GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

References

• Ackerman, S. A., Strabala, K. I., Menzel, W. P., Frey, R. A., Moeller, C. C., & Gumley, L. E. (1998). Discriminating clear sky from clouds with MODIS. Journal of Geophysical Research, 103(32), 141–52. doi:10.1029/1998JD200032
   Google Scholar
• Ahlqvist, O. (2005). Using uncertain conceptual spaces to translate between land cover categories. International Journal of Geographical Information Science, 19, 831–857.
   Web of Science ®Google Scholar
• Arvor, D., Madiela, B. D., & Corpetti, T. (2016). Semantic pre-classification of vegetation gradient based on linearly unmixed Landsat time series. Geoscience and Remote Sensing Symposium (IGARSS), IEEE International (pp. 4422–4425).
   Google Scholar
• Baraldi, A. (2011). Fuzzification of a crisp near-real-time operational automatic spectral-rule-based decision-tree preliminary classifier of multisource multispectral remotely sensed images. IEEE Transactions on Geoscience and Remote Sensing, 49, 2113–2134. doi:10.1109/TGRS.2010.2091137
   Web of Science ®Google Scholar
• Baraldi, A. (2015). “Automatic Spatial Context-Sensitive Cloud/Cloud-Shadow Detection in Multi-Source Multi-Spectral Earth Observation Images – AutoCloud+,” Invitation to tender ESA/AO/1-8373/15/I-NB – “VAE: Next Generation EO-based Information Services”, DOI: 10.13140/RG.2.2.34162.71363. arXiv: 1701.04256. Retrieved Jan., 8, 2017 from https://arxiv.org/ftp/arxiv/papers/1701/1701.04256.pdf
   Google Scholar
• Baraldi, A. (2017). Pre-processing, classification and semantic querying of large-scale earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations”. Ph.D. dissertation in Agricultural and Food Sciences, University of Naples “Federico II”. Department of Agricultural Sciences, Italy. Ph.D. defense: 16 May 2017. doi:10.13140/RG.2.2.25510.52808. Retrieved January, 30 2018, from https://www.researchgate.net/publication/317333100_Pre-processing_classification_and_semantic_querying_of_large-scale_Earth_observation_spaceborneairborneterrestrial_image_databases_Process_and_product_innovations
   Google Scholar
• Baraldi, A., & Boschetti, L. (2012a). Operational automatic remote sensing image understanding systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA) – part 1: Introduction. Remote Sensing, 4, 2694–2735. doi:10.3390/rs4092694
   Web of Science ®Google Scholar
• Baraldi, A., & Boschetti, L. (2012b). Operational automatic remote sensing image understanding systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA) – part 2: Novel system architecture, information/knowledge representation, algorithm design and implementation. Remote Sensing, 4, 2768–2817.
   Web of Science ®Google Scholar
• Baraldi, A., Boschetti, L., & Humber, M. (2014). Probability sampling protocol for thematic and spatial quality assessments of classification maps generated from spaceborne/airborne very high resolution images. IEEE Transactions on Geoscience and Remote Sensing, 52(1), 701–760. doi:10.1109/TGRS.2013.2243739
   Web of Science ®Google Scholar
• Baraldi, A., Durieux, L., Simonetti, D., Conchedda, G., Holecz, F., & Blonda, P. (2010a). Automatic spectral rule-based preliminary classification of radiometrically calibrated SPOT-4/-5/IRS, AVHRR/ MSG,AATSR, IKONOS/QuickBird/OrbView/GeoEye and DMC/SPOT-1/-2 imagery – part I: System design and implementation. IEEE Transactions on Geoscience and Remote Sensing, 48, 1299–1325. doi:10.1109/TGRS.2009.2032457
   Web of Science ®Google Scholar
• Baraldi, A., Durieux, L., Simonetti, D., Conchedda, G., Holecz, F., & Blonda, P. (2010b). Automatic spectral rule-based preliminary classification of radiometrically calibrated SPOT-4/-5/IRS, AVHRR/ MSG, AATSR, IKONOS/QuickBird/OrbView/GeoEye and DMC/SPOT-1/-2 imagery – part II: Classification accuracy assessment. IEEE Transactions on Geoscience and Remote Sensing, 48, 1326–1354. doi:10.1109/TGRS.2009.2032064
   Web of Science ®Google Scholar
• Baraldi, A., Gironda, M., & Simonetti, D. (2010c). Operational three-stage stratified topographic correction of spaceborne multi-spectral imagery employing an automatic spectral rule-based decision-tree preliminary classifier. IEEE Transactions Geosci Remote Sensing, 48(1), 112–146. doi:10.1109/TGRS.2009.2028017
   Web of Science ®Google Scholar
• Baraldi, A., & Humber, M. (2015). Quality assessment of pre-classification maps generated from spaceborne/airborne multi-spectral images by the Satellite Image Automatic Mapper™ and Atmospheric/Topographic Correction™-Spectral Classification software products: Part 1 – theory. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3), 1307–1329.
