مدل‌سازی تغییرات پوشش جنگل‌های استان خوزستان مبتنی بر الگوریتم یادگیری ماشین و تصاویر ماهواره‌ای

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Dias, F., Suhadolnik, N., Camargo, H., & Da Silva, S. (2024). Predicting the pulse of the Amazon: Machine learning insights into deforestation dynamics. Journal of Environmental Management, 362, 121359. ‏

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Moscovini, L., Ortenzi, L., Pallottino, F., Figorilli, S., Violino, S., Pane, C., ... & Costa, C. (2024). An open-source machine-learning application for predicting pixel-to-pixel NDVI regression from RGB calibrated images. Computers and Electronics in Agriculture, 216, 108536.‏

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Saha, S., Bhattacharjee, S., Shit, P. K., Sengupta, N., & Bera, B. (2022). Deforestation probability assessment using integrated machine learning algorithms of Eastern Himalayan foothills (India). Resources, Conservation & Recycling Advances, 14, 200077.‏

Saha, S., Saha, M., Mukherjee, K., Arabameri, A., Ngo, P. T. T., & Paul, G. C. (2020). Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India. Science of the Total Environment, 730, 139197.‏

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Wang, Z., Wang, Y., Liu, Y., Wang, F., Deng, W., & Rao, P. (2023). Spatiotemporal characteristics and natural forces of grassland NDVI changes in Qilian Mountains from a sub-basin perspective. Ecological Indicators, 157, 111186.‏

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Zhao, J., Li, L., Liu, J., Yan, Y., Wang, Q., Newman, C., & Zhou, Y. (2024). A bibliometric analysis using machine learning to track paradigm shifts and analytical advances in forest ecology and forestry journal publications from 2010 to 2022. Forest Ecosystems, 100233.‏

Zhou, C., Lin, K., Xu, D., Chen, L., Guo, Q., Sun, C., & Yang, X. (2018). Near infrared computer vision and neuro-fuzzy model-based feeding decision system for fish in aquaculture. Computers and electronics in agriculture, 146, 114-124.‏

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