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Articles

Subpixel land-cover classification for improved urban area estimates using Landsat

ORCID Icon, , &
Pages 5763-5792 | Received 28 Feb 2017, Accepted 13 Jun 2017, Published online: 06 Jul 2017

References

  • ABS. 2015. Australian National Accounts 1988-2015. Australian Bureau of Statistics. Belconnen, ACT, Australia.
  • Aguilar, M. A., R. Vicente, F. J. Aguilar, A. Fernández, and M. M. Saldaña. 2012. “Optimizing Object-Based Classification in Urban Environments Using Very High Resolution Geoeye-1 Imagery.” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1–7: 99–104. doi:10.5194/isprsannals-I-7-99-2012.
  • Akbari, H., S. Rose, and H. Taha. 2003. “Analyzing the Land Cover of an Urban Environment Using High-Resolution Orthophotos.” Landscape and Urban Planning 63 (1): 1–14. doi:10.1016/S0169-2046(02)00165-2.
  • ARUP, and The Rockefeller Foundation. 2015. City Resilience Framework—100 Resilient Cities. New York, NY, USA: Rockefeller Foundation.
  • Bagan, H., and Y. Yamagata. 2014. “Land-Cover Change Analysis in 50 Global Cities by Using a Combination of Landsat Data and Analysis of Grid Cells.” Environmental Research Letters 9(6). IOP Publishing: 64015. doi:10.1088/1748-9326/9/6/064015.
  • Bettencourt, L., and G. West. 2010. “A Unified Theory of Urban Living.” Nature 467 (913): 9–10. doi:10.1038/467912a.
  • Braun, A. C., U. Weidner, and S. Hinz. 2012. “Classification in High-Dimensional Feature Spaces-Assessment Using SVM, IVM and RVM with Focus on Simulated EnMAP Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (2): 436–443. doi:10.1109/JSTARS.2012.2190266.
  • Caccetta, P., S. Collings, A. Devereux, K. Hingee, D. Mcfarlane, A. Traylen, and W. Xiaoliang. 2012. Urban Monitor : Enabling Effective Monitoring and Management of Urban and Coastal Environments Using Digital Aerial Photography Final Report – Transformation of Aerial Photography into Digital Raster Information Products. Australia: CSIRO.
  • Cai, Y., H. Zhang, P. Zheng, and W. Pan. 2016. “Quantifying the Impact of Land use/Land Cover Changes on the Urban Heat Island: A Case Study of the Natural Wetlands Distribution Area of Fuzhou City, China.” Wetlands. doi:10.1007/s13157-016-0738-7.
  • Chen, T., R. A. M. De Jeu, Y. Y. Liu, G. R. Van Der Werf, and A. J. Dolman. 2014. “Using Satellite Based Soil Moisture to Quantify the Water Driven Variability in NDVI: A Case Study over Mainland Australia.” Remote Sensing of Environment 140: 330–338. doi:10.1016/j.rse.2013.08.022.
  • Collings, S., P. Caccetta, and N. Campbell. 2011. “Empirical Models for Radiometric Calibration of Digital Aerial Frame Mosaics.” IEEE Transactions on Geoscience and Remote Sensing 49 (7): 2573–2588. doi:10.1109/TGRS.2011.2108301.
  • Congalton, R. G. 2001. “Accuracy Assessment and Validation of Remotely Sensed and Other Spatial Information.” International Journal of Wildland Fire 10: 321–328. doi:10.1071/WF01031.
  • Cunningham, S., J. Rogan, D. Martin, V. DeLauer, S. McCauley, and A. Shatz. 2015. “Mapping Land Development through Periods of Economic Bubble and Bust in Massachusetts Using Landsat Time Series Data.” GIScience & Remote Sensing 1603: 2016. doi:10.1080/15481603.2015.1045277.
  • Department for Communities and Local Government. 2015. English Housing Survey: Housing Stock Report, 2014-2015. London, UK: English Housing Survey. doi:10.1017/CBO9781107415324.004.
  • Dhakal, S. P. 2014. “Glimpses of Sustainability in Perth, Western Australia : Capturing and Communicating the Adaptive Capacity of an Activist Group.” Consilience: the Jounral of Sustainable Development 11 (1): 167–182.
