A geostatistical temporal mixture analysis approach to address endmember variability for estimating regional impervious surface distributions

References

• Asner, G. P., and D. B. Lobell. 2000. “A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation.” Remote Sensing of Environment 74: 99–112. doi:10.1016/S0034-4257(00)00126-7.
   Web of Science ®Google Scholar
• Ball, J. E., L. M. Bruce, and N. H. Younan. 2007. “Hyperspectral Pixel Unmixing via Spectral Band Selection and DC-Insensitive Singular Value Decomposition.” IEEE Transactions on Geoscience and Remote Sensing Letters 4: 382–386. doi:10.1109/LGRS.2007.895686.
   Web of Science ®Google Scholar
• Bateson, A., and B. Curtiss. 1996. “A Method for Manual Endmember Selection and Spectral Unmixing.” Remote Sensing of Environment 55: 229–243. doi:10.1016/S0034-4257(95)00177-8.
   Web of Science ®Google Scholar
• Bateson, C. A., G. P. Asner, and C. A. Wessman. 2000. “Endmember Bundles: A New Approach to Incorporating Endmember Variability into Spectral Mixture Analysis.” IEEE Transactions on Geoscience and Remote Sensing 38: 1083–1094. doi:10.1109/36.841987.
   Web of Science ®Google Scholar
• Bedini, E. 2009. “Mapping Lithology of the Sarfartoq Carbonatite Complex, Southern West Greenland, Using Hymap Imaging Spectrometer Data.” Remote Sensing of Environment 113: 1208–1219. doi:10.1016/j.rse.2009.02.007.
   Web of Science ®Google Scholar
• Bhatti, S., and N. Tripathi. 2014. “Built-Up Area Extraction Using Landsat 8 OLI Imagery.” GIScience & Remote Sensing 51: 445–467. doi:10.1080/15481603.2014.939539.
   Web of Science ®Google Scholar
• Chang, C.-I., and B. H. Ji. 2006. “Weighted Abundance-Constrained Linear Spectral Mixture Analysis.” IEEE Transactions on Geoscience and Remote Sensing 44: 378–388. doi:10.1109/TGRS.2005.861408.
   Web of Science ®Google Scholar
• Cressie, N. 1993. Statistics for Spatial Data. Revised ed. New York: Wiley.
   Google Scholar
• 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 52: 397–415. doi:10.1080/15481603.2015.1045277.
   Web of Science ®Google Scholar
• Curran, P. J., and P. M. Atkinson. 1998. “Geostatistics and Remote Sensing.” Progress in Physical Geography 22: 61–78. doi:10.1177/030913339802200103.
   Web of Science ®Google Scholar
• Deng, C., and C. Wu. 2013. “A Spatially Adaptive Spectral Mixture Analysis for Mapping Subpixel Urban Impervious Surface Distribution.” Remote Sensing of Environment 133: 62–70. doi:10.1016/j.rse.2013.02.005.
   Web of Science ®Google Scholar
• Dennison, P. E., K. Q. Halligan, and D. A. Roberts. 2004. “A Comparison of Error Metrics and Constraints for Multiple Endmember Spectral Mixture Analysis and Spectral Angle Mapper.” Remote Sensing of Environment 93: 359–367. doi:10.1016/j.rse.2004.07.013.
   Web of Science ®Google Scholar
• Dennison, P. E., and D. A. Roberts. 2003. “Endmember Selection for Multiple Endmember Spectral Mixture Analysis Using Endmember Average RMSE.” Remote Sensing of Environment 87: 123–135. doi:10.1016/S0034-4257(03)00135-4.
   Web of Science ®Google Scholar
• Eckmann, T. C., C. J. Still, D. A. Roberts, and J. C. Michaelsen. 2010. “Variations in Subpixel Fire Properties with Season and Land Cover in Southern Africa.” Earth Interactions 14: 1–29. doi:10.1175/2010EI328.1.
   Web of Science ®Google Scholar
• Elmore, A. J., J. F. Mustard, S. J. Manning, and D. B. Lobell. 2000. “Quantifying Vegetation Change in Semiarid Environments: Precision and Accuracy of Spectral Mixture Analysis and the Normalized Difference Vegetation Index.” Remote Sensing of Environment 73: 87–102. doi:10.1016/S0034-4257(00)00100-0.
   Web of Science ®Google Scholar
• Gilabert, M. A., F. J. Garcı́a-Haro, and J. Meliá. 2000. “A Mixture Modeling Approach to Estimate Vegetation Parameters for Heterogeneous Canopies in Remote Sensing.” Remote Sensing of Environment 72: 328–345. doi:10.1016/S0034-4257(99)00109-1.
