A Comparison of Mangrove Canopy Height Using Multiple Independent Measurements from Land, Air, and Space

Remote Sensing

Marc Simard¹  SeungKuk Lee²  Emanuelle Feliciano²  Temilola Fatoyinbo²  Carl Trettin³  David Lagomasino⁴ 

[1] Jet Propulsion Laboratory, Pasadena, CA 91109, USA;NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA;US Department of Agriculture, Forest Service, Cordesville, SC 29434, USA;Universities Space Research Association/GESTAR, 7178 Columbia Gateway Dr., Columbia, MD 21046, USA;

关键词: canopy height;    DSM;    biomass;    Africa;    H100;    blue carbon;    TDX;    VHR;    MRV;   

DOI  :  10.3390/rs8040327

来源: DOAJ

【 摘 要 】

Canopy height is one of the strongest predictors of biomass and carbon in forested ecosystems. Additionally, mangrove ecosystems represent one of the most concentrated carbon reservoirs that are rapidly degrading as a result of deforestation, development, and hydrologic manipulation. Therefore, the accuracy of Canopy Height Models (CHM) over mangrove forest can provide crucial information for monitoring and verification protocols. We compared four CHMs derived from independent remotely sensed imagery and identified potential errors and bias between measurement types. CHMs were derived from three spaceborne datasets; Very-High Resolution (VHR) stereophotogrammetry, TerraSAR-X add-on for Digital Elevation Measurement, and Shuttle Radar Topography Mission (TanDEM-X), and lidar data which was acquired from an airborne platform. Each dataset exhibited different error characteristics that were related to spatial resolution, sensitivities of the sensors, and reference frames. Canopies over 10 m were accurately predicted by all CHMs while the distributions of canopy height were best predicted by the VHR CHM. Depending on the guidelines and strategies needed for monitoring and verification activities, coarse resolution CHMs could be used to track canopy height at regional and global scales with finer resolution imagery used to validate and monitor critical areas undergoing rapid changes.

【 授权许可】

Unknown