Quality index approach for analysis of urban green infrastructure in Himalayan cities

In fast urbanizing cities, fragmentation of urban green infrastructure (UGI) commonly arises due to lack of efficient planning to maintain the quantity and improve their quality. As ecological processes and landscape patterns are closely intertwined, it is a prerequisite to investigate landscape structure when aiming at better provision of ecosystem services. This study integrates remote sensing, geographic information system, combination of landscape metrics, and multi-variated statistics to delineate structural attributes influencing UGI Quality (UGIQ). We exemplify our methodology in three capital cities of Indian Himalayan states at administrative ward level. The UGIQ is derived by comparing landscape characters defined by nine metrics denoting area, shape, and aggregation attributes. By employing principal component analysis (PCA) and multi-collinearity diagnosis, a set of quality defining metrics are obtained for each city. Further, to gain insightful spatial basis for improving connectivity, Morphological Spatial Pattern Analysis (MSPA) is used to visualize and classify patches into seven morphological classes. Landscape characterization highlights a pattern of low-quality wards having a limited number and area of UGI patches in urban centers, and high-quality wards with complex and aggregated patches towards fringes. PCA identifies the positive influence of area (LPI, AREA_MN) and shape (LSI, FRAC_AM, CONTIG) metrics and negative influence of patch distance (ENN_MN) and fragmentation (PD) on UGIQ in different combinations across the cities. Higher shares of morphological core and edge classes are recognized for overall UGIQ improvement. The results provide quantitative measures to develop integrated spatial planning strategies.