minor updates

NEONScience · Feb 17, 2024 · bef576f · bef576f
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diff --git a/tutorials/R/Geospatial-skills/intro-raster-r/00-Raster-Structure/00-Raster-Structure.R b/tutorials/R/Geospatial-skills/intro-raster-r/00-Raster-Structure/00-Raster-Structure.R
@@ -12,7 +12,7 @@ setwd(wd)
 ##           year='2022',
 ##           easting=732000,
 ##           northing=4713500,
-##           check.size=FALSE, # set to TRUE or can leave out if you want to check the size before downloading
+##           check.size=FALSE, # set to TRUE or remove if you want to check the size before downloading
 ##           savepath = wd)
 
 
@@ -33,8 +33,6 @@ plot(DSM_HARV,
 
 
 ## ----classified-elevation-map, fig.cap="Classified elevation map of NEON's site Harvard Forest", fig.width=10--------------------------------------
-# Demonstration image for the tutorial
-
 # add a color map with 5 colors
 col=terrain.colors(3)
 # add breaks to the colormap (4 breaks = 3 segments)
@@ -82,20 +80,19 @@ min(DSM_HARV)
 ##           year='2022',
 ##           easting=737500,
 ##           northing=4701500,
-##           check.size=FALSE, # set to TRUE or can leave out if you want to check the size before downloading
+##           check.size=FALSE, # set to TRUE or remove if you want to check the size before downloading
 ##           savepath = wd)
 
 
 ## ----demonstrate-no-data-black, fig.cap="Colorized raster image with NoDataValues around the edge rendered in black"-------------------------------
-# no data demonstration code - not being taught 
-# Use stack function to read in all bands
+# Use rast function to read in all bands
 RGB_HARV <- 
   rast(paste0(wd,"DP3.30010.001/neon-aop-products/2022/FullSite/D01/2022_HARV_7/L3/Camera/Mosaic/2022_HARV_7_737000_4701000_image.tif"))
 
-# Create an RGB image from the raster stack
+# Create an RGB image from the raster
 par(col.axis="white",col.lab="white",tck=0)
 plotRGB(RGB_HARV, r = 1, g = 2, b = 3, 
-        axes=TRUE, main="Raster With NoData Values\nRendered in Black")
+        axes=TRUE, main="Raster With NoData Values Rendered in Black")
 
 
 
@@ -107,7 +104,6 @@ func <- function(x) {
 
 newRGBImage <- app(RGB_HARV, func)
 
-
 par(col.axis="white",col.lab="white",tck=0)
 # Create an RGB image from the raster stack
 plotRGB(newRGBImage, r = 1, g = 2, b = 3,

diff --git a/tutorials/R/Geospatial-skills/intro-raster-r/00-Raster-Structure/00-Raster-Structure.Rmd b/tutorials/R/Geospatial-skills/intro-raster-r/00-Raster-Structure/00-Raster-Structure.Rmd
@@ -4,10 +4,10 @@ title: "Raster 00: Intro to Raster Data in R"
 description: "This tutorial reviews the fundamental principles, packages and metadata/raster attributes that are needed to work with raster data in R. It covers the three core metadata elements that we need to understand to work with rasters in R: CRS, Extent and Resolution. It also explores missing and bad data values as stored in a raster and how R handles these elements. Finally, it introduces the GeoTiff file format."	
 dateCreated: 2015-10-23
 authors: Leah A. Wasser, Megan A. Jones, Zack Brym, Kristina Riemer, Jason Williams, Jeff Hollister,  Mike Smorul	
-contributors:	Jason Brown
+contributors:	Jason Brown, Bridget Hass
 estimatedTime: 1 hour
 packagesLibraries: neonUtilities, terra
-topics: raster, spatial-data-gis, lidar
+topics: raster, spatial-data-gis, lidar, camera
 languagesTool: R
 dataProduct: DP3.30024.001, DP3.30010.001
 code1: https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/tutorials/R/Geospatial-skills/intro-raster-r/00-Raster-Structure/00-Raster-Structure.R
@@ -77,8 +77,8 @@ Raster or "gridded" data are stored as a grid of values which are rendered on a
 map as pixels. Each pixel value represents an area on the Earth's surface.
 
