Deforestation and Forest Monitoring With CNN and RNN

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Author(s): Kiran Sree Pokkuluri (Shri Vishnu Engineering College for Women, India), N. S. S. S. N. Usha Devi (Jawaharlal Nehru Technological University, Kakinada, India)and Alex Khang (Global Research Institute of Technology and Engineering, USA)
Copyright: 2025
Pages: 18
Source title: Practical Applications of Machine Learning and AI: Medicine, Environmental Science, Transportation, and Education
Source Author(s)/Editor(s): Toufik Mzili (Chouaib Doukkali University, Morocco)and Adarsh Kumar Arya (Harcourt Butler Technical University, India)
DOI: 10.4018/979-8-3373-1399-3.ch008

Keywords: Artificial Intelligence / Computer Science & IT / Engineering Science Reference

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Abstract

Deforestation poses a significant threat to global biodiversity and climate stability, necessitating effective monitoring and management strategies. It is highly necessary for an effective monitoring strategy to mitigate deforestation as it possesses a potential threat to climate stability and global biodiversity. A novel deep learning technique with Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) is proposed to identify the deforestation and monitoring the forest. CNN is deployed to identify the deforested areas by extracting spatial features and RNN are used to capture the patterns of forest dynamics processing the time series satellite data. This is a novel mechanism where the spatial and temporal analysis is done for the prediction.

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