XGBoost-LSTM Hybrid Model for Predicting Vegetation Cover in Kenya\'s North Rift Region: Leveraging LULC and Environmental Variables

In Kenya's North Rift region, rapid Land Use Land Cover (LULC) transformations have resulted from agricultural activities, population growth, and infrastructure development. While these changes support economic growth and food security, they impact terrestrial vegetation health and ecosystem stability. Accurate mapping and prediction of these changes is crucial for sustainable land use strategies. A key vegetation health metric is the Normalized Difference Vegetation Index (NDVI). However, predicting NDVI from LULC is challenging due to their complex non-linear relationships and environmental influences. Additionally, satellite-based NDVI predictions face limitations from atmospheric distortions and sensor noise complicating spatial and temporal trend analysis. To enhance NDVI prediction, this study explored a hybrid approach that integrates spatial analysis with temporal forecasting for NDVI prediction. It employed ensemble methods for spatial feature extraction and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies. First, LULC changes and their relationship with NDVI were analyzed, followed by a comparison of ensemble methods to identify the better model for capturing non-linear interactions between LULC and environmental variables. The superior ensemble model was then integrated with LSTM to develop a hybrid model. Comparing the performance of ensemble models, the results showed that XGBoost performed superiorly. Therefore, based on XGBoost's superior performance, an XGBoost-LSTM hybrid model was developed, improving prediction accuracy with an RMSE of 0.085 and an R² score of 0.92. The XGBoost-LSTM hybrid model surpassed individual model performance, enhancing predictive accuracy, scalability, and robustness. This study provides a framework for large-scale vegetation health monitoring with practical applications in sustainable land management and climate adaptation.