From b4a03cd66a7e81b5bf8478641fcfe947d2be5bee Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Mon, 2 Sep 2024 13:30:03 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll                                     |   2 +-
 R_session/Compare_BI_plots.html               |  41 +-
 R_session/Tree_detection.html                 |  41 +-
 base/about.html                               |  37 +-
 base/faq.html                                 |  37 +-
 base/impressum.html                           |  37 +-
 ...ribing_and_comparing_spaital_patterns.html | 857 ++++++++++++++++++
 index.html                                    |  37 +-
 mc_session/mc1.html                           |  47 +-
 mc_session/mc3.html                           |  47 +-
 mc_session/mc4.html                           |  47 +-
 ml_session/ML_AOA.html                        |  47 +-
 modules/de-git-module.html                    |  37 +-
 modules/en-git-module.html                    |  37 +-
 modules/slidelist.html                        |  41 +-
 search.json                                   |  79 +-
 sitemap.xml                                   |  40 +-
 slides/slidelist.html                         |  39 +-
 tdv_session/01_TrainingDataCollection.html    |  41 +-
 tdv_session/02_ValidationDataCollection.html  |  41 +-
 templates/allformats-odt-docx-examples.odt    | Bin 14637 -> 14639 bytes
 templates/journals-examples-default.odt       | Bin 14890 -> 14891 bytes
 22 files changed, 1388 insertions(+), 244 deletions(-)
 create mode 100644 block6/describing_and_comparing_spaital_patterns.html

diff --git a/.nojekyll b/.nojekyll
index 3649b50..7cf2ee6 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-55ce3a51
\ No newline at end of file
+30e75a13
\ No newline at end of file
diff --git a/R_session/Compare_BI_plots.html b/R_session/Compare_BI_plots.html
index 0d0a94c..b1b1749 100644
--- a/R_session/Compare_BI_plots.html
+++ b/R_session/Compare_BI_plots.html
@@ -138,7 +138,7 @@
       <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" role="button" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
         <i class="bi bi-layout-text-sidebar-reverse"></i>
       </button>
-        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../R_session/Compare_BI_plots.html">Remote Sensing applications in forest management planing</a></li><li class="breadcrumb-item"><a href="../R_session/Compare_BI_plots.html">Number_Trees_BI_nDOM</a></li></ol></nav>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../R_session/Compare_BI_plots.html">Block 2: Remote Sensing applications in forest management planning</a></li><li class="breadcrumb-item"><a href="../R_session/Compare_BI_plots.html">Number_Trees_BI_nDOM</a></li></ol></nav>
         <a class="flex-grow-1" role="navigation" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
         </a>
     </div>
@@ -171,7 +171,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -195,7 +195,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="true">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="true" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -219,7 +219,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+ <span class="menu-text">Block 3: Machine Learning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -227,6 +227,24 @@
       <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Microclimate Sensors &amp; Power supply Units</span></a>
   </div>
@@ -248,22 +266,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
@@ -281,7 +298,7 @@ <h2 id="toc-title">On this page</h2>
 <!-- main -->
 <main class="content" id="quarto-document-content">
 
-<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../R_session/Compare_BI_plots.html">Remote Sensing applications in forest management planing</a></li><li class="breadcrumb-item"><a href="../R_session/Compare_BI_plots.html">Number_Trees_BI_nDOM</a></li></ol></nav>
+<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../R_session/Compare_BI_plots.html">Block 2: Remote Sensing applications in forest management planning</a></li><li class="breadcrumb-item"><a href="../R_session/Compare_BI_plots.html">Number_Trees_BI_nDOM</a></li></ol></nav>
 <div class="quarto-title">
 <h1 class="title">Number_Trees_BI_nDOM</h1>
 </div>
diff --git a/R_session/Tree_detection.html b/R_session/Tree_detection.html
index e54d97b..cc84fb4 100644
--- a/R_session/Tree_detection.html
+++ b/R_session/Tree_detection.html
@@ -138,7 +138,7 @@
       <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" role="button" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
         <i class="bi bi-layout-text-sidebar-reverse"></i>
       </button>
-        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../R_session/Compare_BI_plots.html">Remote Sensing applications in forest management planing</a></li><li class="breadcrumb-item"><a href="../R_session/Tree_detection.html">Tree detection</a></li></ol></nav>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../R_session/Compare_BI_plots.html">Block 2: Remote Sensing applications in forest management planning</a></li><li class="breadcrumb-item"><a href="../R_session/Tree_detection.html">Tree detection</a></li></ol></nav>
         <a class="flex-grow-1" role="navigation" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
         </a>
     </div>
@@ -171,7 +171,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -195,7 +195,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="true">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="true" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -219,7 +219,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+ <span class="menu-text">Block 3: Machine Learning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -227,6 +227,24 @@
       <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Microclimate Sensors &amp; Power supply Units</span></a>
   </div>
@@ -248,22 +266,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
@@ -281,7 +298,7 @@ <h2 id="toc-title">On this page</h2>
 <!-- main -->
 <main class="content" id="quarto-document-content">
 
-<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../R_session/Compare_BI_plots.html">Remote Sensing applications in forest management planing</a></li><li class="breadcrumb-item"><a href="../R_session/Tree_detection.html">Tree detection</a></li></ol></nav>
+<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../R_session/Compare_BI_plots.html">Block 2: Remote Sensing applications in forest management planning</a></li><li class="breadcrumb-item"><a href="../R_session/Tree_detection.html">Tree detection</a></li></ol></nav>
 <div class="quarto-title">
 <h1 class="title">Tree detection</h1>
 </div>
diff --git a/base/about.html b/base/about.html
index 19dd01c..be61d04 100644
--- a/base/about.html
+++ b/base/about.html
@@ -136,7 +136,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -160,7 +160,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -184,7 +184,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+ <span class="menu-text">Block 3: Machine Learning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -192,6 +192,24 @@
       <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Microclimate Sensors &amp; Power supply Units</span></a>
   </div>
@@ -213,22 +231,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
diff --git a/base/faq.html b/base/faq.html
index 73dd3b2..0d9a7e8 100644
--- a/base/faq.html
+++ b/base/faq.html
@@ -134,7 +134,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -158,7 +158,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -182,7 +182,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+ <span class="menu-text">Block 3: Machine Learning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -190,6 +190,24 @@
       <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Microclimate Sensors &amp; Power supply Units</span></a>
   </div>
@@ -211,22 +229,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
diff --git a/base/impressum.html b/base/impressum.html
index 4496da4..86ccaeb 100644
--- a/base/impressum.html
+++ b/base/impressum.html
@@ -138,7 +138,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -162,7 +162,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -186,7 +186,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+ <span class="menu-text">Block 3: Machine Learning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -194,6 +194,24 @@
       <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Microclimate Sensors &amp; Power supply Units</span></a>
   </div>
@@ -215,22 +233,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
diff --git a/block6/describing_and_comparing_spaital_patterns.html b/block6/describing_and_comparing_spaital_patterns.html
new file mode 100644
index 0000000..5bc8a07
--- /dev/null
+++ b/block6/describing_and_comparing_spaital_patterns.html
@@ -0,0 +1,857 @@
+<!DOCTYPE html>
+<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
+
+<meta charset="utf-8">
+<meta name="generator" content="quarto-1.5.57">
+
+<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
+
+
+<title>describing_and_comparing_spaital_patterns – EON Summer School 2024</title>
+<style>
+code{white-space: pre-wrap;}
+span.smallcaps{font-variant: small-caps;}
+div.columns{display: flex; gap: min(4vw, 1.5em);}
+div.column{flex: auto; overflow-x: auto;}
+div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+ul.task-list{list-style: none;}
+ul.task-list li input[type="checkbox"] {
+  width: 0.8em;
+  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
+  vertical-align: middle;
+}
+</style>
+
+
+<script src="../site_libs/quarto-nav/quarto-nav.js"></script>
+<script src="../site_libs/clipboard/clipboard.min.js"></script>
+<script src="../site_libs/quarto-search/autocomplete.umd.js"></script>
+<script src="../site_libs/quarto-search/fuse.min.js"></script>
+<script src="../site_libs/quarto-search/quarto-search.js"></script>
+<meta name="quarto:offset" content="../">
+<script src="../site_libs/quarto-html/quarto.js"></script>
+<script src="../site_libs/quarto-html/popper.min.js"></script>
+<script src="../site_libs/quarto-html/tippy.umd.min.js"></script>
+<script src="../site_libs/quarto-html/anchor.min.js"></script>
+<link href="../site_libs/quarto-html/tippy.css" rel="stylesheet">
+<link href="../site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" class="quarto-color-scheme" id="quarto-text-highlighting-styles">
+<link href="../site_libs/quarto-html/quarto-syntax-highlighting-dark.css" rel="prefetch" class="quarto-color-scheme quarto-color-alternate" id="quarto-text-highlighting-styles">
+<script src="../site_libs/bootstrap/bootstrap.min.js"></script>
+<link href="../site_libs/bootstrap/bootstrap-icons.css" rel="stylesheet">
+<link href="../site_libs/bootstrap/bootstrap.min.css" rel="stylesheet" class="quarto-color-scheme" id="quarto-bootstrap" data-mode="light">
+<link href="../site_libs/bootstrap/bootstrap-dark.min.css" rel="prefetch" class="quarto-color-scheme quarto-color-alternate" id="quarto-bootstrap" data-mode="dark">
+<script id="quarto-search-options" type="application/json">{
+  "location": "navbar",
+  "copy-button": false,
+  "collapse-after": 3,
+  "panel-placement": "end",
+  "type": "textbox",
+  "limit": 50,
+  "keyboard-shortcut": [
+    "f",
+    "/",
+    "s"
+  ],
+  "language": {
+    "search-no-results-text": "No results",
+    "search-matching-documents-text": "matching documents",
+    "search-copy-link-title": "Copy link to search",
+    "search-hide-matches-text": "Hide additional matches",
+    "search-more-match-text": "more match in this document",
+    "search-more-matches-text": "more matches in this document",
+    "search-clear-button-title": "Clear",
+    "search-text-placeholder": "",
+    "search-detached-cancel-button-title": "Cancel",
+    "search-submit-button-title": "Submit",
+    "search-label": "Search"
+  }
+}</script>
+
+
+<link rel="stylesheet" href="../css/styles.css">
+<meta property="og:title" content="EON Summer School 2024">
+<meta property="og:description" content="">
+<meta property="og:site_name" content="EON Summer School 2024">
+<meta name="twitter:title" content="EON Summer School 2024">
+<meta name="twitter:description" content="">
+<meta name="twitter:creator" content="@gisma">
+<meta name="twitter:card" content="summary">
+</head>
+
+<body class="nav-sidebar docked nav-fixed">
+
+<div id="quarto-search-results"></div>
+  <header id="quarto-header" class="headroom fixed-top">
+    <nav class="navbar navbar-expand-lg " data-bs-theme="dark">
+      <div class="navbar-container container-fluid">
+      <div class="navbar-brand-container mx-auto">
+    <a href="../index.html" class="navbar-brand navbar-brand-logo">
+    <img src="../images/logooil.jpg" alt="" class="navbar-logo">
+    </a>
+    <a class="navbar-brand" href="../index.html">
+    <span class="navbar-title">EON Summer School 2024</span>
+    </a>
+  </div>
+        <div class="quarto-navbar-tools tools-end">
+  <a href="" class="quarto-color-scheme-toggle quarto-navigation-tool  px-1" onclick="window.quartoToggleColorScheme(); return false;" title="Toggle dark mode"><i class="bi"></i></a>
+</div>
+          <div id="quarto-search" class="" title="Search"></div>
+      </div> <!-- /container-fluid -->
+    </nav>
+  <nav class="quarto-secondary-nav">
+    <div class="container-fluid d-flex">
+      <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" role="button" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+        <i class="bi bi-layout-text-sidebar-reverse"></i>
+      </button>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../block6/describing_and_comparing_spaital_patterns.html">Block 6: Describing and comparing spatial patterns</a></li><li class="breadcrumb-item"><a href="../block6/describing_and_comparing_spaital_patterns.html">describing_and_comparing_spaital_patterns.html</a></li></ol></nav>
+        <a class="flex-grow-1" role="navigation" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
+        </a>
+    </div>
+  </nav>
+</header>
+<!-- content -->
+<div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
+<!-- sidebar -->
+  <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal quarto-sidebar-collapse-item sidebar-navigation docked overflow-auto">