   Web of Science ®Google Scholar
• Baraldi, A., Humber, M., & Boschetti, L. (2013). Quality assessment of pre-classification maps generated from spaceborne/airborne multi-spectral images by the Satellite Image Automatic Mapper™ and Atmospheric/Topographic Correction™-Spectral Classification software products: Part 2 – experimental results. Remote Sensing, 5, 5209–5264. doi:10.3390/rs5105209
   Web of Science ®Google Scholar
• Baraldi, A., Puzzolo, V., Blonda, P., Bruzzone, L., & Tarantino, C. (2006). Automatic spectral rule-based preliminary mapping of calibrated Landsat TM and ETM+ images. IEEE Transactions on Geoscience and Remote Sensing, 44, 2563–2586. doi:10.1109/TGRS.2006.874140
   Web of Science ®Google Scholar
• Baraldi, A., & Soares, J. V. B. (2017). Multi-objective software suite of two-dimensional shape descriptors for object-based image analysis. Subjects: Computer Vision and Pattern Recognition (cs.CV), arXiv:1701.01941. Retrieved January 8, 2017, from [Online] https://arxiv.org/ftp/arxiv/papers/1701/1701.01941.pdf
   Google Scholar
• Baraldi, A., Tiede, D., Sudmanns, M., Belgiu, M., & Lang, S. (2016). Automated near real-time earth observation level 2 product generation for semantic querying. Enschede, The Netherlands: GEOBIA 2016, 14-16 Sept., University of Twente Faculty of Geo-Information and Earth Observation (ITC).
   Google Scholar
• Baraldi, A., Tiede, D., Sudmanns, M., Belgiu, M., & Lang, S. (2017, March 28–30). Systematic ESA EO level 2 product generation as pre-condition to semantic content-based image retrieval and information/knowledge discovery in EO image databases. 2017 Conf. on Big Data From Space, BiDS’17, Toulouse, France.
   Google Scholar
• Baraldi, A., Wassenaar, T., & Kay, S. (2010d). Operational performance of an automatic preliminary spectral rule-based decision-tree classifier of spaceborne very high resolution optical images. IEEE Transactions on Geoscience and Remote Sensing, 48, 3482–3502. doi:10.1109/TGRS.2010.2046741
   Web of Science ®Google Scholar
• Beauchemin, M., & Thomson, K. (1997). The evaluation of segmentation results and the overlapping area matrix. International Journal of Remote Sens, 18, 3895–3899. doi:10.1080/014311697216720
   Web of Science ®Google Scholar
• Benavente, R., Vanrell, M., & Baldrich, R. (2008). Parametric fuzzy sets for automatic color naming. Journal of the Optical Society of America, 25, 2582–2593. doi:10.1364/JOSAA.25.002582
   Web of Science ®Google Scholar
• Berlin, B., & Kay, P. (1969). Basic color terms: Their universality and evolution. Berkeley, CA: University of California.
   Google Scholar
• Bernus, P., & Noran, O. (2017). Data rich – but information poor. In L. Camarinha-Matos, H. Afsarmanesh, & R. Fornasiero (Eds.), Collaboration in a data-rich world: PRO-VE 2017. IFIP advances in information and communication technology (Vol. 506, pp. 206–214).
   Google Scholar
• Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford, UK: Clarendon.
