Global land cover mapping at 30m resolution: A POK-based operational approach

Jun Chen1, Jin Chen2, Anping Liao1, Xin Cao2, Lijun Chen1, Xuehong Chen2, Chaoying He1, Gang Han1, Peng Shu1, Miao Lu1, Weiwei Zhang1, Xiaohua Tong3, J. P. Mills4
1National Geomatics Center of China, Beijing 100830, China
2State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China#TAB#
3College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
4School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom

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Aitkenhead, 2011, Automating land cover mapping of Scotland using expert system and knowledge integration methods, Remote Sens. Environ., 115, 1285, 10.1016/j.rse.2011.01.012

Angel, 2011, The dimensions of global urban expansion: estimates and projections for all countries, 2000–2050, Progr. Plann., 75, 53, 10.1016/j.progress.2011.04.001

Aplin, 2011, Introduction to object-based landscape analysis, Int. J. Geograph. Inf. Sci., 25, 869, 10.1080/13658816.2011.566570

Ban, 2003, Multitemporal ERS-1 SAR and Landsat TM data for agricultural crop classification: comparison and synergy, Can. J. Remote Sens., 29, 518, 10.5589/m03-014

Ban, 2013, Object-based fusion of multi temporal multi angle ENVISAT ASAR and HJ-1 multispectral data for urban land-cover mapping, IEEE Trans. Geo Sci. Remote Sens., 51, 1998, 10.1109/TGRS.2012.2236560

Ban, 2010, Fusion of quick bird MS and RADARSAT-1 SAR data for land-cover mapping: object-based and knowledge-based approach, Int. J. Remote Sens., 31, 1391, 10.1080/01431160903475415

Bartholomé, 2005, GLC2000: a new approach to GLC mapping from earth observation data, Int. J. Remote Sens., 26, 1959, 10.1080/01431160412331291297

Berk, A., Anderson, G., Bernstein, L., Acharya, P., Dothe, H., Matthew, M., Adler-Golden, S., Chetwynd Jr., J., Richtsmeier, S., Pukall, B., Allred, C., Jeong, L., Hoke, M., 1999. MODTRAN4 radiative transfer modeling for atmospheric correction. In: Proceedings SPIE Annual Meeting 3756, Denver, CO, pp. 348–353.

Blaschke, 2010, Object based image analysis for remote sensing, ISPRS J. Photogrammetry Remote Sens., 65, 2, 10.1016/j.isprsjprs.2009.06.004

Bontemps, S., Defourney, P., Van Bogaert, E., Arino, O., Kalogirou, V., Perez, J.P., 2010. GLOBCOVER2009 Products Description and Validation Report, <http://due.esrin.esa.int/globcover/LandCover2009/GLOBCOVER2009_Validation_Report_2.2.pdf>.

Brisaboa, 2014, An inconsistency measure of spatial data sets with respect to topological constraints, Int. J. Geograph. Inf. Sci., 28, 56, 10.1080/13658816.2013.811243

Cao, 2014, Preliminary analysis of the spatio-temporal patter of global land surface water, Sci. China-Earth Sci., 40, 1661

Carneggie, 1966, Uses of multiband remote sensing in forest and range inventory, Photogrammetria, 21, 115, 10.1016/0031-8663(66)90014-7

Chen, 1984, Utilization of DTM in improving remote sensing classification accuracy, J. Wuhan Univ. Surveying Mapp., 9, 69

Chen, 2007, Detection of spatial conflicts between rivers and contours in digital map updating, Int. J. Geograph. Inf. Sci., 21, 1093, 10.1080/13658810701300071

Chen, 2011, Higher resolution GLC mapping, Geomatics World, 4, 12

Chen, 2011, A simple and effective method for filling gaps in Landsat ETM+ SLC-off images, Remote Sens. Environ., 115, 1053, 10.1016/j.rse.2010.12.010

Chen, 2012, An automated approach for updating land cover maps based on integrated change detection and classification methods, ISPRS J. Photogrammetry Remote Sens., 71, 86, 10.1016/j.isprsjprs.2012.05.006

Chen, 2013, A spectral gradient difference based approach for land cover change detection, ISPRS J. Photogrammetry Remote Sens., 85, 1, 10.1016/j.isprsjprs.2013.07.009

Chen, 2013, Temporal logic and operation relations based knowledge representation for land cover change web services, ISPRS J. Photogrammetry Remote Sens., 80, 140, 10.1016/j.isprsjprs.2013.02.005