+    <div class="sidebar-menu-container"> 
+    <ul class="list-unstyled mt-1">
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-1" role="navigation" aria-expanded="false">
+ <span class="menu-text">Welcome</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-1" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-1" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../index.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Welcome EON Summer School 2024 (01-06.09.2024)</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-2" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../tdv_session/01_TrainingDataCollection.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Collection of training data for remote sensing model building</span></a>
+  </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../tdv_session/02_ValidationDataCollection.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Collection of validation data in the context of remote sensing based forest monitoring</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-3" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../R_session/Compare_BI_plots.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Number_Trees_BI_nDOM</span></a>
+  </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../R_session/Tree_detection.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Tree detection</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 3: Machine Learning</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Microclimate Sensors &amp; Power supply Units</span></a>
+  </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../mc_session/mc3.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Spatial Interpolation</span></a>
+  </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../mc_session/mc4.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Data Sources</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
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+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="true">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="true" aria-label="Toggle section">
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+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
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+
+<p>You can find all of the workshop materials at <a href="https://github.com/Nowosad/eon2024-exercises" class="uri">https://github.com/Nowosad/eon2024-exercises</a>.</p>
+
+
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+  var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
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+    const citeInfo = findCites(ref);
+    if (citeInfo) {
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+        citeInfo.cites.forEach(function(cite) {
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+          citeDiv.classList.add('csl-entry');
+          var biblioDiv = window.document.getElementById('ref-' + cite);
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+          }
+          popup.appendChild(citeDiv);
+        });
+        return popup.innerHTML;
+      });
+    }
+  }
+});
+</script>
+</div> <!-- /content -->
+<footer class="footer">
+  <div class="nav-footer">
+    <div class="nav-footer-left">
+      &nbsp;
+    </div>   
+    <div class="nav-footer-center">
+<p>Earth Observation Network Harz Summerschool (2024)</p>
+<div class="toc-actions"><ul><li><a href="https://github.com/earth-observation-network/EON2024/edit/main/block6/describing_and_comparing_spaital_patterns.qmd" class="toc-action"><i class="bi bi-github"></i>Edit this page</a></li><li><a href="https://github.com/earth-observation-network/EON2024/issues/new" class="toc-action"><i class="bi empty"></i>Report an issue</a></li></ul></div></div>
+    <div class="nav-footer-right">
+      &nbsp;
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+  </div>
+</footer>
+
+
+
+
+</body></html>
\ No newline at end of file
diff --git a/index.html b/index.html
index 0d93ce9..702d944 100644
--- a/index.html
+++ b/index.html
@@ -142,7 +142,7 @@
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       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -166,7 +166,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -190,7 +190,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+ <span class="menu-text">Block 3: Machine Learning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -198,6 +198,24 @@
       <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="./ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="./mc_session/mc1.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Microclimate Sensors &amp; Power supply Units</span></a>
   </div>
@@ -219,22 +237,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="./block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
diff --git a/mc_session/mc1.html b/mc_session/mc1.html
index 7dbdb89..29dd6fc 100644
--- a/mc_session/mc1.html
+++ b/mc_session/mc1.html
@@ -107,7 +107,7 @@
       <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" role="button" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
         <i class="bi bi-layout-text-sidebar-reverse"></i>
       </button>
-        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Microclimate Data Retrieval &amp; Spatial Data Interpolation</a></li><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Microclimate Sensors &amp; Power supply Units</a></li></ol></nav>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</a></li><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Microclimate Sensors &amp; Power supply Units</a></li></ol></nav>
         <a class="flex-grow-1" role="navigation" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
         </a>
     </div>
@@ -140,7 +140,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -164,7 +164,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -187,13 +187,31 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="true">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="true" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 3: Machine Learning</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 show">  
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="true">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="true" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 show">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link active">
@@ -217,22 +235,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
@@ -268,7 +285,7 @@ <h2 id="toc-title">On this page</h2>
 <div class="quarto-about-solana column-body">
   <div class="about-entity">
     <div class="entity-contents">
-      <header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Microclimate Data Retrieval &amp; Spatial Data Interpolation</a></li><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Microclimate Sensors &amp; Power supply Units</a></li></ol></nav>
+      <header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</a></li><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Microclimate Sensors &amp; Power supply Units</a></li></ol></nav>
 <div class="quarto-title">
 <h1 class="title">Microclimate Sensors &amp; Power supply Units</h1>
 <p class="subtitle lead">Tutorial: EON Summer School 2024</p>
diff --git a/mc_session/mc3.html b/mc_session/mc3.html
index 5e3efca..a69cb0f 100644
--- a/mc_session/mc3.html
+++ b/mc_session/mc3.html
@@ -169,7 +169,7 @@
       <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" role="button" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
         <i class="bi bi-layout-text-sidebar-reverse"></i>
       </button>
-        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Microclimate Data Retrieval &amp; Spatial Data Interpolation</a></li><li class="breadcrumb-item"><a href="../mc_session/mc3.html">Spatial Interpolation</a></li></ol></nav>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</a></li><li class="breadcrumb-item"><a href="../mc_session/mc3.html">Spatial Interpolation</a></li></ol></nav>
         <a class="flex-grow-1" role="navigation" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
         </a>
     </div>
@@ -202,7 +202,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -226,7 +226,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -249,13 +249,31 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="true">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="true" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 3: Machine Learning</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 show">  
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="true">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="true" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 show">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
@@ -279,22 +297,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
@@ -342,7 +359,7 @@ <h2 id="toc-title">On this page</h2>
 <div class="quarto-about-solana column-body">
   <div class="about-entity">
     <div class="entity-contents">
-      <header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Microclimate Data Retrieval &amp; Spatial Data Interpolation</a></li><li class="breadcrumb-item"><a href="../mc_session/mc3.html">Spatial Interpolation</a></li></ol></nav>
+      <header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</a></li><li class="breadcrumb-item"><a href="../mc_session/mc3.html">Spatial Interpolation</a></li></ol></nav>
 <div class="quarto-title">
 <h1 class="title">Spatial Interpolation</h1>
 <p class="subtitle lead">Tutorial: EON Summer School 2024</p>
diff --git a/mc_session/mc4.html b/mc_session/mc4.html
index a8ee56c..4d3f804 100644
--- a/mc_session/mc4.html
+++ b/mc_session/mc4.html
@@ -111,7 +111,7 @@
       <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" role="button" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
         <i class="bi bi-layout-text-sidebar-reverse"></i>
       </button>
-        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Microclimate Data Retrieval &amp; Spatial Data Interpolation</a></li><li class="breadcrumb-item"><a href="../mc_session/mc4.html">Data Sources</a></li></ol></nav>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</a></li><li class="breadcrumb-item"><a href="../mc_session/mc4.html">Data Sources</a></li></ol></nav>
         <a class="flex-grow-1" role="navigation" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
         </a>
     </div>
@@ -144,7 +144,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -168,7 +168,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -191,13 +191,31 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="true">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="true" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 3: Machine Learning</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 show">  
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="true">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="true" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 show">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
@@ -221,22 +239,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
@@ -252,7 +269,7 @@
 <div class="quarto-about-solana column-body">
   <div class="about-entity">
     <div class="entity-contents">
-      <header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Microclimate Data Retrieval &amp; Spatial Data Interpolation</a></li><li class="breadcrumb-item"><a href="../mc_session/mc4.html">Data Sources</a></li></ol></nav>
+      <header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../mc_session/mc1.html">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</a></li><li class="breadcrumb-item"><a href="../mc_session/mc4.html">Data Sources</a></li></ol></nav>
 <div class="quarto-title">
 <h1 class="title">Data Sources</h1>
 <p class="subtitle lead">Tutorial: EON Summer School 2024</p>
diff --git a/ml_session/ML_AOA.html b/ml_session/ML_AOA.html
index 23139c1..49d7e6d 100644
--- a/ml_session/ML_AOA.html
+++ b/ml_session/ML_AOA.html
@@ -161,7 +161,7 @@
       <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" role="button" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
         <i class="bi bi-layout-text-sidebar-reverse"></i>
       </button>
-        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../ml_session/ML_AOA.html">Machine Learning</a></li><li class="breadcrumb-item"><a href="../ml_session/ML_AOA.html">Machine learning for remote sensing applications</a></li></ol></nav>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../ml_session/ML_AOA.html">Block 3: Machine Learning</a></li><li class="breadcrumb-item"><a href="../ml_session/ML_AOA.html">Machine learning for remote sensing applications</a></li></ol></nav>
         <a class="flex-grow-1" role="navigation" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
         </a>
     </div>
@@ -194,7 +194,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -218,7 +218,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -241,13 +241,31 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="true">
+ <span class="menu-text">Block 3: Machine Learning</span></a>
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="true" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 show">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
@@ -271,22 +289,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="true">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="true" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 show">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link active">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
@@ -341,7 +358,7 @@ <h2 id="toc-title">On this page</h2>
 <!-- main -->
 <main class="content" id="quarto-document-content">
 
-<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../ml_session/ML_AOA.html">Machine Learning</a></li><li class="breadcrumb-item"><a href="../ml_session/ML_AOA.html">Machine learning for remote sensing applications</a></li></ol></nav>
+<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../ml_session/ML_AOA.html">Block 3: Machine Learning</a></li><li class="breadcrumb-item"><a href="../ml_session/ML_AOA.html">Machine learning for remote sensing applications</a></li></ol></nav>