   Google Scholar
• Bishop, M. P., & Colby, J. D. (2002). Anisotropic reflectance correction of SPOT-3 HRV imagery. International Journal of Remote Sens, 23(10), 2125–2131. doi:10.1080/01431160110097231
   Web of Science ®Google Scholar
• Bishop, M. P., Shroder, J. F., & Colby, J. D. (2003). Remote sensing and geomorphometry for studying relief production in high mountains. Geomorphology, 55(1–4), 345–361. doi:10.1016/S0169-555X(03)00149-1
   Web of Science ®Google Scholar
• Boschetti, L., Flasse, S. P., & Brivio, P. A. (2004). Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: The Pareto boundary. Remote Sensing of Environment, 91, 280–292. doi:10.1016/j.rse.2004.02.015
   Web of Science ®Google Scholar
• Boschetti, L., Roy, D. P., Justice, C. O., & Humber, M. L. (2015). MODIS–Landsat fusion for large area 30 m burned area mapping. Remote Sensing of Environment, 161, 27–42. doi:10.1016/j.rse.2015.01.022
   Web of Science ®Google Scholar
• Capurro, R., & Hjørland, B. (2003). The concept of information. Annual Review of Information Science and Technology, 37, 343–411. doi:10.1002/aris.1440370109
   Web of Science ®Google Scholar
• Cherkassky, V., & Mulier, F. (1998). Learning from data: Concepts, theory, and methods. New York, NY: Wiley.
   Google Scholar
• CNES. (2015). Venμs satellite sensor level 2 product. Retrieved January 5, 2016, from https://venus.cnes.fr/en/VENUS/prod_l2.htm
   Google Scholar
• Congalton, R. G, & Green, K. (1999). Assessing the accuracy of remotely sensed data. In Boca raton, fl. USA: Lewis Publishers.
   Google Scholar
• Dai, X., & Khorram, S. (1998). The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE Transactions on Geoscience and Remote Sensing, 36, 1566–1577. doi:10.1109/36.718860
   Web of Science ®Google Scholar
• Despini, F., Teggi, S., & Baraldi, A. (2014, September 22). Methods and metrics for the assessment of pan-sharpening algorithms. In L. Bruzzone, J. A. Benediktsson, & F. Bovolo (Eds.), SPIE proceedings, Vol. 9244: Image and signal processing for remote sensing XX. Amsterdam, Netherlands.
   Google Scholar
• Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) and VEGA Technologies. (2011). Sentinel-2 MSI – Level 2A products algorithm theoretical basis document. Document S2PAD-ATBD-0001. European Space Agency.
   Google Scholar
• Di Gregorio, A., & Jansen, L. (2000). Land cover classification system (LCCS): Classification concepts and user manual.FAO: Rome, Italy, FAO Corporate Document Repository. Retrieved February 10, 2015, from http://www.fao.org/DOCREP/003/X0596E/X0596e00.htm
   Google Scholar
• Dillencourt, M. B., Samet, H., & Tamminen, M. (1992). A general approach to connected component labeling for arbitrary image representations. Journal of Association for Computing Machinery, 39, 253–280. doi:10.1145/128749.128750
   Web of Science ®Google Scholar
• Dorigo, W., Richter, R., Baret, F., Bamler, R., & Wagner, W. (2009). Enhanced automated canopy characterization from hyperspectral data by a novel two step radiative transfer model inversion approach. Remote Sensing, 1, 1139–1170. doi:10.3390/rs1041139
   Web of Science ®Google Scholar
• Duke Center for Instructional Technology. (2016). Measurement: Process and outcome indicators. Retrieved June 20, 2016, from http://patientsafetyed.duhs.duke.edu/module_a/measurement/measurement.html
   Google Scholar
• Environmental Protection Agency (EPA). (2007). “Definitions” in multi-resolution land characteristics consortium (MRLC). Retrieved November 13, 2013, from http://www.epa.gov/mrlc/definitions.html#2001
   Google Scholar
• Environmental Protection Agency (EPA). (2013). Western ecology division. Retrieved November 13, 2013, from http://www.epa.gov/wed/pages/ecoregions.htm
   Google Scholar
• European Space Agency (ESA). (2015). Sentinel-2 User Handbook, Standard Document, Issue 1 Rev 2.
   Google Scholar
• Foody, G. (2016). “Observations on accuracy assessments of object-based image classifications,” GEOBIA 2016, 14-16 sept. University of Twente, Faculty of Geo-Information and Earth Observation (ITC), Enschede, The Netherlands.
   Google Scholar
• Foody, G. (2006). The evaluation and comparison of thematic maps derived from remote sensing.In Proc. of the 7th International Symposium on spatial accuracy in natural resources and environmental sciences M. Caetano (& M. Painho, eds), 7-9 July, 2006, Lisbon. Instituto Geográfico Português, Lisbon, 18-31.
