Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia

نویسندگان

چکیده

Land Use/Land Cover (LULC) change has inhibited sustainable development for the last millennia by affecting climate, biological cycles, and ecosystem services functions. In this regard, understanding historical future patterns of LULC plays a crucial role in implementing effective natural resource management. This study aimed to model characterize spatiotemporal trajectories landscape between 1984 2060 periods. The satellite image spectral information was segmented into seven classes using hybrid approach recognition. supervised classification technique Support Vector Machine (SVM) used classify images, whilst Change Modeler (LCM) Module TerrSet software assess trend simulation dynamics. To predict changes, transition potential maps were generated Multi-layer Perceptron (MLP) neural network algorithm. findings demonstrated that Goang Watershed experienced significant since 1984. During 1984–2001, 2001–2022, 1984–2022 periods, farmland showed dramatic increasing with 7.5 km2/yr−1, 110.3 64.3 respectively. A similar also observed built-up areas 0.5 3.2 2 km2/yr−1. expansion area at expense forest, shrubland, grasslands. With business-as-usual scenario, extent will continue increase 2022 while rapid reduction is expected alarming rate put pressure on biodiversity area. As result, eco-friendly conservation approaches should be implemented as soon possible maintain health encourage development.

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ژورنال

عنوان ژورنال: Heliyon

سال: 2023

ISSN: ['2405-8440']

DOI: https://doi.org/10.1016/j.heliyon.2023.e20088