 <div class="quarto-title">
 <h1 class="title">Machine learning for remote sensing applications</h1>
 <p class="subtitle lead">Mapping the MarburgOpenForest</p>
diff --git a/modules/de-git-module.html b/modules/de-git-module.html
index e21b46f..30b8999 100644
--- a/modules/de-git-module.html
+++ b/modules/de-git-module.html
@@ -170,7 +170,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -194,7 +194,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -218,7 +218,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+ <span class="menu-text">Block 3: Machine Learning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -226,6 +226,24 @@
       <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Microclimate Sensors &amp; Power supply Units</span></a>
   </div>
@@ -247,22 +265,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
diff --git a/modules/en-git-module.html b/modules/en-git-module.html
index 2c54c96..9e89a4b 100644
--- a/modules/en-git-module.html
+++ b/modules/en-git-module.html
@@ -136,7 +136,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -160,7 +160,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -184,7 +184,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+ <span class="menu-text">Block 3: Machine Learning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -192,6 +192,24 @@
       <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Microclimate Sensors &amp; Power supply Units</span></a>
   </div>
@@ -213,22 +231,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
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 </nav>
diff --git a/modules/slidelist.html b/modules/slidelist.html
index 8ec01ea..d63eab2 100644
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@@ -194,7 +194,7 @@
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             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
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@@ -218,7 +218,7 @@
         <li class="sidebar-item sidebar-item-section">
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             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
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@@ -242,7 +242,7 @@
         <li class="sidebar-item sidebar-item-section">
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             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+ <span class="menu-text">Block 3: Machine Learning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
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@@ -250,6 +250,24 @@
       <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Microclimate Sensors &amp; Power supply Units</span></a>
   </div>
@@ -271,22 +289,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
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 </nav>
@@ -323,7 +340,7 @@ <h1 class="title">Self-study modules</h1>
 
 <div class="quarto-listing quarto-listing-container-grid" id="listing-listing">
 <div class="list grid quarto-listing-cols-3">
-<div class="g-col-1" data-index="0" data-listing-file-modified-sort="1725283320459" data-listing-reading-time-sort="17" data-listing-word-count-sort="3270">
+<div class="g-col-1" data-index="0" data-listing-file-modified-sort="1725283762697" data-listing-reading-time-sort="17" data-listing-word-count-sort="3270">
 <a href="../modules/de-git-module.html" class="quarto-grid-link">
 <div class="quarto-grid-item card h-100 card-left">
 <p class="card-img-top"><img src="../images/github-Rstudio.jpg" style="height: 150px;"  class="thumbnail-image card-img"/></p>
@@ -343,7 +360,7 @@ <h5 class="no-anchor card-title listing-title">
 </div>
 </a>
 </div>
-<div class="g-col-1" data-index="1" data-listing-file-modified-sort="1725283320459" data-listing-reading-time-sort="1" data-listing-word-count-sort="30">
+<div class="g-col-1" data-index="1" data-listing-file-modified-sort="1725283762697" data-listing-reading-time-sort="1" data-listing-word-count-sort="30">
 <a href="../modules/en-git-module.html" class="quarto-grid-link">
 <div class="quarto-grid-item card h-100 card-left">
 <p class="card-img-top"><img src="../images/github-Rstudio.jpg" style="height: 150px;"  class="thumbnail-image card-img"/></p>
diff --git a/search.json b/search.json
index ba15c8f..95372a5 100644
--- a/search.json
+++ b/search.json
@@ -6,7 +6,7 @@
     "section": "",
     "text": "In this tutorial we will go through the basic workflow of training machine learning models for spatial mapping based on remote sensing. To do this we will look at two case studies located in the MarburgOpenForest in Germany: one has the aim to produce a land cover map including different tree species; the other aims at producing a map of Leaf Area Index.\nBased on “default” models, we will further discuss the relevance of different validation strategies and the area of applicability.\n\n\nFor this tutorial we need the terra package for processing of the satellite data as well as the caret package as a wrapper for machine learning (here: randomForest) algorithms. Sf is used for handling of the training data available as vector data (polygons). Mapview is used for spatial visualization of the data. CAST will be used to account for spatial dependencies during model validation as well as for the estimation of the AOA.\n\nrm(list=ls())\n#major required packages:\n\nrequire(devtools)\ndevtools::install_github(\"HannaMeyer/CAST\")\n\nlibrary(terra)\nlibrary(caret)\nlibrary(randomForest)\nlibrary(mapview)\nlibrary(sf)\nlibrary(CAST)\nlibrary(tmap)\nlibrary(rprojroot)\n\n\n# create a string containing the current working directory\nwd = paste0(find_rstudio_root_file(),\"/ml_session/data/\")",
     "crumbs": [
-      "Machine Learning",
+      "Block 3: Machine Learning",
       "Machine learning for remote sensing applications"
     ]
   },
@@ -17,7 +17,7 @@
     "section": "",
     "text": "For this tutorial we need the terra package for processing of the satellite data as well as the caret package as a wrapper for machine learning (here: randomForest) algorithms. Sf is used for handling of the training data available as vector data (polygons). Mapview is used for spatial visualization of the data. CAST will be used to account for spatial dependencies during model validation as well as for the estimation of the AOA.\n\nrm(list=ls())\n#major required packages:\n\nrequire(devtools)\ndevtools::install_github(\"HannaMeyer/CAST\")\n\nlibrary(terra)\nlibrary(caret)\nlibrary(randomForest)\nlibrary(mapview)\nlibrary(sf)\nlibrary(CAST)\nlibrary(tmap)\nlibrary(rprojroot)\n\n\n# create a string containing the current working directory\nwd = paste0(find_rstudio_root_file(),\"/ml_session/data/\")",
     "crumbs": [
-      "Machine Learning",
+      "Block 3: Machine Learning",
       "Machine learning for remote sensing applications"
     ]
   },
@@ -28,7 +28,7 @@
     "section": "Data preparation",
     "text": "Data preparation\nTo start with, let’s load and explore the remote sensing raster data as well as the vector data that include the training sites.\n\nRaster data (predictor variables)\n\nmof_sen &lt;- rast(paste0(wd,\"sentinel_uniwald.grd\"))\nprint(mof_sen)\n\nclass       : SpatRaster \ndimensions  : 522, 588, 10  (nrow, ncol, nlyr)\nresolution  : 10, 10  (x, y)\nextent      : 474200, 480080, 5629540, 5634760  (xmin, xmax, ymin, ymax)\ncoord. ref. : +proj=utm +zone=32 +datum=WGS84 +units=m +no_defs \nsource      : sentinel_uniwald.grd \nnames       : T32UM~1_B02, T32UM~1_B03, T32UM~1_B04, T32UM~1_B05, T32UM~1_B06, T32UM~1_B07, ... \nmin values  :         723,         514,         294,    341.8125,    396.9375,    440.8125, ... \nmax values  :        8325,        9087,       13810,   7368.7500,   8683.8125,   9602.3125, ... \n\n\nThe raster data contain a subset of the optical data from Sentinel-2 (see band information here: https://en.wikipedia.org/wiki/Sentinel-2) given in scaled reflectances (B02-B11). In addition,the NDVI was calculated. Let’s plot the data to get an idea how the variables look like.\n\nplot(mof_sen)\n\n\n\n\n\n\n\nplotRGB(mof_sen,r=3,g=2,b=1,stretch=\"lin\")\n\n\n\n\n\n\n\n\n\n\nVector data (Response variable)\nThe vector file is read as sf object. It contains the training sites that will be regarded here as a ground truth for the land cover classification.\n\ntrainSites &lt;- read_sf(paste0(wd,\"trainingsites_LUC.gpkg\"))\n\nUsing mapview we can visualize the aerial image channels in the geographical context and overlay it with the polygons. Click on the polygons to see which land cover class is assigned to a respective polygon.\n\nmapview(mof_sen[[1]], map.types = \"Esri.WorldImagery\") +\n  mapview(trainSites)\n\n\n\n\n\n\n\nDraw training samples and extract raster information\nIn order to train a machine learning model between the spectral properties and the land cover class, we first need to create a data frame that contains the predictor variables at the location of the training sites as well as the corresponding class information. However, using each pixel overlapped by a polygon would lead to a overly huge dataset, therefore, we first draw training samples from the polygon. Let’s use 1000 randomly sampled (within the polygons) pixels as training data set.\n\ntrainlocations &lt;- st_sample(trainSites,1000)\ntrainlocations &lt;- st_join(st_sf(trainlocations), trainSites)\nmapview(trainlocations)\n\n\n\n\n\nNext, we can extract the raster values for these locations. The resulting data frame contains the predictor variables for each training location that we can merged with the information on the land cover class from the sf object.\n\ntrainDat &lt;- extract(mof_sen, trainlocations, df=TRUE)\ntrainDat &lt;- data.frame(trainDat, trainlocations)\nhead(trainDat)\n\n  ID T32UMB_20170510T103031_B02 T32UMB_20170510T103031_B03\n1  1                        839                        769\n2  2                        920                        801\n3  3                        865                        764\n4  4                        943                        923\n5  5                        853                        832\n6  6                        850                        921\n  T32UMB_20170510T103031_B04 T32UMB_20170510T103031_B05\n1                        508                   915.3125\n2                        573                   935.5000\n3                        502                   848.5000\n4                        695                  1204.1875\n5                        490                   987.3125\n6                        524                  1220.8125\n  T32UMB_20170510T103031_B06 T32UMB_20170510T103031_B07\n1                   1615.625                   1864.812\n2                   2702.750                   3693.938\n3                   2459.188                   3228.000\n4                   2472.188                   2900.500\n5                   1765.438                   2037.375\n6                   2217.875                   2491.062\n  T32UMB_20170510T103031_B08 T32UMB_20170510T103031_B11\n1                       1957                   1425.438\n2                       3426                   1308.750\n3                       3114                   1142.625\n4                       2787                   2058.312\n5                       2323                   1530.188\n6                       2842                   1953.438\n  T32UMB_20170510T103031_B12      NDVI id  LN    Type           trainlocations\n1                   759.5625 0.5878296 NA   1   Eiche POINT (476573.9 5632268)\n2                   674.9375 0.7134284 22 403 Strasse POINT (478543.3 5632763)\n3                   551.6875 0.7223451 NA 102  Felder   POINT (478118 5631136)\n4                  1091.0000 0.6008042 12 303   Wiese POINT (478396.1 5632345)\n5                   821.3750 0.6516175 NA   2   Buche POINT (477062.7 5632419)\n6                  1014.6250 0.6886512 NA   2   Buche   POINT (477609 5631575)",
     "crumbs": [
-      "Machine Learning",
+      "Block 3: Machine Learning",
       "Machine learning for remote sensing applications"
     ]
   },
@@ -39,7 +39,7 @@
     "section": "Model training",
     "text": "Model training\n\nPredictors and response\nFor model training we need to define the predictor and response variables. As predictors we can use basically all information from the raster stack as we might assume they could all be meaningful for the differentiation between the land cover classes. As response variable we use the “Label” column of the data frame.\n\npredictors &lt;- names(mof_sen)\nresponse &lt;- \"Type\"\n\n\n\nA first “default” model\nWe then train a Random Forest model to lean how the classes can be distinguished based on the predictors (note: other algorithms would work as well. See https://topepo.github.io/caret/available-models.html for a list of algorithms available in caret). Caret’s train function is doing this job.\nSo let’s see how we can then train a “default” random forest model. We specify “rf” as method, indicating that a Random Forest is applied. We reduce the number of trees (ntree) to 75 to speed things up. Note that usually a larger number (&gt;250) is appropriate.\n\nmodel &lt;- train(trainDat[,predictors],\n               trainDat[,response],\n               method=\"rf\",\n               ntree=75)\nmodel\n\nRandom Forest \n\n1000 samples\n  10 predictor\n  10 classes: 'Buche', 'Duglasie', 'Eiche', 'Felder', 'Fichte', 'Laerche', 'Siedlung', 'Strasse', 'Wasser', 'Wiese' \n\nNo pre-processing\nResampling: Bootstrapped (25 reps) \nSummary of sample sizes: 1000, 1000, 1000, 1000, 1000, 1000, ... \nResampling results across tuning parameters:\n\n  mtry  Accuracy   Kappa    \n   2    0.8327506  0.7927715\n   6    0.8317421  0.7918213\n  10    0.8261596  0.7851166\n\nAccuracy was used to select the optimal model using the largest value.\nThe final value used for the model was mtry = 2.\n\n\nTo perform the classification we can then use the trained model and apply it to each pixel of the raster stack using the predict function.\n\nprediction &lt;- predict(mof_sen,model)\n\nThen we can then create a map with meaningful colors of the predicted land cover using the tmap package.