Aggregation-Scale and Terrain-Zone Variation in Multifeature Classification of Land-Cover Samples in a Karst Landscape

Land-cover studies often compare feature configurations at one pixel size and report aggregate accuracy, leaving their behavior across aggregation supports, classes, and landscape strata incompletely characterized. We compared five pixel sizes (2, 5, 10, 15, and 30 m) derived from one pan-sharpened Gaofen-2 image in Huajiang Town, Guizhou Province, China. Random forests with eight combinations of spectral, index, texture, and terrain features were evaluated for seven classes using the same 1583 reference polygons and five repeated spatial-block partitions. The reported performance compares predictions for these polygons, not accuracy over all map pixels. Macro-F1 for spectral decreased from 70.59 ± 0.62% at 2 m to 62.47 ± 0.38% at 30 m; the standard deviations describe sensitivity to the repeated partitions. Averaged across the five pixel sizes, the index-, texture-, and terrain-augmented pipelines exceeded their separately screened spectral baselines by 1.68, 2.35, and 5.39 percentage points, respectively. These contrasts include changes in feature screening and do not isolate individual feature effects. Performance also varied across classes and terrain-zone sample groups. Within one image, one sampled region, and the specified aggregation and validation design, feature-pipeline contrasts varied across scale, class, and terrain-zone strata; external map accuracy and transferability remain to be tested with independent, support-matched reference data.