Integrating Sparse LiDAR and Multisensor Time-Series Imagery From Spaceborne Platforms for Deriving Localized Canopy Height Model

Canopy height is a fundamental metric for extracting valuable information about forested areas. Over the past decade, the light detection and ranging (LiDAR) technology has provided a straightforward method for measuring canopy height using various platforms, including terrestrial, uncrewed aerial vehicles (UAVs), airborne, and satellite sensors. However, despite its global reach, spaceborne LiDAR data suffers from a sparse sampling pattern that fails to provide continuous global coverage. In contrast, satellites like LANDSAT deliver seamless and extensive coverage of the Earth’s surface through spectral data. This study aims to develop a deep learning model to infer canopy heights from sparsely observed LiDAR data, utilizing the multisensor spectral data from spaceborne platforms. Specifically tailored for localized sites, the model focuses on regional-level canopy height estimation by leveraging the relationship between canopy height and multisensor time-series data from Landsat, Sentinel-2, and Sentinel-1. We first demonstrate the importance of integrating multisensor data by training three separate models: one using only Landsat data, one using only Sentinel-2 data, and a multimodal model that incorporates Landsat, Sentinel 1, and Sentinel 2 data to estimate LiDAR-derived canopy height. These models were tested on two sites in Indiana—Tippecanoe and Monroe counties—where the multimodal approach produced the best results, achieving RMSEs of 3.895 and 4.993 m, respectively. We then tested our multimodal model in two additional counties—Baker County, FL, USA and Piute County, UT, USA—where the model achieved an RMSE of 5.397 and 3.742 m, respectively.