 <figure>
-    <a href="https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/dev-aten/graphics/raster-general/raster_concept.png">
-    <img src="https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/dev-aten/graphics/raster-general/raster_concept.png"
+    <a href="https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/graphics/raster-general/raster_concept.png">
+    <img src="https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/graphics/raster-general/raster_concept.png"
     alt="Satellite (raster) image with an inset map of a smaller extent. The inset map's structure is further shown as a grid of numeric values represented by colors on a the legend." >
     </a>
     <figcaption> Source: National Ecological Observatory Network (NEON)
@@ -107,7 +107,7 @@ byTileAOP(dpID='DP3.30024.001', # lidar elevation
           year='2022',
           easting=732000,
           northing=4713500,
-          check.size=FALSE, # set to TRUE or can leave out if you want to check the size before downloading
+          check.size=FALSE, # set to TRUE or remove if you want to check the size before downloading
           savepath = wd)
 ```
 This file will be downloaded into a nested subdirectory under the ~/data folder, inside a folder named DP3.30024.001 (the Data Product ID). The file should show up in this location: ~/data/DP3.30024.001/neon-aop-products/2022/FullSite/D01/2022_HARV_7/L3/DiscreteLidar/DSMGtif/NEON_D01_HARV_DP3_732000_4713000_DSM.tif.
@@ -180,8 +180,6 @@ maps include:
 The legend of this map shows the colors representing each discrete class. 
 
 ```{r classified-elevation-map, fig.cap="Classified elevation map of NEON's site Harvard Forest", fig.width=10}
-# Demonstration image for the tutorial
-
 # add a color map with 5 colors
 col=terrain.colors(3)
 # add breaks to the colormap (4 breaks = 3 segments)
@@ -373,7 +371,7 @@ byTileAOP(dpID='DP3.30010.001',
           year='2022',
           easting=737500,
           northing=4701500,
-          check.size=FALSE, # set to TRUE or can leave out if you want to check the size before downloading
+          check.size=FALSE, # set to TRUE or remove if you want to check the size before downloading
           savepath = wd)
 ```
 
@@ -382,15 +380,14 @@ This file will be downloaded into a nested subdirectory under the ~/data folder,
 In the image below, the pixels that are black have `NoDataValue`s. The camera did not collect data in these areas. 
 
 ```{r demonstrate-no-data-black, fig.cap="Colorized raster image with NoDataValues around the edge rendered in black"}
-# no data demonstration code - not being taught 
-# Use stack function to read in all bands
+# Use rast function to read in all bands
 RGB_HARV <- 
   rast(paste0(wd,"DP3.30010.001/neon-aop-products/2022/FullSite/D01/2022_HARV_7/L3/Camera/Mosaic/2022_HARV_7_737000_4701000_image.tif"))
 