Chen, 2014, Some concepts and considerations for 30m global land cover mapping, Acta Geodaeticaet Cartographica Sinica, 43, 551

Chen, 2014, Fast updating of national geo-spatial databases with high resolution imagery: China’s methodology and experiences, Proc. ISPRS Commission IV Symp., 1

Cihlar, 2000, Land cover mapping of large areas from satellites: status and research priorities, Int. J. Remote Sens., 21, 1093, 10.1080/014311600210092

Comber, 2004, Integrating land-cover data with different ontologies: identifying change from inconsistency, Int. J. Geograph. Inf. Sci., 18, 691, 10.1080/13658810410001705316

Coppin, 2004, Digital change detection methods in ecosystem monitoring: a review, Int. J. Remote Sens., 25, 1565, 10.1080/0143116031000101675

Costa, 2014, Combining per-pixel and object-based classifications for mapping land cover over large areas, Int. J. Remote Sens., 35, 738, 10.1080/01431161.2013.873151

Croke, 2004, A dynamic model for predicting hydrologic response to land cover changes in gauged and ungauged catchments, J. Hydrol., 291, 115, 10.1016/j.jhydrol.2003.12.012

Defries, 1999, GLC characterization from satellite data: from research to operational implementation?, Glob. Ecol. Biogeogr., 8, 367, 10.1046/j.1365-2699.1999.00139.x

Ehlers, 1989, Integration of remote sensing with geographic information systems: a necessary evolution, Photogrammetric Eng. Remote Sens., 55, 1619

Elvidge, 2007, Global distribution and density of constructed impervious surfaces, Sensors, 7, 1962, 10.3390/s7091962

Foley, 2005, Global consequences of land use, Science, 309, 570, 10.1126/science.1111772

Frazier, 2000, Water body detection and delineation with Landsat TM data, Photogrammetric Eng. Remote Sens., 66, 1461

Friedl, 2002, Global land cover mapping from MODIS: algorithms and early results, Remote Sens. Environ., 83, 287, 10.1016/S0034-4257(02)00078-0

Friedl, 2010, MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets, Remote Sens. Environ., 114, 168, 10.1016/j.rse.2009.08.016

Fritz, 2010, Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa, Int. J. Remote Sens., 31, 2237, 10.1080/01431160902946598

Fritz, 2012, Geo-Wiki: an online platform for improving global land cover, Environ. Modell. Software, 31, 110, 10.1016/j.envsoft.2011.11.015

Gao, 2004, Thematic knowledge for the generalization of land use data, Cartographic J., 41, 245, 10.1179/00087040X13959

Gerke, 2004, Graph-supported verification of road databases, ISPRS J. Photogrammetry Remote Sens., 58, 152, 10.1016/j.isprsjprs.2003.09.003

Giri, 2013, Next generation of global land cover characterization, mapping, and monitoring, Int. J. Appl. Earth Observation Geo Inf., 25, 30, 10.1016/j.jag.2013.03.005

Goldewijk, 2004, Land cover change over the last three centuries due to human activities: the availability of new global data sets, Geo J., 61, 335

Gong, 2009, Assessment of GLC map accuracies using flux net location data, Progr. Nat. Sci., 19, 754

Gong, 1992, Frequency-based contextual classification and gray level vector reduction for land-use identification, Photogrammetric Eng. Remote Sens., 58, 423

Gong, 2013, Finer resolution observation and monitoring of GLC: first mapping results with Landsat TM and ETM+ data, Int. J. Remote Sens., 34, 2607, 10.1080/01431161.2012.748992

Goward, 2011, The future of Landsat-class remote sensing, 807

Grimm, 2008, Global change and the ecology of cities, Science, 319, 756, 10.1126/science.1150195

Han, 2015, A web-based system for supporting GLC data production, ISPRS J. Photogrammetry Remote Sens., 103, 66, 10.1016/j.isprsjprs.2014.07.012

Hansen, 2012, A review of large area monitoring of land cover change using Landsat data, Remote Sens. Environ., 122, 66, 10.1016/j.rse.2011.08.024

Hansen, 2000, GLC classification at 1km spatial resolution using a classification tree approach, Int. J. Remote Sens., 21, 1331, 10.1080/014311600210209

Hansen, 2013, High-resolution global maps of 21st-century forest cover change, Science, 342, 851

