Habitat Categorization and Vegetation Mapping of Kumana National Park, Sri Lanka

Abstract

Remote sensing constitutes a broad and influential discipline that has assumed a significant role in vegetation mapping on a global scale in recent years. The availability of an accurate vegetation map assists future ecological studies and the management of protected areas. This study was conducted to identify and map the available habitats in Kumana National Park (KNP), Sri Lanka. We utilized multiple environmental covariates obtained via field surveys and remote sensing techniques for the initial categorization of habitats based on principal component analysis. Vegetation maps for KNP were generated by applying multiple classification algorithms to Sentinel 2 multispectral satellite imagery. The maximum likelihood classification (MLC) model generated the most accurate and detailed vegetation map for KNP, which was verified with ground truth data (overall accuracy of 93%; Kappa, 87%). The study’s findings furnish precise insights into the vegetation cover of KNP, thereby augmenting knowledge on the spatial distribution of habitats to support the future work of researchers and park managers. This map offers significantly improved resolution and spatial detail compared to previous maps. It also increased the number of identified habitat types from four to six. These findings can be used to identify critical areas for both terrestrial and aquatic fauna within KNP and support habitat conservation and management strategies in the park.