updates and ROIs

BioViewPoint.Rproj

@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX

Code/01_Creating spatial BBS routes.R

@@ -184,8 +184,15 @@ bbs_route.data_sf.point$geometry.1 <- NULL
 # Create a dataframe with all the information in both the bbs routes df and the
 # updated.route df but with the route shape (linestring) geometry only
 bbs_route.data_sf.line <- cbind(bbs_route.data_sf, updated.route.shapefile) 
-bbs_route.data_sf.line$geometry <- NULL
-names(bbs_route.data_sf.line)[21] <- "geometry"
+
+# Set the geometry column to the linestring geometry
+st_geometry(bbs_route.data_sf.line) <- bbs_route.data_sf.line$geometry.1
+
+# Remove the old geometry column
+bbs_route.data_sf.line$geometry.1 <- NULL
+
+# Ensure that 'geometry' is now the active geometry column
+st_geometry(bbs_route.data_sf.line) <- st_geometry(bbs_route.data_sf.line)
 
 
 ###############################################
@@ -281,10 +288,10 @@ plot(bbs_route.data_sf.point$geometry,
 # Write each sf dataframe as a geo json file
 
 st_write(bbs_route.data_sf.point, 
-         paste(path, "/00_Data/Processed/BBS_Rtes_Point.geojson",
+         paste(path, "/00_Data/Processed/BBS/BBS_Rtes_Point.shp",
                                         sep = ""))
 st_write(bbs_route.data_sf.line, 
-         paste(path, "/00_Data/Processed/BBS_Rtes_Linestring.geojson",
+         paste(path, "/00_Data/Processed/BBS/BBS_Rtes_Linestring.shp",
                                        sep = ""))
 
 

Code/03_Generating 40km ROIs.R

@@ -0,0 +1,148 @@
+###############################################################################
+########                                                               ########
+########             Developing 40x40km Regions of Interest            ######## 
+########                     Around Each BBS Route                     ########
+###############################################################################
+
+# Author: Kimberly Thompson   
+
+# This code generates square polygons around each BBS route, using the midpoint
+# of each linestring, such that the midpoint is 20 km from each side of the 
+# square.
+
+
+########## clean workspace and load required packages ####################
+
+# clean workspace to improve efficiency: #
+rm(list = ls() )
+gc() #releases memory
+
+library(tidyverse) # Data cleaning
+library(sf)
+
+
+###############################################
+###                                         ###
+###             Data Loading                ###
+###                                         ###
+###############################################
+
+# Set path to Bioviewpoint folder (where data resides)
+# path <- "~/Library/CloudStorage/[email protected]/.shortcut-targets-by-id/1HhR4gs3fKXyAXBhQom6t69gAqR6SeKhx/BioViewPoint"
+# path <- "~/share/groups/MAS/04_personal/Kim_T/BioViewPoint"
+path <- "I:/mas/04_personal/Kim_T/BioViewPoint"
+
+
+# Load the spatial linestring file
+spatial.data <- sf :: st_read(paste(path,
+                                    "/00_Data/Processed/BBS/BBS_Rtes_Linestring.shp",
+                                    sep = ""))
+
+# Load USA and Canada shapefile
+usa.shape <- sf :: st_read(paste(path, "/00_Data/Raw/USACANAB.shp",
+                                 sep = ""))
+
+# Define the coordinate system
+albers = sp:: CRS("+init=epsg:5070")
+
+# Define coordintate system as albers:
+usa.shape <- sf :: st_set_crs( usa.shape, albers )
+
+
+###############################################
+###                                         ###
+###           Calculating Midpoints         ###
+###                                         ###
+###############################################
+
+# Use st_length to get the total length of each linestring
+# each is approximately 40km but will vary
+# Use st_line_interpolate with half the length of each line 
+# to find the midpoint
+
+# Calculate midpoints
+midpoints <- spatial.data %>%
+  # Calculate length of each linestring
+  mutate(length = st_length(geometry),
+         # Find midpoint
+         midpoint = st_line_interpolate(geometry, length / 2)) %>%  
+  select(midpoint)
+
+###############################################
+###                                         ###
+###           Generating Squares            ###
+###                                         ###
+###############################################
+
+# Define the side length of the square (40km)
+side_length <- 40000 # in meters
+
+# Define function to create a square polygon around each point
+create_square <- function(point, side_length) {
+
+  half_side <- side_length / 2
+
+  # Create a matrix of corner points for the square
+  coords <- matrix(c(
+    st_coordinates(point)[1] - half_side, st_coordinates(point)[2] - half_side,
+    st_coordinates(point)[1] + half_side, st_coordinates(point)[2] - half_side,
+    st_coordinates(point)[1] + half_side, st_coordinates(point)[2] + half_side,
+    st_coordinates(point)[1] - half_side, st_coordinates(point)[2] + half_side,
+    # Closing the square
+    st_coordinates(point)[1] - half_side, st_coordinates(point)[2] - half_side), 
+    ncol = 2, byrow = TRUE)
+
+  # Create a polygon from the coordinates
+  polygon <- st_polygon(list(coords))
+
+  return(st_sfc(polygon, crs = st_crs(spatial.data)))
+
+}
+
+# Apply the function to create squares for each point
+squares <- midpoints %>%
+  rowwise() %>%
+  mutate(square = list(create_square(midpoint, side_length))) %>%
+  unnest(square) %>%
+  select(square)
+
+# Convert squares to sf object
+squares_sf <- st_sf(squares)
+
+
+###############################################
+###                                         ###
+###             Housekeeping                ###
+###                                         ###
+###############################################
+
+
+# Create an ID column for both dataframes
+squares_sf <- squares_sf %>%
+  mutate(id = row_number())  # Create an ID for squares_sf
+
+spatial.data <- spatial.data %>%
+  mutate(id = row_number()) %>%  # Create an ID for spatial.data
+  select(1:20, id)  # Select only the first 20 columns and the ID
+
+# Combine the dataframes by binding columns
+combined_data <- bind_cols(squares_sf, spatial.data)
+
+# Set squares as the active geometry
+st_geometry(combined_data) <- combined_data$square
+
+# Remove unwanted columns (e.g., LINESTRING and geometry.1)
+combined_data <- combined_data %>%
+  select(-c(2, 3, 24, 25))  
+
+
+# Write the new sf object
+st_write(combined_data, 
+         paste(path, "/00_Data/Processed/BBS/40km ROIs.shp",
+               sep = ""))
+
+
+
+
+
+

