Utilizing Artificial Intelligence and Remote Sensing to Detect Prosopis juliflora Invasion: Environmental Drivers and Community Insights in Rangelands of Kenya

Abstract

The remarkable adaptability and rapid proliferation of Prosopis juliflora have led to its invasive status in the rangelands of Kenya, detrimentally impacting native vegetation and biodiversity. Exacerbated by human activities such as overgrazing, deforestation, and land degradation, these conditions make the spread and management of this species a critical ecological concern. This study assesses the effectiveness of artificial intelligence (AI) and remote sensing in monitoring the invasion of Prosopis juliflora in Baringo County, Kenya. We investigated the environmental drivers, including weather conditions, land cover, and biophysical attributes, that influence its distinction from native vegetation. By analyzing data on the presence and absence of Prosopis juliflora, coupled with datasets on weather, land cover, and elevation, we identified key factors facilitating its detection. Our findings highlight the Decision Tree/Random Forest classifier as the most effective, achieving a 95% accuracy rate in instance classification. Key variables such as the Normalized Difference Vegetation Index (NDVI) for February, precipitation, land cover type, and elevation were significant in the accurate identification of Prosopis juliflora. Community insights reveal varied perspectives on the impact of Prosopis juliflora, with differing views based on professional experiences with the species. Integrating these technological advancements with local knowledge, this research contributes to developing sustainable management practices tailored to the unique ecological and social challenges posed by this invasive species. Our results highlight the contribution of advanced technologies for environmental management and conservation within rangeland ecosystems.