Spatiotemporal mapping of riparian vegetation through multi-sensor data fusion and deep learning techniques

Riparian ecosystems are biodiversity hotspots and provide crucial services to human wellbeing. Currently, the knowledge of how riparian ecosystems respond to and in turn influence the variations of the environment remains considerably limited. As a first step toward filling the gap, this research aims to characterize the dynamics of riparian vegetation during the past several decades across multiple aquatic sites operated by the National Ecological Observatory Network (NEON) of the US. Specifically, it leverages high-resolution hyperspectral and lidar data collected by NEON’s airborne observational platform (AOP) surveys, the long-term records of satellite optical and radar imagery, and advanced data fusion and classification techniques to generate a time-series record of riparian vegetation on a seasonal-to-yearly basis. The maps derived will provide a new basis for understanding how riparian vegetation has changed across continental US, and for predicting how it is likely to change in the future. This work is sponsored by NSF’s Macrosystems Biology and NEON-Enabled Science (MSB-NES) Program (2021/9–2024/8), and the overarching goal of the project is to mechanistically link riparian vegetation dynamics to hydroclimate variations and assess the functional importance of riparian ecosystems to macrosystem fluxes of carbon and water.