ecosystem-services-assessment

Ecosystem services assessment maps indicators by integrating land-cover rasters, biophysical data, and study area polygons to quantify spatial ES values for conservation and land-use planning.

Mapping carbon storage and erosion control requires integrating land-cover rasters with biophysical proxies to produce spatial es_indicator_maps and summary tables for land-use planning.

Yes, you can generate ecosystem service trade-offs using Python tools like numpy and rasterio, which process land-cover and biophysical inputs to output a tradeoff_matrix for PES design.

Yes, you need a land-cover raster, carbon pools, and a study area polygon as minimum inputs to satisfy the requirements for generating ES indicator maps, summary tables, and reports.

The best way to summarize ecosystem service indicators by land-cover class is to process land-cover maps with biophysical data, generating per-class summaries and an es_summary_table.csv output.

Limitations of land-cover based pollination habitat assessment include relying on habitat quality estimates and biophysical proxies rather than direct species observations, which may affect accuracy for detailed ecological studies.