Space-Based Mapping of Mangrove Canopy Height with Multi-Sensor Observations and Deep Learning Techniques

Boya Zhang, Kaleb E. Smith, Shimon Wdowinski, Chaohao Lin, Daniel Gann, Selena Chavez, Jingchao Zhang

Abstract: This study maps canopy height (CH) of mangrove forests using multi-sensor remote-sensing observations with deep learning methods. The multi-sensor data include satellite observations of Synthetic Aperture Radar (SAR) and Landsat-8 optical, and airborne Light Detection and Ranging (LiDAR) data. The study focuses on the south Florida Everglades mangrove forests, which experienced severe damage caused by Hurricane Irma in September 2017. We developed two innovative neural network models to predict CH from SAR and optical data. The models mapped mangrove CH the year following Hurricane Irma with an RMSE of about 3 m. The innovative deep learning methods achieves better accuracy than other traditional machine learning models.