  • Dorais, A., and J. Cardille. 2011. “Strategies for Incorporating High-Resolution Google Earth Databases to Guide and Validate Classifications: Understanding Deforestation in Borneo.” Remote Sensing 3 (6): 1157–1176. doi:10.3390/rs3061157.
  • Downs, A. 2005. “Smart Growth: Why We Discuss It More than We Do It.” Journal of the American Planning Association 71 (4): 367–378. doi:10.1080/01944360508976707.
  • Feyisa, G. L., G. Henrik Meilby, D. Jenerette, and S. Pauliet. 2016. “Locally Optimized Separability Enhancement Indices for Urban Land Cover Mapping: Exploring Thermal Environmental Consequences of Rapid Urbanization in Addis Ababa, Ethiopia.” Remote Sensing of Environment 175: 14–31. doi:10.1016/j.rse.2015.12.026.
  • Foody, G. M., and A. Mathur. 2006. “The Use of Small Training Sets Containing Mixed Pixels for Accurate Hard Image Classification: Training on Mixed Spectral Responses for Classification by a SVM.” Remote Sensing of Environment 103 (2): 179–189. doi:10.1016/j.rse.2006.04.001.
  • Friedl, M. A., D. K. McIver, J. C. F. Hodges, X. Y. Zhang, D. Muchoney, A. H. Strahler, C. E. Woodcock, et al. 2002. “Global Land Cover Mapping from MODIS: Algorithms and Early Results.” Remote Sensing of Environment 83 (1–2): 287–302. doi:10.1016/S0034-4257(02)00078-0.
  • Ghimire, B., J. Rogan, and J. Miller. 2010. “Contextual Land-Cover Classification: Incorporating Spatial Dependence in Land-Cover Classification Models Using Random Forests and the Getis Statistic.” Remote Sensing Letters 1 (1): 45–54. doi:10.1080/01431160903252327.
  • Gislason, P. O., J. A. Benediktsson, and J. R. Sveinsson. 2006. “Random Forests for Land Cover Classification.” Pattern Recognition Letters 27 (4): 294–300. doi:10.1016/j.patrec.2005.08.011.
  • Hansen, M. C., and T. R. Loveland. 2012. “A Review of Large Area Monitoring of Land Cover Change Using Landsat Data.” Remote Sensing of Environment 122: 66–74. doi:10.1016/j.rse.2011.08.024.
  • Hepinstall-Cymerman, J., S. Coe, and L. R. Hutyra. 2013. “Urban Growth Patterns and Growth Management Boundaries in the Central Puget Sound, Washington, 1986-2007.” Urban Ecosystems 16 (1): 109–129. doi:10.1007/s11252-011-0206-3.
  • Herold, M., M. Gardner, B. Hadley, and D. Roberts. 2002. “The Spectral Dimension in Urban Land Cover Mapping from High-Resolution Optical Remote Sensing Data.” Symposium A Quarterly Journal In Modern Foreign Literatures 6: 1–8. doi:10.1109/TGRS.2003.815238.
  • Howard, L. 1988. The Climate of London. London, UK: Cambridge University Press.
  • Hu, L., and N. A. Brunsell. 2015. “A New Perspective to Assess the Urban Heat Island through Remotely Sensed Atmospheric Profiles.” Remote Sensing of Environment 158: 393–406. doi:10.1016/j.rse.2014.10.022.
  • Hu, X., and Q. Weng. 2009. “Estimating Impervious Surfaces from Medium Spatial Resolution Imagery Using the Self-Organizing Map and Multi-Layer Perceptron Neural Networks.” Remote Sensing of Environment 113 (10): 2089–2102. doi:10.1016/j.rse.2009.05.014.
  • Hu, Y., G. Jia, M. Hou, X. Zhang, F. Zheng, and Y. Liu. 2015. “The Cumulative Effects of Urban Expansion on Land Surface Temperatures in Metropolitan Jingjintang, China Yonghong.” Journal of Geophysical RESEARCH: Atmospheres RESEARCH 9932–9943. doi:10.1002/2014JD022994.Received.
