Evaluation of urban community digital landscape and runoff regulation effect by GIS and InVEST model - Lina Yan; Xin Gu (PLoS ONE, 2026)
In high-density urban communities, the expansion of impervious surfaces and the fragmentation of green spaces increase surface runoff. Accurate identification of runoff regulation effects at the community scale has therefore become an important issue in landscape optimization. This study examined a typical high-density urban community. A Geographic Information System (GIS) was used to process high-resolution remote sensing images, terrain data, and meteorological records. An eight-class digital landscape classification system was then established. The Integrated Valuation of Ecosystem Services and Trade-offs–Sediment Delivery Ratio (InVEST-SDR) module was combined with landscape pattern metrics to construct a community-scale runoff regulation assessment framework. Model parameters were calibrated using observed runoff data. Runoff regulation results under current conditions and optimized scenarios were subsequently compared. The results showed that the model achieved a coefficient of determination of 0.89 and an average relative error of 1.3%, indicating reliable performance at the community scale. When impervious surfaces accounted for 37.2% of the area, the mean annual runoff depth reached 41.8 mm and the runoff coefficient was 0.52. After increasing green-space coverage by 15% and patch aggregation by 23%, the mean annual runoff depth decreased to 27.3 mm. Runoff regulation efficiency increased by 34.7%. Landscape fragmentation showed a significant positive correlation with runoff volume. Patch aggregation showed a significant positive correlation with runoff reduction rate. A dispersed green-space layout improved regulation efficiency by 18.6% compared with a centralized layout. These results indicate that the spatial structure of digital landscapes influences runoff regulation at the community scale. The GIS–InVEST coupling framework provides quantitative support for landscape optimization and spatial planning of sponge communities.