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All Journal Journal of Degraded and Mining Lands Management
Gupta, Rupesh
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology

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Geo-spatial dynamics and machine learning insights: SVM and RF-driven land use and cover change detection in São Paulo, Brazil Lavanya, G; Rajamurugadoss, J; Sujatha, V; Kachancheeri, Muhammed Shameem; Kannan, SPM; Gupta, Rupesh; Sivakumar, Vivek
Journal of Degraded and Mining Lands Management Vol. 12 No. 5 (2025)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2025.125.8509

Abstract

A quantitative evaluation of land-use/land-cover (LULC) changes is required due to the rapid acceleration of LULC change in recent decades, which has been influenced by population growth, economic expansion, and industrial development, particularly in emergent nations. The Sao Paulo region in Brazil faces significant LULC changes due to industrial development, urbanization, and agricultural growth, impacting ecosystems, biodiversity, and water supplies. Addressing these changes involves using Landsat satellite data, sustainable land management, conservation programs, and community involvement. This study compares random forest (RF) and support vector machine (SVM) techniques for classifying LULC features. RF achieved higher accuracy (0.89) compared to SVM (0.76). LULC distribution in 1993 was 3% water, 20% agriculture, 49% forests, 27% built-up, and 1% barren. Projections for 2023 show changes to 2% water, 35% agriculture, 42% forests, 35% built-up, 3% barren, and 5% mining. RF is identified as the superior classifier, though further testing in diverse conditions is recommended.
Ecological environment quality assessment using remote sensing models and a machine learning approach for land consolidation Kanchanamala, P; Venu Madhav, T; Kachancheeri, Muhammed Shameem; Sutar, Ajim Shabbir; Muthukumaran, N; Kannan, S.P.M.; Gupta, Rupesh
Journal of Degraded and Mining Lands Management Vol. 12 No. 5 (2025)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2025.125.8605

Abstract

The development of land consolidation (LC) in Kochi leads to ecological alterations, requiring effective methods to assess ecological environmental quality (EEQ). This study introduced a remote sensing (RS)-based framework using pre-, during-, and post-LC data. The Remote Sensing Ecological Index (RSEI), incorporating four factors wetness (LSM), greenness (NDVI), heat (LST), and dryness (NDBSI)—was applied to evaluate EEQ variations. Land Use Land Cover (LULC) was derived using Random Forest classification to highlight changes during Kochi’s smart city project. RSEI values range from 0 (low EEQ) to 1 (high EEQ). The results showed that EEQ was good before LC, declined during LC, and significantly dropped after LC, with areas such as Pazhangad, Fort Kochi, and Mattancherry experiencing severe degradation. Recovery of EEQ took over a decade. This approach offers a reliable, scientific method for long-term EEQ monitoring at the project scale and supports sustainable planning in ecologically sensitive urban developments.
Co-Authors Kachancheeri, Muhammed Shameem Kanchanamala, P Kannan, S.P.M. Kannan, SPM Lavanya, G Muthukumaran, N Rajamurugadoss, J Sivakumar, Vivek Sujatha, V Sutar, Ajim Shabbir Venu Madhav, T