   Google Scholar
• GeoTerraImage. (2015). Provincial and national land cover 30 m. Retrieved September 22, 2015, from. http://www.geoterraimage.com/productslandcover.php
   Google Scholar
• Gevers, T., Gijsenij, A., Van De Weijer, J., & Geusebroek, J. M. (2012). Color in computer vision. Hoboken, NJ: Wiley.
   Google Scholar
• Griffin, L. D. (2006). Optimality of the basic color categories for classification. Journal of the Royal Society, Interface, 3, 71–85. doi:10.1098/rsif.2005.0076
   PubMed Web of Science ®Google Scholar
• Griffith, G. E., & Omernik, J. M. (2009). Ecoregions of the United States-Level III (EPA). In C. J. Cleveland (Ed.), Encyclopedia of earth. Washington, D.C.: Environmental Information Coalition, National Council for Science and the Environment.
   Google Scholar
• Group on Earth Observation (GEO). (2005). The global earth observation system of systems (GEOSS) 10-year implementation plan, adopted 16 February 2005. Retrieved January 10, 2012, from http://www.earthobservations.org/docs/10-Year%20Implementation%20Plan.pdf
   Google Scholar
• Group on Earth Observation/Committee on Earth Observation Satellites (GEO/CEOS). (2010). A quality assurance framework for earth observation, version 4.0. Retrieved November 15, 2012, from. http://qa4eo.org/docs/QA4EO_Principles_v4.0.pdf
   Google Scholar
• Group on Earth Observation/Committee on Earth Observation Satellites (GEO-CEOS) - Working Group on Calibration and Validation (WGCV). (2015). Land product validation (LPV). Retrieved March 20, 2015, from http://lpvs.gsfc.nasa.gov/
   Google Scholar
• Hadamard, J. (1902). Sur les problemes aux deriveespartielles et leur signification physique. Princet University Bulletin, 13, 49–52.
   Google Scholar
• Homer, C., Huang, C. Q., Yang, L. M., Wylie, B., & Coan, M. (2004). Development of a 2001 National Land-Cover Database for the United States. Photogrammetric Engineering & Remote Sensing, 70, 829–840. doi:10.14358/PERS.70.7.829
   Web of Science ®Google Scholar
• Hunt, N., & Tyrrell, S. (2012). Stratified sampling. Coventry University. Retrieved February 7, 2012from http://www.coventry.ac.uk/ec/~nhunt/meths/strati.html
   Google Scholar
• IBM. (2016). The four V’s of big data, IBM big data & analytics hub.Retrieved January 30, 2018, from http://www.ibmbigdatahub.com/infographic/four-vs-big-data
   Google Scholar
• Kuzera, K., & Pontius, R. G. (2008). Importance of matrix construction for multiple-resolution categorical map comparison. GIScience & Remote Sensing, 45, 249–274. doi:10.2747/1548-1603.45.3.249
   Web of Science ®Google Scholar
• Lee, D. S., Storey, J. C., Choate, M. J., & Hayes, R. (2004). Four years of Landsat-7 on-orbit geometric calibration and performance. IEEE Transactions on Geoscience and Remote Sensing, 42, 2786–2795. doi:10.1109/TGRS.2004.836769
   Web of Science ®Google Scholar
• Liang, S. (2004). Quantitative remote sensing of land surfaces. Hoboken, NJ: Wiley.
   Google Scholar
• Lillesand, T., & Kiefer, R. (1979). Remote sensing and image interpretation. New York, NY: Wiley.
   Google Scholar
• Lipson, H. (2007). Principles of modularity, regularity, and hierarchy for scalable systems. Journal of Biological Physics and Chemistry, 7, 125–128. doi:10.4024/40701.jbpc.07.04
   Google Scholar
• Liu, S., Hairong Liu, L. J., Latecki, S. Y., Xu, C., & Lu, H. (2011). Size adaptive selection of most informative features. Association for the Advancement of Artificial Intelligence.
   Google Scholar
• Lück, W., & Van Niekerk, A. (2016). Evaluation of a rule-based compositing technique for Landsat-5 TM and Landsat-7 ETM+ images. International Journal of Applied Earth Observation and Geoinformation, 47, 1–14. doi:10.1016/j.jag.2015.11.019
   Web of Science ®Google Scholar
• Lunetta, R., & Elvidge, D. (1999). Remote sensing change detection: Environmental monitoring methods and applications. London, UK: Taylor & Francis.
