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Reprojection: Reprojection is the process of converting spatial data from one coordinate system to another. It allows you to align data from different sources or to display data in a desired projection for analysis or visualization.
Resampling: Resampling involves changing the cell size or resolution of a raster dataset. It can be used to increase or decrease the spatial resolution of data, often to match the resolution of other datasets or to reduce file size.
Reclassification: Reclassification is the process of reassigning values in a raster dataset based on specified criteria. It's commonly used to simplify complex data, create categorical maps from continuous data, or to recode values for analysis.
Filtering: Filtering in spatial analysis refers to selecting or highlighting specific data based on certain criteria. It can involve removing unwanted data or emphasizing particular features, often used to focus on areas of interest or to remove noise from datasets.
Clipping: Clipping is the process of extracting a portion of a spatial dataset that falls within a defined boundary. It's used to focus on a specific area of interest or to reduce the size of a dataset to a manageable extent.
Distance Calculation: Distance calculation in GIS involves computing the spatial distance between features or locations. It can be used to create buffer zones, analyze proximity relationships, or generate distance-based raster surfaces for various spatial analyses
Environment setupsetup
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library(ForestForesight) # Make sure you have run ff_environment previously, if not you have to assign the ff_folder yourself ff_folder <- "/path/to/ff_folder"Sys.getenv("DATA_FOLDER") template_folder <- list.files(file.path(ff_folder, "preprocessed", "input"), pattern = "^[0-9]{2}[NS]_[0-9]{3}[EW]$", full.names = TRUE)[1] template_raster <- rast(list.files(template_folder, pattern = "\\.tif$", full.names = TRUE)[1]) |
For more information about the config file please refer to the configuration page here Open-Source Contribution .
Reprojecting a raster dataset
This is useful when your input raster is in a different coordinate reference system (CRS) than your template or has a different resolution or extent.
Below we give different example scripts on how to preprocess your own dataset. Please make sure you enter your own datasets when you load your dataset using rast or vect
Example: Reprojecting a land cover raster
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# Load a land cover raster in a different CRS
land_cover <- rast("landcover_wgs84path/to/your/own/dataset.tif") #this is an example! Enter your own dataset
# Check CRS
print(crs(land_cover))
print(crs(template_raster))
# Reproject using nearest neighbor (best for categorical data)
land_cover_nearest <- project(land_cover, template_raster, method = "near")
# Reproject using cubic (often better for continuous data)
land_cover_cubic <- project(land_cover, template_raster, method = "cubic")
# Compare results
par(mfrow = c(1, 2))
plot(land_cover_nearest, main = "Nearest Neighbor")
plot(land_cover_cubic, main = "Cubic") |
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# Load a multi-band Landsat image
landsat <- rast("landsat_image.tif") #this is an example! Enter your own dataset
# Check number of layers
nlyr(landsat)
# Select the Near-Infrared band (usually band 5 in Landsat 8)
nir_band <- landsat[[5]]
# Standardize to template
nir_standardized <- project(nir_band, template_raster, method = "cubic")
# Plot
plot(nir_standardized, main = "Near-Infrared Band") |
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# Load a land cover raster
lulc <- rast("land_use_land_cover.tif") #this is an example! Enter your own dataset
# Create a reclassification matrix
# Let's say we want to simplify to Forest (1), Agriculture (2), and Other (3)
rcl_matrix <- matrix(c(
1, 5, 1, # Classes 1-5 become Forest (1)
6, 10, 2, # Classes 6-10 become Agriculture (2)
11, 20, 3 # Classes 11-20 become Other (3)
), ncol = 3, byrow = TRUE)
# Reclassify
lulc_reclass <- classify(lulc, rcl_matrix)
# Standardize to template
lulc_standardized <- project(lulc_reclass, template_raster, method = "near")
# Plot
plot(lulc_standardized, main = "Reclassified Land Cover") |
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# Load forest cover polygons
forest_polygons <- vect("forest_cover.shp") #this is an example! Enter your own dataset
# Rasterize (1 for forest, 0 for non-forest)
forest_raster <- rasterize(forest_polygons, template_raster, field = 1, background = 0)
# Plot
plot(forest_raster, main = "Forest Cover Mask") |
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# Load administrative boundaries
admin_boundaries <- vect("admin_boundaries.shp") #this is an example! Enter your own dataset
# Assuming there's a 'population' column in the vector data
population_raster <- rasterize(admin_boundaries, template_raster, field = "population", background = 0)
# Plot
plot(population_raster, main = "Population Density") |
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# Load road network
roads <- vect("road_network.shp") #this is an example! Enter your own dataset
# Create distance raster (in meters)
distance_to_roads <- distance(template_raster, roads)
# Convert to kilometers
distance_to_roads_km <- distance_to_roads / 1000
# Plot
plot(distance_to_roads_km, main = "Distance to Roads (km)") |
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