\n\ncols &lt;- rev(c(\"palegreen\", \"blue\", \"grey\", \"red\", \"lightgreen\", \"forestgreen\", \"beige\",\"brown\",\"darkgreen\",\"yellowgreen\"))\n\ntm_shape(prediction) +\n  tm_raster(palette = cols,title = \"LUC\")+\n  tm_scale_bar(bg.color=\"white\",bg.alpha=0.75)+\n  tm_layout(legend.bg.color = \"white\",\n            legend.bg.alpha = 0.75)\n\n\n\n\n\n\n\n\nBased on this we can now discuss more advanced aspects of cross-validation for performance assessment as well as spatial variable selection strategies.\n\n\nModel training with spatial CV and variable selection\nBefore starting model training we can specify some control settings using trainControl. For hyperparameter tuning (mtry) as well as for error assessment we use a spatial cross-validation. Here, the training data are split into 5 folds by trying to resemble the geographic distance distribution required when predicting the entire area from the trainign data,\n\n## define prediction area:\nstudyArea &lt;- as.polygons(mof_sen, values = FALSE, na.all = TRUE) |&gt;\n    st_as_sf() |&gt;\n    st_transform(st_crs(trainlocations))|&gt;\n    st_union()\nmapview(studyArea)\n\n\n\n\nindices &lt;- knndm(trainlocations,studyArea,k=5)\ngd &lt;- geodist(trainlocations,studyArea,cvfolds = indices$indx_train )\nplot(gd)+ scale_x_log10(labels=round)\n\n\n\n\n\n\n\nctrl &lt;- trainControl(method=\"cv\", \n                     index = indices$indx_train,\n                     indexOut = indices$indx_test,\n                     savePredictions = TRUE)\n\nModel training is then again performed using caret’s train function. However we use a wrapper around it that is selecting the predictor variables which are relevant for making predictions to new spatial locations (forward feature selection, fss). We use the Kappa index as metric to select the best model.\n\n# train the model\nset.seed(100)\nmodel &lt;- ffs(trainDat[,predictors],\n             trainDat[,response],\n             method=\"rf\",\n             metric=\"Kappa\",\n             trControl=ctrl,\n             importance=TRUE,\n             ntree=100,\n             verbose=FALSE)\n\n\nprint(model)\n\nSelected Variables: \nT32UMB_20170510T103031_B05 T32UMB_20170510T103031_B06 T32UMB_20170510T103031_B02 T32UMB_20170510T103031_B12\n---\nRandom Forest \n\n1000 samples\n   4 predictor\n  10 classes: 'Buche', 'Duglasie', 'Eiche', 'Felder', 'Fichte', 'Laerche', 'Siedlung', 'Strasse', 'Wasser', 'Wiese' \n\nNo pre-processing\nResampling: Cross-Validated (10 fold) \nSummary of sample sizes: 816, 803, 830, 661, 890 \nResampling results across tuning parameters:\n\n  mtry  Accuracy   Kappa    \n  2     0.7812635  0.5674632\n  3     0.7448999  0.5188131\n  4     0.7317119  0.5178565\n\nKappa was used to select the optimal model using the largest value.\nThe final value used for the model was mtry = 2.\n\nplot(varImp(model))\n\n\n\n\n\n\n\n\n\n\nModel validation\nWhen we print the model (see above) we get a summary of the prediction performance as the average Kappa and Accuracy of the three spatial folds. Looking at all cross-validated predictions together we can get the “global” model performance.\n\n# get all cross-validated predictions:\ncvPredictions &lt;- model$pred[model$pred$mtry==model$bestTune$mtry,]\n# calculate cross table:\ntable(cvPredictions$pred,cvPredictions$obs)\n\n          \n           Buche Duglasie Eiche Felder Fichte Laerche Siedlung Strasse Wasser\n  Buche        4        0     6      0      7       0        0       0      0\n  Duglasie     0        2     0      0      1       0        0       0      0\n  Eiche        3        0     0      1      2       0        0       0      0\n  Felder       3        0     0    260      0       0        0      11      0\n  Fichte       0        0     0      6      1       0        0       0      0\n  Laerche      0        0     0      0      0       0        0       0      0\n  Siedlung     0        0     0      0      0       0        0       0      0\n  Strasse      0        0     0     14      0       0        0      35      0\n  Wasser       0        0     0      1      0       0        0       1      0\n  Wiese        0        0     0     21      0       0        0       5      0\n          \n           Wiese\n  Buche        0\n  Duglasie     0\n  Eiche        1\n  Felder      15\n  Fichte       0\n  Laerche      0\n  Siedlung     0\n  Strasse      7\n  Wasser       1\n  Wiese       69\n\n\n\n\nVisualize the final model predictions\n\nprediction &lt;- predict(mof_sen,model)\ncols &lt;- rev(c(\"palegreen\", \"blue\", \"grey\", \"red\", \"lightgreen\", \"forestgreen\", \"beige\",\"brown\",\"darkgreen\",\"yellowgreen\"))\n\ntm_shape(prediction) +\n  tm_raster(palette = cols,title = \"LUC\")+\n  tm_scale_bar(bg.color=\"white\",bg.alpha=0.75)+\n  tm_layout(legend.bg.color = \"white\",\n            legend.bg.alpha = 0.75)",
     "crumbs": [
-      "Machine Learning",
+      "Block 3: Machine Learning",
       "Machine learning for remote sensing applications"
     ]
   },
@@ -50,7 +50,7 @@
     "section": "Area of Applicability",
     "text": "Area of Applicability\nWe have seen that technically, the trained model can be applied to the entire area of interest (and beyond…as long as the sentinel predictors are available which they are, even globally). But we should assess if we SHOULD apply our model to the entire area. The model should only be applied to locations that feature predictor properties that are comparable to those of the training data. If dissimilarity to the training data is larger than the dissimmilarity within the training data, the model should not be applied to this location.\n\nAOA &lt;- aoa(mof_sen,model,LPD=TRUE, verbose=FALSE)\nplot(AOA$AOA)\n\n\n\n\n\n\n\n\nThe result of the aoa function has two layers: the dissimilarity index (DI) and the area of applicability (AOA). The DI can take values from 0 to Inf, where 0 means that a location has predictor properties that are identical to properties observed in the training data. With increasing values the dissimilarity increases. The AOA has only two values: 0 and 1. 0 means that a location is outside the area of applicability, 1 means that the model is inside the area of applicability. As an option, we cal also calculate the Local Point Density (LPD), which tells us, for a prediction location, how MANY similar training data points were used during modle training.\n\nError profiles\nLet’s assume there is a relationship between the density of training data points in the predictor space (LPD) and the model performance. Let’s analyze that and use that to predict the prediction performance.\n\nplot(AOA$LPD)\n\n\n\n\n\n\n\nep &lt;- errorProfiles(model,AOA,variable=\"LPD\")\nplot(ep)\n\n\n\n\n\n\n\nplot(predict(AOA$LPD,ep))",
     "crumbs": [
-      "Machine Learning",
+      "Block 3: Machine Learning",
       "Machine learning for remote sensing applications"
     ]
   },
@@ -61,7 +61,7 @@
     "section": "Prepare data",
     "text": "Prepare data\n\nmof_sen &lt;- rast(paste0(wd,\"sentinel_uniwald.grd\"))\nLAIdat &lt;- st_read(paste0(wd,\"trainingsites_LAI.gpkg\"))\n\nReading layer `trainingsites_LAI' from data source \n  `/home/hanna/Documents/Github/HannaMeyer/EON2024/ml_session/data/trainingsites_LAI.gpkg' \n  using driver `GPKG'\nSimple feature collection with 67 features and 10 fields\nGeometry type: POINT\nDimension:     XY\nBounding box:  xmin: 476350 ymin: 5631537 xmax: 478075 ymax: 5632765\nProjected CRS: WGS 84 / UTM zone 32N\n\ntrainDat &lt;- extract(mof_sen,LAIdat,na.rm=TRUE)\ntrainDat$LAI &lt;- LAIdat$LAI\n\n\nmeanmodel &lt;- mof_sen[[1]]\nvalues(meanmodel) &lt;- mean(trainDat$LAI)\nplot(meanmodel)\n\n\n\n\n\n\n\nrandommodel &lt;- mof_sen[[1]]\nvalues(randommodel)&lt;- runif(ncell(randommodel),min = 0,4)\n\nplot(randommodel)",
     "crumbs": [
-      "Machine Learning",
+      "Block 3: Machine Learning",
       "Machine learning for remote sensing applications"
     ]
   },
@@ -72,7 +72,7 @@
     "section": "A simple linear model",
     "text": "A simple linear model\nAs a simple first approach we might develop a linear model. Let’s assume a linear relationship between the NDVI and the LAI\n\nplot(trainDat$NDVI,trainDat$LAI)\nmodel_lm &lt;- lm(LAI~NDVI,data=trainDat)\nsummary(model_lm)\n\n\nCall:\nlm(formula = LAI ~ NDVI, data = trainDat)\n\nResiduals:\n     Min       1Q   Median       3Q      Max \n-1.87314 -0.52143 -0.03363  0.63668  2.25252 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)   \n(Intercept)  -0.8518     1.4732  -0.578  0.56515   \nNDVI          6.8433     2.3160   2.955  0.00435 **\n---\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.8887 on 65 degrees of freedom\nMultiple R-squared:  0.1184,    Adjusted R-squared:  0.1049 \nF-statistic: 8.731 on 1 and 65 DF,  p-value: 0.004354\n\nabline(model_lm,col=\"red\")\n\n\n\n\n\n\n\nprediction_LAI &lt;- predict(mof_sen,model_lm,na.rm=T)\nplot(prediction_LAI)\n\n\n\n\n\n\n\nlimodelpred &lt;- -0.8518+mof_sen$NDVI*6.8433\nmapview(limodelpred)",
     "crumbs": [
-      "Machine Learning",
+      "Block 3: Machine Learning",
       "Machine learning for remote sensing applications"
     ]
   },
@@ -83,7 +83,7 @@
     "section": "The machine learning way",
     "text": "The machine learning way\n\nDefine CV folds\nLet’s use the NNDM cross-validation approach.\n\nstudyArea &lt;- as.polygons(mof_sen, values = FALSE, na.all = TRUE) |&gt;\n    st_as_sf() |&gt;\n    st_transform(st_crs(LAIdat))|&gt;\n    st_union()\n\nnndm_folds &lt;- knndm(LAIdat,studyArea,k=3)\n\nLet’s explore the geodistance\n\ngd &lt;- geodist(LAIdat,studyArea,cvfolds = nndm_folds$indx_test)\nplot(gd)\n\n\n\n\n\n\n\n\n\n\nModel training\n\nctrl &lt;- trainControl(method=\"cv\",\n                     index=nndm_folds$indx_train,\n                     indexOut = nndm_folds$indx_test,\n                    savePredictions = \"all\")\n\n\nmodel &lt;- ffs(trainDat[,predictors],\n             trainDat$LAI,\n             method=\"rf\",\n             trControl = ctrl,\n             importance=TRUE,\n             verbose=FALSE)\n\n\nmodel\n\nSelected Variables: \nT32UMB_20170510T103031_B07 T32UMB_20170510T103031_B08 NDVI\n---\nRandom Forest \n\n67 samples\n 3 predictor\n\nNo pre-processing\nResampling: Cross-Validated (10 fold) \nSummary of sample sizes: 41, 44, 49 \nResampling results across tuning parameters:\n\n  mtry  RMSE       Rsquared   MAE      \n  2     0.8488074  0.2173559  0.7078165\n  3     0.8547139  0.2092237  0.7142045\n\nRMSE was used to select the optimal model using the smallest value.\nThe final value used for the model was mtry = 2.\n\n\n\n\nLAI prediction\nLet’s then use the trained model for prediction.\n\nLAIprediction &lt;- predict(mof_sen,model)\nplot(LAIprediction)\n\n\n\n\n\n\n\n\n\nQuestion?! Why does it look so different than the linear model?\n\n\n\nAOA estimation\n\nAOA &lt;- aoa(mof_sen,model,LPD = TRUE, verbose=FALSE)\nplot(AOA$AOA)\n\n\n\n\n\n\n\nplot(AOA$LPD)\n\n\n\n\n\n\n\n\n\n\nError profiles\nLet’s assume there is a relationship between the density of training data points in the predictor space (LPD) and the model performance. Let’s analyze that and use that to predict the prediction performance.\n\nep &lt;- errorProfiles(model,AOA,variable=\"DI\")\nplot(ep)\n\n\n\n\n\n\n\nplot(predict(AOA$DI,ep))",
     "crumbs": [
-      "Machine Learning",
+      "Block 3: Machine Learning",
       "Machine learning for remote sensing applications"
     ]
   },
@@ -94,7 +94,7 @@
     "section": "",
     "text": "library(sf)\nlibrary(RStoolbox)\nlibrary(terra)\nlibrary(ggplot2)\nlibrary(mapview)\nlibrary(kableExtra)\nlibrary(dplyr)\nlibrary(rprojroot)\nlibrary(patchwork)\nlibrary(rmarkdown)",
     "crumbs": [
-      "Reference Data Collection",
+      "Block 1: Reference Data Collection",
       "Collection of training data for remote sensing model building"
     ]
   },
@@ -105,7 +105,7 @@
     "section": "Dimension reduction (PCA)",
     "text": "Dimension reduction (PCA)\nIn a fist step we reduce the dimensions of the 9 Sentinel-2 bands while maintaining most of the information, using a principal component analysis (PCA).\n\n# Calculation of the principlal components using the RStoolbox\npca&lt;-RStoolbox::rasterPCA(s2,nSamples = 5000, spca=TRUE )\n\n\n# Extracting the first three components\nrgb_raster &lt;- subset(pca$map, 1:3)\n\n# Function to scale the pixel values to 0-255\nscale_fun &lt;- function(x) {\n  # Calculation of the 2% and 98% quantile\n  q &lt;- quantile(x, c(0.02, 0.98), na.rm = TRUE)\n  \n  # scaling the values\n  x &lt;- (x - q[1]) / (q[2] - q[1]) * 255\n  \n  # restrict the values to 0-255\n  x &lt;- pmin(pmax(x, 0), 255)\n  \n  return(x)\n}\n\n# Scaling of each band\nfor (i in 1:3) {\n  rgb_raster[[i]] &lt;- app(rgb_raster[[i]], scale_fun)\n}\n\n# Plot the first three principal components as RGB\nplotRGB(rgb_raster, r = 1, g = 2, b = 3)\n\n# Show importance of componentes\nsummary(pca$model)\n\nFrom the output of the PCA we see that we can capture 92% of the variability with the first two components. Thus we will only use the PC1 and PC2 for the subsequent analysis.",
     "crumbs": [
-      "Reference Data Collection",
+      "Block 1: Reference Data Collection",
       "Collection of training data for remote sensing model building"
     ]
   },
@@ -116,7 +116,7 @@
     "section": "Unsupervised clustering",
     "text": "Unsupervised clustering\nIn the next step we run an unsupervised classification of the PC1 and PC2 to get a clustered map. For the unsupervised classification we need to take a decision on the number of classes/clusters to be created. Here we will take \\(n=5\\) classes. However, depending on the target variable this value need to be adjusted.\n\nset.seed(2222)\ncluster &lt;- RStoolbox::unsuperClass(pca$map[[c('PC1','PC2')]], nSamples = 100, nClasses = 5, nStarts = 5)\n\n\n## Plots\ncolors &lt;- rainbow(5)\nplot(cluster$map, col = colors, legend = TRUE, axes = TRUE, box =TRUE)\n\nThe map shows a clear spatial patterns related to the elevation, tree species and vitality status of the Nationalpark forests.",
     "crumbs": [
-      "Reference Data Collection",
+      "Block 1: Reference Data Collection",
       "Collection of training data for remote sensing model building"
     ]
   },
@@ -127,7 +127,7 @@
     "section": "Implement a plot design",
