DOI: 10.1016/j.compenvurbsys.2019.101444
论文题名: Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night
作者: Nieves J.J. ; Sorichetta A. ; Linard C. ; Bondarenko M. ; Steele J.E. ; Stevens F.R. ; Gaughan A.E. ; Carioli A. ; Clarke D.J. ; Esch T. ; Tatem A.J.
刊名: Computers, Environment and Urban Systems
ISSN: 1989715
出版年: 2020
卷: 80 语种: 英语
英文关键词: Built-settlements
; Dasymetric modelling
; Population
; Random forest
; Spatial growth
; Urban features
Scopus关键词: Decision trees
; Pixels
; Population dynamics
; Population statistics
; Remote sensing
; Sustainable development
; Built-settlements
; Population
; Random forests
; Spatial growth
; Urban features
; Urban growth
; annual variation
; demography
; greenspace
; light
; pixel
; population dynamics
; remote sensing
; spatiotemporal analysis
; urban area
英文摘要: Mapping urban features/human built-settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban features/human built-settlement datasets have become available, issues still exist in remotely-sensed imagery due to spatial and temporal coverage, adverse atmospheric conditions, and expenses involved in producing such datasets. Remotely-sensed annual time-series of urban/built-settlement extents therefore do not yet exist and cover more than specific local areas or city-based regions. Moreover, while a few high-resolution global datasets of urban/built-settlement extents exist for key years, the observed date often deviates many years from the assigned one. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we describe an interpolative and flexible modelling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modelling with open source subnational data to produce annual 100 m × 100 m resolution binary built-settlement datasets in four test countries located in varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85 and 99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to built-settlement in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban features/built-settlement datasets derived from remotely-sensed imagery, provides a base upon which to create urban future/built-settlement extent projections, and enables further exploration of the relationships between urban/built-settlement area and population dynamics. © 2019 The Authors Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158973
Appears in Collections: 气候变化与战略
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作者单位: WorldPop Project, United Kingdom; Department of Geography and Environment, University of Southampton, United Kingdom; Department of Geography, Université de Namur, Belgium; Department of Geography and Geosciences, University of LouisvilleKY, United States; German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, Germany
Recommended Citation:
Nieves J.J.,Sorichetta A.,Linard C.,et al. Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night[J]. Computers, Environment and Urban Systems,2020-01-01,80