VCI Estimation Using Multispectral Imagery Sample Notebook

[shivanip32]

VCI Estimation Using Multispectral Imagery Sample Notebook

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Checklist

Please go through each entry in the below checklist and mark an 'X' if that condition has been met. Every entry should be marked with an 'X' to be get the Pull Request approved.

• All imports are in the first cell?
  • First block of imports are standard libraries
  • Second block are 3rd party libraries
  • Third block are all arcgis imports? Note that in some cases, for samples, it is a good idea to keep the imports next to where they are used, particularly for uncommonly used features that we want to highlight.
• All GIS object instantiations are one of the following?
  • gis = GIS()
  • gis = GIS('home') or gis = GIS('pro')
  • gis = GIS(profile="your_online_portal")
  • gis = GIS(profile="your_enterprise_portal")
• If this notebook requires setup or teardown, did you add the appropriate code to ./misc/setup.py and/or ./misc/teardown.py?
• If this notebook references any portal items that need to be staged on AGOL/Python API playground, did you coordinate with a Python API team member to stage the item the correct way with the api_data_owner user?
• If the notebook requires working with local data (such as CSV, FGDB, SHP, Raster files), upload the files as items to the Geosaurus Online Org using api_data_owner account and change the notebook to first download and unpack the files.
• Code simplified & split out across multiple cells, useful comments?
• Consistent voice/tense/narrative style? Thoroughly checked for typos?
• All images used like <img src="base64str_here"> instead of <img src="https://some.url">? All map widgets contain a static image preview? (Call mapview_inst.take_screenshot() to do so)
• All file paths are constructed in an OS-agnostic fashion with os.path.join()? (Instead of r"\foo\bar", os.path.join(os.path.sep, "foo", "bar"), etc.)
• Is your code formatted using Jupyter Black? You can use Jupyter Black to format your code in the notebook.
• If this notebook showcases deep learning capabilities, please go through the following checklist:
  • Are the inputs required for Export Training Data Using Deep Learning tool published on geosaurus org (api data owner account) and added in the notebook using gis.content.get function?
  • Is training data zipped and published as Image Collection? Note: Whole folder is zipped with name same as the notebook name.
  • Are the inputs required for model inferencing published on geosaurus org (api data owner account) and added in the notebook using gis.content.get function? Note: This includes providing test raster and trained model.
  • Are the inferenced results displayed using a webmap widget?
• IF YOU WANT THIS SAMPLE TO BE DISPLAYED ON THE DEVELOPERS.ARCGIS.COM WEBSITE, ping @jyaistMap so he can add it to the list for the next deploy.

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priyankatuteja commented on 2024-09-10T07:09:34Z
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data path is directing to a deldevs org

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priyankatuteja commented on 2024-09-10T07:09:35Z
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item on deldevs org

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priyankatuteja commented on 2024-09-10T07:09:36Z
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item on private org

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priyankatuteja commented on 2024-09-10T07:09:37Z
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item on private org

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priyankatuteja commented on 2024-09-10T07:09:37Z
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item on private org

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BP-Ent commented on 2024-09-30T20:48:49Z
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Traditionally, VCI from Landsat is calculated using a chain of formulas that are complex and demanding of resources. Fortunately, deep learning models provide an efficient way to compute and predict VCI. In this study, we propose an approach to predicting VCI from Landsat 5 & 8 imagery using the Pix2PixHD deep learning model. The VCI will be computed for both Landsat 5 & Landsat 8 using the Normalized Difference Vegetation Index (NDVI). The calculated VCI will then be used to train an image translation Pix2Pix model. The model will then be capable of translating Landsat-5 & Landsat-8 multispectral imagery to VCI, allowing the predictions to be used for multitemporal monitoring of VCI.

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BP-Ent commented on 2024-09-30T20:48:50Z
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The Vegetation Condition Index (VCI) compares the current NDVI to a range of values observed in the same period in previous years.

The Normalized Difference Vegetation Index (NDVI) was calculated using the formula for all the images of Landsat 5 & Landsat 8. Using the time series NDVI rasters, NDVI Minimum and NDVI Maximum rasters were created that represent the minimum & maximum values of the NDVI for each pixel. Using these rasters, VCI rasters were created that we will use to train an image translation Pix2Pix model.

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BP-Ent commented on 2024-09-30T20:48:51Z
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Common bands of Landsat 5 and Landsat 8 were extracted and used to create training data. The common bands are shown in the table below:

The common bands of Landsat, NDVI min, and NDVI max were stacked together to create an 8 band composite raster for all four years. The composite raster and VCI raster were used to export the training data in Export Tiles format.

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BP-Ent commented on 2024-09-30T20:48:51Z
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In the maps above, the Vegetation Condition Index increases from dark green to red. 1996 was a year with normal rainfall, which means the vegetation had enough water to sustain it. As such, in the map it can be seen that most of the pixels are green, indicating good vegetation conditions. Conversely, 2022 was a year with a rain deficit, meaning stressful conditions for vegetation. The map displays corresponding red pixels, indicating extremely unfavorable conditions for vegetation growth.

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BP-Ent commented on 2024-09-30T20:48:52Z
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In this notebook, we have demonstrated how to use a Pix2Pix model using ArcGIS API for Python to translate imagery from one domain to another. Additionally, this notebook demonstrated how the deep learning model can be trained on imagery from different satellites to increase the temporal coverage of the trained deep learning model. The model trained in the notebook works on both Landsat-5 & Landsat-8 imagery representing approximately 30 years.

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