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Analyze CDL crops dynamically

Source: R/04-crop-analysis.R
analyze_cdl_crops_dynamic.Rd

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)

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

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

Examples

if (FALSE) { # \dontrun{
# 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"
)
} # }

# \donttest{
# 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")
#> Starting dynamic CDL crop analysis...
#> Analyzing 1 crop codes for corn
#> CDL analysis completed!
print(class(result))  # Should be SpatRaster
#> [1] "SpatRaster"
#> attr(,"package")
#> [1] "terra"
# }