Hayakawa, 2008, Comparison of new and existing global digital elevation models: ASTER G-DEM and SRTM-3, Geophys. Res. Lett., 35, L17404, 10.1029/2008GL035036

He, 2006, Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China, Appl. Geography, 26, 323, 10.1016/j.apgeog.2006.09.006

Heipke, 2008, Updating geospatial databases from images, 355

Herold, 2008, Some challenges in GLC mapping: an assessment of agreement and accuracy in existing 1km datasets, Remote Sens. Environ., 112, 2538, 10.1016/j.rse.2007.11.013

Hu, 2014, Landsat TM/ETM+ and HJ-1A/b CCD data automatic relative radiometric normalisation and accuracy verification, J. Remote Sens., 18, 267

Huang, 2012, Spatiotemporal reflectance fusion via sparse representation, IEEE Trans. Geosci. Remote Sens., 50, 3707, 10.1109/TGRS.2012.2186638

Huang, 2012, Integrating remotely sensed data, GIS and expert knowledge to update object-based land use/land cover information, Int. J. Remote Sens., 33, 905, 10.1080/01431161.2010.536182

Hussain, 2013, Change detection from remotely sensed images: from pixel-based to object-based approaches, ISPRS J. Photogrammetry Remote Sens., 80, 91, 10.1016/j.isprsjprs.2013.03.006

Iwao, 2006, Validating land cover maps with degree confluence project information, Geophys. Res. Lett., 33, L23404, 10.1029/2006GL027768

Johnson, 2010, The 2009 crop land data layer, Photogram Metric Eng. Remote Sens., 11, 1201

Kushwaha, 1990, Forest-type mapping and change detection from satellite imagery, ISPRS J. Photogrammetry Remote Sens., 45, 175, 10.1016/0924-2716(90)90057-I

Liao, 2014, High-resolution remote sensing mapping of global land water, Sci. China Earth Sci., 40, 1634

Liu, 1999, The land use and land cover database and its relative studies in China, J. Geographic Sci., 12, 275

Loveland, 2000, Development of a GLC characteristics database and IGBP discover from 1km AVHRR data, Int. J. Remote Sens., 21, 1303, 10.1080/014311600210191

Lu, 2007, A survey of image classification methods and techniques for improving classification performance, Int. J. Remote Sens., 28, 823, 10.1080/01431160600746456

Lu, 2004, Change detection techniques, Int. J. Remote Sens., 28, 823, 10.1080/01431160600746456

Malinverni, 2011, Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery, Int. J. Geographical Inf. Sci., 25, 1025, 10.1080/13658816.2011.566569

MDA, 2014. EarthSatGeoCover LC Overview, <http://www.mdafederal.com/geocover/geocoverlc>.

Mills, 1996, A new approach to the verification and revision of large-scale mapping, ISPRS J. Photogrammetry Remote Sens., 51, 17, 10.1016/0924-2716(96)00005-6

Myint, 2011, Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery, Remote Sens. Environ., 115, 1145, 10.1016/j.rse.2010.12.017

Ok, 2012, A segment-based approach to classify agricultural lands by using multi-temporal optical and microwave data, Int. J. Remote Sens., 33, 7184, 10.1080/01431161.2012.700423

Olson, 2001, Terrestrial ecoregions of the world: a new map of life on earth, Bioscience, 51, 933, 10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2

Otsu, N., 1979. A threshold selection method from grey-level histograms, IEEE Transactions on Systems, Man, and Cybernetics SMC-9, pp. 62–66.

Potere, 2009, Mapping urban areas on a global scale: which of the eight maps now available is more accurate?, Int. J. Remote Sens., 30, 6531, 10.1080/01431160903121134

Ramankutty, 2008, Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000, Global Biogeochem. Cycles, 22, GB1003, 10.1029/2007GB002952

Rao, Y., Zhu, X., Chen, J., Wang, J., 2014. A direct method for producing high spatial-resolution NDVI time series datasets with multi-temporal MODIS NDVI data and a Landsat TM/ETM+ image. IEEE Trans. Geosci. Remote Sens. (submitted for publication).

