globalchange  > 过去全球变化的重建
DOI: 10.1371/journal.pone.0112856
论文题名:
Hydrologic Landscape Regionalisation Using Deductive Classification and Random Forests
作者: Stuart C. Brown; Rebecca E. Lester; Vincent L. Versace; Jonathon Fawcett; Laurie Laurenson
刊名: PLOS ONE
ISSN: 1932-6203
出版年: 2014
发表日期: 2014-11-14
卷: 9, 期:11
语种: 英语
英文关键词: Principal component analysis ; Permutation ; Trees ; Spatial and landscape ecology ; Rivers ; Eigenvalues ; Geology ; Wetlands
英文摘要: Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment) which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic trends at the scale of catchments and continents.
URL: http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0112856&type=printable
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/17875
Appears in Collections:过去全球变化的重建
影响、适应和脆弱性
科学计划与规划
气候变化与战略
全球变化的国际研究计划
气候减缓与适应
气候变化事实与影响

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作者单位: School of Life and Environmental Sciences, Deakin University, Warrnambool, Victoria, Australia;School of Life and Environmental Sciences, Deakin University, Warrnambool, Victoria, Australia;Greater Green Triangle University, Department of Rural Health, Flinders University, Warrnambool, Victoria, Australia;Centre for Environmental Management, Faculty of Science and Technology, Federation University Australia, Ballarat, Victoria, Australia;Sinclair-Knight-Merz, Bendigo, Victoria, Australia;School of Life and Environmental Sciences, Deakin University, Warrnambool, Victoria, Australia

Recommended Citation:
Stuart C. Brown,Rebecca E. Lester,Vincent L. Versace,et al. Hydrologic Landscape Regionalisation Using Deductive Classification and Random Forests[J]. PLOS ONE,2014-01-01,9(11)
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