气候变化领域集成服务门户(CCPortal): Mapping understory invasive plant species with field and remotely sensed data in Chitwan, Nepal

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DOI	10.1016/j.rse.2020.112037
	Mapping understory invasive plant species with field and remotely sensed data in Chitwan, Nepal
	Dai J.; Roberts D.A.; Stow D.A.; An L.; Hall S.J.; Yabiku S.T.; Kyriakidis P.C.
发表日期	2020
ISSN	00344257
卷号	250
英文摘要	Monitoring invasive species distribution and prevalence is important, but direct field-based assessment is often impractical. In this paper, we introduce and validate a cost-effective method for mapping understory invasive plant species. We utilized Landsat imagery, spectral mixture analysis (SMA) and a maximum entropy (Maxent) modeling framework to map the spatial extent of Mikania micrantha in Chitwan National Park, Nepal and community forests within its buffer zone. We developed a spectral library from reference and image sources and applied multiple endmember SMA (MESMA) to selected Landsat imagery. Incorporating the resultant green vegetation and shade fractions into Maxent, we mapped the distribution of understory M. micrantha in the study area, with training and testing Area under Curve (AUC) values around 0.80, and kappa around 0.55. In vegetated places, especially mature forests, an increase in green vegetation fraction and decrease in shade fraction was associated with higher likelihood of M. micrantha presence. In addition, the inclusion of elevation as a model input further improved map accuracy (AUC around 0.95; kappa around 0.80). Elevation, a surrogate for distance to water in this case, proved to be the determining factor of M. micrantha's distribution in the study area. The combination of MESMA and Maxent can provide significant opportunities for understanding understory vegetation distribution, and contribute to ecological restoration, biodiversity conservation, and provision of sustainable ecosystem services in protected areas. © 2020 The Authors
英文关键词	Chitwan National Park; Invasive species; Landsat; Maxent; Mikania micrantha; Spectral mixture analysis; Understory vegetation
语种	英语
scopus关键词	Biodiversity; Conservation; Cost effectiveness; Ecosystems; Environmental protection; Forestry; Mapping; Vegetation; Biodiversity conservation; Cost-effective methods; Ecological restoration; Remotely sensed data; Spectral mixture analysis; Sustainable ecosystems; Understory vegetation; Vegetation fractions; Maximum entropy methods; ecosystem service; forest ecosystem; invasive species; maximum entropy analysis; remote sensing; satellite imagery; software; understory; vegetation mapping; vegetation structure; Chitwan National Park; Narayani; Nepal; Mikania micrantha
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179156
作者单位	Department of Geography, San Diego State University, San Diego, CA 92182, United States; Department of Geography, University of California, Santa Barbara, CA 93106, United States; Center for Complex Human-Environment Systems, San Diego State University, San Diego, CA 92182, United States; School of Life Sciences, Arizona State University, Tempe, AZ 85281, United States; Department of Sociology and Criminology, Pennsylvania State University, University Park, PA 16802, United States; Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, 3036, Cyprus
推荐引用方式 GB/T 7714	Dai J.,Roberts D.A.,Stow D.A.,et al. Mapping understory invasive plant species with field and remotely sensed data in Chitwan, Nepal[J],2020,250.
APA	Dai J..,Roberts D.A..,Stow D.A..,An L..,Hall S.J..,...&Kyriakidis P.C..(2020).Mapping understory invasive plant species with field and remotely sensed data in Chitwan, Nepal.Remote Sensing of Environment,250.
MLA	Dai J.,et al."Mapping understory invasive plant species with field and remotely sensed data in Chitwan, Nepal".Remote Sensing of Environment 250(2020).