Validation of the US Geological Survey's Land Change Monitoring, Assessment and Projection (LCMAP) Collection 1.0 annual land cover products 1985-2017

被引:45
作者
V. Stehman, Stephen [1 ]
Pengra, Bruce W. [2 ,3 ]
Horton, Josephine A. [4 ,5 ]
Wellington, Danika F. [2 ,3 ]
机构
[1] SUNY Syracuse, Coll Environm Sci & Forestry, Syracuse, NY 13210 USA
[2] KBR, Sioux Falls, SD 57198 USA
[3] US Geol Survey, Earth Resources Observat & Sci EROS Ctr, Sioux Falls, SD 57198 USA
[4] Innovate Inc, Sioux Falls, SD 57198 USA
[5] US Geol Survey, EROS Ctr, Sioux Falls, SD 57198 USA
关键词
Accuracy assessment; Remote sensing; Environmental monitoring; Landsat; Time series; CONTERMINOUS UNITED-STATES; ACCURACY ASSESSMENT; ESTIMATING AREA; DESIGN; TIME; DATABASE;
D O I
10.1016/j.rse.2021.112646
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) has released a suite of annual land cover and land cover change products for the conterminous United States (CONUS). The accuracy of these products was assessed using an independently collected land cover reference sample dataset produced by analysts interpreting Landsat data, high-resolution aerial photographs, and other ancillary data. The reference sample of nearly 25,000 pixels and the accompanying 33-year time series of annual land cover reference labels allowed for a comprehensive assessment of accuracy of the LCMAP land cover and land cover change products. Overall accuracy (+/- standard error) for the per-pixel assessment across all years for the eight land cover classes was 82.5% (+/- 0.2%). Overall accuracy was consistent year-to-year within a range of 1.5% but varied regionally with lower accuracy in the eastern United States. User's accuracy (UA) and producer's accuracy (PA) for CONUS ranged from the higher accuracies of Water (UA = 96%, PA = 93%) and Tree Cover (UA = 90%, PA = 83%) to the lower accuracies of Wetland (UA = 69%, PA = 74%) and Barren (UA = 43%, PA = 57%). For a binary change/no change classification, UA of change was 13% (+/- 0.5%) and PA was 16% (+/- 0.6%) for CONUS when agreement was defined as a match by the exact year of change. UA and PA improved to 28% and 34% when agreement was defined as the change being detected by the map and reference data within a +/- 2-year window. Change accuracy was higher in the eastern United States compared to the western US. UA was 49% (+/- 0.3) and PA was 54% (+/- 0.3) for the footprint of change (defined as the area experiencing at least one land cover change from 1985 to 2017). For class-specific loss and gain when agreement was defined as an exact year match, UA and PA were generally below 30%, with Tree Cover loss being the most accurately mapped change (UA = 25%, PA = 31%). These accuracy results provide users with information to assess the suitability of LCMAP data and information to guide future research for improving LCMAP products, particularly focusing on the challenges of accurately mapping annual land cover change.
引用
收藏
页数:16
相关论文
共 53 条
[1]  
[Anonymous], 1976, GEOLOGICAL SURVEY PR
[2]  
Bontemps S., 2011, Biogeosci. Discuss, V8, P7713, DOI DOI 10.5194/BGD-8-7713-2011
[3]   A stratified random sampling design in space and time for regional to global scale burned area product validation [J].
Boschetti, Luigi ;
Stehman, Stephen V. ;
Roy, David P. .
REMOTE SENSING OF ENVIRONMENT, 2016, 186 :465-478
[4]   Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach [J].
Brown, Jesslyn F. ;
Tollerud, Heather J. ;
Barber, Christopher P. ;
Zhou, Qiang ;
Dwyer, John L. ;
Vogelmann, James E. ;
Loveland, Thomas R. ;
Woodcock, Curtis E. ;
Stehman, Stephen V. ;
Zhu, Zhe ;
Pengra, Bruce W. ;
Smith, Kelcy ;
Horton, Josephine A. ;
Xian, George ;
Auch, Roger F. ;
Sohl, Terry L. ;
Sayler, Kristi L. ;
Gallant, Alisa L. ;
Zelenak, Daniel ;
Reker, Ryan R. ;
Rover, Jennifer .
REMOTE SENSING OF ENVIRONMENT, 2020, 238
[5]  
Buttner G., 2017, CLC2018 Technical Guidelines
[6]  
Büttner G, 2014, REMOTE SENS DIGIT IM, V18, P55, DOI 10.1007/978-94-007-7969-3_5
[7]   High-resolution wall-to-wall land-cover mapping and land change assessment for Australia from 1985 to 2015 [J].
Calderon-Loor, Marco ;
Hadjikakou, Michalis ;
Bryan, Brett A. .
REMOTE SENSING OF ENVIRONMENT, 2021, 252
[8]  
CARD DH, 1982, PHOTOGRAMM ENG REM S, V48, P431
[9]   XGBoost: A Scalable Tree Boosting System [J].
Chen, Tianqi ;
Guestrin, Carlos .
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :785-794
[10]   Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation [J].
Cohen, Warren B. ;
Yang, Zhigiang ;
Kennedy, Robert .
REMOTE SENSING OF ENVIRONMENT, 2010, 114 (12) :2911-2924