     "text": "Implement a plot design\n\n# Create a training data set by extracting the mean value of all pixels touching\n# a buffered area with 13m around the plot center\nplots &lt;- sf::st_buffer(sf_samples,dist = 13)\ntrain&lt;-terra::extract(s2,plots,fun='mean',bind=FALSE,na.rm=TRUE)\n\nplots &lt;- plots %&gt;% mutate(ID=row_number())\ntrain &lt;- plots %&gt;% left_join(train, by= \"ID\")\nmapview::mapview(train, zcol=\"class_unsupervised\",\n        map.types = c(\"Esri.WorldShadedRelief\", \"OpenStreetMap.DE\"))+\n  mapview(np_boundary,alpha.regions = 0.2, aplha = 1)",
     "crumbs": [
-      "Reference Data Collection",
+      "Block 1: Reference Data Collection",
       "Collection of training data for remote sensing model building"
     ]
   },
@@ -138,7 +138,7 @@
     "section": "",
     "text": "library(here)\nlibrary(sf)\nlibrary(dplyr)\nlibrary(terra)\nlibrary(ggplot2)\nlibrary(foreign)\n\nLoad BI plots, BI trees and trees detected in ndom (created in previous script).\n\nplots_bi &lt;- st_read(here(\"R_session\", \"data\", \"buffer13_bi_mittelpunkt_solling_2023.gpkg\"))\n\ntrees_bi &lt;- st_read(here(\"R_session\", \"data\", \"bi_baeume_vorrat_solling_2023.gpkg\"))\n\ntrees_ndom &lt;- st_read(here(\"R_session\", \"data\", \"ttops_bi_ndom.gpkg\"))\n\nLoad table with median slope in the BI plot and bind with plots BI table\n\nslope_bi_plot &lt;- read.dbf(here(\"R_session\", \"data\", \"bi_plot_median_slope.dbf\"))\nplots_bi &lt;- bind_cols(plots_bi, slope_bi_plot %&gt;% select(X_median))\nrm(slope_bi_plot)\n\nCount how many bi trees inside plot\n\nintersection_bi_trees_plots &lt;- st_intersection(x = plots_bi, y = trees_bi)\n\ncount_bi_trees_plot &lt;- intersection_bi_trees_plots %&gt;% \n    add_count(KSPNR, name = \"n_trees_bi\") %&gt;% \n    group_by(KSPNR) %&gt;% \n    slice(n()) %&gt;% \n    ungroup() \n\nOptional: Trees_bi 2638 observations, intersection_bi_trees_plots 2606 observations, check reason from this difference\n\n#st_erase = function(x, y) st_difference(x, st_union(st_combine(y)))\n\n#difference &lt;- st_erase(trees_bi , intersection_bi_trees_plots)\n\n#writeVector(vect(difference), here(\"output\", \"trees_not_bi.gpkg\"), overwrite=TRUE)\n\nCount how many ndom trees inside plot\n\nintersection_ndom_trees_plots &lt;- st_intersection(x = plots_bi, y = trees_ndom)\n\ncount_ndom_trees_plot &lt;- intersection_ndom_trees_plots %&gt;% \n    add_count(KSPNR, name = \"n_trees_ndom\") %&gt;% \n    group_by(KSPNR) %&gt;% \n    slice(n()) %&gt;% \n    ungroup() \n\nMerge table with number trees from ndom and table with number trees from BI\n\ncount_trees_bi_ndom &lt;- merge(as.data.frame(count_ndom_trees_plot), as.data.frame(count_bi_trees_plot), by = \"KSPNR\")\n\nPlot to compare the number of trees in each plot taking slope of plot into account\n\nx &lt;- ggplot(count_trees_bi_ndom, aes(x=n_trees_bi, y=n_trees_ndom,        colour=X_median.x)) + \n              xlim(0,30) + ylim(0,30) +\n              geom_abline(slope=1) +\n              geom_point() +\n              scale_colour_gradientn(colours=rainbow(4)) +\n              labs(title = \"Number of trees in each plot measured in BI vs detected with nDOM\", \n              x = \"Number of trees measured in BI\",\n              y = \"Number of trees detected in nDOM\",\n              color = \"Terrain slope [degree]\") +\n              theme(plot.title = element_text(hjust = 0.5))\n\nWhat could be the reasons for the difference?",
     "crumbs": [
-      "Remote Sensing applications in forest management planing",
+      "Block 2: Remote Sensing applications in forest management planning",
       "Number_Trees_BI_nDOM"
     ]
   },
@@ -149,7 +149,7 @@
     "section": "",
     "text": "library(here)\nlibrary(sf)\nlibrary(dplyr)\nlibrary(terra)\nlibrary(ggplot2)\nlibrary(foreign)\n\nLoad BI plots, BI trees and trees detected in ndom (created in previous script).\n\nplots_bi &lt;- st_read(here(\"R_session\", \"data\", \"buffer13_bi_mittelpunkt_solling_2023.gpkg\"))\n\ntrees_bi &lt;- st_read(here(\"R_session\", \"data\", \"bi_baeume_vorrat_solling_2023.gpkg\"))\n\ntrees_ndom &lt;- st_read(here(\"R_session\", \"data\", \"ttops_bi_ndom.gpkg\"))\n\nLoad table with median slope in the BI plot and bind with plots BI table\n\nslope_bi_plot &lt;- read.dbf(here(\"R_session\", \"data\", \"bi_plot_median_slope.dbf\"))\nplots_bi &lt;- bind_cols(plots_bi, slope_bi_plot %&gt;% select(X_median))\nrm(slope_bi_plot)\n\nCount how many bi trees inside plot\n\nintersection_bi_trees_plots &lt;- st_intersection(x = plots_bi, y = trees_bi)\n\ncount_bi_trees_plot &lt;- intersection_bi_trees_plots %&gt;% \n    add_count(KSPNR, name = \"n_trees_bi\") %&gt;% \n    group_by(KSPNR) %&gt;% \n    slice(n()) %&gt;% \n    ungroup() \n\nOptional: Trees_bi 2638 observations, intersection_bi_trees_plots 2606 observations, check reason from this difference\n\n#st_erase = function(x, y) st_difference(x, st_union(st_combine(y)))\n\n#difference &lt;- st_erase(trees_bi , intersection_bi_trees_plots)\n\n#writeVector(vect(difference), here(\"output\", \"trees_not_bi.gpkg\"), overwrite=TRUE)\n\nCount how many ndom trees inside plot\n\nintersection_ndom_trees_plots &lt;- st_intersection(x = plots_bi, y = trees_ndom)\n\ncount_ndom_trees_plot &lt;- intersection_ndom_trees_plots %&gt;% \n    add_count(KSPNR, name = \"n_trees_ndom\") %&gt;% \n    group_by(KSPNR) %&gt;% \n    slice(n()) %&gt;% \n    ungroup() \n\nMerge table with number trees from ndom and table with number trees from BI\n\ncount_trees_bi_ndom &lt;- merge(as.data.frame(count_ndom_trees_plot), as.data.frame(count_bi_trees_plot), by = \"KSPNR\")\n\nPlot to compare the number of trees in each plot taking slope of plot into account\n\nx &lt;- ggplot(count_trees_bi_ndom, aes(x=n_trees_bi, y=n_trees_ndom,        colour=X_median.x)) + \n              xlim(0,30) + ylim(0,30) +\n              geom_abline(slope=1) +\n              geom_point() +\n              scale_colour_gradientn(colours=rainbow(4)) +\n              labs(title = \"Number of trees in each plot measured in BI vs detected with nDOM\", \n              x = \"Number of trees measured in BI\",\n              y = \"Number of trees detected in nDOM\",\n              color = \"Terrain slope [degree]\") +\n              theme(plot.title = element_text(hjust = 0.5))\n\nWhat could be the reasons for the difference?",
     "crumbs": [
-      "Remote Sensing applications in forest management planing",
+      "Block 2: Remote Sensing applications in forest management planning",
       "Number_Trees_BI_nDOM"
     ]
   },
@@ -325,7 +325,7 @@
     "section": "",
     "text": "froggit shop [DE]\n  \n  \n    \n     ecowitt shop [US]\n  \n  \n    \n     Fine Offset\n  \n\n      \nThe sensors from Fine Offset are re-branded and partly modified by the resellers. This article deals with sensors from the german re-seller froggit and the US re-seller ecowitt. More precise the DP-/GW SmartHubs WiFi Gateway with temperature, humidity & Pressure which is developed by fine offset. The unique selling point of the LoRa-Wifi gateway is the extraordinarily extensive possibility of connecting radio-bound sensors.",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Microclimate Sensors & Power supply Units"
     ]
   },
@@ -336,7 +336,7 @@
     "section": "Calibration Concept",
     "text": "Calibration Concept\nThe low budget sensors are usually lacking of a stable measurement quality. To obtain reliable micro climate data a two step calibration process is suggested.\n\nThe measurements of all sensors (preferably in a climate chamber) will be statistically analysed to identify sensor which produce systematic and significant outliers.\nThe sensors are calibrated against an operational running high price reference station in the field.\n\n\n\n\n\n\n\nFuture Calibration Plans\n\n\n\n\n\nFor the future a machine learning approach including the radiation, azimuth, temperature and humidity as predictors for the calibrated temperature as the response variable will be used as an rolling calibration tool.",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Microclimate Sensors & Power supply Units"
     ]
   },
@@ -347,7 +347,7 @@
     "section": "Switching scheme",
     "text": "Switching scheme\nThe battery box has a very simple design. Besides the cabling, it contains a solar charge regulator, a fuse panel for the protection of the consumers and an AGM 120aH battery.\n ## Components * Sealable, durable Wham Bam Heavy Duty Box, 62 L, 59,5 x 40 x 37 cm, PP Recycling Plastic Wham Bam Box. The “Wham Bam Box” made of recycled PP plastic was chosen for its extreme mechanical strength and almost complete biochemical resistance. The bad temperature spectrum for thermal stability is from approx. -10 -140 °C., it is acid and alkali resistant and waterproof. By additionally equipping the box with a fire protection mat, the almost airtight closure offers a virtually complete reduction of fire load inside and outside the box. * 12V deep-cycle battery BSA Audio Solar Technologie 120 Ah 12V C100 * 3 x Neutrik powerCON TRUE1 NAC3FPX outlets and Neutrik SCNAC-FPX sealing cover. * Fuse Box for car fuses up to max. 15A per fuse, maximum 30A per fuse box, With sealed cover, splash-proof, Material: PA6.6, 12 connections on the side * Nominal voltage: 32 V/DC * Nominal current (per output): 15 A * Temperature range: -20 - +85 °C * Connections: Flat plug 8x 6,3 x 0,8 mm lateral * Solar charge controller, 20A (ALLPOWERS, available from various brands) Specification ALLPOWERS",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Microclimate Sensors & Power supply Units"
     ]
   },
@@ -358,7 +358,7 @@
     "section": "Wiring",
     "text": "Wiring\n\nBattery to solar charger:\n\nPole terminal connectors (+ and -)\n6 mm2 cables (red and black)\n2 x Crimp cable shoes\n\nSolar panel to solar charger\n\nMC4 photovoltaic connectors (+ and -) Weidemüller\n6 mm2 cables (red and black)\n2x Crimp cable shoes\n\nSolar charger fuse box outlets\n\n6 x 1,5 mm2 cables, red\n6 x 1/4’’ FASTON terminals Fuse Box\n3 x 1,5 mm2 cables, black\n2 x Crimp cable shoes (holding 3 wires)\n6 x 6,35mm / 1/4’’ crimp FASTON terminals\n\n\nPlease note the following points: * Silicone cables, solar cables, plugs and fuse box fulfills industry standards. All cable lugs are crimped and checked. * The cable lugs are not screwed to the charging cables with cable lugs but through the crimp connection with the end sleeve. * A main fuse (e.g. 40A automatic circuit breaker) must be installed\nSee also the figure below.",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Microclimate Sensors & Power supply Units"
     ]
   },
@@ -369,7 +369,7 @@
     "section": "Mounting",
     "text": "Mounting\n\nOutlets: 6x M3 screw (12mm), washers and nuts\nSolar connectors: 2 x waterproof cable glands\nSolar charger and fuse box:\n\nWooden plate, glued to the box\n4 screws for Solar Charge Controller\n4 screws for fuse box Cable lugs and plugs are covered with self-vulcanizing tape and additionally insulated.",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Microclimate Sensors & Power supply Units"
     ]
   },
@@ -380,7 +380,7 @@
     "section": "Station setup in the field",
     "text": "Station setup in the field\nFor safe operation, the following points must be taken into account when setting up the box:\n1.) The box must be placed horizontally. Preferable at on a clearing to reduce impacts of falling branches or similar.  2.) One square meter around the box must be cleared of any vegetation and the A-horizon (depending on the slope, even more).\n 3.) Around this area a further strip with a diameter of at least 1 meter must also be cleared of organic material, especially leaves. Dig up the A-horizon and exclude roots and organic stuff. Note that the wiring sections must also be cleared of combustible organic material.\n 4.) Check cables and screws for proper seating and integrity.\n\n5.) Check proper installation of the solar panel. Mount the panel on a simple wooden slat attached to the frame to avoid damage to the protective foil on the back. Such damage will destroy the panel.\n\n6.) Attach the solar connectors to the panel. This avoids ground contact and provides good weather protection. This can be done very easily by threading cable ties through the plugs and the junction box. {% include figure image_path=“../images/battery_box/07_solar_plugs.jpg” alt=“Attach the solar connectors to the panel.” %}  7.) Finally, the box should be secured against unauthorized or accidental opening. For this purpose there is a steel cable with a number lock, which is to be attached in the way it is placed there.",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Microclimate Sensors & Power supply Units"
     ]
   },
@@ -391,7 +391,7 @@
     "section": "Final check",
     "text": "Final check\n\nAll contacts and cables must be checked for proper seating and integrity. Especially the charging cables on the battery must be screwed tightly.\nAll cables are to be laid without tension.\nThe solar cables are to be laid separately to avoid a short circuit, so that an animal crossing etc. does not cause them to come into contact.\nThe box is secured and tight.",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Microclimate Sensors & Power supply Units"
     ]
   },
@@ -402,7 +402,7 @@
     "section": "Risk Assessment",
     "text": "Risk Assessment\nHere you find the preliminary risk assesment for the installation and operation of 12 V solar power based energy supply units and measuring sensor systems.",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Microclimate Sensors & Power supply Units"
     ]
   },
@@ -420,6 +420,17 @@
     "section": "",
     "text": "This is a senseless question to meet a meaningfull answer\n\n\n\n\n\n\n\n\n\nThis is a meaningful answer to a senseless question\n\n\n\n\n\n\n\n\n\nLearn More…\n\n\n\n\n\nThis is a even more meaningful answer to a senseless question"
   },
+  {
+    "objectID": "block6/describing_and_comparing_spaital_patterns.html",
+    "href": "block6/describing_and_comparing_spaital_patterns.html",
+    "title": "EON Summer School 2024",
+    "section": "",
+    "text": "You can find all of the workshop materials at https://github.com/Nowosad/eon2024-exercises.",
+    "crumbs": [
