Evaluating landscape capacity to provide spatially explicit valued ecosystem services for sustainable coastal resource management

Abstract

Ecosystem Services (ESs) are the direct and indirect benefits and opportunities that human obtained from the ecosystem. This study evaluated landscape capacity of providing multiple key ESs in a tropical coastal ecosystem (Sundarbans Biodiversity Region (SBR)India). Multiple supervised machine learning algorithms were utilized to classify the regions into several landscape zones. The provisioning capacities of ESs for each landscape type were derived separately from an expert opinion survey and the remote sensing based methods, and the association of the outcomes between these two approaches was evaluated using the Pearson correlation coefficient test. A total of nine ESs were selected to quantify their economic values for several reference years. The benefit transfer and equivalent value coefficient approaches were used to aggregate the economic values for each ES. Research results indicated that the water bodies are the most important landscape units in the SBR region. This ecosystem has the highest relevant capacity to provide the necessary regulatory, supporting, provisioning, and cultural ESs. Water regulation (WR), waste treatment (WT), aesthetic, recreation, and cultural (ARC), and climate regulation (CR) are the main ESs of the SBR. These services are immensely important not only for upgrading the livelihood status of coastal communities but also for the climatic and environmental suitability of the Kolkata urban region. The correlation results between the remote sensing and expert-based capacity estimates have suggested that the proposed remote sensing approach could be an alternative to evaluate the landscape capacity of providing multiple ESs in any given ecosystem. Except for the mangrove region, a very high (>0.7) correlation was observed between the model and expert-derived capacity values. The outcome of this study could be an important reference to the land administrators, planners, decision makers for adopting suitable land resource management plans for sustainable uses of natural resources in coastal region. © 2019 Elsevier Ltd