Raymond, 2013, Global carbon dioxide emissions from inland waters, Nature, 503, 355, 10.1038/nature12760

Robertson, 2011, Comparison of pixel- and object-based classification in land cover change mapping, Int. J. Remote Sens., 32, 1505, 10.1080/01431160903571791

Rogana, 2004, Remote sensing technology for mapping and monitoring land-cover and land-use change, Progr. Plann., 61, 301, 10.1016/S0305-9006(03)00066-7

Roger, 2005, Land use and climate change, Science, 310, 1625, 10.1126/science.1120529

Running, 2008, Ecosystem disturbance, carbon, and climate, Science, 321, 652, 10.1126/science.1159607

Ryherd, 1996, Combining spectral and texture data in the segmentation of remotely sensed images, Photogrammetric Eng. Remote Sens., 62, 181

Smith, 2013, Hybrid pixel- and object-based approach to habitat condition monitoring, 552

Sterling, 2013, The impact of global land-cover change on the terrestrial water cycle, Nat. – Climate Change, 3, 385, 10.1038/nclimate1690

Sulla-Menashe, 2011, Hierarchical mapping of northern Eurasian land cover using MODIS data, Remote Sens. Environ., 115, 392, 10.1016/j.rse.2010.09.010

Sun, 2012, Comparison and improvement of methods for identifying water bodies in remotely sensed imagery, Int. J. Remote Sens., 33, 6854, 10.1080/01431161.2012.692829

Sutton, 2009, Paving the planet: impervious surface as proxy measure of the human ecological footprint, Prog. Phys. Geogr., 33, 510, 10.1177/0309133309346649

Tang, 2014, Practice and thoughts of the automatic processing of multispectral images with 30m spatial resolution on the global scale, J. Remote Sens., 18, 231

Tong, 2012, Fuzzy acceptance sampling plans for inspection of geospatial data with ambiguity in quality characteristics, Comput. Geosci., 48, 256, 10.1016/j.cageo.2012.01.013

Tong, 2011, Designing a two-rank acceptance sampling plan for quality inspection of geospatial data products, Comput. Geosci., 37, 1570, 10.1016/j.cageo.2011.02.006

Townshend, 1991, GLC classification by remote sensing: present capabilities and future possibilities, Remote Sens. Environ., 35, 243, 10.1016/0034-4257(91)90016-Y

Townshend, 2012, Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges, Int. J. Digital Earth, 5, 373, 10.1080/17538947.2012.713190

Trias-Sanz, 2008, Using colour, texture, and hierarchical segmentation for high-resolution remote sensing, ISPRS J. Photogrammetry Remote Sens., 63, 156, 10.1016/j.isprsjprs.2007.08.005

Verbesselt, 2010, Detecting trend and seasonal changes in satellite image time series, Remote Sens. Environ., 114, 106, 10.1016/j.rse.2009.08.014

Verburg, 2011, Challenges in using land use and land cover data for global change studies, Glob. Change Biol., 17, 974, 10.1111/j.1365-2486.2010.02307.x

Whigham, 2009, Global distribution, diversity and human alterations of wetland resources

Wulder, 2008, Landsat continuity: issues and opportunities for land cover monitoring, Remote Sens. Environ., 112, 955, 10.1016/j.rse.2007.07.004

Xian, 2009, Updating the 2001 national land cover database land cover classification to 2006 by using Landsat imagery change detection methods, Remote Sens. Environ., 113, 1133, 10.1016/j.rse.2009.02.004

Yin, 2013, Comparison of automatic thresholding methods for snow-cover mapping using Landsat TM imagery, Int. J. Remote Sens., 34, 6679, 10.1080/01431161.2013.803631

Yu, 2012, Google earth as a virtual globe tool for earth science applications at the global scale: progress and perspectives, Int. J. Remote Sens., 33, 3966, 10.1080/01431161.2011.636081

Zell, 2012, A user-driven approach to determining critical earth observation priorities for societal benefit, IEEE J. Selected Topics Appl. Earth Observations Remote Sens., 5, 1594, 10.1109/JSTARS.2012.2199467

Zhang, W., Wu, W., Cui, Y., Wen, Q., 2012. HJ-1 satellite image geometric correction system design, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 22–27, Munich, pp. 4351–4354.

Zhou, 2014, Restoration of information obscured by mountainous shadows through Landsat TM/ETM+ images without the use of DEM data: a new method, IEEE Trans. Geosci. Remote Sens., 52, 313, 10.1109/TGRS.2013.2239651

Zhu, 2012, A modified neighborhood similar pixel interpolator approach for removing thick clouds in Landsat Images, IEEE Geosci. Remote Sens. Lett., 9, 521, 10.1109/LGRS.2011.2173290