+      "Block 6: Describing and comparing spatial patterns",
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+    ]
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     "objectID": "base/about.html",
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@@ -455,7 +466,7 @@
     "section": "",
     "text": "Station inside the dead Fir plot (Ecowitt Dashboard Login required)\n  \n  \n    \n     Station inside the clear cut (Ecowitt Dashboard Login required)\n  \n  \n    \n     Position Data of the climate stations\n  \n\n      \nData logger and measurement data",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Data Sources"
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   },
@@ -529,7 +540,7 @@
     "section": "",
     "text": "Goal is to detect tree tops using a variable window size and using the ndom raster in the BI plots in Solling 2023.\n\nlibrary(lidR)\nlibrary(terra)\nlibrary(here)\n\nLoad ndom previously clipped to BI plots in Solling (BI 2023)\n\nndom_bi &lt;- rast(here(\"R_session\", \"data\", \"ndom_bi_plots_solling_2023.tif\"))\nmapview(ndom_bi)\n\nMake tiles because data too big for R\n\ntiles &lt;- makeTiles(x=ndom_bi, \n          y=c(3000,3000),\n          filename=\n            here(\"R_session\", \"output\",\"tiles\",\"ndom_bi_.tif\"), \n          na.rm=TRUE)\n\n# In case the tiles are already there\ntiles &lt;-list.files(here(\"R_session\", \"output\",\"tiles\"), pattern='*\\\\.tif', recursive=TRUE, full.names=TRUE)\n\nTree detection function with variable window size. Any points below 2 m will equate to a window size of 3 m, while points above 20 meters equate to a window size of 5 m. Anything between 2 and 20 meter will have a non-linear relationship.\n\nf &lt;- function(x) {\n  y &lt;- 2.6 * (-(exp(-0.08*(x-2)) - 1)) + 3 \n  # from https://r-lidar.github.io/lidRbook/itd.html\n  y[x &lt; 2] &lt;- 3\n  y[x &gt; 20] &lt;- 5\n  return(y)\n}\n\nheights &lt;- seq(-5,30,0.5)\nws &lt;- f(heights)\nplot(heights, ws, type = \"l\",  ylim = c(0,5))\n\nTree detection using function for variable window size\n\nout &lt;- sapply(tiles, \\(tile) {\n        x &lt;- rast(tile)\n        ttops &lt;- lidR::locate_trees(x, lidR::lmf(f))\n        vect(ttops)\n    })\n\nout &lt;- vect(out)\n\nSave tree tops\n\nwriteVector(out, here(\"R_session\", \"output\",\"ttops_ndom_.gpkg\"), overwrite=TRUE)",
     "crumbs": [
-      "Remote Sensing applications in forest management planing",
+      "Block 2: Remote Sensing applications in forest management planning",
       "Tree detection"
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@@ -540,7 +551,7 @@
     "section": "",
     "text": "Goal is to detect tree tops using a variable window size and using the ndom raster in the BI plots in Solling 2023.\n\nlibrary(lidR)\nlibrary(terra)\nlibrary(here)\n\nLoad ndom previously clipped to BI plots in Solling (BI 2023)\n\nndom_bi &lt;- rast(here(\"R_session\", \"data\", \"ndom_bi_plots_solling_2023.tif\"))\nmapview(ndom_bi)\n\nMake tiles because data too big for R\n\ntiles &lt;- makeTiles(x=ndom_bi, \n          y=c(3000,3000),\n          filename=\n            here(\"R_session\", \"output\",\"tiles\",\"ndom_bi_.tif\"), \n          na.rm=TRUE)\n\n# In case the tiles are already there\ntiles &lt;-list.files(here(\"R_session\", \"output\",\"tiles\"), pattern='*\\\\.tif', recursive=TRUE, full.names=TRUE)\n\nTree detection function with variable window size. Any points below 2 m will equate to a window size of 3 m, while points above 20 meters equate to a window size of 5 m. Anything between 2 and 20 meter will have a non-linear relationship.\n\nf &lt;- function(x) {\n  y &lt;- 2.6 * (-(exp(-0.08*(x-2)) - 1)) + 3 \n  # from https://r-lidar.github.io/lidRbook/itd.html\n  y[x &lt; 2] &lt;- 3\n  y[x &gt; 20] &lt;- 5\n  return(y)\n}\n\nheights &lt;- seq(-5,30,0.5)\nws &lt;- f(heights)\nplot(heights, ws, type = \"l\",  ylim = c(0,5))\n\nTree detection using function for variable window size\n\nout &lt;- sapply(tiles, \\(tile) {\n        x &lt;- rast(tile)\n        ttops &lt;- lidR::locate_trees(x, lidR::lmf(f))\n        vect(ttops)\n    })\n\nout &lt;- vect(out)\n\nSave tree tops\n\nwriteVector(out, here(\"R_session\", \"output\",\"ttops_ndom_.gpkg\"), overwrite=TRUE)",
     "crumbs": [
-      "Remote Sensing applications in forest management planing",
+      "Block 2: Remote Sensing applications in forest management planning",
       "Tree detection"
     ]
   },
@@ -551,7 +562,7 @@
     "section": "",
     "text": "The use of quantitative methods, especially statistical methods, is of considerable importance for describing and explaining spatial patterns (e.g. landscape ecology). The central concept on which these methods are based is that of proximity, or location in relation to each other.",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Spatial Interpolation"
     ]
   },
@@ -562,7 +573,7 @@
     "section": "Distance and data representation",
     "text": "Distance and data representation\nLet’s take a closer look at proximity, which is mentioned frequently. What exactly is it? How can proximity/neighborliness be expressed in such a way that the space becomes meaningful?\nIn general, spatial relationships are described in terms of neighborhoods (positional) and distances (metric). In spatial analysis or prediction, however, it is important to be able to name the spatial influence, i.e. the evaluation or weighting of this relationship, either qualitatively or quantitatively. Tobler did this for a specific objective by stating that “near” is more important than “far”. But what about in other cases? The challenge is that spatial influence can only be measured directly in exceptional cases. There are many ways to estimate it, however.\n\nNeighborhood\nNeighborhood is perhaps the most important concept. Higher dimensional geo-objects can be considered neighboring if they touch each other, e.g. neighboring countries. For zero-dimensional objects (points), the most common approach is to use distance in combination with a number of points to determine neighborhood.\n\n\nDistance\nProximity or neighborhood analyses are often concerned with areas of influence or catchment areas, i.e. spatial patterns of effects or processes.\nThis section discusses some methods for calculating distances between spatial objects. Because of the different ways of discretizing space, we must make the – already familiar – distinction between vector and raster data models.\nInitially, it is often useful to work without spatially restrictive conditions in a first analysis, e.g. when this information is missing. The term “proximity” inherently implies a certain imprecision. Qualitative terms that can be used for this are: “near”, “far” or “in the neighborhood of”. Representation and data-driven analysis require these terms to be objectified and operationalized. So, this metric must be based on a distance concept, e.g. Euclidean distance or travel times. In a second interpretative step, we must decide which units define this type of proximity. In terms of the objective of a question, there are only suitable and less-suitable measures; there is no correct or incorrect. Therefore, it is critical to define a meaningful neighborhood relationship for the objects under investigation.",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Spatial Interpolation"
     ]
   },
@@ -573,7 +584,7 @@
     "section": "Filling spatial gaps",
     "text": "Filling spatial gaps\nNow that we have learned the basic concepts of distance, neighborhood and filling spatial gaps, let’s take a look at interpolating or predicting values in space.\nFor many decades, deterministic interpolation techniques (inverse distance weighting, nearest neighbor, kriging) have been the most popular spatial interpolation techniques. External drift kriging and regression kriging, in particular, are fundamental techniques that use spatial autocorrelation and covariate information, i.e. sophisticated regression statistics.\nMachine learning algorithms like random forest have become very popular for spatial environmental prediction. One major reason for this is that they are can take into account non-linear and complex relationships, i.e. compensate for certain disadvantages that are present in the usual regression methods.",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Spatial Interpolation"
     ]
   },
@@ -584,7 +595,7 @@
     "section": "Proximity concepts",
     "text": "Proximity concepts\n\nVoronoi polygons – dividing space geometrically\nVoronoi polygons (aka Thiessen polygons) are an elementary method for geometrically determining proximity or neighborhoods. Voronoi polygons (see figure below) divide an area into regions that are closest to a given point in a set of irregularly distributed points. In two dimensions, a Voronoi polygon encompasses an area around a point, such that every spatial point within the Voronoi polygon is closer to this point than to any other point in the set. Such constructs can also be formed in higher dimensions, giving rise to Voronoi polyhedra.\n&lt;/frame&gt;\n&lt;iframe width=\"780\" height=\"500\" src=\"https://geomoer.github.io/geoAI//assets/images/unit01/suisse6.html\" title=\"Interpol\"&gt;\n\n\nThe blue dots are a typical example of irregularly distributed points in space – in this case, rain gauges in Switzerland. The overlaid polygons are the corresponding Voronoi segments that define the corresponding closest geometrical areas (gisma 2021)“\n\n\nSince Voronoi polygons correspond to an organizational principle frequently observed in both nature (e.g. plant cells) and in the spatial sciences (e.g. central places , according to Christaller), there are manifold possible applications. Two things must be assumed, however: First, that nothing else is known about the space between the sampled locations and, second, that the boundary line between two samples is incomplete idea.\nVoronoi polygons can also be used to delineate catchment areas of shops, service facilities or wells, like in the example of the Soho cholera outbreak. Please note that within a polygon, one of the spatial features is isomorphic, i.e. the spatial features are identical.\nBut what if we know more about the spatial relationships of the features? Let’s have a look at some crucial concepts.\n\n\nSpatial interpolation of data\nSpatially interpolating data points provides us with a modeled quasi-continuous estimation of features under the corresponding assumptions. But what is spatial interpolation? Essentially, this means using known values to calculate neighboring values that are unknown. Most of these techniques are among the most complex methods of spatial analysis, so we will deliberately limit ourselves here to a basic overview of the methods. Some of the best-known and common interpolation methods found in spatial sciences are nearest neighbor inverse distance, spline interpolations, kriging, and regression methods.\n\n\nContinously filling the gaps by interpolation\nTo get started, take a look at the following figure, which shows six different interpolation methods to derive the spatial distribution of precipitation in Switzerland (in addition to the overlaid Voronoi tessellation).\n\n\n\nThe blue dots are a typical example of irregularly distributed points in space – in this case, rain gauges in Switzerland. The size of each dot corresponds to the amount of precipitation in mm. The overlaid polygons are the corresponding Voronoi segments that define the corresponding closest geometrical areas (gisma 2021)” top left: Nearest neighbor interpolation based on 3-5 nearest neighbors, top right: Inverse Distance weighting (IDW) interpolation method middle left: AutoKriging with no additional parameters, middle right: Thin plate spline regression interpolation method bottom left: Triangular irregular net (TIN) surface interpolation, bottom right: additive model (GAM) interpolation\n\n\nIn the example of precipitation in Switzerland, the positions of the weather stations are fixed and cannot be freely chosen.\nWhen choosing an appropriate interpolation method, we need to pay attention to several properties of the samples (distribution and properties of the measurement points):\n\nRepresentativeness of measurement points: The sample should represent the phenomenon being analyzed in all of its manifestations.\nHomogeneity of measurement points: The spatial interdependence of the data is a very important basic requirement for further meaningful analysis.\nSpatial distribution of measurement points: The spatial distribution is of great importance. It can be completely random, regular or clustered.\nNumber of measurement points: The number of measurement points depends on the phenomenon and the area. In most cases, the choice of sample size is subject to practical limitations.\n\nWhat makes things even more complex is that these four factors – representativeness, homogeneity, spatial distribution and size – are all interrelated. For example, a sample size of 5 measuring stations for estimating precipitation for all of Switzerland is hardly meaningful and therefore not representative. Equally unrepresentative would be selecting every measuring station in German-speaking Switzerland to estimate precipitation for the entire country. In this case, the number alone might be sufficient, but the spatial distribution would not be. If we select every station at an altitude below 750 m asl, the sample could be correct in terms of both size and spatial distribution, but the phenomenon is not homogeneously represented in the sample. An estimate based on this sample would be clearly distorted, especially in areas above 750 m asl. In practice, virtually every natural spatially-continuous phenomenon is governed by stochastic fluctuations, so, mathematically speaking, it can only be described in approximate terms.\n\n\nMachine learning\nMachine learning (ML) methods such as random forest can also produce spatial and temporal predictions (i.e. produce maps from point observations). These methods are particularly robust because they take spatial autocorrelation into account, which can improve predictions or interpolations by adding geographic distances. This ultimately leads to better maps with much more complex relationships and dependencies.\nIn the simplest case, the results are comparable to the well-known model-based geostatistics. The advantage of ML methods over model-based geostatistics, however, is that they make fewer assumptions, can take non-linearities into account and are easier to automate.\n\n\n\nThe original dataset (top left) is a terrain model reduced to 8 meters with 48384 single pixels. For interpolation, 1448 points were randomly drawn and interpolated with conventional kriging (top right), support vector machines (SVM) (middle left), neural networks (middle right), and two variants of random forest (bottom row). In each method, only the distance of the drawn points is used as a dependency.\n\n\n\nEach interpolation method was applied using the “default” settings. Tuning could possibly lead to significant changes in all of them. Fascinatingly, the error measures correlate to the visual results: Kriging and the neural network show the best performance, followed by the random forest models and the support-vector machine.\n\n\n\nmodel\ntotal_error\nmean_error\nsd_error\n\n\n\n\nKriging\n15797773.0\n54.2\n67.9\n\n\nNeural Network\n19772241.0\n67.8\n80.5\n\n\nRandom Forest\n20540628.1\n70.4\n82.5\n\n\nNormalized Random Forest\n20597969.8\n70.6\n82.7\n\n\nSupport Vector Machine\n21152987.7\n72.5\n68.3\n\n\n\n\n\nAdditional references\nGet the Most Out of AI, Machine Learning, and Deep Learning Part 1 (10:52) and Part 2 (13:18)\nWhy You Should NOT Learn Machine Learning! (6:17)\nGeoAI: Machine Learning meets ArcGIS (8:50)",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Spatial Interpolation"
