Disentangling spatial extent, spatial grain, and geomorphic context in land-use change and landscape pattern analyses of a subtropical mountain–lake plain region

Land-use/cover change (LUCC) and landscape metrics are scale sensitive, yet geomorphic context can confound their responses and statistical support can alter explanatory associations. We separated administrative–geomorphic comparisons, replicated extent and grain experiments, and fixed-support GeoDetector analyses in Hunan Province, China. Using the 30 m China Land Cover Dataset (1985–2025), we descriptively compared LUCC across four unequal administrative–geomorphic units. Separately, extent and grain responses were tested at five independent anchors in each of the two county contexts, and GeoDetector associations were assessed for three LUCC periods at a fixed 1000 m support. Forest and cropland dominated regionally; Anhua retained a relatively stable forest matrix, whereas Yuanjiang underwent active cropland–water turnover and impervious-surface expansion. Within the tested design, a significant spatial-extent response was detected only for the landscape shape index (LSI) across 5.01–15.03 km windows; no context × extent interaction was detected. Grain effects were significant for all five metrics; context × grain interactions occurred for patch density (PD), LSI, Shannon's diversity index (SHDI), and Simpson's diversity index (SIDI), but not for the aggregation index (AI). PD and LSI responded most strongly from 30 to 60 m. Slope and distance to built-up land had comparatively large q values for total change and cropland–forest turnover in Anhua; in Yuanjiang, distance to water bodies, surface-water frequency, and elevation ranked highly for total change and cropland–water turnover. All eligible interactions had joint q values exceeding both single-factor q values, and factor rankings were generally robust across temporal alignment, support size, and most discretization settings. These findings distinguish geomorphic context, spatial extent, spatial grain, and statistical support as distinct dimensions to be matched to the landscape attribute and research question.