Update with consistent bullet points/colons

05-geometry-operations.Rmd

@@ -770,17 +770,18 @@ tmap_arrange(tm1, tm2, tm3, nrow = 1)
 ```
 
 
-Several methods for recalculating (estimating) values for a raster with different resolutions/origins exist (Figure \@ref(fig:resampl)).
-It includes:
-
-- Nearest neighbor - assigns the value of the nearest cell of the original raster to the cell of the target one.
-It is fast and usually suitable for categorical rasters
-- Bilinear interpolation - assigns a weighted average of the four nearest cells from the original raster to the cell of the target one (Figure \@ref(fig:bilinear)). The fastest method for continuous rasters
-- Cubic interpolation - uses values of 16 nearest cells of the original raster to determine the output cell value, applying third-order polynomial functions. Used for continuous rasters. It results in a more smoothed surface than the bilinear interpolation, but is also more computationally demanding
-- Cubic spline interpolation - also uses values of 16 nearest cells of the original raster to determine the output cell value, but applies cubic splines (piecewise third-order polynomial functions) to derive the results. Used for continuous rasters
-- Lanczos windowed sinc resampling - uses values of 36 nearest cells of the original raster to determine the output cell value. Used for continuous rasters^[More detailed explanation of this method can be found at https://gis.stackexchange.com/a/14361/20955.]
+There are several methods for estimating values for a raster with different resolutions/origins, as shown in Figure \@ref(fig:resampl).
+These resampling methods include:
+
+- Nearest neighbor: assigns the value of the nearest cell of the original raster to the cell of the target one. This is a fast simple technique that is usually suitable for resampling categorical rasters.
+- Bilinear interpolation: assigns a weighted average of the four nearest cells from the original raster to the cell of the target one (Figure \@ref(fig:bilinear)). This is the fastest method that is appropriate for continuous rasters.
+- Cubic interpolation: uses values of 16 nearest cells of the original raster to determine the output cell value, applying third-order polynomial functions. Used for continuous rasters and results in a smoother surface that results bilinear interpolation, but is more computationally intensive.
+- Cubic spline interpolation: also uses values of 16 nearest cells of the original raster to determine output cell, but applies cubic splines (piecewise third-order polynomial functions) to derive the results. Used for continuous rasters.
+- Lanczos windowed sinc resampling: uses values of 36 nearest cells of the original raster to determine the output cell value. Used for continuous rasters.^[
+More detailed explanation of this method can be found at https://gis.stackexchange.com/a/14361/20955.
+]
 
-As you can find in the above explanation, only *nearest neighbor* is suitable for categorical rasters, while all the methods can be used (with different outcomes) for the continuous rasters.
+The above explanation highlights that only *nearest neighbor* resampling is suitable for categorical rasters, while all the methods can be used (with different outcomes) for continuous rasters.
 Additionally, each successive method requires more processing time.
 
 To apply resampling, the **terra** package provides a `resample()` function.

12-spatial-cv.Rmd

@@ -128,11 +128,11 @@ knitr::kable(lsl_table[c(1, 2, 350), ], caption = "Structure of the lsl dataset.
 To model landslide susceptibility, we need some predictors.
 Since terrain attributes are frequently associated with landsliding [@muenchow_geomorphic_2012], we have already extracted following terrain attributes from `ta` to `lsl`:
 
-- `slope` -  slope angle (°)
-- `cplan` - plan curvature (rad m^−1^) expressing the convergence or divergence of a slope and thus water flow
-- `cprof` - profile curvature (rad m^-1^) as a measure of flow acceleration, also known as downslope change in slope angle
-- `elev` - elevation (m a.s.l.) as the representation of different altitudinal zones of vegetation and precipitation in the study area
-- `log10_carea` - the decadic logarithm of the catchment area (log10 m^2^) representing the amount of water flowing towards a location
+- `slope`:  slope angle (°)
+- `cplan`: plan curvature (rad m^−1^) expressing the convergence or divergence of a slope and thus water flow
+- `cprof`: profile curvature (rad m^-1^) as a measure of flow acceleration, also known as downslope change in slope angle
+- `elev`: elevation (m a.s.l.) as the representation of different altitudinal zones of vegetation and precipitation in the study area
+- `log10_carea`: the decadic logarithm of the catchment area (log10 m^2^) representing the amount of water flowing towards a location
 
 It might be a worthwhile exercise to compute the terrain attributes with the help of R-GIS bridges (see Chapter \@ref(gis)) and extract them to the landslide points (see Exercise section at the end of this Chapter).