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Frequent use cases of cropping a raster file include reducing file size and
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creating maps. Sometimes we have a raster file that is much larger than our
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study area or area of interest. It is often more efficient to crop the
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raster to the extent of our study area to reduce file sizes as we process
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our data. Cropping a raster can also be useful when creating pretty maps so
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that the raster layer matches the extent of the desired vector layers.
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study area or area of interest. It is often more efficient to crop the raster
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to the extent of our study area to reduce file sizes as we process our data.
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Cropping a raster can also be useful when creating pretty maps so that the
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raster layer matches the extent of the desired vector layers.
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## Crop a Raster Using Vector Extent
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@@ -125,7 +130,10 @@ spatial object. To do this, we need to specify the raster to be cropped and the
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spatial object that will be used to crop the raster. R will use the `extent` of
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the spatial object as the cropping boundary.
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To illustrate this, we will crop the Canopy Height Model (CHM) to only include the area of interest (AOI). Let's start by plotting the full extent of the CHM data and overlay where the AOI falls within it. The boundaries of the AOI will be colored blue, and we use `fill = NA` to make the area transparent.
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To illustrate this, we will crop the Canopy Height Model (CHM) to only include
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the area of interest (AOI). Let's start by plotting the full extent of the CHM
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data and overlay where the AOI falls within it. The boundaries of the AOI will
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be colored blue, and we use `fill = NA` to make the area transparent.
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```{r crop-by-vector-extent}
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ggplot() +
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CHM_HARV_Cropped <- crop(x = CHM_HARV, y = aoi_boundary_HARV)
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```
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Now we can plot the cropped CHM data, along with a boundary box showing the full
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CHM extent. However, remember, since this is raster data, we need to convert to
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a data frame in order to plot using `ggplot`. To get the boundary box from CHM,
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the `st_bbox()` will extract the 4 corners of the rectangle that encompass all
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the features contained in this object. The `st_as_sfc()` converts these 4
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coordinates into a polygon that we can plot:
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Now we can plot the cropped CHM data, along with a boundary box showing the
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full CHM extent. However, remember, since this is raster data, we need to
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convert to a data frame in order to plot using `ggplot`. To get the boundary
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box from CHM, the `st_bbox()` will extract the 4 corners of the rectangle that
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encompass all the features contained in this object. The `st_as_sfc()` converts
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these 4 coordinates into a polygon that we can plot:
Now we can plot this cropped data. We will show the AOI boundary on the same plot for scale.
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Now we can plot this cropped data. We will show the AOI boundary on the same
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plot for scale.
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```{r show-manual-crop-area}
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ggplot() +
@@ -292,38 +303,41 @@ ggplot() +
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## Extract Raster Pixels Values Using Vector Polygons
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Often we want to extract values from a raster layer for particular locations -
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for example, plot locations that we are sampling on the ground. We can extract all pixel values within 20m of our x,y point of interest. These can then be summarized into some value of interest (e.g. mean, maximum, total).
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for example, plot locations that we are sampling on the ground. We can extract
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all pixel values within 20m of our x,y point of interest. These can then be
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summarized into some value of interest (e.g. mean, maximum, total).
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{alt='Image shows raster information extraction using 20m polygon boundary.'}
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Image Source: National Ecological Observatory Network (NEON)
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To do this in R, we use the `extract()` function. The `extract()` function
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requires:
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- The raster that we wish to extract values from,
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- The vector layer containing the polygons that we wish to use as a boundary or
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boundaries,
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- we can tell it to store the output values in a data frame using
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`df = TRUE`. (This is optional, the default is to return a list, NOT a data frame.) .
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- we can tell it to store the output values in a data frame using
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`raw = FALSE` (this is optional).
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We will begin by extracting all canopy height pixel values located within our
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`aoi_boundary_HARV` polygon which surrounds the tower located at the NEON Harvard
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Forest field site.
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`aoi_boundary_HARV` polygon which surrounds the tower located at the NEON
We often want to extract summary values from a raster. We can tell R the type
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of summary statistic we are interested in using the `fun =` argument. Let's extract
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a mean height value for our AOI. Because we are extracting only a single number, we will
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not use the `df = TRUE` argument.
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of summary statistic we are interested in using the `fun =` argument. Let's
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extract a mean height value for our AOI.
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```{r summarize-extract}
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mean_tree_height_AOI <- extract(x = CHM_HARV, y = aoi_boundary_HARV, fun = mean)
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mean_tree_height_AOI <- extract(x = CHM_HARV, y = aoi_boundary_HARV,
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fun = mean)
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mean_tree_height_AOI
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```
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## Extract Data using x,y Locations
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We can also extract pixel values from a raster by defining a buffer or area
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surrounding individual point locations using the `extract()` function. To do this
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we define the summary argument (`fun = mean`) and the buffer distance (`buffer = 20`)
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which represents the radius of a circular region around each point. By default, the units of the
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buffer are the same units as the data's CRS. All pixels that are touched by the buffer region are included in the extract.
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surrounding individual point locations using the `st_buffer()` function. To do
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this we define the summary argument (`fun = mean`) and the buffer distance
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(`dist = 20`) which represents the radius of a circular region around each
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point. By default, the units of the buffer are the same units as the data's
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CRS. All pixels that are touched by the buffer region are included in the
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extract.
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Source: National Ecological Observatory Network (NEON).
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{alt='Image shows raster information extraction using 20m buffer region.'}
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Image Source: National Ecological Observatory Network (NEON)
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Let's put this into practice by figuring out the mean tree height in the
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20m around the tower location (`point_HARV`). Because we are extracting only a single number, we
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will not use the `df = TRUE` argument.
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Let's put this into practice by figuring out the mean tree height in the 20m
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around the tower location (`point_HARV`).
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```{r extract-point-to-buffer}
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mean_tree_height_tower <- extract(x = CHM_HARV,
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y = point_HARV,
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buffer = 20,
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y = st_buffer(point_HARV, dist = 20),
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fun = mean)
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mean_tree_height_tower
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## Challenge: Extract Raster Height Values For Plot Locations
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1) Use the plot locations object (`plot_locations_sp_HARV`)
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to extract an average tree height for the
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area within 20m of each vegetation plot location in the study area. Because there are
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multiple plot locations, there will be multiple averages returned, so the `df = TRUE`
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argument should be used.
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1) Use the plot locations object (`plot_locations_sp_HARV`) to extract an
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average tree height for the area within 20m of each vegetation plot location
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in the study area. Because there are multiple plot locations, there will be
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multiple averages returned.
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2) Create a plot showing the mean tree height of each area.
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