DOI: 10.1016/j.ecolind.2019.105979
论文题名: Fine scale plant community assessment in coastal meadows using UAV based multispectral data
作者: Villoslada M. ; Bergamo T.F. ; Ward R.D. ; Burnside N.G. ; Joyce C.B. ; Bunce R.G.H. ; Sepp K.
刊名: Ecological Indicators
ISSN: 1470160X
出版年: 2020
卷: 111 语种: 英语
英文关键词: Coastal plant communities
; Random forests
; UAV
; Unsupervised classification
; Vegetation indices
Scopus关键词: Biodiversity
; Decision trees
; Ecosystems
; Fixed wings
; Restoration
; Sampling
; Vegetation
; Classification accuracy
; Classification technique
; Plant communities
; Random forest classifier
; Random forests
; Spectral characteristics
; Unsupervised classification
; Vegetation index
; Unmanned aerial vehicles (UAV)
; aboveground biomass
; global change
; habitat management
; image classification
; meadow
; multispectral image
; plant community
; species diversity
; unmanned vehicle
; unsupervised classification
; vegetation index
; Estonia
英文摘要: Coastal meadows worldwide are subjected to habitat degradation due to abandonment, intensification and the impacts of global change. In order to protect and restore these habitats and ensure the supply of valuable ecosystem services, it is necessary to know the extent and location of plant communities in coastal meadows. In this study, five plant communities were mapped at very high resolution in three different study sites in West Estonia. A fixed wing UAV was used to obtain multispectral images and derive a set of vegetation indices. Two different image classification techniques were used to cluster the vegetation indices maps and produce plant community distribution maps. The highest classification accuracy was obtained using a Random Forest classifier and 13 vegetation indices. Additionally, the spectral characteristics of the training samples were correlated with aboveground biomass and species diversity. Both biomass and species diversity were positively correlated with the spectral diversity of training samples and are thus likely to have an effect on the classification accuracy. The results of this study highlight the need to utilize a wide array of vegetation indices and assess the spectral characteristics of training samples in order to obtain high classification accuracies and understand the nature of misclassification errors. The resulting maps provide a solid foundation for global change impact assessment and habitat management and restoration in coastal meadows. © 2019 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158449
Appears in Collections: 气候变化与战略
There are no files associated with this item.
作者单位: Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 13 5, Tartu, EE-51014, Estonia; Centre for Aquatic Environments, School of the Environment and Technology, University of Brighton, Cockcroft Building, Moulsecoomb, Brighton, BN2 4GJ, United Kingdom
Recommended Citation:
Villoslada M.,Bergamo T.F.,Ward R.D.,et al. Fine scale plant community assessment in coastal meadows using UAV based multispectral data[J]. Ecological Indicators,2020-01-01,111