A Study on Deep Learning-Based Predictive Modeling of Vegetation Dynamics in Kazakhstan through the Integration of CNNs, RNNs, and Satellite Imagery for Ecological Monitoring

Abstract

The present study focused on applying deep learning methods to analyze the dynamics of vegetation in Kazakhstan's ecosystem. Satellite-based indices, such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were utilized to develop predictive models assessing the effects of environmental parameters on vegetation health. Convolutional Neural Networks (CNNs) were employed for spatial feature extraction along with Recurrent Neural Networks (RNNs) for capturing temporal dependencies. Initially, the ResNet-50 model was deployed with the results revealing a poor correlation with the actual vegetation patterns. Extensive preprocessing and optimization methods, such as batch normalization and gradient clipping, were applied, and the findings demonstrated a significant improvement in predictive precision, enabling accurate forecasts of vegetation changes. This work highlighted the potential of combining advanced technology with ecological research to develop solutions for managing climate impacts.