Fixes for c4

04-spatial-operations.Rmd

@@ -1061,12 +1061,12 @@ r_focal = focal(elev, w = matrix(1, nrow = 3, ncol = 3), fun = min)
 
 The `min()` function has an additional argument to determine whether to remove NAs in the process (`na.rm = TRUE`) or not (`na.rm = FALSE`, the default).
 
-```{r focal-example, echo = FALSE, fig.cap = "Input raster (left) and resulting output raster (right) due to a focal operation - finding the minimum value in 3-by-3 moving windows.", fig.scap="Illustration of a focal operation."}
+```{r focal-example, echo = FALSE, fig.cap = "Input raster (left) and resulting output raster (right) due to a focal operation, finding the minimum value in 3-by-3 moving windows.", fig.scap="Illustration of a focal operation."}
 knitr::include_graphics("images/04_focal_example.png")
 ```
 
 We can quickly check if the output meets our expectations.
-In our example, the minimum value has to be always the upper left corner of the moving window (remember we have created the input raster by row-wise incrementing the cell values by one starting at the upper left corner).
+In our example, the minimum value has to be always the upper left corner of the moving window (remember, we have created the input raster by row-wise incrementing the cell values by one starting at the upper left corner).
 In this example, the weighting matrix consists only of 1s, meaning each cell has the same weight on the output, but this can be changed.
 
 Focal functions or filters play a dominant role in image processing.
@@ -1087,18 +1087,18 @@ Chapter \@ref(gis) shows how to access such GIS functionality from within R.
 Just like focal operations, *zonal* operations apply an aggregation function to multiple raster cells.
 However, a second raster, usually with categorical values, defines the *zonal filters* (or 'zones') in the case of zonal operations, as opposed to a neighborhood window in the case of focal operations presented in the previous section.
 Consequently, raster cells defining the zonal filter do not necessarily have to be neighbors.
-Our grain size raster is a good example, as illustrated in the right panel of Figure \@ref(fig:cont-raster): different grain sizes are spread irregularly throughout the raster.
+Our grain-size raster is a good example, as illustrated in the right panel of Figure \@ref(fig:cont-raster): different grain sizes are spread irregularly throughout the raster.
 Finally, the result of a zonal operation is a summary table grouped by zone which is why this operation is also known as *zonal statistics* in the GIS world\index{GIS}. 
 This is in contrast to focal operations which return a raster object by default.
 
-The following code chunk uses the `zonal()` function to calculate the mean elevation associated with each grain size class.
+The following code chunk uses the `zonal()` function to calculate the mean elevation associated with each grain-size class.
 
 ```{r 04-spatial-operations-43}
 z = zonal(elev, grain, fun = "mean")
 z
 ```
 
-This returns the statistics\index{statistics} for each category, here the mean altitude for each grain size class.
+This returns the statistics\index{statistics} for each category, here the mean altitude for each grain-size class.
 Note that it is also possible to get a raster with calculated statistics for each zone by setting the `as.raster` argument to `TRUE`.
 
 ### Global operations and distances
@@ -1109,9 +1109,9 @@ The most common global operations are descriptive statistics\index{statistics} f
 Aside from that, global operations are also useful for the computation of distance and weight rasters.
 In the first case, one can calculate the distance from each cell to a specific target cell.
 For example, one might want to compute the distance to the nearest coast (see also `terra::distance()`).
-We might also want to consider topography, that means, we are not only interested in the pure distance but would like also to avoid the crossing of mountain ranges when going to the coast.
-To do so, we can weight the distance with elevation so that each additional altitudinal meter 'prolongs' the Euclidean distance (in Exercises 8 and 9 at the end of this chapter you will do exactly that).
-Visibility and viewshed computations also belong to the family of global operations (in the exercises of Chapter \@ref(gis), you will compute a viewshed raster).
+We might also want to consider topography, for example to avoid the crossing mountain ranges on the way to the coast.
+This can be done by weighting distance by elevation so that each additional altitudinal meter 'prolongs' the Euclidean distance (in Exercises E8 and E9 at the end of this chapter you will do exactly that).
+Visibility and viewshed computations also belong to the family of global operations (in the Exercises of Chapter \@ref(gis), you will compute a viewshed raster).
 
 ### Map algebra counterparts in vector processing
 
@@ -1129,13 +1129,13 @@ Zonal operations dissolve the cells of one raster in accordance with the zones (
 ### Merging rasters
 
 \index{raster!merge}
-Suppose we would like to compute the NDVI (see Section \@ref(local-operations)), and additionally want to compute terrain attributes from elevation data for observations within a study area.
+Suppose we would like to compute the NDVI (see Section \@ref(local-operations)), and additionally we want to compute terrain attributes from elevation data for observations within a study area.
 Such computations rely on remotely sensed information. 
 The corresponding imagery is often divided into scenes covering a specific spatial extent, and frequently, a study area covers more than one scene.
 Then, we would need to merge the scenes covered by our study area. 
 In the easiest case, we can just merge these scenes, that is put them side by side.
 This is possible, for example, with digital elevation data.
-In the following code chunk we first download the SRTM elevation data for Austria and Switzerland (for the country codes, see the **geodata** function `country_codes()`).
+In the following code chunk, we first download the Shuttle Radar Topography Mission (SRTM) elevation data for Austria and Switzerland (for the country codes, see the **geodata** function `country_codes()`).
 In a second step, we merge the two rasters into one.
 
 ```{r 04-spatial-operations-44, eval = FALSE}
@@ -1148,9 +1148,9 @@ aut_ch = merge(aut, ch)
 
 The merging approach is of little use when the overlapping values do not correspond to each other.
 This is frequently the case when you want to combine spectral imagery from scenes that were taken on different dates.
-The `merge()` command will still work but you will see a clear border in the resulting image.
+The `merge()` command will still work, but you will see a clear border in the resulting image.
 On the other hand, the `mosaic()` command lets you define a function for the overlapping area. 
-For instance, we could compute the mean value -- this might smooth the clear border in the merged result but it will most likely not make it disappear.
+For instance, we could compute the mean value --- this might smooth the clear border in the merged result, but it will most likely not make it disappear.
 For a more detailed introduction to remote sensing with R, see @wegmann_remote_2016.
 
 ## Exercises

_04-ex.Rmd

@@ -65,7 +65,7 @@ nz_height_combined |>
 E4. Test your knowledge of spatial predicates by finding out and plotting how US states relate to each other and other spatial objects.
 
 The starting point of this exercise is to create an object representing Colorado state in the USA. Do this with the command 
-`colorado = us_states[us_states$NAME == "Colorado",]` (base R) or with with the  `filter()` function (tidyverse) and plot the resulting object in the context of US states.
+`colorado = us_states[us_states$NAME == "Colorado",]` (base R) or with the  `filter()` function (tidyverse) and plot the resulting object in the context of US states.
 
 - Create a new object representing all the states that geographically intersect with Colorado and plot the result (hint: the most concise way to do this is with the subsetting method `[`).
 - Create another object representing all the objects that touch (have a shared boundary with) Colorado and plot the result (hint: remember you can use the argument `op = st_intersects` and other spatial relations during spatial subsetting operations in base R).