Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1117/1.JRS.10.046003 |
Upscaling coniferous forest above-ground biomass based on airborne LiDAR and satellite ALOS PALSAR data | |
Li, Wang1; Niu, Zheng1; Li, Zengyuan2; Wang, Cheng3; Wu, Mingquan1; Muhammad, Shakir1 | |
通讯作者 | Li, Wang |
来源期刊 | JOURNAL OF APPLIED REMOTE SENSING |
ISSN | 1931-3195 |
出版年 | 2016 |
卷号 | 10 |
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 R-2 increase of 21% and a root-mean-square error ( RMSE) decrease of 10 Mgha(-1). One of the four nonparametric models, namely, the random forest regression model, obtained comparable performance ( R-2 = 0.78, RMSE = 14.95 Mgha(-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. (C) 2016 Society of PhotoOptical Instrumentation Engineers (SPIE) |
英文关键词 | airborne LiDAR ALOS PALSAR forest aboveground biomass forest age parametric and nonparametric models |
类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
收录类别 | SCI-E |
WOS记录号 | WOS:000397623100001 |
WOS关键词 | LASER SCANNER DATA ; REMOTE-SENSING DATA ; L-BAND RADAR ; TEMPERATE FOREST ; PRIOR KNOWLEDGE ; CANOPY COVER ; BACKSCATTER ; ATTRIBUTES ; COMPONENTS ; TEXTURE |
WOS类目 | Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/194026 |
作者单位 | 1.Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, CAS Olymp S&T 20,Tun Rd,POB 9718, Beijing 100101, Peoples R China; 2.Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China; 3.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Lab Digital Earth Sci, 9 Dengzhuang South Rd, Beijing 100094, Peoples R 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],2016,10. |
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. |
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(2016). |
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