analyze_cdl_crops_dynamic: Analyze CDL crops dynamically

In geospatialsuite: Comprehensive Geospatiotemporal Analysis and Multimodal Integration Toolkit

Analyze CDL crops dynamically

Description

Perform dynamic analysis of CDL crop data including area calculations, classification, and masking for any crop combination. Now accepts file paths directly.

Usage

analyze_cdl_crops_dynamic(
  cdl_data,
  crop_selection,
  region_boundary = NULL,
  analysis_type = "classify",
  output_folder = NULL,
  year = NULL
)

Arguments

cdl_data	CDL raster data (file path, directory, or SpatRaster object)
crop_selection	Crop selection (names, codes, or categories)
region_boundary	Region boundary
analysis_type	Type of analysis: "mask", "classify", "area"
output_folder	Output directory
year	Year for analysis (optional)

Details

Usage Tips:

Area Analysis Results:

# Access specific results
result$total_area_ha          # Total area in hectares
result$crop_areas_ha          # Area by crop code
result$crop_areas_ha[["5"]]   # Soybean area (code 5)

Visualization:

# For mask/classify results (SpatRaster):
terra::plot(result)           # Plot the raster

# For area results (list):
print(result$total_area_ha)   # Print total area
barplot(unlist(result$crop_areas_ha))  # Simple bar plot

Value

Analysis results depend on analysis_type:

• "mask": SpatRaster with binary mask (1 = crop, 0 = other)

• "classify": SpatRaster with binary classification

• "area": List with detailed area analysis:

  • crop_areas_ha: Named list of areas by crop code (hectares)

  • total_area_ha: Total crop area (hectares)

  • pixel_size_ha: Individual pixel size (hectares)

  • crop_codes: CDL codes analyzed

  • crop_name: Crop name(s)

  • region: Region analyzed

  • raster_info: Technical raster information

  • total_pixels: Total number of pixels

  • valid_pixels: Number of valid (non-NA) pixels

Examples

## Not run: 
# These examples require actual CDL data files
# Analyze soybean area in Iowa - accepts file path directly
soybean_area <- analyze_cdl_crops_dynamic(
  "/path/to/cdl_2023.tif", "soybeans", "Iowa", "area"
)

# Access area results
soybean_area$total_area_ha                    # Total hectares
soybean_area$crop_areas_ha[["5"]]            # Soybean area (code 5)
soybean_area$total_area_ha * 2.47105         # Convert to acres

# Create grain classification
grain_classes <- analyze_cdl_crops_dynamic(
  cdl_data, "grains", "CONUS", "classify"
)
terra::plot(grain_classes)  # Plot the classification

# Works with directories too
results <- analyze_cdl_crops_dynamic(
  "/path/to/cdl/files/", "corn", "Ohio", "area"
)

## End(Not run)


# Example with mock CDL data (this can run)
# Create sample CDL raster
mock_cdl <- terra::rast(nrows = 10, ncols = 10, xmin = 0, xmax = 10,
                       ymin = 0, ymax = 10, crs = "EPSG:4326")
terra::values(mock_cdl) <- sample(c(1, 5, 24), 100, replace = TRUE)  # corn, soy, wheat

# Analyze mock data
result <- analyze_cdl_crops_dynamic(mock_cdl, "corn", analysis_type = "mask")
print(class(result))  # Should be SpatRaster

geospatialsuite documentation built on Nov. 6, 2025, 1:06 a.m.