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Journal of Applied Remote Sensing

Upscaling coniferous forest above-ground biomass based on airborne LiDAR and satellite ALOS PALSAR data
Author(s): Wang Li; Zheng Niu; Zengyuan Li; Cheng Wang; Mingquan Wu; Shakir Muhammad
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Paper Abstract

Forest above-ground biomass (AGB) is an important indicator for understanding the global carbon cycle. It is hard to obtain a geographically and statistically representative AGB dataset, which is limited by unpredictable environmental conditions and high economical cost. A spatially explicit AGB reference map was produced by airborne LiDAR data and calibrated by field measurements. Three different sampling strategies were designed to sample the reference AGB, PALSAR backscatter, and texture variables. Two parametric and four nonparametric models were established and validated based on the sampled dataset. Results showed that random stratified sampling that used LiDAR-evaluated forest age as stratification knowledge performed the best in the AGB sampling. The addition of backscatter texture variables improved the parametric model performance by an R2 increase of 21% and a root-mean-square error (RMSE) decrease of 10  Mg ha−1. One of the four nonparametric models, namely, the random forest regression model, obtained comparable performance (R2=0.78, RMSE=14.95  Mg ha−1) to the parametric model. Higher estimation errors occurred in the forest stands with lower canopy cover or higher AGB levels. In conclusion, incorporating airborne LiDAR and PALSAR data was proven to be efficient in upscaling the AGB estimation to regional scale, which provides some guidance for future forest management over cold and arid areas.

Paper Details

Date Published: 17 October 2016
PDF: 19 pages
J. Appl. Rem. Sens. 10(4) 046003 doi: 10.1117/1.JRS.10.046003
Published in: Journal of Applied Remote Sensing Volume 10, Issue 4
Show Author Affiliations
Wang Li, Institute of Remote Sensing and Digital Earth (China)
Zheng Niu, Institute of Remote Sensing and Digital Earth (China)
Zengyuan Li, Chinese Academy of Forestry (China)
Cheng Wang, Institute of Remote Sensing and Digital Earth (China)
Mingquan Wu, Institute of Remote Sensing and Digital Earth (China)
Shakir Muhammad, Institute of Remote Sensing and Digital Earth (China)


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