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

	Upscaling coniferous forest above-ground biomass based on airborne LiDAR and satellite ALOS PALSAR data
	Li, Wang 1; Niu, Zheng 1; Li, Zengyuan 1; Wang, Cheng 1; Wu, Mingquan 1; Muhammad, Shakir 1
刊名	Journal of Applied Remote Sensing
	2016
卷号	10 期号:4
通讯作者	Li, Wang (lwwhdz@sina.com)
英文摘要	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 R2increase of 21% and a root-mean-square error (RMSE) decrease of 10Mg ha-1. One of the four nonparametric models, namely, the random forest regression model, obtained comparable performance (R2=0.78, RMSE=14.95Mg 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. © 2016 Society of Photo-Optical Instrumentation Engineers (SPIE).
收录类别	EI
语种	英语
WOS记录号	WOS:20164603013307
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39589]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. Institute of Remote Sensing and Digital Earth, State Key Laboratory of Remote Sensing Science, CAS Olympic SandT Park, P. O. Box 9718, Beijing 2.100101, China 3. Chinese Academy of Forestry, Research Institute of Forest Resource Information Techniques, Wanshoushanhou, Beijing 4.100091, China 5. Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Laboratory of Digital Earth Sciences, No. 9 Dengzhuang South Road, Beijing 6.100094, China
推荐引用方式 GB/T 7714	Li, Wang,Niu, Zheng,Li, Zengyuan,et al. Upscaling coniferous forest above-ground biomass based on airborne LiDAR and satellite ALOS PALSAR data[J]. Journal of Applied Remote Sensing,2016,10(4).
APA	Li, Wang,Niu, Zheng,Li, Zengyuan,Wang, Cheng,Wu, Mingquan,&Muhammad, Shakir.(2016).Upscaling coniferous forest above-ground biomass based on airborne LiDAR and satellite ALOS PALSAR data.Journal of Applied Remote Sensing,10(4).
MLA	Li, Wang,et al."Upscaling coniferous forest above-ground biomass based on airborne LiDAR and satellite ALOS PALSAR data".Journal of Applied Remote Sensing 10.4(2016).