气候变化领域集成服务门户(CCPortal): Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series

CCPortal

DOI	10.1016/j.rse.2020.111792
	Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series
	Pickens A.H.; Hansen M.C.; Hancher M.; Stehman S.V.; Tyukavina A.; Potapov P.; Marroquin B.; Sherani Z.
发表日期	2020
ISSN	00344257
卷号	243
英文摘要	Global surface water extent is changing due to natural processes as well as anthropogenic drivers such as reservoir construction and conversion of wetlands to agriculture. However, the extent and change of global inland surface water are not well quantified. To address this, we classified land and water in all 3.4 million Landsat 5, 7, and 8 scenes from 1999 to 2018 and performed a time-series analysis to produce maps that characterize inter-annual and intra-annual open surface water dynamics. We also used a probability sample and reference time-series classification of land and water for 1999–2018 to provide unbiased estimators of area of stable and dynamic surface water extent and to assess the accuracy of the surface water maps. From the reference sample data, we estimate that permanent surface water covers 2.93 (standard error ±0.09) million km2, and during this time period an estimated 138,011 (±28,163) km2 underwent only gain of surface water, over double the estimated 53,154 (±10,883) km2 that underwent only loss of surface water. The estimated area of 950,719 (±104,034) km2 that experienced recurring change between land and water states is far greater than the area undergoing these unidirectional trends. From a probability sample of high resolution imagery, an estimated 10.9% (±1.9%) of global inland surface water is within mixed pixels at Landsat resolution indicating that monitoring of surface water changes requires improved spatial detail. We provide the first unbiased area estimators of open surface water extent and its changes with associated uncertainties and illustrate the challenges of tracking changes in surface water area using medium spatial and temporal resolution data. © 2020 The Authors
英文关键词	Area estimation; Change detection; Global; Landsat; Surface water; Time-series
语种	英语
scopus关键词	Agricultural robots; Image enhancement; Reservoirs (water); Time series analysis; Dynamic surface; High resolution imagery; Inland surface water; Landsat time series; Natural process; Reservoir constructions; Spatial and temporal resolutions; Unbiased estimator; Surface waters; annual variation; Landsat; mapping method; satellite data; spatiotemporal analysis; surface water; time series; tracking; wetland
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/179337
作者单位	Department of Geographical Sciences, University of Maryland, College Park, MD 20740, United States; Google, Mountain View, CA 94043, United States; Department of Forest and Natural Resources Management, State University of New York, Syracuse, NY 13210, United States
推荐引用方式 GB/T 7714	Pickens A.H.,Hansen M.C.,Hancher M.,et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series[J],2020,243.
APA	Pickens A.H..,Hansen M.C..,Hancher M..,Stehman S.V..,Tyukavina A..,...&Sherani Z..(2020).Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series.Remote Sensing of Environment,243.
MLA	Pickens A.H.,et al."Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series".Remote Sensing of Environment 243(2020).