![]() ![]() These scientific trends were used to gather local perceptions on the described socio-economic settings. At the same time, the increase of the built-up areas dominated 6.04% of the existing agricultural areas in 2021. It was proposed that the projected BaU in 2050 revealed the decrease of forest land, whereas 6.04% and 1.59% of forests were converted to agricultural areas and built-up areas, respectively. The simulated 2021 BaU scenario reflected the current trends of socio-economic development. While the accuracy in PDS and BDS were comparatively low compared to BaU and EPS. As a result of model validation using 534 ground survey points in 2021, the BaU in 2021 generated the highest overall accuracy (82.77%) with a Kappa value of 0.7846, quantity disagreement value of 0.0693, and allocation disagreement value of 0.1030. The different scenarios were based on socio-economic schemes, which included the business-as-usual scenario (BaU), the ecological protection scenario (EPS), the pessimistic development scenario (PDS), and the baseline development scenario (BDS). Then, the future land cover maps for 20 were developed based on the 2021 transition probability metrics. In 2021, Markov-cellular automata (Markov-CA) and a multi-layer perceptron (MLP) were employed to model land cover changes using four scenarios. In this study, manifold geographic information system techniques were utilized for data extraction, management, and analysis. This thesis aims to understand the mechanism of land cover change in the past, the present, and the future that can be used as supplemental data for environmental planning and conservation. Increase of building residential areas and farming areas influenced by local socio-economic factors has resulted in declining the forest covers and may have negative impacts on ecosystems. Changes in land cover in the Chiang Mai-Lamphun basin have been influenced by the pressure of rapid socio-economic developments. ![]()
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