Analyzing Land Use and Land Cover Changes in Bhaktapur District Using Landsat Data with SVM and MLC Classification Approaches
DOI:
https://doi.org/10.64862/Keywords:
LULC, Change Detection, Support Vector MachineAbstract
Reliable land cover information is essential for effective planning, yet its absence can hinder decision-making. This study analyzes land use/land cover (LULC) changes in Bhaktapur district, Nepal, between 2015 and 2025 using Landsat data and two supervised classifiers: Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC). Findings show rapid urban expansion, with built-up areas rising from 14% to 26% (SVM) and 14% to 18% (MLC), largely replacing agricultural, barren, and some vegetated areas. Vegetation increased slightly due to local restoration efforts, while forest cover remained mostly unchanged. SVM produced higher classification accuracy (79%, kappa 0.69) than MLC (73%, kappa 0.60). Overall, the study demonstrates the usefulness of remote sensing and GIS for monitoring LULC trends and shows the need for sustainable land management as Bhaktapur undergoes rapid urban growth.
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