Merge pull request #162 from r-spatialecology/master

v1.4.2

.travis.yml

@@ -12,7 +12,7 @@ before_install:
   - sudo apt-get -qq install gcc
   - mkdir ~/.R
   - echo -e "CXXFLAGS = -Ofast -march=native -fpic -pipe -std=c++11\nMAKE = make -j8" > ~/.R/Makevars
-  - R -q -e 'install.packages(c("remotes", "knitr", "rmarkdown", "tidyverse", "SDMTools", "NLMR", "bench")); remotes::install_github("ropenscilabs/tic"); tic::prepare_all_stages(); remotes::install_deps(dependencies = TRUE); tic::before_install()'
+  - R -q -e 'install.packages(c("remotes", "knitr", "rmarkdown", "tidyverse", "SDMTools", "bench")); remotes::install_github("ropenscilabs/tic"); remotes::install_github("ropensci/NLMR"); tic::prepare_all_stages(); remotes::install_deps(dependencies = TRUE); tic::before_install()'
 install: R -q -e 'tic::install()'
 after_install: R -q -e 'tic::after_install()'
 before_script: R -q -e 'tic::before_script()'

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: landscapemetrics
 Title: Landscape Metrics for Categorical Map Patterns
-Version: 1.4.1
+Version: 1.4.2
 Authors@R: c(person("Maximillian H.K.", "Hesselbarth",
              role = c("aut", "cre"),
              email = "[email protected]",

NEWS.md

@@ -1,4 +1,10 @@
-# landscapemetrics 1.4
+# landscapemetrics 1.4.2
+* Bugfixes
+    * Bug in total edge calculation
+* Improvements
+    * Adds a support for sf polygons in sample_lsm
+
+# landscapemetrics 1.4.1
 * Bugfixes
     * Bug in the connected-component labelling algorithm for non-rectangular landscapes
 

R/lsm_c_te.R

@@ -194,15 +194,20 @@ lsm_c_te_calc <- function(landscape, count_boundary, directions, resolution = NU
                                              directions = directions,
                                              return_raster = FALSE)[[1]]
 
-            # set all non-class patches to -999
-            landscape_labeled[is.na(landscape_labeled)] <- -999
+            # set all non-class patches, but not NAs, to -999
+            edge_cells <- which(!is.na(landscape) & landscape != patches_class)
+
+            landscape_labeled[edge_cells] <- -999
 
             # add one row/coloumn to count landscape boundary
-            if(count_boundary){
+            if (count_boundary) {
                 landscape_labeled <- pad_raster(landscape = landscape_labeled,
                                                 pad_raster_value = -999,
                                                 pad_raster_cells = 1,
                                                 return_raster = FALSE)[[1]]
+
+                # set NA to -999
+                landscape_labeled[is.na(landscape_labeled)] <- -999
             }
 
             # resolution identical in x and y direction

R/lsm_l_te.R

@@ -146,10 +146,17 @@ lsm_l_te_calc <- function(landscape, count_boundary, resolution = NULL){
     resolution_y <- resolution[[2]]
 
     if (count_boundary) {
+
+        # get background value not present as class
+        background_value <- max(landscape, na.rm = TRUE) + 1
+
+        # add row/col around raster
         landscape <- pad_raster(landscape = landscape,
-                                pad_raster_value = max(landscape, na.rm = TRUE) + 1,
+                                pad_raster_value = background_value,
                                 pad_raster_cells = 1,
                                 return_raster = FALSE)[[1]]
+        # set NA to background value
+        landscape[is.na(landscape)] <- background_value
     }
 
     if (resolution_x == resolution_y) {

R/lsm_p_perim.R

@@ -148,7 +148,7 @@ lsm_p_perim_calc <- function(landscape, directions, resolution = NULL) {
 
         top_bottom_matrix <- matrix(c(NA, NA, NA,
                                       1,  0, 1,
-                                     NA, NA, NA), 3, 3, byrow = TRUE)
+                                      NA, NA, NA), 3, 3, byrow = TRUE)
 
         left_right_matrix <- matrix(c(NA, 1, NA,
                                       NA, 0, NA,

R/sample_lsm.R

@@ -3,7 +3,7 @@
 #' @description Sample metrics
 #'
 #' @param landscape Raster* Layer, Stack, Brick or a list of rasterLayers.
-#' @param y 2-column matrix with coordinates, SpatialPoints, SpatialLines, SpatialPolygons of sf points.
+#' @param y 2-column matrix with coordinates, SpatialPoints, SpatialLines, SpatialPolygons, sf points or sf polygons.
 #' @param plot_id Vector with id of sample points. If not provided, sample
 #' points will be labelled 1...n.
 #' @param shape String specifying plot shape. Either "circle" or "square"
@@ -68,7 +68,7 @@
 #' poly_2 <- sp::Polygons(list(poly_2), "p2")
 #' sample_plots <- sp::SpatialPolygons(list(poly_1, poly_2))
 #'
-#' sample_lsm(landscape, y = sample_plots, what = "lsm_l_np", size = 5)
+#' sample_lsm(landscape, y = sample_plots, what = "lsm_l_np")
 #' }
 #'
 #' @aliases sample_lsm
@@ -309,6 +309,10 @@ sample_lsm_int <- function(landscape,
                            ...) {
 
