High-Resolution Gridded Forest Biomass Maps
Transform FIA point samples into continuous 30-meter resolution raster maps. Generate spatial datasets for carbon mapping, species diversity analysis, and forest monitoring.
View on GitHub DocumentationGenerate gridded biomass and carbon maps at 30m resolution, matching Landsat imagery for seamless remote sensing integration.
Comprehensive allometric equation library covering all major North American tree species with validated biomass models.
Calculate Shannon diversity, Simpson index, and species richness maps for biodiversity assessment and conservation planning.
Output data in Zarr format for efficient cloud storage, streaming access, and integration with modern data pipelines.
Advanced interpolation methods to generate continuous surfaces from sparse FIA plot data with uncertainty quantification.
Create multi-band raster stacks with biomass, carbon, species counts, and diversity metrics in a single dataset.
# Install gridFIA
pip install gridfia
# Import and create biomass maps
from gridfia import GridFIA
# Initialize with FIA data
grid = GridFIA(state="NC")
# Generate 30m biomass raster
biomass_map = grid.create_biomass_grid(
resolution=30,
output_format="zarr"
)
# Calculate species diversity
diversity = grid.calculate_diversity(
index="shannon"
)
# Export to GeoTIFF
grid.export_geotiff("nc_biomass.tif")Create wall-to-wall carbon stock maps for carbon accounting, offset projects, and climate mitigation planning.
Map species diversity patterns to identify biodiversity hotspots and prioritize conservation areas.
Combine FIA-derived maps with satellite imagery for enhanced forest monitoring and change detection.
Generate spatial timber volume estimates for forest management planning and harvest scheduling.
Map fuel loads and forest structure to support wildfire risk assessment and mitigation strategies.
Quantify and map ecosystem services including carbon sequestration, water yield, and habitat provision.