AI-DRIVEN FOREST MANAGEMENT: LEVERAGING REMOTE SENSING AND MACHINE LEARNING FOR SUSTAINABLE FORESTRY

Abstract

Nowadays, we rely on advanced technologies for sustainable forest management tasks to address the challenges of monitoring vast and ecologically diverse forests. There are no existing scalable and automated solutions for assessing forest conditions in Kazakhstan, making monitoring and preserving forest environments difficult. This project’s main goal is to develop a comprehensive, computing-based application that integrates aspects of remote sensing, geospatial data processing, and artificial intelligence to support modern tools for forest monitoring. The core objective is to construct a dataset for specific forest regions in Kazakhstan using biweekly satellite images from Sentinel-2 and LANDSAT satellites. Specifically, the dataset consists of forest masks generated through the threshold classification of vegetative indices, such as NDVI, and a range of vegetation indices for assessment of forest health and disturbance detection. Data gaps caused by cloud cover were addressed using temporal interpolation and reprojection techniques to produce complete forest masks. Moreover, the application includes a chatbot based on a retrieval augmented generation (RAG) system, which enables users to query the system by passing their questions as prompts and receiving contextualized responses from the database. The chatbot’s role is to assist with forest management questions for the user, leveraging modern smart query systems in combination with Large Language models (LLM). The resulting mobile application provides functionalities such as forest mask visualization, deforestation and fire detection, and access to vegetation metrics and their analysis. The application is designed to be intuitive and user-friendly, ensuring ease of use for all stakeholders, regardless of their technical background. To assess the quality of the application, the satisfaction levels of users are evaluated through their direct feedback. This work illustrates a comprehensive approach for designing, implementing, and validating a forest management application, with scalable potential for broader use in making decisions for environmental challenges.