A UAV ‐based assessment for alpine meadows micro‐patch pattern: Spatial scale thresholds and landscape indices extraction

The spatial configuration of alpine meadow micro‐patches (<5 m 2 ) on the Qinghai‐Tibet Plateau (QTP) serves as a critical indicator for early warning of ecological degradation. While unmanned aerial vehicle (UAV) remote sensing enables micro‐patch detection, two methodological challenges persist: unclear response thresholds of landscape indices to spatial extent variations and diminished ecological interpretability due to redundancy in multidimensional indices. This study develops a novel scale‐adaptive framework integrating spatial extent effect analysis with principal component analysis‐driven (PCA‐driven) dimensionality reduction. Based on 34 landscape indices derived from UAV imagery (0.02‐m resolution), we systematically quantified sensitivity thresholds through spatial autocorrelation–heterogeneity trade‐off analysis across 2–50‐m spatial extents. The results showed that (1) Six critical indices, including number of patches (NP) and mean patch size (AREA_MN), exhibited significant sensitivity to spatial extent variations. The spatial extent effect curves identified 10–21‐m as the optimal domain, with 17‐m spatial extent optimally balancing spatial autocorrelation and heterogeneity. (2) PCA reduced dimensionality to three factors (area‐based aggregation, spatial shape, and edge‐separation features), explaining 84% cumulative variance. Four indices—AREA_MN, mean patch euclidean nearest neighbor distance (ENN_MN), perimeter‐area fractal dimension (PAFRAC), and mean patch fractal dimension (FRAC_MN)—were identified as key characterization indices, establishing an early‐warning diagnostic system for degradation. This framework provides a replicable protocol for micro‐patch dynamics monitoring in fragile ecosystems, supporting targeted restoration policies on the QTP and analogous regions.