ItemArticle

Land Use/Land Cover (LULC) Change Classification for Change Detection Analysis of Remotely Sensed Data Using Machine Learning-Based Random Forest Classifier

Title
Land Use/Land Cover (LULC) Change Classification for Change Detection Analysis of Remotely Sensed Data Using Machine Learning-Based Random Forest Classifier
Creator
Mahendra, H.N.; Pushpalatha, V.; Rekha, V.; Sharmila, N.; Kumar, D. Mahesh; Pavithra, G.S.; Basavaraj, N.M.; Mallikarjunaswamy, S.
Description
Land Use and Land Cover (LULC) classification is critical for monitoring and managing natural resources and urban development. This study focuses on LULC classification for change detection analysis of remotely sensed data using a machine learning-based Random Forest classifier. The research aims to provide a detailed analysis of LULC changes between 2010 and 2020. The Random Forest classifier is chosen for its robustness and high accuracy in handling complex datasets. The classifier achieved a classification accuracy of 86.56% for the 2010 data and 88.42% for the 2020 data, demonstrating an improvement in classification performance over the decade. The results indicate significant LULC changes, highlighting areas of urban expansion, deforestation, and agricultural transformation. These findings highlight the importance of continuous monitoring and provide valuable insights for policymakers and environmental managers. The study demonstrates the effectiveness of using advanced machine-learning techniques for accurate LULC classification and change detection in remotely sensed data. 2025 by the authors.
Source
Nature Environment and Pollution Technology;Volume;24;Issue;2;Article No.;B4238;
Date
01-01-2025
Publisher
Technoscience Publications
Subject
Land use/land cover; Linear Imaging Self-Scanning Sensor-III; Machine learning; Multispectral data; Random forest classifier; Remote sensing
Coverage
Mahendra H.N., Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, Bengaluru, 560060, India; Pushpalatha V., Department of Information Science and Engineering, JSS Academy of Technical Education, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, Bengaluru, 560060, India; Rekha V., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bangalore, 560060, India; Sharmila N., Department of Electrical and Electronics Engineering, JSS Science and Technology University, Karnataka, Mysuru, 570015, India; Kumar D.M., Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, Bengaluru, 560060, India; Pavithra G.S., Department of Computer Science and Engineering, AI-ML, RNS Institute of Technology, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, Bengaluru, 560098, India; Basavaraj N.M., Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, Bengaluru, 560060, India; Mallikarjunaswamy S., Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, Bengaluru, 560060, India
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 9726268;
Format
online
Language
English
Type
Article

Citation

Mahendra, H.N.; Pushpalatha, V.; Rekha, V.; Sharmila, N.; Kumar, D. Mahesh; Pavithra, G.S.; Basavaraj, N.M.; Mallikarjunaswamy, S., “Land Use/Land Cover (LULC) Change Classification for Change Detection Analysis of Remotely Sensed Data Using Machine Learning-Based Random Forest Classifier,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23614.

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