Resolution-dependent divergence of optimal segmentation scale across land-cover types in a coastal wetland landscape: A systematic empirical characterization from 0.02 to 30 m near the Yellow River estuary, Dongying, China

Optimal segmentation scale is a core parameter in object-based image analysis (OBIA). However, its variation with spatial resolution and land-cover type remains insufficiently characterized, constraining cross-resolution scale transfer in multi-source remote sensing mapping workflows. Using a coastal wetland landscape near the Yellow River estuary in Dongying, China, as the study area, this study systematically characterized optimal segmentation scales for seven land-cover types—tidal flat, bare land, buildings, water bodies, roads, cropland, and vegetation—across six spatial-resolution levels from 0.02 to 30 m. The optimal scale of each land-cover type was extracted using the Ratio of Mean Difference to Neighbors (absolute) to Standard Deviation (RMAS) method. Local variance (LV) quantified spatial heterogeneity, and perimeter-area fractal dimension (PAFRAC) provided an auxiliary descriptor of boundary complexity. Empirical regression models relating optimal scale to spatial resolution and LV were developed, and their utility was evaluated through classification and search-efficiency analyses. The results show that: (1) at the same spatial resolution, land-cover types with lower LV, namely tidal flat and water bodies, were consistently associated with larger optimal segmentation scales, whereas land-cover types with higher LV, namely buildings and roads, were associated with smaller optimal segmentation scales, indicating a stable negative cross-sectional correspondence. The PAFRAC results provided auxiliary support for this correspondence from the perspective of boundary complexity. (2) The functional form of the resolution–scale response systematically diverged by land-cover type: tidal flat and water bodies followed exponential-function models (R 2 = 0.985–0.994), whereas bare land, buildings, vegetation, roads, and cropland followed power-function models (R 2 = 0.931–0.996). This difference in function type was also reproduced in the LV–scale model, showing a consistent empirical pattern across the two model formulations. (3) In the 0.02 m ultra-high-resolution unmanned aerial vehicle (UAV) imagery, scale reversal was observed for buildings and vegetation: their optimal segmentation scales were lower than the corresponding values at 0.5 m and 2 m, deviating from the monotonic trend over the 0.5–30 m range. This phenomenon may reflect a shift in the dominant source of spatial heterogeneity from macroscopic patch morphology to micro-scale internal texture; however, because of the limited coverage of the UAV data, this interpretation requires further verification. (4) Across the five full-area satellite-image resolutions, overall accuracy (OA) varied non-monotonically with spatial resolution. The 10 m imagery achieved the highest OA (OA = 97.85%, Kappa = 0.97). Tidal flat and water bodies showed stable accuracy across the five satellite-image resolutions, whereas the accuracies of buildings and roads decreased markedly at spatial resolutions coarser than 10 m. (5) In an in-sample model evaluation, direct use of the model-predicted scales yielded lower performance than the RMAS-derived optimal scales, whereas prediction-centered local searches covered all 35 spatial-resolution–land-cover-type combinations and reduced candidate scales and estimated segmentation-only processing time by 41.8% and 38.7%, respectively. The empirical equations provide a quantitative basis for cross-resolution scale-search initialization in the present study area under the present data and processing conditions. Their applicability to structurally similar coastal wetlands requires independent multi-site validation.