   Google Scholar
• Luo, Y., Trishchenko, A. P., & Khlopenkov, K. V. (2008). Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America. Remote Sensing of Environment, 112, 4167–4185. doi:10.1016/j.rse.2008.06.010
   Web of Science ®Google Scholar
• Markham, B., & Helder, D. (2012). Forty-year calibrated record of earth-reflected radiance from Landsat: A review. Paper 70. NASA Publications.
   Google Scholar
• Marr, D. (1982). Vision. New York, NY: Freeman and C.
   Google Scholar
• Matsuyama, T., & Hwang, V. S. (1990). SIGMA – a knowledge-based aerial image understanding system. New York, NY: Plenum Press.
   Google Scholar
• Muirhead, K., & Malkawi, O. (1989, September 5–8). Proceedings Fourth AVHRR Data Users Meeting (pp. 31–34). Rottenburg, Germany.
   Google Scholar
• Nagao, M., & Matsuyama, T. (1980). A structural analysis of complex aerial photographs. New York, NY: Plenum.
   Google Scholar
• National Aeronautics and Space Administration (NASA). (2016). Getting petabytes to people: How the EOSDIS facilitates earth observing data discovery and use. [Online]. Retrieved December 29, 2016, from https://earthdata.nasa.gov/getting-petabytes-to-people-how-the-eosdis-facilitates-earth-observing-data-discovery-and-use
   Google Scholar
• Open Geospatial Consortium (OGC) Inc. (2015). OpenGIS® implementation standard for geographic information – simple feature access – part 1: Common architecture. Retrieved March 8, 2015, from http://www.opengeospatial.org/standards/is
   Google Scholar
• Ortiz, A., & Oliver, G. (2006). On the use of the overlapping area matrix for image segmentation evaluation: A survey and new performance measures. Pattern Recognition Letters, 27, 1916–1926. doi:10.1016/j.patrec.2006.05.002
   Web of Science ®Google Scholar
• Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions Pattern Analysis Machine Intelligent, 27, 1226–1238. doi:10.1109/TPAMI.2005.159
   PubMed Web of Science ®Google Scholar
• Pontius, R. G., Jr., & Connors, J. (2006, July 5–7). Expanding the conceptual, mathematical and practical methods for map comparison. In M. Caetano & M. Painho (Eds). Proceedings of the 7th international symposium on spatial accuracy assessment in natural resources and environmental sciences (pp. 64–79). Lisbon: Instituto Geográfico Português.
   Google Scholar
• Pontius, R. G., Jr., & Millones, M. (2011). Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407–4429. doi:10.1080/01431161.2011.552923
   Web of Science ®Google Scholar
• Riano, D., Chuvieco, E., Salas, J., & Aguado, I. (2003). Assessment of different topographic corrections in landsat-TM data for mapping vegetation types. IEEE Transactions on Geoscience and Remote Sensing, 41(5), 1056–1061. doi:10.1109/TGRS.2003.811693
   Web of Science ®Google Scholar
• Richter, R., & Schläpfer, D. (2012a). Atmospheric/topographic correction for satellite imagery – ATCOR-2/3 User Guide, Version 8.2 BETA. Retrieved April 12, 2013, from http://www.dlr.de/eoc/Portaldata/60/Resources/dokumente/5_tech_mod/atcor3_manual_2012.pdf
   Google Scholar
• Richter, R., & Schläpfer, D. (2012b). Atmospheric/Topographic correction for airborne imagery – ATCOR-4 User Guide, Version 6.2 BETA, 2012. Retrieved April 12, 2013, from http://www.dlr.de/eoc/Portaldata/60/Resources/dokumente/5_tech_mod/atcor4_manual_2012.pdf
   Google Scholar
• Ridd, M. K. (1995). Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities. International Journal of Remote Sens, 16(12), 2165–2185. doi:10.1080/01431169508954549
   Web of Science ®Google Scholar
• Roy, D., Ju, J., Kline, K., Scaramuzza, P. L., Kovalskyy, V., Hansen, M., … Zhang, C. S. (2010). Web-enabled Landsat data (WELD): Landsat ETM plus composited mosaics of the conterminous United States. Remote Sensing of Environment, 114, 35–49. doi:10.1016/j.rse.2009.08.011
   Web of Science ®Google Scholar
• SIAM-WELD-NLCD FTP. (2016). Retrieved December 13, 2016, from http://tinyurl.com/j4nzwzl
   Google Scholar
• Simonetti, D., Simonetti, E., Szantoi, Z., Lupi, A., & Eva, H. D. (2015). First results from the phenology based synthesis classifier using Landsat-8 imagery. IEEE Geoscience and Remote Sensing Letters, 12(7), 1496–1500. doi:10.1109/LGRS.2015.2409982
   Web of Science ®Google Scholar
• Sonka, M., Hlavac, V., & Boyle, R. (1994). Image processing, analysis and machine vision. London, UK: Chapman & Hall.
