Continuous fields of land cover for the conterminous United States using Landsat data: first results from the Web-Enabled Landsat Data (WELD) project

被引：110

作者：

Hansen, Matthew C. ^{[1
]}

Egorov, Alexey ^{[1
]}

Roy, David P. ^{[1
]}

Potapov, Peter ^{[1
]}

Ju, Junchang ^{[1
]}

Turubanova, Svetlana ^{[1
]}

Kommareddy, Indrani ^{[1
]}

Loveland, Thomas R. ^{[2
]}

机构：

[1] S Dakota State Univ, Geog Informat Sci Ctr Excellence, Brookings, SD 57007 USA

[2] US Geol Survey, Earth Resources Observat & Sci Ctr, Sioux Falls, SD 57198 USA

来源：

REMOTE SENSING LETTERS | 2011年 / 2卷 / 04期

关键词：

FOREST COVER; MODIS; CLASSIFICATION; SATELLITE; CALIBRATION; VALIDATION; SET; TM;

D O I：

10.1080/01431161.2010.519002

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

Vegetation Continuous Field (VCF) layers of 30 m percent tree cover, bare ground, other vegetation and probability of water were derived for the conterminous United States (CONUS) using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data sets from the Web-Enabled Landsat Data (WELD) project. Turnkey approaches to land cover characterization were enabled due to the systematic WELD Landsat processing, including conversion of digital numbers to calibrated top of atmosphere reflectance and brightness temperature, cloud masking, reprojection into a continental map projection and temporal compositing. Annual, seasonal and monthly WELD composites for 2008 were used as spectral inputs to a bagged regression and classification tree procedure using a large training data set derived from very high spatial resolution imagery and available ancillary data. The results illustrate the ability to perform Landsat land cover characterizations at continental scales that are internally consistent while retaining local spatial and thematic detail.

引用

页码：279 / 288

页数：10

共 31 条

[1]

[Anonymous], P PEC 16 GLOB PRIOR

[2]

[Anonymous], STRAT PLAN CLIM CHAN

[3] Bagging predictors [J].

Breiman, L .

MACHINE LEARNING, 1996, 24 (02) :123-140

[4]

Breiman L., 1984, Classification and Regression Trees, DOI 10.1201/9781315139470

[5] Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors [J].

Chander, Gyanesh ;

Markham, Brian L. ;

Helder, Dennis L. .

REMOTE SENSING OF ENVIRONMENT, 2009, 113 (05) :893-903

[6] Subpixel forest cover in central Africa from multisensor, multitemporal data [J].

DeFries, R ;

Hansen, M ;

Steininger, M ;

Dubayah, R ;

Sohlberg, R ;

Townshend, J .

REMOTE SENSING OF ENVIRONMENT, 1997, 60 (03) :228-246

[7] Training set size requirements for the classification of a specific class [J].

Foody, Giles M. ;

Mathur, Ajay ;

Sanchez-Hernandez, Carolina ;

Boyd, Doreen S. .

REMOTE SENSING OF ENVIRONMENT, 2006, 104 (01) :1-14

[8] Decision tree classification of land cover from remotely sensed data [J].

Friedl, MA ;

Brodley, CE .

REMOTE SENSING OF ENVIRONMENT, 1997, 61 (03) :399-409

[9] Global land cover mapping from MODIS: algorithms and early results [J].

Friedl, MA ;

McIver, DK ;

Hodges, JCF ;

Zhang, XY ;

Muchoney, D ;

Strahler, AH ;

Woodcock, CE ;

Gopal, S ;

Schneider, A ;

Cooper, A ;

Baccini, A ;

Gao, F ;

Schaaf, C .

REMOTE SENSING OF ENVIRONMENT, 2002, 83 (1-2) :287-302

[10]

*GLOB CLIM OBS SYS, 2003, GCOS82 WMOIOCUNEPICS

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