   Web of Science ®Google Scholar
• Herold, M., D. A. Roberts, M. E. Gardner, and P. E. Dennison. 2004. “Spectrometry for Urban Area Remote Sensing-Development and Analysis of a Spectral Library from 350 to 2400 Nm.” Remote Sensing of Environment 91: 304–319. doi:10.1016/j.rse.2004.02.013.
   Web of Science ®Google Scholar
• Hestir, E. L., S. Khanna, M. E. Andrew, M. J. Santos, J. H. Viers, J. A. Greenberg, S. S. Rajapakse, and S. L. Ustin. 2008. “Identification of Invasive Vegetation Using Hyperspectral Remote Sensing in the California Delta Ecosystem.” Remote Sensing of Environment 112: 4034–4047. doi:10.1016/j.rse.2008.01.022.
   Web of Science ®Google Scholar
• Jia, G. J., I. C. Burke, A. F. H. Goetz, M. R. Kaufmann, and B. C. Kindel. 2006. “Assessing Spatial Patterns of Forest Fuel Using AVIRIS Data.” Remote Sensing of Environment 102: 318–327. doi:10.1016/j.rse.2006.02.025.
   Web of Science ®Google Scholar
• Johnson, P. E., M. O. Smith, S. Taylorgeorge, and J. B. Adams. 1993. “A Semiempirical Method for Analysisi of the Reflectance Spectra of Binary Mineral Mixtures.” Journal of Geographical Research 88: 3557–3561. doi:10.1029/JB088iB04p03557.
   Web of Science ®Google Scholar
• Keshava, N. 2003. “Survey of Spectral Unmixing Algorithms.” Lincoln Laboratory Journal 14: 55–78.
   Google Scholar
• Knight, J. F., and M. Voth. 2011. “Mapping Impervious Cover Using Multi-Temporal MODIS NDVI Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4: 303–309. doi:10.1109/JSTARS.2010.2051535.
   Web of Science ®Google Scholar
• Lelong, C. C. D., P. C. Pinet, and H. Poilvé. 1998. “Hyperspectral Imaging and Stress Mapping in Agriculture: A Case Study on Wheat in Beauce (France).” Remote Sensing of Environment 66: 179–191. doi:10.1016/S0034-4257(98)00049-2.
   Web of Science ®Google Scholar
• Li, G., D. Lu, E. Moran, and S. Hetrick. 2013. “Mapping Impervious Surface Area in the Brazilian Amazon Using Landsat Imagery.” GIScience & Remote Sensing 50: 172–183. doi:10.1080/15481603.2013.780452.
   PubMed Web of Science ®Google Scholar
• Li, J. 2004. “Wavelet-Based Feature Extraction for Improved Endmember Abundance Estimation in Linear Unmixing of Hyperspectral Signals.” IEEE Transactions on Geoscience and Remote Sensing 42: 644–649. doi:10.1109/TGRS.2003.822750.
   Web of Science ®Google Scholar
• Li, W., and C. Wu. 2014. “Phenology-Based Temporal Mixture Analysis for Estimating Large-Scale Impervious Surface Distributions.” International Journal of Remote Sensing 35: 779–795. doi:10.1080/01431161.2013.873147.
   Web of Science ®Google Scholar
• Li, W., and C. Wu. 2015. “Incorporating Land Use Land Cover Probability Information into Endmember Class Selections for Temporal Mixture Analysis.” ISPRS Journal of Photogrammetry and Remote Sensing 101: 163–173. doi:10.1016/j.isprsjprs.2014.12.007.
   Web of Science ®Google Scholar
• Liu, J., J. R. Miller, D. Haboudane, E. Pattey, and K. Hochheim. 2008. “Crop Fraction Estimation from Casi Hyperspectral Data Using Linear Spectral Unmixing and Vegetation Indices.” Canadian Journal of Remote Sensing 34: S124–S138. doi:10.5589/m07-062.
   Web of Science ®Google Scholar
• Lobell, D. B., G. P. Asner, B. E. Law, and R. N. Treuhaft. 2002. “View Angle Effects on Canopy Reflectance and Spectral Mixture Analysis of Coniferous Forests Using AVIRIS.” International Journal of Remote Sensing 23: 2247–2262. doi:10.1080/01431160110075613.
   Web of Science ®Google Scholar
• McBratney, A. B., and R. Webster. 1986. “Choosing Functions for Semi-Variograms of Soil Properties and Fitting Them to Sampling Estimates.” Journal of Soil Science 37: 617–639. doi:10.1111/j.1365-2389.1986.tb00392.x.