     # use polygon
+    if (inherits(x = y, what = "sf") && all(sf::st_geometry_type(y) %in% c("POLYGON", "MULTIPOLYGON"))){
+        y <- methods::as(y, "Spatial")
+    }
+
     if (inherits(x = y, what = c("SpatialPolygons", "SpatialPolygonsDataFrame"))) {
 
         # convert to SpatialPolygons
@@ -328,7 +332,7 @@ sample_lsm_int <- function(landscape,
 
             if (verbose) {
 
-                warning("Package 'rgeos' not installed. Please make sure polygons are disaggregated.",
+                warning("Package 'rgeos' is not installed. Please make sure polygons are disaggregated.",
                         call. = FALSE)
             }
         }
@@ -378,12 +382,10 @@ sample_lsm_int <- function(landscape,
                 if (all(sf::st_geometry_type(y) %in% c("POINT", "MULTIPOINT"))) {
 
                     y <- matrix(sf::st_coordinates(y)[, 1:2], ncol = 2)
-                }
-
-                else {
+                } else {
 
                     stop(
-                        "landscapemetrics currently only supports sf point features for landscape metrics sampling"
+                        "landscapemetrics currently only supports sf point and polygon features for landscape metrics sampling"
                     )
                 }
             }
@@ -392,7 +394,7 @@ sample_lsm_int <- function(landscape,
                                               "MULTILINESTRING", "MULTIPOLYGON"))) {
 
                 stop(
-                    "landscapemetrics currently only supports sf point features for landscape metrics sampling"
+                    "landscapemetrics currently only supports sf point and polygon features for landscape metrics sampling"
                 )
             }
 

README.Rmd

@@ -56,9 +56,7 @@ devtools::install_github("r-spatialecology/landscapemetrics")
 ```
 
 #### Announcement
-Due to an improved connected-component labelling algorithm, the patch ID differs between **landscapemetrics** v1.4 and older versions. However, results for all metrics are identical.
-
-The algorithm labels the patches in a different order and therefore may use different patch IDs compared to previous versions.
+Due to an improved connected-component labelling algorithm (**landscapemetrics** v1.4 and higher), patches are labeled in a different order than before and therefore different patch IDs might be used compared to previous versions. However, results for all metrics are identical.
 
 ## Using landscapemetrics
 

README.md

@@ -59,12 +59,11 @@ devtools::install_github("r-spatialecology/landscapemetrics")
 
 #### Announcement
 
-Due to an improved connected-component labelling algorithm, the patch ID
-differs between **landscapemetrics** v1.4 and older versions. However,
-results for all metrics are identical.
-
-The algorithm labels the patches in a different order and therefore may
-use different patch IDs compared to previous versions.
+Due to an improved connected-component labelling algorithm
+(**landscapemetrics** v1.4 and higher), patches are labeled in a
+different order than before and therefore different patch IDs might be
+used compared to previous versions. However, results for all metrics are
+identical.
 
 ## Using landscapemetrics
 
@@ -134,7 +133,7 @@ lsm_p_enn(landscape)
 #>  8     1 patch     1     8 enn     4.12
 #>  9     1 patch     1     9 enn     4.24
 #> 10     1 patch     2    10 enn     4.47
-#> # … with 17 more rows
+#> # ... with 17 more rows
 
 # calculate the total area and total class edge length
 lsm_l_ta(landscape)
@@ -171,7 +170,7 @@ calculate_lsm(landscape, level = "patch")
 #>  8     1 patch     1     8 area   0.0005
 #>  9     1 patch     1     9 area   0.0001
 #> 10     1 patch     2    10 area   0.0035
-#> # … with 314 more rows
+#> # ... with 314 more rows
 ```
 
 ### Utility functions

cran-comments.md

@@ -1,3 +1,6 @@
+# landscapemetrics 1.4.2
+Minor improvments and bug fixes
+
 # landscapemetrics 1.4.1
 Major bugfixes
 

tests/testthat/test-lsm-c-te.R

@@ -2,6 +2,17 @@ context("class level lsm_c_te metric")
 
 landscapemetrics_class_landscape_value <- lsm_c_te(landscape)
 