  • Huang, C., L. S. Davis, and J. R. G. Townshend. 2002. “An Assessment of Support Vector Machines for Land Cover Classification.” International Journal of Remote Sensing 23 (4): 725–749. doi:10.1080/01431160110040323.
  • Imhoff, M. L., W. T. Lawrence, D. C. Stutzer, and C. D. Elvidge. 1997. “A Technique for Using Composite DMSP/OLS ‘City Lights’ Satellite Data to Map Urban Area.” Remote Sensing of Environment 61 (3): 361–370. doi:10.1016/S0034-4257(97)00046-1.
  • Ju, J., D. P. Roy, E. Vermote, J. Masek, and V. Kovalskyy. 2012. “Continental-Scale Validation of MODIS-Based and LEDAPS Landsat ETM+ Atmospheric Correction Methods.” Remote Sensing of Environment 122: 175–184. doi:10.1016/j.rse.2011.12.025.
  • Kalnay, E., and M. Cai. 2003. “Impact of Urbanization and Land-Use Change on Climate.” Nature 423: 528–531. doi:10.1038/nature01649.1.
  • Kelly, J. F., B. Weidmann, and M. Walsh. 2011. The Housing We’d Choose. Grattan Institute. Melbourne, Australia: Grattan Institute.
  • Kennewell, C., and B. J. Shaw. 2008. “Perth, Western Australia.” Cities 25 (4): 243–255. doi:10.1016/j.cities.2008.01.002.
  • Kotsiantis, S. B., I. D. Zaharakis, and P. E. Pintelas. 2006. “Machine Learning: A Review of Classification and Combining Techniques.” Artificial Intelligence Review 26 (3): 159–190. doi:10.1007/s10462-007-9052-3.
  • Li, C., J. Wang, L. Wang, H. Luanyun, and P. Gong. 2014. “Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery.” Remote Sensing 6 (2): 964–983. doi:10.3390/rs6020964.
  • Liang, S., H. Fang, and M. Chen. 2001. “Atmospheric Correction of Landsat ETM+ Land Surface Imagery. I.\nMethods.” IEEE Transactions on Geoscience and Remote Sensing 39 (11): 2490–2498. doi:10.1109/36.964986.
  • Lu, D., L. Guiying, W. Kuang, and E. Moran. 2014. “Methods to Extract Impervious Surface Areas from Satellite Images.” International Journal of Digital Earth 7 (2015): 93–112. doi:10.1080/17538947.2013.866173.
  • Lu, D., E. Moran, and S. Hetrick. 2011. “Detection of Impervious Surface Change with Multitemporal Landsat Images in an Urban-Rural Frontier.” ISPRS Journal of Photogrammetry and Remote Sensing : Official Publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) 66 (3): 298–306. doi:10.1016/j.isprsjprs.2010.10.010.
  • Lu, D., and Q. Weng. 2006. “Use of Impervious Surface in Urban Land-Use Classification.” Remote Sensing of Environment 102 (1–2): 146–160. doi:10.1016/j.rse.2006.02.010.
  • Luo, J., D. Peijun, S. Alim, X. Xie, and Z. Xue. 2014. “Annual Landsat Analysis of Urban Growth of Nanjing City from 1980 to 2013.” 2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA) 357–361. doi:10.1109/EORSA.2014.6927912.
  • MacLachlan, A., E. Biggs, G. Roberts, and B. Boruff. 2017a. “Urban Growth Dynamics in Perth, Western Australia: Using Applied Remote Sensing for Sustainable Future Planning.” Land 6 (1): 9. doi:10.3390/land6010009.
  • MacLachlan, A., E. Biggs, G. Roberts, and B. Boruff. 2017b. Classified Earth Observation Data between 1990 and 2015 for the Perth Metropolitan Region, Western Australia Using the Import Vector Machine Algorithm. PANGAEA. doi:10.1594/PANGAEA.871017.
  • Maiersperger, T. K., P. L. Scaramuzza, L. Leigh, S. Shrestha, K. P. Gallo, C. B. Jenkerson, and J. L. Dwyer. 2013. “Characterizing LEDAPS Surface Reflectance Products by Comparisons with AERONET, Field Spectrometer, and MODIS Data.” Remote Sensing of Environment 136: 1–13. doi:10.1016/j.rse.2013.04.007.
