R语言：无需重分类快速获取指定cell value对应的raster extent

Great question—dealing with large rasters always means finding ways to avoid processing the entire dataset when you only need a subset. Reclassifying the whole raster to set non-target values to NA is a huge waste of compute and time, so here are several efficient, targeted workflows to get the extent of cells with value 100:

Using R's terra Package (Recommended for Programmers) #

The terra package is optimized for large raster handling with block-processing (no need to load the entire raster into memory). Here's how to target only cells with value 100:

library(terra)

# Load your large raster (doesn't load full data into memory yet)
r <- rast("large_raster.tif")

# Get indices of cells with value = 100 (processes in blocks)
target_cells <- which(r == 100)

# Convert cell indices to geographic coordinates
cell_coords <- xyFromCell(r, target_cells)

# Calculate the bounding extent of these coordinates
target_extent <- ext(
  min(cell_coords[,1]), max(cell_coords[,1]),
  min(cell_coords[,2]), max(cell_coords[,2])
)

# Crop the original raster to this extent (only loads the needed portion)
cropped_raster <- crop(r, target_extent)

This workflow skips modifying the entire raster and only focuses on the cells you care about, drastically reducing compute time and memory usage.

Using GDAL Command Line (Great for Batch/Non-Programmatic Workflows) #

GDAL tools are designed for efficient raster processing, and you can target specific cell values without full reclassification:

# Step 1: Convert only value=100 cells to a small vector polygon
gdal_polygonize.py large_raster.tif -f "GeoJSON" temp_extent.geojson -where "value=100"

# Step 2: Extract the extent from the vector file
ogrinfo -so -al temp_extent.geojson | grep Extent

# Step 3: Crop the original raster using the extracted extent (replace xmin/ymax/etc. with your values)
gdal_translate -projwin xmin ymax xmax ymin large_raster.tif cropped_raster.tif

# Clean up temporary file if needed
rm temp_extent.geojson

The -where clause in gdal_polygonize.py ensures only cells with value 100 are processed, keeping the temporary vector file tiny even for massive rasters.

Using ArcGIS Desktop/Pro #

If you prefer a GUI workflow, ArcGIS tools can target specific cells without full reclassification:

• Run Raster to Points, set the query expression to "VALUE" = 100, and output a point feature class (only points for your target value are generated).
• Run Minimum Bounding Geometry on the point feature class, select "ENVELOPE" as the geometry type, and output a polygon representing the extent.
• Use Clip Raster with the original raster and the envelope polygon to get your cropped subset.

Make sure to enable "Background Processing" and set appropriate tile sizes in the Raster Analysis environment to avoid loading the full raster into memory.

Key Notes #

• If your value=100 cells are spread across multiple disconnected regions, these methods will return a single bounding extent that covers all of them. To get individual extents for each region, split the polygon output into separate features first.
• Always verify your raster's spatial reference is correct to ensure the calculated extent aligns with your projection.
• For ultra-large rasters (10GB+), GDAL or terra are the most efficient options due to their built-in block-processing.

内容的提问来源于stack exchange，提问作者user