-# Create an RGB image from the raster stack
+# Create an RGB image from the raster
 par(col.axis="white",col.lab="white",tck=0)
 plotRGB(RGB_HARV, r = 1, g = 2, b = 3, 
-        axes=TRUE, main="Raster With NoData Values\nRendered in Black")
+        axes=TRUE, main="Raster With NoData Values Rendered in Black")
 
 ```
 
@@ -406,7 +403,6 @@ func <- function(x) {
 
 newRGBImage <- app(RGB_HARV, func)
 
-
 par(col.axis="white",col.lab="white",tck=0)
 # Create an RGB image from the raster stack
 plotRGB(newRGBImage, r = 1, g = 2, b = 3,
@@ -437,10 +433,11 @@ Examples of Bad Data Values:
 * The normalized difference vegetation index (NDVI), which is a measure of 
 greenness, has a valid range of -1 to 1. Any value outside of that range would 
 be considered a "bad" value.
-* Reflectance data in an image will often range from 0-1 or 0-10,000 depending 
-upon how the data are scaled. Thus a value greater than 1 or greater than 10,000
+* Reflectance data in an image should range from 0-1 (or 0-10,000 depending 
+upon how the data are scaled). Thus a value greater than 1 or greater than 10,000
 is likely caused by an error in either data collection or processing. These 
-erroneous values can occur, for example, in water vapor absorption bands.
+erroneous values can occur, for example, in water vapor absorption bands, which 
+contain invalid data, and are meant to be disregarded.
 
 ### Find Bad Data Values
 Sometimes a raster's metadata will tell us the range of expected values for a

diff --git a/tutorials/R/Geospatial-skills/intro-raster-r/00-Raster-Structure/00-Raster-Structure.html b/tutorials/R/Geospatial-skills/intro-raster-r/00-Raster-Structure/00-Raster-Structure.html
@@ -136,8 +136,8 @@ <h2 id="about-raster-data">About Raster Data</h2>
 <p>Raster or “gridded” data are stored as a grid of values which are rendered on a
 map as pixels. Each pixel value represents an area on the Earth’s surface.</p>
 <figure>
-    <a href="https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/dev-aten/graphics/raster-general/raster_concept.png">
-    <img src="https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/dev-aten/graphics/raster-general/raster_concept.png"
+    <a href="https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/graphics/raster-general/raster_concept.png">
+    <img src="https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/graphics/raster-general/raster_concept.png"
     alt="Satellite (raster) image with an inset map of a smaller extent. The inset map's structure is further shown as a grid of numeric values represented by colors on a the legend." >
     </a>
     <figcaption> Source: National Ecological Observatory Network (NEON)
@@ -167,7 +167,7 @@ <h2 id="download-lidar-raster-data">Download LiDAR Raster Data</h2>
 
           northing=4713500,
 
-          check.size=FALSE, # set to TRUE or can leave out if you want to check the size before downloading
+          check.size=FALSE, # set to TRUE or remove if you want to check the size before downloading
 
           savepath = wd)
 </code></pre>
@@ -247,11 +247,7 @@ <h4 id="categorical-landcover-map-for-the-united-states">Categorical Landcover M
 </figure>
 <h4 id="categorical-elevation-map-of-the-neon-harvard-forest-site">Categorical Elevation Map of the NEON Harvard Forest Site</h4>
 <p>The legend of this map shows the colors representing each discrete class.</p>
-<pre><code># Demonstration image for the tutorial
-
-
-
-# add a color map with 5 colors
+<pre><code># add a color map with 5 colors
 
 col=terrain.colors(3)
 
@@ -449,27 +445,25 @@ <h2 id="nodata-values-in-rasters">NoData Values in Rasters</h2>
 
           northing=4701500,
 
-          check.size=FALSE, # set to TRUE or can leave out if you want to check the size before downloading
+          check.size=FALSE, # set to TRUE or remove if you want to check the size before downloading
 
           savepath = wd)
 </code></pre>
 <p>This file will be downloaded into a nested subdirectory under the ~/data folder, inside a folder named DP3.30010.001 (the Camera Data Product ID). The file should show up in this location: ~/data/DP3.30010.001/neon-aop-products/2022/FullSite/D01/2022_HARV_7/L3/Camera/Mosaic/2022_HARV_7_737000_4701000_image.tif.</p>
 <p>In the image below, the pixels that are black have <code>NoDataValue</code>s. The camera did not collect data in these areas.</p>
-<pre><code># no data demonstration code - not being taught 
-
-# Use stack function to read in all bands
+<pre><code># Use rast function to read in all bands
 
 RGB_HARV &lt;- 
   rast(paste0(wd,&quot;DP3.30010.001/neon-aop-products/2022/FullSite/D01/2022_HARV_7/L3/Camera/Mosaic/2022_HARV_7_737000_4701000_image.tif&quot;))
 
 
 
-# Create an RGB image from the raster stack
+# Create an RGB image from the raster
 
 par(col.axis=&quot;white&quot;,col.lab=&quot;white&quot;,tck=0)
 
 plotRGB(RGB_HARV, r = 1, g = 2, b = 3, 
-        axes=TRUE, main=&quot;Raster With NoData Values\nRendered in Black&quot;)
+        axes=TRUE, main=&quot;Raster With NoData Values Rendered in Black&quot;)
 </code></pre>
 <p><img src="https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/tutorials/R/Geospatial-skills/intro-raster-r/00-Raster-Structure/rfigs/demonstrate-no-data-black-1.png" alt="Colorized raster image with NoDataValues around the edge rendered in black" /></p>
 <p>In the next image, the black edges have been assigned <code>NoDataValue</code>. R doesn’t
@@ -514,10 +508,11 @@ <h2 id="bad-data-values-in-rasters">Bad Data Values in Rasters</h2>
 <li>The normalized difference vegetation index (NDVI), which is a measure of
 greenness, has a valid range of -1 to 1. Any value outside of that range would
 be considered a “bad” value.</li>
-<li>Reflectance data in an image will often range from 0-1 or 0-10,000 depending
-upon how the data are scaled. Thus a value greater than 1 or greater than 10,000
+<li>Reflectance data in an image should range from 0-1 (or 0-10,000 depending
+upon how the data are scaled). Thus a value greater than 1 or greater than 10,000
