Remote Sensing and GIS-Based LULC Prediction in Shah Alam: A Strategy for Sustainable Urban Growth and Development
DOI:
https://doi.org/10.24191/bioenv.v2i3.71Keywords:
Remote Sensing, GIS, Urban Development, Landuse Landcover, Machine LearningAbstract
Land use refers to the development of an area due to human activities, such as agriculture, industry, and residential. Land use and land cover (LULC) dynamics significantly impact agricultural productivity and food security, necessitating a comprehensive understanding and prediction of these changes. This study addresses the importance of accurate land use predictions in the context of rapid urbanisation, population growth, and climate change. This research aims to bridge the gap between land management and food security by utilising remote sensing, machine learning, and GIS technologies to predict dynamic LULC patterns. The transition potential modelling module (MOLUSCE) was used as a plugin in QGIS software to create land use and land cover (LULC) in Shah Alam City using Landsat 8 satellite images and dual sensors from 2014-2023. The ANN model was chosen to predict LULC in 2032. LULC are classified as water, developed, bare land, forests and vegetation. As a result, the developed area was the largest in 2014 and 2023, occupying 51.50% and 63.54% of the total area, respectively. Water was the smallest land use in both years. The study integrated spatial variables such as Digital Elevation Model (DEM) data and road network maps to enhance land use predictions. The findings show a fair agreement (kappa value of 0.619) between predicted and observed land-use changes, highlighting the potential for evidence-based decision-making in sustainable urban development and food security. This research contributes to the field by providing insights into future land use patterns, supporting informed policy decisions, and promoting sustainable agricultural practices for global food security efforts.
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Copyright (c) 2024 Roslina Idris, Mohamad Taufiq Mohamad Saleh
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