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

) 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.