DETECTION OF FOREST CHANGES USING NEURAL NETWORKS BASED ON TERRAIN IMAGE ANALYSIS - RAZUMOVSKYY, MYKOLA; KROSHNYI, IGOR (Herald of Khmelnytskyi National University. Technical sciences, 2024)
The preservation of forest resources is an urgent task in addressing the ecological problems of the present day. Forests play a key role in climate regulation, biodiversity conservation, and the provision of numerous ecosystem services. They act as natural carbon sinks, help maintain water balance, and contribute to soil stability, which is critical for sustaining a healthy environment. However, due to increasing anthropogenic pressures such as deforestation, urbanization, and climate change, forest resources worldwide are under threat. Forests are disappearing at an alarming rate, leading to biodiversity loss, land degradation, and the intensification of the greenhouse effect.Monitoring the condition of forests and promptly detecting changes in their structure are critically important for preventing the degradation of natural ecosystems and developing effective conservation strategies. Traditional methods, such as ground surveys and satellite image analysis, are often labor-intensive, resource-demanding, and prone to human error. Ground surveys, in particular, require a lot of time and effort, and they have limited territorial coverage, making them less effective for large-scale studies. Satellite image analysis, although more efficient on a larger scale, still requires significant human resources for data processing and interpretation.The application of modern technologies, specifically artificial neural networks (ANNs), for image analysis opens new prospects in this field. ANNs can automatically learn to recognize complex visual patterns and detect changes in images, making them a powerful tool for monitoring forest resources. The use of ANNs allows for significant reduction of human influence by automating the data processing and improving the accuracy of change detection. This, in turn, facilitates a rapid response to negative changes and the development of prompt measures for forest conservation.This article examines methods for using ANNs to detect and classify changes in forests, such as logging, windthrow, forest fires, and others. Particular attention is paid to the analysis of satellite images and aerial photography, which allow for effective monitoring of changes over large areas. Research results show that the use of ANNs significantly improves the efficiency and accuracy of monitoring forest changes compared to traditional methods. Automatic change detection allows for the timely identification of threats and the implementation of necessary measures for forest conservation. This is especially important in the context of rapidly increasing anthropogenic impact and climate change, which require prompt response and adaptive management strategies for forest resources.Thus, the application of artificial neural networks for forest resource monitoring represents a promising research direction that can have a significant impact on the preservation of natural ecosystems and the maintenance of ecological balance on the planet.
Keywords: artificial neural networks; convolutional neural networks; deep learning; image analysis; segmentation; satellite images; forest conservation; forest monitoring