  • Marfai, M. A., A. B. Sekaranom, and P. Ward. 2014. “Community Responses and Adaptation Strategies toward Flood Hazard in Jakarta, Indonesia.” Natural Hazards 75 (2): 1127–1144. doi:10.1007/s11069-014-1365-3.
  • Masek, J. G., E. F. Vermote, N. E. Saleous, R. Wolfe, F. G. Hall, K. F. Huemmrich, F. Gao, J. Kutler, and T.-K. Lim. 2006. “A Landsat Surface Reflectance Dataset.” IEEE Geoscience and Remote Sensing Letters 3 (1): 68–72. doi:10.1109/LGRS.2005.857030.
  • Masek, J. G., F. E. Lindsay, and S. N. Goward. 2000. “Dynamics of Urban Growth in the Washington DC Metropolitan Area, 1973-1996, from Landsat Observations.” International Journal of Remote Sensing 21 (18): 3473–3486. doi:10.1080/014311600750037507.
  • Miller, R. B., and C. Small. 2003. “Cities from Space: Potential Applications of Remote Sensing in Urban Environmental Research and Policy.” Environmental Science and Policy 6 (2): 129–137. doi:10.1016/S1462-9011(03)00002-9.
  • Mountrakis, G., I. Jungho, and C. Ogole. 2011. “Support Vector Machines in Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (3): 247–259. doi:10.1016/j.isprsjprs.2010.11.001.
  • Myint, S. W., P. Gober, A. Brazel, S. Grossman-Clarke, and Q. Weng. 2011. “Per-Pixel Vs. Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery.” Remote Sensing of Environment 115 (5): 1145–1161. doi:10.1016/j.rse.2010.12.017.
  • Pal, M., and P. M. Mather. 2003. “An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification.” Remote Sensing of Environment 86 (4): 554–565. doi:10.1016/S0034-4257(03)00132-9.
  • Pan, J., M. Wang, D. Li, and J. Li. 2009. “Automatic Generation of Seamline Network Using Area Voronoi Diagrams with Overlap.” IEEE Transactions on Geoscience and Remote Sensing 47 (6): 1737–1744. doi:10.1109/TGRS.2008.2009880.
  • Powell, R., and D. Roberts. 2008. “Characterizing Variability of the Urban Physical Environment for a Suite of Cities in Rondônia, Brazil.” Earth Interactions 12 (13): 1–32. doi:10.1175/2008EI246.1.
  • Powell, R., and D. Roberts. 2010. “Characterizing Urban Land-Cover Change in Rondônia, Brazil: 1985 to 2000.” Journal of Latin American Geography 9 (3): 183–211. doi:10.1353/lag.2010.0028.
  • Powell, R., D. Roberts, P. Dennison, and L. Hess. 2007. “Sub-Pixel Mapping of Urban Land Cover Using Multiple Endmember Spectral Mixture Analysis: Manaus, Brazil.” Remote Sensing of Environment 106 (2): 253–267. doi:10.1016/j.rse.2006.09.005.
  • Poznanska, A., S. Bayer, and T. Bucher. 2013. “Derivation of Urban Objects and Their Attributes for Large-Scale Urban Areas Based on Very High Resolution UltraCam True Orthophotos and nDSM – a Case Study Berlin, Germany.” Proceedings of the SPIE 13. doi:10.1117/12.2030000.
  • Pravitasari, A. E., I. Saizen, N. Tsutsumida, E. Rustiadi, and D. O. Pribadi. 2015. “Local Spatially Dependent Driving Forces of Urban Expansion in an Emerging Asian Megacity: The Case of Greater Jakarta (Jabodetabek).” Journal of Sustainable Development 8 (1): 108–120. doi:10.5539/jsd.v8n1p108.
  • Ridd, M. K. 1995. “Exploring A V-I-S Model for Urban Ecosystem through Remote Sensing: A Comparative Ananomy for Cities.” International Journal of Remote Sensing. doi:10.1080/01431169508954549.
  • Roscher, R., W. Förstner, and B. Waske. 2012. “I 2VM: Incremental Import Vector Machines.” Image and Vision Computing 30: 263–278. doi:10.1016/j.imavis.2012.04.004.
