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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Indenesian Journal of Tropical Biology Indenesian Journal of Tropical Biology
Çoban, Hüseyin Oğuz
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Environmental Science Health Professions Immunology & microbiology Materials Science & Nanotechnology Medicine & Pharmacology Social Sciences

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Integrating Multi-Variable Driving Factors to Improve Land Use & Land Cover Classification Accuracy using Machine Learning Approaches: A Case Study from Lombok Island Purnama, Miftahul Irsyadi; Çoban, Hüseyin Oğuz
Jurnal Manajemen Hutan Tropika Vol. 31 No. 2 (2025)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7226/jtfm.31.2.123

Abstract

Accurate classification of land cover is essential for effective land management and environmental monitoring. This study aimed to enhance land cover classification for Lombok Island using advanced machine learning algorithms. The models employed include Random Forest, Gradient Boosting, Decision Tree, and Naive Bayes, integrating a wide range of variables, such as Landsat satellite imagery, spectral indices, physiographic, climatic, and socio-economic data. Among these, Random Forest demonstrated the highest model accuracy at 82%, followed by Gradient Boosting at 80%, Decision Tree at 73%, and Naïve Bayes at 61%. In field validation assessments, comparing the predictions of these machine learning models with ground truth data, Random Forest was the most reliable, achieving an overall accuracy of 88%. This superior performance is largely due to the multi-variable approach, which allows the model to mitigate issues like cloud cover in satellite images. The key variables that significantly influenced the land cover classification on Lombok Island include proximity to settlements, temperature, and distance to roads. These results provide essential insights for land management strategies, enabling policymakers and stakeholders to make informed decisions on sustainable development, urban planning, and environmental conservation in rapidly changing landscapes.
Modelling Current and Future Mangrove Distribution under RCP 8.5 Climate Scenario: A Machine Learning Approach on Lombok Island, Indonesia Purnama, Miftahul Irsyadi; Azizah, Lutfia; Grendis, Nuraqila Waida Bintang; Zulkurniawan, Muhamad; ÇOBAN, Hüseyin Oğuz
Indonesian Journal of Tropical Biology Vol. 2 No. 1 (2026): April (In Progrees)
Publisher : Yayasan Siti Widhatul Faeha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65622/ijtb.v2i1.235

Abstract

Mangrove ecosystems are essential for coastal protection and biodiversity, yet their distribution is highly influenced by climate variability. This study aims to predict current and future distribution of mangrove habitats on Lombok Island-Indonesia, using environmental predictor variables derived from topographic data and Köppen–Geiger climate classification. Mangrove distribution data were classified into presence and absence categories and integrated with climatic and terrain variables to develop habitat suitability models using five machine learning algorithms: Random Forest (RF), Decision Tree (DT), Naïve Bayes (NB), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Model performance was evaluated using accuracy metrics, and the best-performing model was selected for spatial projection under the Representative Concentration Pathway (RCP) 8.5 scenario for 2050 and 2080. The RF model showed the highest predictive performance. The results indicate a substantial decline in suitable mangrove habitats, decreasing from 12,443 ha under current conditions to 7,255 ha in 2050 and 6,336 ha in 2080, representing a reduction of nearly 50%. This decline is associated with changes in precipitation and temperature regimes that influence hydrological conditions and habitat suitability. The application of machine learning provides a robust spatial approach for predicting mangrove distribution and supports conservation planning and climate-adaptive coastal management.
Modelling Current and Future Mangrove Distribution under RCP 8.5 Climate Scenario: A Machine Learning Approach on Lombok Island, Indonesia Purnama, Miftahul Irsyadi; Azizah, Lutfia; Grendis, Nuraqila Waida Bintang; Zulkurniawan, Muhamad; ÇOBAN, Hüseyin Oğuz
Indonesian Journal of Tropical Biology Vol. 2 No. 1 (2026): April (In Progrees)
Publisher : Yayasan Siti Widhatul Faeha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65622/ijtb.v2i1.235

Abstract

Mangrove ecosystems are essential for coastal protection and biodiversity, yet their distribution is highly influenced by climate variability. This study aims to predict current and future distribution of mangrove habitats on Lombok Island-Indonesia, using environmental predictor variables derived from topographic data and Köppen–Geiger climate classification. Mangrove distribution data were classified into presence and absence categories and integrated with climatic and terrain variables to develop habitat suitability models using five machine learning algorithms: Random Forest (RF), Decision Tree (DT), Naïve Bayes (NB), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Model performance was evaluated using accuracy metrics, and the best-performing model was selected for spatial projection under the Representative Concentration Pathway (RCP) 8.5 scenario for 2050 and 2080. The RF model showed the highest predictive performance. The results indicate a substantial decline in suitable mangrove habitats, decreasing from 12,443 ha under current conditions to 7,255 ha in 2050 and 6,336 ha in 2080, representing a reduction of nearly 50%. This decline is associated with changes in precipitation and temperature regimes that influence hydrological conditions and habitat suitability. The application of machine learning provides a robust spatial approach for predicting mangrove distribution and supports conservation planning and climate-adaptive coastal management.
Co-Authors Azizah, Lutfia Grendis, Nuraqila Waida Bintang Purnama, Miftahul Irsyadi Zulkurniawan, Muhamad