Code/04_LUC in ROIs.R

@@ -0,0 +1,102 @@
+###############################################################################
+########                                                               ########
+########             Calculating Amount of Land-use Change             ######## 
+########        Around Each BBS Route's 40km Region of Interest        ########
+###############################################################################
+
+# Author: Kimberly Thompson   
+
+# This code reclassfies land-use change from type of change (e.g., habitat
+# type 1 to habitat type 2) to a presence/absences of change and then 
+# the sum of presences from 2001 to 2021 to identify regions of interest
+# with the highest and loweest change.
+
+
+########## clean workspace and load required packages ####################
+
+# clean workspace to improve efficiency: #
+rm(list = ls() )
+gc() #releases memory
+
+library(tidyverse) # Data cleaning
+library(sf)
+library(terra)
+
+###############################################
+###                                         ###
+###             Data Loading                ###
+###                                         ###
+###############################################
+
+# Set path to Bioviewpoint folder (where data resides)
+path <- "~/Library/CloudStorage/[email protected]/.shortcut-targets-by-id/1HhR4gs3fKXyAXBhQom6t69gAqR6SeKhx/BioViewPoint"
+# path <- "~/share/groups/MAS/04_personal/Kim_T/BioViewPoint"
+# path <- "I:/mas/04_personal/Kim_T/BioViewPoint"
+
+# Load the square polygons (ROIs)
+bbs.roi <- sf :: st_read(paste(path,
+                                    "/00_Data/Processed/BBS/40km ROIs.shp",
+                                    sep = ""))
+
+# Load the National Land Cover Database Change
+nlcd <- terra :: rast(paste(path,
+                               "/00_Data/Raw/National Land Cover Database/nlcd_2001_2021_land_cover_change_index_l48_20230630.img",
+                               sep = ""))
+
+
+###############################################
+###                                         ###
+###   Reclassify Land Use Change Raster     ###
+###                                         ###
+###############################################
+
+# We want to know primarily, which areas have experienced the most (and least)
+# change, as opposed to knowing the specific types of change.
+# Because the goal is to filter the BBS routes based on amounts of change.
+
+# Display categories
+levels(nlcd)
+
+#       value                           Class_Names
+# 1       0                                      
+# 2       1                             no change
+# 3       2                          water change
+# 4       3                          urban change
+# 5       4           wetland within class change
+# 6       5             herbaceous wetland change
+# 7       6       agriculture within class change
+# 8       7                cultivated crop change
+# 9       8                    hay/pasture change
+# 10      9 rangeland herbaceous and shrub change
+# 11     10                         barren change
+# 12     11                         forest change
+# 13     12                  woody wetland change
+# 14     13                           snow change
+# And numbers above with no cat name
+
+# Create a reclassification matrix
+# This matrix tells R to:
+#         Reclassify value 1 to 0
+#         Reclassify values 2 through 13 to 1
+#         Reclassify values 14 and above to 0
+rcl <- matrix(c(1, 1, 0,
+                2, 13, 1,
+                14, Inf, 0), 
+              ncol=3, byrow=TRUE)
+
+# Apply the reclassification
+nlcd_reclass <- classify(nlcd, rcl)
+
+# Define the levels of the reclassification
+levels(nlcd_reclass) <- data.frame(id=c(0,1), class=c("None", "Target"))
+
+
+
+
+
+# Define the coordinate system
+albers = sp:: CRS("+init=epsg:5070")
+
+crs(nlcd) == crs(bbs.roi)
+
+