     ]
   },
@@ -595,7 +606,7 @@
     "section": "Hands on our data",
     "text": "Hands on our data\n\nSetup the environment\nPlease download the data from the repository or take the USB-stick\n\n#------------------------------------------------------------------------------\n# Author: creuden@gmail.com\n# Description:  interpolates the air temp\n# Copyright:GPL (&gt;= 3)  Date: 2023-08-28 \n#------------------------------------------------------------------------------\n\n# 0 ---- project setup ----\n\n# load packages (if not installed please install via install.packages())\nlibrary(\"raster\")\nlibrary(\"terra\")\nlibrary(\"sp\")\nlibrary(\"sf\")\nlibrary(\"dplyr\")\nlibrary(\"lwgeom\")\nlibrary(\"readxl\")\nlibrary(\"highfrequency\")\nlibrary(\"tidyverse\")\nlibrary(\"rprojroot\")\nlibrary(\"tibble\")\nlibrary(\"xts\")\nlibrary(\"data.table\")\nlibrary(\"mapview\")\nlibrary(stars)\nlibrary(gstat)\n\n# create a string containing the current working directory\nwd=paste0(find_rstudio_root_file(),\"/mc_session/data/\")\n\n# define time period to aggregate temp dat\ntime_period = 3\n\n# multiplication factor for blowing up the Copernicus DEM\nblow_fac = 15\n\n# reference system as proj4 string for old SP package related stuff\ncrs = raster::crs(\"+proj=utm +zone=33 +datum=WGS84 +units=m +no_defs\")\nsfcrs &lt;- st_crs(\"EPSG:32633\")\n\n# Copernicus DEM (https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1)\nfnDTM = paste0(wd,\"copernicus_DEM.tif\")  \n\n# Weather Data adapt if you download a new file \n# https://www.ecowitt.net/home/index?id=20166)\n# https://www.ecowitt.net/home/index?id=149300\nfn_dataFC29 = paste0(wd,\"all_GW1000A-WIFIFC29.xlsx\")\nfn_dataDB2F =paste0(wd,\"all_GW1000A-WIFIDB2F.xlsx\")\n\n# station data as derived by the field group\nfn_pos_data= paste0(wd,\"stations_prelim.shp\")\n\n# arbitrary plot borders just digitized for getting a limiting border of the plot area\nfn_area =paste0(wd,\"plot.shp\")\n\n# rds file for saving the cleaned up weather data\ncleandata = paste0(wd,\"climdata.RDS\")\n\n# 1 ---- read data ----\n# read_sf(\"data/de_nuts1.gpkg\") |&gt; st_transform(crs) -&gt; de\n# read DEM data\nDTM = terra::rast(fnDTM) # DTM.\n# increase resolution by 15\nDTM=disagg(DTM, fact=c(blow_fac, blow_fac)) \n#rename layer to altitude\nnames(DTM)=\"altitude\"\nr=DTM*0\n\n# read station position data\n pos=st_read(fn_pos_data)\n\nReading layer `stations_prelim' from data source \n  `/home/creu/edu/gisma-courses/EON2024/mc_session/data/stations_prelim.shp' \n  using driver `ESRI Shapefile'\nSimple feature collection with 14 features and 1 field\nGeometry type: POINT\nDimension:     XY\nBounding box:  xmin: 183055.8 ymin: 5748366 xmax: 183170.3 ymax: 5748499\nProjected CRS: WGS 84 / UTM zone 33N\n\n # read station position data\narea=st_read(fn_area)\n\nReading layer `plot' from data source \n  `/home/creu/edu/gisma-courses/EON2024/mc_session/data/plot.shp' \n  using driver `ESRI Shapefile'\nSimple feature collection with 1 feature and 1 field\nGeometry type: POLYGON\nDimension:     XY\nBounding box:  xmin: 10.40154 ymin: 51.79506 xmax: 10.40658 ymax: 51.79803\nGeodetic CRS:  WGS 84\n\n# reproject the dataset to the project crs\narea=st_transform(area,crs)\n# read temperature data we need to skip row 1 due to excel format\nclim_dataFC29 = as_tibble(read_excel(fn_dataFC29, skip = 1)) \nclim_dataDB2F = as_tibble(read_excel(fn_dataDB2F, skip = 1))\n\n\n\nCleaning data\nWe need to do an ugly cleaning job. This is basically the most cumbersome part of dealing with data analysis.\n\n# select the required cols\ntempFC29 = clim_dataFC29 %&gt;% dplyr::select(c(1,2,32,36,40,44,48))\ntempDB2F = clim_dataDB2F %&gt;% dplyr::select(c(1,25,29,33,37,41,45,49,53))\n# rename header according to the pos file names and create a merge field time\nnames(tempDB2F) = c(\"time\",\"ch1_r\",\"ch2_r\",\"ch3_r\",\"ch4_r\",\"ch5_r\",\"ch6_r\",\"ch7_r\",\"ch8_r\")\nnames(tempFC29) = c(\"time\",\"base\",\"ch1\",\"ch2\",\"ch3\",\"ch4\",\"ch5\")\n#merge files\ntemp=merge(tempFC29,tempDB2F)\n# convert datum which is a string to date format\ntemp$time=as.POSIXct(temp$time)\n# aggregate timeslots according to the value in time_period\ntemp3h = aggregateTS(as.xts(temp), alignBy = \"hours\",dropna = T,alignPeriod = time_period)\n# add the datum colum (which is now a pointer of the timeseries) as first col in the dataset\ntemp_fin=as_tibble(temp3h) %&gt;% add_column(time = index(temp3h), .before = 1)\n# transpose and combine the table\ntemp_fin=as_tibble(cbind(nms = names(temp_fin), t(temp_fin)))\n# delete first row \nnames(temp_fin) = temp_fin[1,]\ntemp_fin=temp_fin[-1,]\n# replace names specially time by stationid\nnames(temp_fin)[names(temp_fin) == 'time'] = 'stationid'\n# extract altitudes for positions\npos$altitude= exactextractr::exact_extract(DTM,st_buffer(pos,1),\"mean\")\n\n\n  |                                                                            \n  |                                                                      |   0%\n  |                                                                            \n  |=====                                                                 |   7%\n  |                                                                            \n  |==========                                                            |  14%\n  |                                                                            \n  |===============                                                       |  21%\n  |                                                                            \n  |====================                                                  |  29%\n  |                                                                            \n  |=========================                                             |  36%\n  |                                                                            \n  |==============================                                        |  43%\n  |                                                                            \n  |===================================                                   |  50%\n  |                                                                            \n  |========================================                              |  57%\n  |                                                                            \n  |=============================================                         |  64%\n  |                                                                            \n  |==================================================                    |  71%\n  |                                                                            \n  |=======================================================               |  79%\n  |                                                                            \n  |============================================================          |  86%\n  |                                                                            \n  |=================================================================     |  93%\n  |                                                                            \n  |======================================================================| 100%\n\n# merge positions and values via id\nm=merge(pos,temp_fin)\n# make the var name working for gstat by replacing all patterns\nn= gsub(x = names(m),pattern = \"-\",replacement = \"\")\nn= gsub(x = n,pattern = \" \",replacement = \"\")\nn= gsub(x = n,pattern = \":\",replacement = \"\")\nn= gsub(x = n,pattern = \"2023\",replacement = \"A2023\")\n# and rename couse this as new names\nnames(m)=n\nm= st_transform(m,sfcrs)\n\nsaveRDS(m,cleandata)\n\n\n\nPreparing and converting spatial basis data sets\nAfter the basic cleaning is finished we prepare some specific datasets according to the technical needs.\n\n# grep the varnames for an interpolation loop\nvars=grep(glob2rx(\"A2023*\"), n, value = TRUE)\nvars\n\n[1] \"A20230828230000\" \"A20230829020000\" \"A20230829050000\" \"A20230829080000\"\n[5] \"A20230829110000\" \"A20230829140000\" \"A20230829170000\" \"A20230829200000\"\n[9] \"A20230829220000\"\n\n# convert final sf vector to terra vector\ntemperature_vect = vect(m)\ntemperature_vect \n\n class       : SpatVector \n geometry    : points \n dimensions  : 14, 11  (geometries, attributes)\n extent      : 183055.8, 183170.3, 5748366, 5748499  (xmin, xmax, ymin, ymax)\n coord. ref. : WGS 84 / UTM zone 33N (EPSG:32633) \n names       : stationid altitude A20230828230000 A20230829020000\n type        :     &lt;chr&gt;    &lt;num&gt;           &lt;chr&gt;           &lt;chr&gt;\n values      :      base    579.1            10.3            10.1\n                     ch1      578            10.6            10.2\n                   ch1_r    575.3            10.7            10.0\n A20230829050000 A20230829080000 A20230829110000 A20230829140000\n           &lt;chr&gt;           &lt;chr&gt;           &lt;chr&gt;           &lt;chr&gt;\n             9.5             9.6            14.8              13\n             9.5             9.7              12            14.3\n             9.6            10.4            13.4            18.3\n A20230829170000 A20230829200000 A20230829220000\n           &lt;chr&gt;           &lt;chr&gt;           &lt;chr&gt;\n            15.2            12.8            11.5\n            16.9            12.9            10.4\n            19.2             9.9             8.1\n\n# create table containing x, y, value (A20230829220000) to interpolate this values in space\nxyz=cbind(geom(temperature_vect)[,3],geom(temperature_vect)[,4],as.numeric(temperature_vect$A20230829220000))\n# convert to data frame and name header\nxyz=data.frame(xyz)\nnames(xyz) =c(\"x\",\"y\",\"temp\")\nxyz\n\n          x       y temp\n1  183137.7 5748385 11.5\n2  183087.0 5748438 10.4\n3  183130.3 5748437  8.1\n4  183055.8 5748460 10.7\n5  183098.0 5748374  9.7\n6  183081.0 5748499 10.6\n7  183110.5 5748389  9.7\n8  183095.5 5748465 10.7\n9  183144.5 5748366  8.9\n10 183120.1 5748476 10.5\n11 183170.3 5748415  6.4\n12 183144.8 5748427  7.4\n13 183156.8 5748389  7.4\n14 183134.0 5748399  7.8\n\n# the same just for x,y\nxy=cbind(geom(temperature_vect)[,3],geom(temperature_vect)[,4])\n#the same just for z\nz=as.numeric(temperature_vect$A20230829220000)",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Spatial Interpolation"
     ]
   },
@@ -606,7 +617,7 @@
     "section": "Further Hands on examples",
     "text": "Further Hands on examples\nThe Forgenius Pinus Pinaster Project provides an fully integrated GIS source code and field data dealing with prediction classificaten of UAV and station realted data.",
     "crumbs": [
-      "Microclimate Data Retrieval & Spatial Data Interpolation",
+      "Block 4 and 5: Microclimate Data Retrieval & Spatial Data Interpolation",
       "Spatial Interpolation"
     ]
   },
@@ -617,7 +628,7 @@
     "section": "",
     "text": "rm(list=ls())\nlibrary(sf)\n\nWarning: Paket 'sf' wurde unter R Version 4.3.3 erstellt\n\n\nLinking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUE\n\nlibrary(terra)\n\nWarning: Paket 'terra' wurde unter R Version 4.3.3 erstellt\n\n\nterra 1.7.78\n\nlibrary(ggplot2)\n\nWarning: Paket 'ggplot2' wurde unter R Version 4.3.3 erstellt\n\nlibrary(rprojroot)\n\nWarning: Paket 'rprojroot' wurde unter R Version 4.3.3 erstellt\n\nlibrary(patchwork)\n\nWarning: Paket 'patchwork' wurde unter R Version 4.3.3 erstellt\n\n\n\nAttache Paket: 'patchwork'\n\n\nDas folgende Objekt ist maskiert 'package:terra':\n\n    area\n\nwd=paste0(find_rstudio_root_file(),\"/tdv_session/data/\")",
     "crumbs": [
-      "Reference Data Collection",
+      "Block 1: Reference Data Collection",
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   },
@@ -628,7 +639,7 @@
     "section": "Systematic sample to collect reference data for map validation",
     "text": "Systematic sample to collect reference data for map validation\nTo validate the map we use a systematic sample grid. In a real world application we do not know the true population values. Therefore, field work would be needed to collect reference data at the selected sample points. In this workshop we assume that the agp_pop map represents the true value without any errors. Thus, we don’t need to go to field but we can sample the data by extracting the true values from the map at the sample locations.\n\n# we will use n=100 sample plots\nn=100\np1 = sf::st_sample(np_boundary,size=n,type='regular')\n\nggplot()+geom_sf(data=np_boundary,fill=NA)+\n  geom_sf(data=p1)\n\n\n\n\n\n\n\n\nAt each sample point we extract the predicted and observed AGB value.\n\nobs &lt;- terra::extract(agb_pop,vect(p1))\nnames(obs)&lt;-c('ID','obs')\n\npred &lt;- terra::extract(agb_model,vect(p1))\nnames(pred)&lt;-c('ID','pred')\nvalidation&lt;-data.frame(observed=obs$obs, predicted=pred$pred)\n\n# we need to remove the na values from this dataframe. In real world applications\n# such NA values can,  occur for example at inaccessible field plots.\n\nvalidation&lt;-validation[complete.cases(validation),]",
