Dynamic Quantification and Characterization of Spatial Heterogeneity in Mid-Sized Urban Landscape of India

Quantifying landscape features and linking them to ecological processes is a key goal of landscape ecology. Urbanization, socio-economic growth, political influences, and morphology have extended built-up and urban regions from the core to the boundaries. Population expansion and human activity in districts have increased outlying areas and living space borders, segmenting the urban area and affecting the local ecosystem. Current space-based remote sensing (RS) techniques could be used to visualize conditions and future prognoses for district growth to plan the infrastructure. The Land Use Land Cover (LULC) patterns in the Sonipat district, located within the National Capital Region (NCR), were examined using RS data from 2011 (Landsat 7) and 2021 (Sentinel-2) and analyzed on the Google Earth Engine (GEE) cloud platform. LULC datasets for both years were generated, followed by calculations of landscape metrics to evaluate changes across the study area. These metrics, computed using R software version 4.4.2, include analyses at three levels: five metrics at the patch level, five at the landscape level, and nine at the class level. This paper provides detailed insights into these landscape metrics, illustrating the extent and nature of landscape changes within the study area over the decade. Aggregation and fragmentation are observed in the study area, as the results indicate that urban, fallow, and barren areas have merged into larger, contiguous patches over time. This shows a consolidation of smaller patches into more extensive, connected land cover areas. Fragmentation is described as occurring between 2011 and 2021, especially in the cropland LULC class, where the landscape was divided into smaller, isolated patches. This means that larger, continuous land cover types were broken down into numerous smaller patches, increasing the overall patchiness and separation across the area, which might have an ecological impact. Landscape metrics and spatial-temporal monitoring of the landscape would aid the district council and planners in better planning and livelihood sustainability.