Remote Sensing Applications and Decision Support

Upscaling coniferous forest above-ground biomass based on airborne LiDAR and satellite ALOS PALSAR data

[+] Author Affiliations
Wang Li, Zheng Niu, Mingquan Wu, Shakir Muhammad

Institute of Remote Sensing and Digital Earth, The State Key Laboratory of Remote Sensing Science, CAS Olympic S&T Park No. 20, Da Tun Road, P. O. Box 9718, Beijing 100101, China

Zengyuan Li

Chinese Academy of Forestry, Research Institute of Forest Resource Information Techniques, Wanshoushanhou, Beijing 100091, China

Cheng Wang

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Laboratory of Digital Earth Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China

J. Appl. Remote Sens. 10(4), 046003 (Oct 17, 2016). doi:10.1117/1.JRS.10.046003
History: Received May 15, 2016; Accepted September 27, 2016
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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  Mgha1. One of the four nonparametric models, namely, the random forest regression model, obtained comparable performance (R2=0.78, RMSE=14.95  Mgha1) 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.

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Citation

Wang Li ; Zheng Niu ; Zengyuan Li ; Cheng Wang ; Mingquan Wu, et al.
"Upscaling coniferous forest above-ground biomass based on airborne LiDAR and satellite ALOS PALSAR data", J. Appl. Remote Sens. 10(4), 046003 (Oct 17, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.046003


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