DOI: 10.1016/j.jag.2017.08.003
Scopus记录号: 2-s2.0-85032462807
论文题名: Identifying tropical dry forests extent and succession via the use of machine learning techniques
作者: Li W ; , Cao S ; , Campos-Vargas C ; , Sanchez-Azofeifa A
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 63 起始页码: 196
结束页码: 205
语种: 英语
英文关键词: Feature-level fusion
; Hyperspectral MAPper dataset
; Machine learning
; Secondary tropical dry forest
; Waveform LIDAR data
Scopus关键词: aboveground biomass
; data set
; dry forest
; ecosystem service
; environmental monitoring
; lidar
; machine learning
; mapping
; tropical forest
; Costa Rica
; Guanacaste
; Santa Rosa National Park
; Bos
英文摘要: Information on ecosystem services as a function of the successional stage for secondary tropical dry forests (TDFs) is scarce and limited. Secondary TDFs succession is defined as regrowth following a complete forest clearance for cattle growth or agriculture activities. In the context of large conservation initiatives, the identification of the extent, structure and composition of secondary TDFs can serve as key elements to estimate the effectiveness of such activities. As such, in this study we evaluate the use of a Hyperspectral MAPper (HyMap) dataset and a waveform LIDAR dataset for characterization of different levels of intra-secondary forests stages at the Santa Rosa National Park (SRNP) Environmental Monitoring Super Site located in Costa Rica. Specifically, a multi-task learning based machine learning classifier (MLC-MTL) is employed on the first shortwave infrared (SWIR1) of HyMap in order to identify the variability of aboveground biomass of secondary TDFs along a successional gradient. Our paper recognizes that the process of ecological succession is not deterministic but a combination of transitional forests types along a stochastic path that depends on ecological, edaphic, land use, and micro-meteorological conditions, and our results provide a new way to obtain the spatial distribution of three main types of TDFs successional stages. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79990
Appears in Collections: 气候变化事实与影响
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作者单位: College of Information and Communication Engineering, Harbin Engineering University, Harbin, China; Alberta Centre for Earth Observation Sciences, Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Canada
Recommended Citation:
Li W,, Cao S,, Campos-Vargas C,et al. Identifying tropical dry forests extent and succession via the use of machine learning techniques[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,63