TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest
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First author draft
Date
2017-01-01
Authors
Lin, Yi
Wei, Tian
Yang, Bin
Knyazikhin, Yuri
Zhang, Yuhu
Sato, Hisashi
Fang, Xing
Liang, Xinlian
Yan, Lei
Sun, Shanlin
Version
Published version
OA Version
Citation
Yi Lin, Tian Wei, Bin Yang, Yuri Knyazikhin, Yuhu Zhang, Hisashi Sato, Xing Fang, Xinlian Liang, Lei Yan, Shanlin Sun. 2017. "TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest." INTERNATIONAL JOURNAL OF DIGITAL EARTH, Volume 10, Issue 7, pp. 701 - 718 (18).
Abstract
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 R2 = 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.