24th Pacific Science Congress (PSC-24)

Local implementation of the Kunming–Montreal Global Biodiversity Framework requires actionable indicators that connect biodiversity patterns to ecosystem functioning at management-relevant scales. We present a county-scale, multi-source assessment framework integrating three complementary dimensions—(i) ecosystem types, (ii) structure and function, and (iii) spatial pattern—using openly available remote sensing and routine field observations. In Laifeng County, Hubei, China, we mapped ecosystem types from high-resolution imagery and derived functional proxies including fractional vegetation cover (FVC), aboveground biomass (AGB), and vegetation indices (e.g., NDVI, kNDVI). Using AGB as a proxy for primary productivity, we quantify the biodiversity–productivity relationship at county scale. Spatial pattern metrics (e.g., SHDI, SHEI, PD, LPI, AI, and a fragmentation index) were computed to quantify landscape configuration, while topography and land-use layers provided covariates. Indicators were normalized and aggregated to an Ecosystem Diversity Index (EDI), with sensitivity and robustness analyses.

Results show that mid-elevation forests form a contiguous ecological backbone (high AI, low fragmentation) coinciding with higher AGB, whereas shrub–grass–wetland mosaics are highly fragmented and functionally heterogeneous. EDI peaks at intermediate elevation and landscape heterogeneity, and its relationship with AGB is positive but saturating, indicating diminishing gains in productivity at very high diversity levels. Across 10×10 km grids, topographic complexity and land-use intensity dominate EDI variation, with results robust to alternative weighting schemes. The framework is transferable and reproducible, enabling county governments to (1) establish baselines, (2) prioritize conservation and restoration corridors, and (3) track progress toward GBF targets. By relying on public Earth Observation data and lightweight field inputs, it lowers entry barriers for routine biodiversity–function monitoring and supports evidence-based spatial planning.

Ecosystem diversity,Biodiversity,Ecosystem Function,Remote sensing (RS),多源数据融合