  • Roscher, R., B. Waske, and W. Forstner. 2010. “Kernel Discriminative Random Fields for Land Cover Classification.” APR Workshop on Pattern Recognition in Remote Sensing. doi:10.1109/PRRS.2010.5742801.
  • Schneider, A. 2012. “Monitoring Land Cover Change in Urban and Peri-Urban Areas Using Dense Time Stacks of Landsat Satellite Data and a Data Mining Approach.” Remote Sensing of Environment 124: 689–704. doi:10.1016/j.rse.2012.06.006.
  • Schneider, A., M. Friedl, and D. Potere. 2010. “Mapping Global Urban Areas Using MODIS 500-M Data: New Methods and Datasets Based on ‘Urban Ecoregions.’.” Remote Sensing of Environment 114 (8): 1733–1746. doi:10.1016/j.rse.2010.03.003.
  • Schneider, A., M. A. Friedl, and D. Potere. 2009. “A New Map of Global Urban Extent from MODIS Satellite Data.” Environmental Research Letters 4 (4): 44003. doi:10.1088/1748-9326/4/4/044003.
  • Schneider, A., and C. M. Mertes. 2014. “Expansion and Growth in Chinese Cities, 1978–2010.” Environmental Research Letters 9 (2): 24008. doi:10.1088/1748-9326/9/2/024008.
  • Schneider, A., K. Seto, and D. Webster. 2005. “Urban Growth in Chengdu, Western China: Application of Remote Sensing to Assess Planning and Policy Outcomes.” Environment and Planning B: Planning and Design 32 (3): 323–345. doi:10.1068/b31142.
  • Sexton, J. O., X.-P. Song, C. Huang, C. Saurabh, M. E. Baker, and J. R. Townshend. 2013. “Urban Growth of the Washington, D.C.–Baltimore, MD Metropolitan Region from 1984 to 2010 by Annual, Landsat-Based Estimates of Impervious Cover.” Remote Sensing of Environment 129: 42–53. doi:10.1016/j.rse.2012.10.025.
  • Sharifi, E., and S. Lehmann. 2014. “Comparative Analysis of Surface Urban Heat Island Effect in Central Sydney.” Journal of Sustainable Development 7 (3): 23–34. doi:10.5539/jsd.v7n3p23.
  • Song, X.-P., J. O. Sexton, C. Huang, S. Channan, and J. R. Townshend. 2016. “Characterizing the Magnitude, Timing and Duration of Urban Growth from Time Series of Landsat-Based Estimates of Impervious Cover.” Remote Sensing of Environment 175: 1–13. doi:10.1016/j.rse.2015.12.027.
  • Sun, G., X. Chen, X. Jia, Y. Yao, and Z. Wang. 2015. “Combinational Build-Up Index (CBI) for Effective Impervious Surface Mapping in Urban Areas.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1–12. doi:10.1109/JSTARS.2015.2478914.
  • Sundarakumar, K., M. Harika, S. K. Aspiya Begum, S. Yamini, and K. Balakrishna. 2012. “Land Use And Land Cover Change Detection And Urban Sprawl Analysis Of Vijayawada City Using Multitemporal Landsat.” International Journal of Engineering Science and Technology 4 (1): 170–178.
  • Suryahadi, A., and S. Sumarto. 2003. “Poverty and Vulnerability in Indonesia before and after the Economic Crisis.” Asian Economic Journal 17 (1): 45–64. doi:10.1111/1351-3958.00161.
  • Turner, B., E. Lambin, and A. Reenberg. 2010. “The Emergence of Land Change Science for Global Environmental Change and Sustainability.” PNAS 103 (128): 13070–13075. doi:10.1073/pnas.0704119104.
  • U.S. Department of Commerce. 2013. 2013 Housing Profile: United States. U.S. Department of Housing and Uban Development. Washington, USA: United States Census Bureau.
  • United Nations, Department of Economic and Social Affairs, Population Division. 2014. World Urbanization Prospects: The 2014 Revision, Highlights (ST/ESA/SER.A/352). New York, NY, USA. doi:10.4054/DemRes.2005.12.9.
