Geospatial Vegetation Forecasting

Geospatial vegetation forecasting aims to predict future vegetation states using spatial data and models, primarily to understand and mitigate the impacts of climate change and extreme weather events on ecosystems. Recent research focuses on developing advanced deep learning models, such as diffusion models and convolutional long short-term memory networks, often incorporating multi-modal data (satellite imagery, meteorological data) to improve prediction accuracy and account for uncertainties inherent in vegetation dynamics. These advancements are crucial for informing effective ecosystem management strategies, improving agricultural practices, and enhancing our understanding of climate change impacts on the biosphere.