Journal
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 102, Issue -, Pages 198-208Publisher
ELSEVIER
DOI: 10.1016/j.isprsjprs.2015.02.007
Keywords
Lidar; Forest biomass; MODIS; Terrestrial; Scale
Categories
Funding
- USDA Forest Service Region 5
- USDA Forest Service Pacific Southwest Research Station
- US Fish and Wildlife Service
- California Department of Water Resources
- California Department of Fish and Game
- California Department of Forestry and Fire Protection
- Sierra Nevada Conservancy
- State Key Laboratory of Earth Processes and Resource Ecology
- National Natural Science Foundation Project of China [41471363]
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Forests play a key role in the global carbon cycle, and forest above ground biomass (AGB) is an important indictor to the carbon storage capacity and the potential carbon pool size of a forest ecosystem. Accurate estimation of forest AGB has become increasingly important for a wide range of end-users. Although satellite remote sensing provides abundant observations to monitor forest coverage, validation of coarse-resolution AGB derived from satellite observations is difficult because of the scale mismatch between the footprints of satellite observations and field measurements. In this study, we use airborne Lidar to bridge the scale gaps between satellite-based and field-based studies, and evaluate satellite-derived indices to estimate regional forest AGB. We found that: (1) Lidar data can be used to accurately estimate forest AGB using tree height and tree quadratic height, (2) linear regression, among four tested models, achieve the best performance (R-2 = 0.74; RMSE = 183.57 Mg/ha); (3) for MODIS-derived vegetation indices at varied spatial resolution (250-1000 m), accumulated NDVI, accumulated LAI, and accumulated FPAR could explain 53-74% variances of forest AGB, whereas accumulated NDVI derived from 1 km MODIS products gives higher R-2 (74%) and lower RMSE (13.4 Mg/ha) than others. We conclude that Lidar data can be used to bridge the scale gap between satellite and field studies. Our results indicate that combining MODIS and Lidar data has the potential to estimate regional forest AGB. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
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