Spatial analysis and classification of land use patterns in Lucknow district, UP, India using GIS and random forest approach

Munizzah Salim, Shubham Bhattacharjee, Neerav Sharma, Kavita Sharma, Rahul Dev Garg

Article ID: 10230
Vol 8, Issue 1, 2025

(Abstract)

Abstract


Mapping land use and land cover (LULC) is essential for comprehending changes in the environment and promoting sustainable planning. To achieve accurate and effective LULC mapping, this work investigates the integration of Geographic Information Systems (GIS) with Machine Learning (ML) methodology. Different types of land covers in the Lucknow district were classified using the Random Forest (RF) algorithm and Landsat satellite images. Since the research area consists of a variety of landforms, there are issues with classification accuracy. These challenges are met by combining supplementary data into the GIS framework and adjusting algorithm parameters like selection of cloud free images and homogeneous training samples. The result demonstrates a net increase of 484.59 km2 in built-up areas. A net decrement of 75.44 km2 was observed in forest areas. A drastic net decrease of 674.52 km2 was observed for wetlands. Most of the wastelands have been converted into urban areas and agricultural land based on their suitability with settlements or crops. The classifications achieved an overall accuracy near 90%. This strategy provides a reliable way to track changes in land cover, supporting resource management, urban planning, and environmental preservation. The results highlight how sophisticated computational methods can enhance the accuracy of LULC evaluations.


Keywords


LULC; GIS; ML; spatial analysis; urban planning

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DOI: https://doi.org/10.24294/jgc10230

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