   Google Scholar
• Stehman, S. V., & Czaplewski, R. L. (1998). Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sensing of Environment, 64, 331–344. doi:10.1016/S0034-4257(98)00010-8
   Web of Science ®Google Scholar
• Stehman, S. V., Wickham, J. D., Wade, T. G., & Smith, J. H. (2008). Designing a multi-objective, multi-support accuracy assessment of the 2001 National Land Cover Data (NLCD 2001) of the conterminous United States. Photogrammetric Engineering & Remote Sensing, 74, 1561–1571. doi:10.14358/PERS.74.12.1561
   Web of Science ®Google Scholar
• Tiede, D., Baraldi, A., Sudmanns, M., Belgiu, M., & Lang, S. (2016, March 15–17). ImageQuerying (IQ) – earth observation image content extraction & querying across time and space, submitted (oral presentation and poster session). ESA 2016 Conf. on Big Data From Space, BIDS ’16, Santa Cruz de Tenerife, Spain.
   Google Scholar
• Vermote, E., & Saleous, N. (2007). LEDAPS surface reflectance product description – Version 2.0. University of Maryland at College Park/Dept Geography and NASA/GSFC Code 614.5
   Google Scholar
• Victor, J. (1994). Images, statistics, and textures: Implications of triple correlation uniqueness for texture statistics and the Julesz conjecture: Comment. Journal of Optical Social American A, 11(5), 1680–1684. doi:10.1364/JOSAA.11.001680
   Google Scholar
• Vogelmann, J. E., Howard, S. M., Yang, L., Larson, C. R., Wylie, B. K., & Van Driel, N. (2001). Completion of the 1990s National Land Cover Data set for the conterminous United States from Landsat Thematic Mapper data and ancillary data sources. Photogrammetric Engineering & Remote Sensing, 67, 650–662.
   Web of Science ®Google Scholar
• Vogelmann, J. E., Sohl, T. L., Campbell, P. V., & Shaw, D. M. (1998). Regional land cover characterization using Landsat Thematic Mapper data and ancillary data sources. Environmental Monitoring and Assessment, 51, 415–428. doi:10.1023/A:1005996900217
   Web of Science ®Google Scholar
• Web-Enabled Landsat Data (WELD) Tile FTP. Retrieved December 12, 2016, from https://weld.cr.usgs.gov/
   Google Scholar
• Wessels, K., Van Den Bergh, F., Roy, D., Salmon, B., Steenkamp, K., MacAlister, B., … Jewitt, D. (2016). Rapid land cover map updates using change detection and robust random forest classifiers. Remote Sensing, 8(888), 1–24. doi:10.3390/rs8110888
   Google Scholar
• Wickham, J. D., Stehman, S. V., Fry, J. A., Smith, J. H., & Homer, C. G. (2010). Thematic accuracy of the USGS NLCD 2001 land cover for the conterminous United States. Remote Sensing of Environment, 114, 1286–1296. doi:10.1016/j.rse.2010.01.018
   Web of Science ®Google Scholar
• Wickham, J. D., Stehman, S. V., Gass, L., Dewitz, J., Fry, J. A., & Wade, T. G. (2013). Accuracy assessment of NLCD 2006 land cover and impervious surface. Remote Sensing of Environment, 130, 294–304. doi:10.1016/j.rse.2012.12.001
   Web of Science ®Google Scholar
• Xian, G., & Homer, C. (2010). Updating the 2001 National Land Cover Database impervious surface products to 2006 using Landsat imagery change detection methods. Remote Sensing of Environment, 114, 1676–1686. doi:10.1016/j.rse.2010.02.018
   Web of Science ®Google Scholar
• Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big data and cloud computing: Innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13–53. doi:10.1080/17538947.2016.1239771
   Web of Science ®Google Scholar