     "crumbs": [
-      "Reference Data Collection",
+      "Block 1: Reference Data Collection",
       "Collection of validation data in the context of remote sensing based forest monitoring"
     ]
   },
@@ -639,7 +650,7 @@
     "section": "Assessment of the ABG-model performance",
     "text": "Assessment of the ABG-model performance\n\nggplot(data=validation,aes(x=observed, y=predicted))+\n  geom_point(alpha=0.5)+\n  xlab('Observed AGB t/ha')+ylab('Predicted AGB t/ha')\n\n\n\n\n\n\n\n\n\nSample RMSE\nAgain we can use the RMSE to express the mean difference between observed and predicted AGB.\n\nRMSE_sample = sqrt(sum((validation$observed-validation$predicted)^2)/nrow(validation))\n\nThe sample RMSE is 39.14* t/ha. To better compare the values between different target variables and models is can also express as a proportion relative to the mean value of the predictions.\n\nrRMSE = RMSE_sample/mean(validation$predicted)\n\nOn average we expect that the AGB estimate of our model has an error of 23.7 %.\n\n\nError distribution\nBut is this RMSE valid for the entire range of the observed values or do we expect higher errors for higher AGB values?\nTo see how the model performs over target value range we can use the following analysis plots.\n\nvalidation$resid&lt;-validation$observed-validation$predicted\n\np1&lt;-ggplot(data=validation,aes(x=observed, y=predicted))+\n  geom_point(alpha=0.5)+\n  xlab('Observed AGB t/ha')+ylab('Predicted AGB t/ha')+\n  xlim(0,250)+ylim(0,250)+\n  geom_abline(slope=1,intercept = 0)+\n  stat_summary(fun.data= mean_cl_normal) + \n  geom_smooth(method='lm')\n\n\n\np2&lt;-ggplot(data=validation,aes(x=observed, y=resid))+\n  geom_point(alpha=0.5)+\n  xlab('Observed AGB t/ha')+ylab('Residuals')+\n  xlim(0,250)+ylim(-50,+50)+\n  geom_abline(slope=0,intercept = 1)\n\np3&lt;-ggplot(data=validation,aes(x=resid))+\n  geom_histogram(aes(y=..density..),fill='grey',binwidth=10)+\n  xlab('Observed AGB t/ha')+ylab('Density')+\n  xlim(-150,150)+\n  stat_function(fun = dnorm, geom=\"polygon\",args = list(mean = mean(validation$resid), sd = sd(validation$resid)),color='blue',alpha=0.4,fill='blue')+\n  geom_vline(xintercept=0,color='blue')+\n  geom_vline(xintercept=mean(validation$resid),color='red')\np1+p2+p3+plot_layout(ncol=3)\n\nWarning: Removed 7 rows containing non-finite outside the scale range\n(`stat_summary()`).\n\n\n`geom_smooth()` using formula = 'y ~ x'\n\n\nWarning: Removed 7 rows containing non-finite outside the scale range\n(`stat_smooth()`).\n\n\nWarning: Removed 7 rows containing missing values or values outside the scale range\n(`geom_point()`).\n\n\nWarning: Removed 91 rows containing missing values or values outside the scale range\n(`geom_segment()`).\n\n\nWarning: Removed 16 rows containing missing values or values outside the scale range\n(`geom_point()`).\n\n\nWarning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.\nℹ Please use `after_stat(density)` instead.\n\n\nWarning: Removed 2 rows containing missing values or values outside the scale range\n(`geom_bar()`).",
     "crumbs": [
-      "Reference Data Collection",
+      "Block 1: Reference Data Collection",
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   }
diff --git a/sitemap.xml b/sitemap.xml
index 95d3d52..25ea163 100644
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-    <lastmod>2024-09-02T13:22:00.471Z</lastmod>
+    <lastmod>2024-09-02T13:29:22.709Z</lastmod>
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-    <lastmod>2024-09-02T13:22:00.063Z</lastmod>
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-    <lastmod>2024-09-02T13:22:00.611Z</lastmod>
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-    <lastmod>2024-09-02T13:22:00.327Z</lastmod>
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-    <lastmod>2024-09-02T13:22:00.459Z</lastmod>
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-    <lastmod>2024-09-02T13:22:00.267Z</lastmod>
+    <lastmod>2024-09-02T13:29:22.505Z</lastmod>
+  </url>
+  <url>
+    <loc>https://earth-observation-network.github.io/EON2024/block6/describing_and_comparing_spaital_patterns.html</loc>
+    <lastmod>2024-09-02T13:29:22.509Z</lastmod>
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     <loc>https://earth-observation-network.github.io/EON2024/base/about.html</loc>
-    <lastmod>2024-09-02T13:22:00.267Z</lastmod>
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   <url>
     <loc>https://earth-observation-network.github.io/EON2024/base/impressum.html</loc>
-    <lastmod>2024-09-02T13:22:00.267Z</lastmod>
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     <loc>https://earth-observation-network.github.io/EON2024/mc_session/mc4.html</loc>
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     <loc>https://earth-observation-network.github.io/EON2024/modules/de-git-module.html</loc>
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     <loc>https://earth-observation-network.github.io/EON2024/R_session/Tree_detection.html</loc>
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diff --git a/slides/slidelist.html b/slides/slidelist.html
index fabf091..05c63e1 100644
--- a/slides/slidelist.html
+++ b/slides/slidelist.html
@@ -194,7 +194,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -218,7 +218,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -242,7 +242,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+ <span class="menu-text">Block 3: Machine Learning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -250,6 +250,24 @@
       <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Microclimate Sensors &amp; Power supply Units</span></a>
   </div>
@@ -271,22 +289,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
@@ -353,7 +370,7 @@ <h1 class="title">Presentations</h1>
 </tr>
 </thead>
 <tbody class="list">
-<tr data-index="0" data-listing-file-modified-sort="1725283320459" data-listing-reading-time-sort="4" data-listing-word-count-sort="610" data-listing-title-sort="Slides and extensions" data-listing-filename-sort="slide1.qmd">
+<tr data-index="0" data-listing-file-modified-sort="1725283762697" data-listing-reading-time-sort="4" data-listing-word-count-sort="610" data-listing-title-sort="Slides and extensions" data-listing-filename-sort="slide1.qmd">
 <td>
 <a href="../slides/slide1.html" class="title listing-title">Slides and extensions</a>
 </td>
diff --git a/tdv_session/01_TrainingDataCollection.html b/tdv_session/01_TrainingDataCollection.html
index d97924f..a77c2ac 100644
--- a/tdv_session/01_TrainingDataCollection.html
+++ b/tdv_session/01_TrainingDataCollection.html
@@ -169,7 +169,7 @@
       <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" role="button" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
         <i class="bi bi-layout-text-sidebar-reverse"></i>
       </button>
-        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../tdv_session/01_TrainingDataCollection.html">Reference Data Collection</a></li><li class="breadcrumb-item"><a href="../tdv_session/01_TrainingDataCollection.html">Collection of training data for remote sensing model building</a></li></ol></nav>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../tdv_session/01_TrainingDataCollection.html">Block 1: Reference Data Collection</a></li><li class="breadcrumb-item"><a href="../tdv_session/01_TrainingDataCollection.html">Collection of training data for remote sensing model building</a></li></ol></nav>
         <a class="flex-grow-1" role="navigation" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
         </a>
     </div>
@@ -202,7 +202,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="true">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="true" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -226,7 +226,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -250,7 +250,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+ <span class="menu-text">Block 3: Machine Learning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -258,6 +258,24 @@
       <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Microclimate Sensors &amp; Power supply Units</span></a>
   </div>
@@ -279,22 +297,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
@@ -323,7 +340,7 @@ <h2 id="toc-title">On this page</h2>
 <!-- main -->
 <main class="content" id="quarto-document-content">
 
-<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../tdv_session/01_TrainingDataCollection.html">Reference Data Collection</a></li><li class="breadcrumb-item"><a href="../tdv_session/01_TrainingDataCollection.html">Collection of training data for remote sensing model building</a></li></ol></nav>
+<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../tdv_session/01_TrainingDataCollection.html">Block 1: Reference Data Collection</a></li><li class="breadcrumb-item"><a href="../tdv_session/01_TrainingDataCollection.html">Collection of training data for remote sensing model building</a></li></ol></nav>
 <div class="quarto-title">
 <h1 class="title">Collection of training data for remote sensing model building</h1>
 <p class="subtitle lead">Tutorial: EON Summer School 2024</p>
diff --git a/tdv_session/02_ValidationDataCollection.html b/tdv_session/02_ValidationDataCollection.html
index 4a8afa2..2ad8409 100644
--- a/tdv_session/02_ValidationDataCollection.html
+++ b/tdv_session/02_ValidationDataCollection.html
@@ -169,7 +169,7 @@
       <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" role="button" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
         <i class="bi bi-layout-text-sidebar-reverse"></i>
       </button>
-        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../tdv_session/01_TrainingDataCollection.html">Reference Data Collection</a></li><li class="breadcrumb-item"><a href="../tdv_session/02_ValidationDataCollection.html">Collection of validation data in the context of remote sensing based forest monitoring</a></li></ol></nav>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../tdv_session/01_TrainingDataCollection.html">Block 1: Reference Data Collection</a></li><li class="breadcrumb-item"><a href="../tdv_session/02_ValidationDataCollection.html">Collection of validation data in the context of remote sensing based forest monitoring</a></li></ol></nav>
         <a class="flex-grow-1" role="navigation" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
         </a>
     </div>
@@ -202,7 +202,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="true">
- <span class="menu-text">Reference Data Collection</span></a>
+ <span class="menu-text">Block 1: Reference Data Collection</span></a>
           <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" role="navigation" aria-expanded="true" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -226,7 +226,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false">
- <span class="menu-text">Remote Sensing applications in forest management planing</span></a>
+ <span class="menu-text">Block 2: Remote Sensing applications in forest management planning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -250,7 +250,7 @@
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false">
- <span class="menu-text">Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+ <span class="menu-text">Block 3: Machine Learning</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -258,6 +258,24 @@
       <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="px-0"><hr class="sidebar-divider hi "></li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 4 and 5: Microclimate Data Retrieval &amp; Spatial Data Interpolation</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="../mc_session/mc1.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Microclimate Sensors &amp; Power supply Units</span></a>
   </div>
@@ -279,22 +297,21 @@
         <li class="px-0"><hr class="sidebar-divider hi "></li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false">
- <span class="menu-text">Machine Learning</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" role="navigation" aria-expanded="false" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false">
+ <span class="menu-text">Block 6: Describing and comparing spatial patterns</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" role="navigation" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../ml_session/ML_AOA.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Machine learning for remote sensing applications</span></a>
+  <a href="../block6/describing_and_comparing_spaital_patterns.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">describing_and_comparing_spaital_patterns.html</span></a>
   </div>
 </li>
       </ul>
   </li>
-        <li class="px-0"><hr class="sidebar-divider hi "></li>
     </ul>
     </div>
 </nav>
@@ -325,7 +342,7 @@ <h2 id="toc-title">On this page</h2>
 <!-- main -->
 <main class="content" id="quarto-document-content">
 
-<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../tdv_session/01_TrainingDataCollection.html">Reference Data Collection</a></li><li class="breadcrumb-item"><a href="../tdv_session/02_ValidationDataCollection.html">Collection of validation data in the context of remote sensing based forest monitoring</a></li></ol></nav>
+<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../tdv_session/01_TrainingDataCollection.html">Block 1: Reference Data Collection</a></li><li class="breadcrumb-item"><a href="../tdv_session/02_ValidationDataCollection.html">Collection of validation data in the context of remote sensing based forest monitoring</a></li></ol></nav>
 <div class="quarto-title">
 <h1 class="title">Collection of validation data in the context of remote sensing based forest monitoring</h1>
 <p class="subtitle lead">Tutorial for the EON Summer School 2024</p>
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