+test_lsm <- matrix(data = NA, nrow = 25, ncol = 30)
+
+test_lsm[c(5:7), c(5:7)] <- 1
+test_lsm[4, 6] <- 1
+test_lsm[6, 8] <- 1
+test_lsm[8, 6] <- 1
+test_lsm[6, 4] <- 1
+test_lsm[6, 6] <- 2
+
+test_lsm <- raster::raster(test_lsm, xmn = 0, xmx = 30, ymn = 0, ymx = 25)
+
 test_that("lsm_c_te is typestable", {
     expect_is(lsm_c_te(landscape), "tbl_df")
     expect_is(lsm_c_te(landscape_stack), "tbl_df")
@@ -30,3 +41,12 @@ test_that("lsm_l_te equals 0 if only one patch is present",  {
 test_that("lsm_c_te can handle raster with different xy resolution", {
     expect_is(lsm_c_te(landscape_diff_res), "tbl_df")
 })
+
+test_that("lsm_c_te is the same if count_boundary = FALSE and vice versa", {
+
+    result_cbF <- lsm_c_te(test_lsm, count_boundary = FALSE)
+    result_cbT <- lsm_c_te(test_lsm, count_boundary = TRUE)
+
+    expect_true(result_cbF$value[[1]] == result_cbF$value[[2]])
+    expect_true(result_cbT$value[[1]] != result_cbF$value[[2]])
+})

tests/testthat/test-lsm-l-te.R

@@ -2,6 +2,17 @@ context("landscape level lsm_l_te metric")
 
 landscapemetrics_landscape_landscape_value <- lsm_l_te(landscape)
 
+test_lsm <- matrix(data = NA, nrow = 25, ncol = 30)
+
+test_lsm[c(5:7), c(5:7)] <- 1
+test_lsm[4, 6] <- 1
+test_lsm[6, 8] <- 1
+test_lsm[8, 6] <- 1
+test_lsm[6, 4] <- 1
+test_lsm[6, 6] <- 2
+
+test_lsm <- raster::raster(test_lsm, xmn = 0, xmx = 30, ymn = 0, ymx = 25)
+
 test_that("lsm_l_te is typestable", {
     expect_is(lsm_l_te(landscape), "tbl_df")
     expect_is(lsm_l_te(landscape_stack), "tbl_df")
@@ -31,3 +42,16 @@ test_that("lsm_l_te option count_boundary is working", {
 test_that("lsm_l_te can handle raster with different xy resolution", {
     expect_is(lsm_l_te(landscape_diff_res), "tbl_df")
 })
+
+
+test_that("lsm_l_te is the same if count_boundary = FALSE", {
+
+    result_cbF <- lsm_c_te(test_lsm, count_boundary = FALSE)
+    result_cbT <- lsm_c_te(test_lsm, count_boundary = TRUE)
+
+    result_l_cbF <- lsm_l_te(test_lsm, count_boundary = FALSE)
+    result_l_cbT <- lsm_l_te(test_lsm, count_boundary = TRUE)
+
+    expect_true(all(result_l_cbF$value == result_cbF$value))
+    expect_true(all(result_l_cbT$value == max(result_cbT$value)))
+})

vignettes/articles/publication_record.Rmd

@@ -23,7 +23,11 @@ To cite `landscapemetrics` or acknowledge its use, please cite the following Sof
 
 - Bosch, M. 2019. PyLandStats: An open-source Pythonic library to compute landscape metrics. bioRxiv, 715052.
 
-- Fletcher, R., Fortin, M.-J. 2018. Spatial Ecology and Conservation Modeling. Applications with R. Basel, Switzerland: Springer International Publishing. 
+- Copot, O., Tanase, C., 2019. Dead wood, forest fragmentation and elevation influences macrofungal diversity on downed coarse woody debris in beech and oak old forest ecosystems from northeastern Romania. Journal of Plant Development 26:161-172.
+
+- Fletcher, R., Fortin, M.-J., 2018. Spatial Ecology and Conservation Modeling. Applications with R. Basel, Switzerland: Springer International Publishing. 
+
+- Gasparini, K.A.C., Silva Junior, C.H.L., Shimabukuro, Y.E., Arai, E., Aragão, L.E.O.C., Silva, C.A., Marshall, P.L., 2019. Determining a Threshold to Delimit the Amazonian Forests from the Tree Canopy Cover 2000 GFC Data. Sensors 19:5020. 
 
 - Heisler, L.M., Simpson, G.L., Poulin, R.G., Wellicome, T.I., Hall, B.D., 2019. Habitat specificity is not universal proxy for sensitivity to habitat conversion among rodents on the Canadian Prairies. bioRxiv 678268.
 
@@ -35,4 +39,4 @@ To cite `landscapemetrics` or acknowledge its use, please cite the following Sof
 
 - Nowosad J., TF Stepinski. 2019. Information theory as a consistent framework for quantification and classification of landscape patterns. Landscape Ecol 2019.
 
-- Rodrigues, L.L. 2019. Effects of domestic megafauna and landscape on diversity of mammals in Atlantic Forest remnants (Dissertation M.Sc.). São Paulo State University
+- Rodrigues, L.L. 2019. Effects of domestic megafauna and landscape on diversity of mammals in Atlantic Forest remnants (Dissertation M.Sc.). São Paulo State University.