updated readme

README.rmd

@@ -48,7 +48,7 @@ m3 <- pixel_metrics(las, ~metrics_set2(X, Y, Z), res = 20)
 #### Simple descriptive statistics - `metrics_basic()`
 
 - `n` - total number of returns
-- `zmin`, `zmax`, `zmean`, `zvar`, `zsd`, `zcv`, `zskew`, `zkurt` - elevation maximum, minimum, mean, standard deviation, coeficient of variation, skewness, and kurtosis
+- `zmin`, `zmax`, `zmean`, `zvar`, `zsd`, `zcv`, `zskew`, `zkurt` - elevation maximum, minimum, mean, standard deviation, coefficient of variation, skewness, and kurtosis
 
 #### Height percentiles - `metrics_percentiles()`
 
@@ -76,7 +76,8 @@ Proportion of returns above a user-defined threshold. By default, percent of ret
 - `CRR` - canopy relief ratio ((mean - min) / (max – min))
 - `zentropy`, `VCI` - normalized Shannon diversity index, Vertical Complexity Index
 
-see: van Ewijk, K. Y., Treitz, P. M., & Scott, N. A. (2011). Characterizing Forest Succession in Central Ontario using LAS-derived Indices. Photogrammetric Engineering and Remote Sensing, 77(3), 261-269
+see: 
+van Ewijk, K. Y., Treitz, P. M., & Scott, N. A. (2011). Characterizing Forest Succession in Central Ontario using LAS-derived Indices. Photogrammetric Engineering and Remote Sensing, 77(3), 261-269
 
 
 #### Cumulative point density - `metrics_canopydensity()` 
@@ -89,7 +90,8 @@ Canopy density metrics as defined by Woods et al. 2008. Elevation range is divid
 - `zpcum8`
 - `zpcum9`
 
-See: M. Woods, K. Lim, and P. Treitz. Predicting forest stand variables from LiDAR data in the Great Lakes – St. Lawrence forest of Ontario. The Forestry Chronicle. 84(6): 827-839.
+See: 
+M. Woods, K. Lim, and P. Treitz. Predicting forest stand variables from LiDAR data in the Great Lakes – St. Lawrence forest of Ontario. The Forestry Chronicle. 84(6): 827-839.
 
 #### L-moments metrics - `metrics_Lmoments()`
 
@@ -126,9 +128,9 @@ Interval metrics - proportion of returns between specified elevation intervals.
 
 #### Number of points by return number - `metrics_echo2()` 
 
-`n_return_1` - total number of 1st returns
-`n_return_2` - total number of 2nd returns 
-`n_return_*` - total number of * returns
+- `n_return_1` - total number of 1st returns
+- `n_return_2` - total number of 2nd returns 
+- `n_return_*` - total number of * returns
 
 
 #### A wrapper function for the rumple metric - `metrics_rumple()` 
@@ -149,14 +151,13 @@ A set of metrics calculated in a voxel space. For convenience, a point cloud is
 - `vzsd`, `vzcv` - voxel elevation standard deviation and coefficient of variation
 - `OpenGapSpace`, `ClosedGapSpace`, `Euphotic`, `Oligophotic` - Canopy volume classes based on Lefsky et al 1999
 
-See: Lefsky, M. A., Cohen, W. B., Acker, S. A., Parker, G. G., Spies, T. A., & Harding, D. (1999). Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests. Remote Sensing of Environment, 70(3), 339–361. doi:10.1016/S0034-4257(99)00052-8
+See: 
+Lefsky, M. A., Cohen, W. B., Acker, S. A., Parker, G. G., Spies, T. A., & Harding, D. (1999). Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests. Remote Sensing of Environment, 70(3), 339–361. doi:10.1016/S0034-4257(99)00052-8
 
 
 #### Metrics based on kernel density estimation - `metrics_kde()`
 
-Kernel density estimation (KDE) applied to the distribution of point cloud elevation (Z). KDE allows to 
-create a probability density function (using a Guassian kernel). The density function is then used to detect
-peaks (function maxima), and attributes of those maxima. Based on similar metric available in Fusion (see references), with significant differences in the list of output statistics as well as the default bandwidth used when estimating kernel density.
+Kernel density estimation (KDE) applied to the distribution of point cloud elevation (Z). KDE allows to create a probability density function (using a Guassian kernel). The density function is then used to detect peaks (function maxima), and attributes of those maxima. Based on similar metric available in Fusion (see references), with significant differences in the list of output statistics as well as the default bandwidth used when estimating kernel density.
 
 - `kde_peaks_count` - number of detected distribution maxima (peaks)
 - `kde_peak1_elev` - elevation (height) corresponding to the 1st peak
@@ -169,15 +170,17 @@ peaks (function maxima), and attributes of those maxima. Based on similar metric
 - `kde_peak2_diff` -  distance (height difference) between peaks 2 and 3
 -  `…`
 
-See: McGaughey, R.J., 2021. FUSION/LDV: Software for LIDAR Data Analysis and Visualization. http://forsys.cfr.washington.edu/software/fusion/FUSION_manual.pdf
+See: 
+McGaughey, R.J., 2021. FUSION/LDV: Software for LIDAR Data Analysis and Visualization. http://forsys.cfr.washington.edu/software/fusion/FUSION_manual.pdf
 
 
 
 #### Height of median energy - `metrics_HOME()`
 
 - `HOME` - calculations based on LAStools' implementation of the HOME metric.
 
-See: http://lastools.org/download/lascanopy_README.txt
+See: 
+http://lastools.org/download/lascanopy_README.txt
 
 
 #### GLCM (Grey-Level Co-Occurence Matrix) metrics of a canopy height model (CHM) - `metrics_texture()`