  • Varshney, A., and E. Rajesh. 2014. “A Comparative Study of Built-Up Index Approaches for Automated Extraction of Built-Up Regions from Remote Sensing Data.” Journal of the Indian Society of Remote Sensing 42: 1–5. doi:10.1007/s12524-013-0333-9.
  • Vermote, E., D. Tanre, J. L. Deuze, M. Herman, and J. J. Morcrette. 1997. Second Simulation of the Satellite Signal in the Solar Spectrum (6S). 6S User Guide Version 2. Appendix III: Description of the Subroutines. Maryland, USA: NASA-Goddard Space Flight Center.
  • Wang, L., D. Liu, Q. Wang, and Y. Wang. 2013. “Spectral Unmixing Model Based on Least Squares Support Vector Machine with Unmixing Residue Constraints.” Ieee Geoscience and Remote Sensing Letters 10 (6): 1592–1596. 10.1109/LGRS.2013.2262371.
  • Wanner, W., X. Li, and A. H. Strahler. 1995. “On the Derivation of Kernels for Kernel-Driven Models of Bidirectional Reflectance.” Journal of Geophysical Research 100 (D10): 21077. doi:10.1029/95JD02371.
  • Watanachaturaporn, P., M. K. Arora, and P. K. Varshney. 2008. “Multisource Classification Using Support Vector Machines: An Empirical Comparison with Decision Tree and Neural Network Classifiers.” Photogrammetric Engineering and Remote Sensing 74 (2): 239–246. doi:10.14358/PERS.74.2.239.
  • Weng, F., and P. Ruiliang. 2013. “Mapping and Assessing of Urban Impervious Areas Using Multiple Endmember Spectral Mixture Analysis: A Case Study in the City of Tampa, Florida.” Geocarto International 28 (7): 594–615. doi:10.1080/10106049.2013.764355.
  • Weng, Q. 2001. “Modeling Urban Growth Effects on Surface Runoff with the Integration of Remote Sensing and GIS.” Environmental Management 28 (6): 737–748. doi:10.1007/s002670010258.
  • Western Australian Planning Commission. 2013a. The Housing We’d Choose: A Study for Perth and Peel. the Government of Western Australia Department of Housing and Department of Planning. Perth,WA, Australia: Western Australian Planning Commission.
  • Western Australian Planning Commission. 2013b. The Housing We’d Choose: A Study for Perth and Peel Summary Report. Perth,WA, Australia: Western Australian Planning Commission.
  • Western Australian Planning Commission. 2015. Perth and Peel @ 3.5 Million. the Government of Western Australia Department of Planning. Perth,WA, Australia: Western Australian Planning Commission.
  • Wilson, E. H., J. D. Hurd, D. L. Civco, M. P. Prisloe, and C. Arnold. 2003. “Development of a Geospatial Model to Quantify, Describe and Map Urban Growth.” Remote Sensing of Environment 86 (3): 275–285. doi:10.1016/S0034-4257(03)00074-9.
  • Wu, C., and A. T. Murray. 2003. “Estimating Impervious Surface Distribution by Spectral Mixture Analysis.” Remote Sensing of Environment 84: 493–505. doi:10.1016/S0034-4257(02)00136-0.
  • Xie, Q., and Z. Zhou. 2015. “Impact Of Urbanization On Urban Heat Island Effect Based On TM Imagery In Wuhan, China.” Environmental Engineering and Management Journal 14 (3): 647–655.
  • Yuan, F., K. E. Sawaya, B. C. Loeffelholz, and M. E. Bauer. 2005. “Land Cover Classification and Change Analysis of the Twin Cities (Minnesota) Metropolitan Area by Multitemporal Landsat Remote Sensing.” Remote Sensing of Environment 98 (2): 317–328. doi:10.1016/j.rse.2005.08.006.
  • Zhu, J., and T. Hastie. 2005. “Kernel Logistic Regression and the Import Vector Machine.” Journal of Computational and Graphical Statistics 14 (2015): 185–205. doi:10.1198/106186005X25619.
  • Zhu, Z., and C. E. Woodcock. 2014. “Continuous Change Detection and Classification of Land Cover Using All Available Landsat Data.” Remote Sensing of Environment 144: 152–171. doi:10.1016/j.rse.2014.01.011.