[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126350-en":3,"doc-seo-126350-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126350,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Land Use/Land Cover (LULC) Change Classification for Change Detection Analysis of Remotely Sensed Data Using Machine Learning-Based Random Forest Classifier - Article","Land Use and Land Cover (LULC) classification underpins effective monitoring and management of natural resources and urban development. This study applies a machine learning approach using a Random Forest classifier to perform LULC classification for change detection on remotely sensed data. The analysis characterizes LULC changes between 2010 and 2020, leveraging Random Forest robustness for complex datasets. Reported classification accuracy is 86.56% for 2010 and 88.42% for 2020. Results reveal substantial urban expansion, deforestation, and agricultural transformation, supporting continuous monitoring for policy and environmental management.","|  | Nature Environment and Pollution Technology\u003Cbr>An International Quarterly Scientific Journal |  |  | p-ISSN: 0972-6268 (Print copies up to 2016)\u003Cbr>e-ISSN: 2395-3454 | Vol. 24 | No. 2 |  | Article ID\u003Cbr>B4238 |  | 2025 |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Original Research Paper |  |  |  [https://doi.org/10.46488/NEPT.2025.v24i02.B4238](https://doi.org/10.46488/NEPT.2025.v24i02.B4238) |  |  |  |  |  | Open Access Journal |  |  |\n\nLand Use/Land Cover (LULC) Change Classification for Change Detection Analysis of Remotely Sensed Data Using Machine Learning-Based Random Forest Classifier  \nH. N. Mahendra1, V. Pushpalatha2†, V. Rekha3, N. Sharmila4, D. Mahesh Kumar1, G. S. Pavithra5, N. M. Basavaraj1 and S. Mallikarjunaswamy1  \n1Department of Electronicsand Communication Engineering, JSS Academy of Technical Education (Affiliated to Visvesvaraya Technological University, Belagavi), Bengaluru-560060, Karnataka, India  \n2Department of Information Science and Engineering, JSS Academy of Technical Education (Affiliated to Visvesvaraya Technological University, Belagavi), Bengaluru-560060, Karnataka, India  \n3Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bangalore-560060, India 4Department of Electrical and Electronics Engineering, JSS Science and Technology University, Mysuru-570015, Karnataka, India 5Pavithra G S, Department of Computer Science and Engineering (AI-ML), RNS Institute of Technology (Affiliated to Visvesvaraya Technological University, Belagavi), Bengaluru-560098, Karnataka, India  \n†Corresponding author: V. Pushpalatha; [pushpav27@gmail.com](pushpav27@gmail.com)  \nAbbreviation: Nat. Env. & Poll. Technol.  \n[Website: www.neptjournal.com](Website: www.neptjournal.com)  \nReceived: 11-07-2024  \nRevised: 23-08-2024  \nAccepted: 26-08-2024  \nKey Words:  \nRemote sensing Multispectral data Machine learning Random forest classifier  \nLinear Imaging Self-Scanning Sensor-III Land use/land cover  \nCitation for the Paper:  \nMahendra, H. N., Pushpalatha, V., Rekha, V., Sharmila, N., Kumar, D. M., Pavithra, G. S., Basavaraj, N. M., and Mallikarjunaswamy, S., 2025. Land Use/Land Cover (LULC) change classification for change detection analysis of remotely sensed data using machine learning-based random forest classifier. Nature Environment and Pollution Technology, 24(2), p. B4238 . [https://doi.org/10.46488/NEPT.2025](https://doi.org/10.46488/NEPT.2025) . v24i02 . B4238.  \nNote: From year 2025, the journal uses Article ID instead of page numbers in citation of the published articles.  \nCopyright: © 2025 by the authors  \nLicensee: Technoscience Publications This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/l](creativecommons.org/l)icenses/by/4 .0/) .  \nABSTRACT  \nLand 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 ","cbCaih1X50slu0Vw","https://ap.wps.com/l/cbCaih1X50slu0Vw","pdf",910369,6,1,11,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What is the main objective of this research paper?\",\"answer\":\"To classify Land Use/Land Cover (LULC) and analyze LULC changes for change detection using remotely sensed data with a machine learning-based Random Forest classifier.\"},{\"question\":\"Which machine learning method is used for LULC classification and change detection?\",\"answer\":\"A Random Forest classifier is used because it is robust and provides high accuracy on complex datasets.\"},{\"question\":\"What do the results for 2010 and 2020 classification accuracy indicate?\",\"answer\":\"The classifier achieved 86.56% accuracy for 2010 and 88.42% for 2020, showing improved performance and revealing significant changes such as urban expansion, deforestation, and agricultural transformation.\"}]","Land Use/Land Cover (LULC) Change Classification for Change Detection Analysis of Remotely Sensed Data Using Machine Learning-Based Random Forest Classifier - 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