气候变化领域集成服务门户(CCPortal): Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach

CCPortal

DOI	10.1016/j.rse.2019.111356
	Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach
	Brown J.F.; Tollerud H.J.; Barber C.P.; Zhou Q.; Dwyer J.L.; Vogelmann J.E.; Loveland T.R.; Woodcock C.E.; Stehman S.V.; Zhu Z.; Pengra B.W.; Smith K.; Horton J.A.; Xian G.; Auch R.F.; Sohl T.L.; Sayler K.L.; Gallant A.L.; Zelenak D.; Reker R.R.; Rover J.
发表日期	2020
ISSN	00344257
卷号	238
英文摘要	Growing demands for temporally specific information on land surface change are fueling a new generation of maps and statistics that can contribute to understanding geographic and temporal patterns of change across large regions, provide input into a wide range of environmental modeling studies, clarify the drivers of change, and provide more timely information for land managers. To meet these needs, the U.S. Geological Survey has implemented a capability to monitor land surface change called the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. This paper describes the methodological foundations and lessons learned during development and testing of the LCMAP approach. Testing and evaluation of a suite of 10 annual land cover and land surface change data sets over six diverse study areas across the United States revealed good agreement with other published maps (overall agreement ranged from 73% to 87%) as well as several challenges that needed to be addressed to meet the goals of robust, repeatable, and geographically consistent monitoring results from the Continuous Change Detection and Classification (CCDC) algorithm. First, the high spatial and temporal variability of observational frequency led to differences in the number of changes identified, so CCDC was modified such that change detection is dependent on observational frequency. Second, the CCDC classification methodology was modified to improve its ability to characterize gradual land surface changes. Third, modifications were made to the classification element of CCDC to improve the representativeness of training data, which necessitated replacing the random forest algorithm with a boosted decision tree. Following these modifications, assessment of prototype Version 1 LCMAP results showed improvements in overall agreement (ranging from 85% to 90%). © 2019
英文关键词	Analysis Ready Data; Change detection; Earth observations; Land cover; Landsat; Monitoring; Time series
语种	英语
scopus关键词	Decision trees; Monitoring; Petroleum reservoir evaluation; Surface measurement; Time series; Time series analysis; Well testing; Analysis Ready Data; Change detection; Earth observations; Land cover; LANDSAT; Classification (of information); algorithm; assessment method; data set; detection method; environmental modeling; environmental monitoring; EOS; land cover; land surface; land use change; spatiotemporal analysis; United States
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/179476
作者单位	U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, SD 57198, United States; ASRC Federal Data Solutions, Contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, SD 57198, United States; Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA 02215, United States; Department of Forest and Natural Resource Management, State University of New York, Syracuse, NY 13210, United States; Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, United States; KBR, Contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, SD 57198, United States; Innovate! Inc., Contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, SD 57198, United States
推荐引用方式 GB/T 7714	Brown J.F.,Tollerud H.J.,Barber C.P.,et al. Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach[J],2020,238.
APA	Brown J.F..,Tollerud H.J..,Barber C.P..,Zhou Q..,Dwyer J.L..,...&Rover J..(2020).Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach.Remote Sensing of Environment,238.
MLA	Brown J.F.,et al."Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach".Remote Sensing of Environment 238(2020).