Spatio-Temporal Forecasting of Post-Fire Vegetation Recovery using ConvLSTM Networks

Abstract

We present a novel deep learning method employing a Convolutional Long Short-Term Memory (ConvLSTM) network designed to forecast post-fire recovery at high spatio-temporal scales. Our model is trained on a diverse multi-temporal dataset of satellite imagery, encompassing various fire severities, ecosystems, and locations throughout the western U.S. to ensure robust and generalizable predictions. By leveraging a ConvLSTM framework, we simultaneously capture both spatial dependencies (such as topography and vegetation types) and temporal dynamics (such as seasonal changes and long-term recovery rates) in burn areas. Uniquely integrating long-term time-series remote sensing imagery, our model predicts landscape change from 1 month to 10 years post-fire, achieving unprecedented monthly granularity over a decade. We show that a long duration of input time-series data is critical for accurate post-fire forecasts. Additionally, our model facilitates the generation of recovery potential surfaces, maps that provide nuanced insights into an area’s ability to regenerate after a fire. The implications of this research are significant for both post-fire management and ecological studies. For land managers and policymakers, our model represents a powerful tool for planning restoration efforts, allocating resources efficiently, and minimizing negative long-term environmental impacts. Ecologically, it provides a deeper understanding of recovery patterns, aiding in the conservation of biodiversity and the resilience of ecosystems to future fires. Our findings contribute to the growing body of knowledge at the intersection of remote sensing, deep learning, and environmental stewardship, highlighting the potential of advanced AI techniques in addressing critical ecological challenges.