Browsing by Author "Abhijith, V S"

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    Assessment of ecosystem health with urbanization and changing climatic patterns of Kochi
    (COLLEGE OF CLIMATE CHANGE AND ENVIRONMENTAL SCIENCE , VELLANIKKARA, 2023-11-25) Abhijith, V S; Bindu, G
    Ecosystem health, which encompasses the delicate balance of ecological processes and the welfare of various species within ecosystems, is crucial to sustainability. preserving key services like clean air, clean water, and climate regulation—all of which support human well-being and economic stability—requires preserving the health of ecosystems. Furthermore, robust ecosystems foster biodiversity, which increases their resistance to environmental change and capacity for adaptation, assuring their long-term viability. Recognising the inherent relationship between ecosystem health and sustainability, we place a high priority on good environmental stewardship, promoting the peaceful coexistence of people and the environment. By contributing to the urban heat island effect, a phenomenon where urban regions experience hotter temperatures than their surrounding rural areas, land surface temperature (LST) plays a crucial role in urban cities. The main cause of elevated LST in cities is heat absorption and retention by structures, asphalt, and other man-made surfaces. The health, energy use, and general well-being of urban dwellers may be significantly impacted by this increasing heat. From our analysis on the LST of Kochi city through the decade, we see an increase of 10C from 2000, with higher amounts of high temperature hotspots. Our work employs a novel fuzzy-VORS (vigour, organisation, resilience, ecosystem services) model that combines fuzzy logic and a VORS model to forecast the past, present, and future health of the ecosystem in Kochi (Greater Cochin Development Authority Area), India. In this work, the land use and land cover (LULC) maps for the years 2000, 2010, and 2022 were classified using a support vector machine (SVM) classifier. Using delta change, the LULCs dynamics for the years 2000–2010, 2010 2022, and 1990–2022 were calculated. Using the cellular automata-artificial neural network model, the LULC map for 2032 was anticipated (ANN-CA). Sensitivity analysis was carried out using the random forest (RF) machine learning technique. The VORS model and the fuzzy inference system were combined to predict the ecosystem health conditions for the years 2000 through 2032. Urban areas grew by 349 percent between 2000 and 2022, according to LULC maps. The rapid and ongoing urbanisation process has resulted in a severe reduction in all natural resources and ecosystem services. According to a LULC map from the year 2032, the built-up area would be 308.23 km2. All sensitivity study methodologies revealed that vegetation, scrubland, and closeness to urban areas are quite sensitive to land suitability models to simulate and forecast LULC maps for 2022 and 2032.