TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest  

作　　者：Yi Lin Tian Wei Bin Yang Yuri Knyazikhin Yuhu Zhang Hisashi Sato Xing Fang Xinlian Liang Lei Yan Shanlin Sun 

机构地区：[1]Institute of Remote Sensing and GIS,Beijing Key Lab of Spatial Information Integration and Its Applications,School of Earth and Space Sciences,Peking University,Beijing,People’s Republic of China [2]North China University of Science and Technology,Tangshan,People’s Republic of China [3]Department of Earth and Environment,Boston University,Boston,USA [4]College of Resources Environment and Tourism,Capital Normal University,Beijing,People’s Republic of China [5]Research Institute for Global Change,JAMSTEC,Kanagawa,Japan [6]School of Geodesy and Geomatics,Wuhan University,Wuhan,People’s Republic of China [7]Finnish Geospatial Research Institute,Masala,Finland [8]Guilin University of Aerospace Technology,Guilin,People’s Republic of China

出　　处：《International Journal of Digital Earth》2017年第7期701-718,共18页国际数字地球学报（英文）

基　　金：This work was financially supported in part by the National Natural Science Foundation of China[grant numbers 41471281 and 31670718];in part by the SRF for ROCS,SEM,China.

摘　　要：In forest ecosystem studies,tree stem structure variables(SSVs)proved to be an essential kind of parameters,and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon cycle.For this newly emerging task,satellite imagery such as WorldView-2 panchromatic images(WPIs)is used as a potential solution for co-prediction of tree-level multifarious SSVs,with static terrestrial laser scanning(TLS)assumed as a‘bridge’.The specific operation is to pursue the allometric relationships between TLS-derived SSVs and WPI-derived feature parameters,and regression analyses with one or multiple explanatory variables are applied to deduce the prediction models(termed as Model1s and Model2s).In the case of Picea abies,Pinus sylvestris,Populus tremul and Quercus robur in a boreal forest,tests showed that Model1s and Model2s for different tree species can be derived(e.g.the maximum R^(2)=0.574 for Q.robur).Overall,this study basically validated the algorithm proposed for co-prediction of multifarious SSVs,and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling,which is useful for large-scale investigations of forest understory,macroecosystem ecology,global vegetation dynamics and global carbon cycle.

关 键 词：Tree stem structure variable(SSV) WorldView-2 panchromatic image(WPI) static terrestrial laser scanning(TLS) allometric relationship co-prediction model 

分 类 号：S71[农业科学—林学]