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Remote Sens. 2015, 7(5), 5534-5564; doi:10.3390/rs70505534

National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

1
Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430071, China
2
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
3
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100732, China
4
Academy of Forest Inventory and Planning, State Forestry Administration, Beijing 100714, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Xiangming Xiao, Jinwei Dong and Prasad S. Thenkabail
Received: 29 January 2015 / Revised: 16 April 2015 / Accepted: 22 April 2015 / Published: 4 May 2015
View Full-Text   |   Download PDF [6792 KB, uploaded 4 May 2015]   |  

Abstract

Forest aboveground biomass (AGB) was mapped throughout China using large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-radiometer (MODIS) imagery and forest inventory data. The entire land of China was divided into seven zones according to the geographic characteristics of the forests. The forest AGB prediction models were separately developed for different forest types in each of the seven forest zones at GLAS footprint level from GLAS waveform parameters and biomass derived from height and diameter at breast height (DBH) field observation. Some waveform parameters used in the prediction models were able to reduce the effects of slope on biomass estimation. The models of GLAS-based biomass estimates were developed by using GLAS footprints with slopes less than 20° and slopes ≥ 20°, respectively. Then, all GLAS footprint biomass and MODIS data were used to establish Random Forest regression models for extrapolating footprint AGB to a nationwide scale. The total amount of estimated AGB in Chinese forests around 2006 was about 12,622 Mt vs. 12,617 Mt derived from the seventh national forest resource inventory data. Nearly half of all provinces showed a relative error (%) of less than 20%, and 80% of total provinces had relative errors less than 50%. View Full-Text
Keywords: forest aboveground biomass; ICESat/GLAS; large footprint LiDAR; MODIS; forest inventory data; China forest aboveground biomass; ICESat/GLAS; large footprint LiDAR; MODIS; forest inventory data; China
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chi, H.; Sun, G.; Huang, J.; Guo, Z.; Ni, W.; Fu, A. National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China. Remote Sens. 2015, 7, 5534-5564.

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