 is likely caused by an error in either data collection or processing. These
-erroneous values can occur, for example, in water vapor absorption bands.</li>
+erroneous values can occur, for example, in water vapor absorption bands, which
+contain invalid data, and are meant to be disregarded.</li>
 </ul>
 <h3 id="find-bad-data-values">Find Bad Data Values</h3>
 <p>Sometimes a raster’s metadata will tell us the range of expected values for a

diff --git a/...s/R/Geospatial-skills/intro-raster-r/00-Raster-Structure/00-Raster-Structure.md b/...s/R/Geospatial-skills/intro-raster-r/00-Raster-Structure/00-Raster-Structure.md
@@ -4,10 +4,10 @@ title: "Raster 00: Intro to Raster Data in R"
 description: "This tutorial reviews the fundamental principles, packages and metadata/raster attributes that are needed to work with raster data in R. It covers the three core metadata elements that we need to understand to work with rasters in R: CRS, Extent and Resolution. It also explores missing and bad data values as stored in a raster and how R handles these elements. Finally, it introduces the GeoTiff file format."	
 dateCreated: 2015-10-23
 authors: Leah A. Wasser, Megan A. Jones, Zack Brym, Kristina Riemer, Jason Williams, Jeff Hollister,  Mike Smorul	
-contributors:	Jason Brown
+contributors:	Jason Brown, Bridget Hass
 estimatedTime: 1 hour
 packagesLibraries: neonUtilities, terra
-topics: raster, spatial-data-gis, lidar
+topics: raster, spatial-data-gis, lidar, camera
 languagesTool: R
 dataProduct: DP3.30024.001, DP3.30010.001
 code1: https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/tutorials/R/Geospatial-skills/intro-raster-r/00-Raster-Structure/00-Raster-Structure.R
@@ -77,8 +77,8 @@ Raster or "gridded" data are stored as a grid of values which are rendered on a
 map as pixels. Each pixel value represents an area on the Earth's surface.
 
 <figure>
-    <a href="https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/dev-aten/graphics/raster-general/raster_concept.png">
-    <img src="https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/dev-aten/graphics/raster-general/raster_concept.png"
+    <a href="https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/graphics/raster-general/raster_concept.png">
+    <img src="https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/graphics/raster-general/raster_concept.png"
     alt="Satellite (raster) image with an inset map of a smaller extent. The inset map's structure is further shown as a grid of numeric values represented by colors on a the legend." >
     </a>
     <figcaption> Source: National Ecological Observatory Network (NEON)
@@ -115,7 +115,7 @@ We can use the neonUtilities function byTileAOP to download a single elevation t
 
               northing=4713500,
 
-              check.size=FALSE, # set to TRUE or can leave out if you want to check the size before downloading
+              check.size=FALSE, # set to TRUE or remove if you want to check the size before downloading
 
               savepath = wd)
 This file will be downloaded into a nested subdirectory under the ~/data folder, inside a folder named DP3.30024.001 (the Data Product ID). The file should show up in this location: ~/data/DP3.30024.001/neon-aop-products/2022/FullSite/D01/2022_HARV_7/L3/DiscreteLidar/DSMGtif/NEON_D01_HARV_DP3_732000_4713000_DSM.tif.
@@ -206,10 +206,6 @@ maps include:
 The legend of this map shows the colors representing each discrete class. 
 
 
-    # Demonstration image for the tutorial
-
-
-
     # add a color map with 5 colors
 
     col=terrain.colors(3)
@@ -439,7 +435,7 @@ Let's take a look at some of the RGB Camera data over HARV, this time downloadin
 
               northing=4701500,
 
-              check.size=FALSE, # set to TRUE or can leave out if you want to check the size before downloading
+              check.size=FALSE, # set to TRUE or remove if you want to check the size before downloading
 
               savepath = wd)
 
@@ -448,21 +444,19 @@ This file will be downloaded into a nested subdirectory under the ~/data folder,
 In the image below, the pixels that are black have `NoDataValue`s. The camera did not collect data in these areas. 
 
 
-    # no data demonstration code - not being taught 
-
-    # Use stack function to read in all bands
+    # Use rast function to read in all bands
 
     RGB_HARV <- 
       rast(paste0(wd,"DP3.30010.001/neon-aop-products/2022/FullSite/D01/2022_HARV_7/L3/Camera/Mosaic/2022_HARV_7_737000_4701000_image.tif"))
 
 
 
-    # Create an RGB image from the raster stack
+    # Create an RGB image from the raster
 
     par(col.axis="white",col.lab="white",tck=0)
 
     plotRGB(RGB_HARV, r = 1, g = 2, b = 3, 
-            axes=TRUE, main="Raster With NoData Values\nRendered in Black")
+            axes=TRUE, main="Raster With NoData Values Rendered in Black")
 
 ![Colorized raster image with NoDataValues around the edge rendered in black](https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/tutorials/R/Geospatial-skills/intro-raster-r/00-Raster-Structure/rfigs/demonstrate-no-data-black-1.png)
 
@@ -515,10 +509,11 @@ Examples of Bad Data Values:
 * The normalized difference vegetation index (NDVI), which is a measure of 
 greenness, has a valid range of -1 to 1. Any value outside of that range would 
 be considered a "bad" value.
-* Reflectance data in an image will often range from 0-1 or 0-10,000 depending 
-upon how the data are scaled. Thus a value greater than 1 or greater than 10,000
+* Reflectance data in an image should range from 0-1 (or 0-10,000 depending 
+upon how the data are scaled). Thus a value greater than 1 or greater than 10,000
 is likely caused by an error in either data collection or processing. These 
-erroneous values can occur, for example, in water vapor absorption bands.
+erroneous values can occur, for example, in water vapor absorption bands, which 
+contain invalid data, and are meant to be disregarded.
 
 ### Find Bad Data Values
 Sometimes a raster's metadata will tell us the range of expected values for a