   Google Scholar
• Miao, X., P. Gong, S. Swope, R. Pu, R. Carruthers, G. L. Anderson, J. S. Heaton, and C. R. Tracy. 2006. “Estimation of Yellow Starthistle Abundance through CASI-2 Hyperspectral Imagery Using Linear Spectral Mixture Models.” Remote Sensing of Environment 101: 329–341. doi:10.1016/j.rse.2006.01.006.
   Web of Science ®Google Scholar
• Qiu, X., S. Wu, and X. Miao. 2014. “Incorporating Road and Parcel Data for Object-Based Classification of Detailed Urban Land Covers from NAIP Images.” GIScience & Remote Sensing 51: 498–520. doi:10.1080/15481603.2014.963982.
   Web of Science ®Google Scholar
• Rhee, J., S. Park, and Z. Lu. 2014. “Relationship between Land Cover Patterns and Surface Temperature in Urban Areas.” GIScience & Remote Sensing 51: 521–536. doi:10.1080/15481603.2014.964455.
   Web of Science ®Google Scholar
• Rivard, B., J. Feng, A. Gallie, and A. Sanchez-Azofeifa. 2008. “Continuous Wavelets for the Improved Use of Spectral Libraries and Hyperspectral Data.” Remote Sensing of Environment 112: 2850–2862. doi:10.1016/j.rse.2008.01.016.
   Web of Science ®Google Scholar
• Roberts, D. A., P. E. Dennison, M. E. Gardner, Y. Hetzel, S. L. Ustin, and C. T. Lee. 2003. “Evaluation of the Potential of Hyperion for Fire Danger Assessment by Comparison to the Airborne Visible/Infrared Imaging Spectrometer.” IEEE Transactions on Geoscience and Remote Sensing 41: 1297–1310. doi:10.1109/TGRS.2003.812904.
   Web of Science ®Google Scholar
• Roberts, D. A., M. Gardner, R. Church, S. Ustin, G. Scheer, and R. O. Green. 1998. “Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models.” Remote Sensing of Environment 65: 267–279. doi:10.1016/S0034-4257(98)00037-6.
   Web of Science ®Google Scholar
• Rosa, D., and D. Wiesmann. 2013. “Land Cover and Impervious Surface Extraction Using Parametric and Non-Parametric Algorithms from the Open-Source Software R: An Application to Sustainable Urban Planning in Sicily.” GIScience & Remote Sensing 50: 231–250. doi:10.1080/15481603.2013.795307.
   Web of Science ®Google Scholar
• Sabol, D. E., J. B. Adams, and M. O. Smith. 1992. “Quantitative Subpixel Spectral Detection of Targets in Multispectral Images.” Journal of Geophysical Research: Planets 97: 2659–2672. doi:10.1029/91JE03117.
   Web of Science ®Google Scholar
• Schaaf, A., P. Dennison, G. Fryer, K. Roth, and D. Roberts. 2011. “Mapping Plant Functional Types at Multiple Spatial Resolutions Using Imaging Spectrometer Data.” GIScience & Remote Sensing 48: 324–344. doi:10.2747/1548-1603.48.3.324.
   Web of Science ®Google Scholar
• Somers, B., G. P. Asner, L. Tits, and P. Coppin. 2011. “Endmember Variability in Spectral Mixture Analysis: A Review.” Remote Sensing of Environment 115: 1603–1616. doi:10.1016/j.rse.2011.03.003.
   Web of Science ®Google Scholar
• Somers, B., S. Delalieux, J. Stuckens, W. W. Verstraeten, and P. Coppin. 2009. “A Weighted Linear Spectral Mixture Analysis Approach to Address Endmember Variability in Agricultural Production Systems.” International Journal of Remote Sensing 30: 139–147. doi:10.1080/01431160802304625.
   Web of Science ®Google Scholar
• Somers, B., J. Verbesselt, E. M. Ampe, N. Sims, W. W. Verstraeten, and P. Coppin. 2010. “Spectral Mixture Analysis to Monitor Defoliation in Mixed-Aged Eucalyptus Globulus Labill Plantations in Southern Australia Using Landsat 5-TM and EO-1 Hyperion Data.” International Journal of Applied Earth Observation and Geoinformation 12: 270–277. doi:10.1016/j.jag.2010.03.005.
   Web of Science ®Google Scholar
• Tompkins, S., J. F. Mustard, C. M. Pieters, and D. W. Forsyth. 1997. “Optimization of Endmembers for Spectral Mixture Analysis.” Remote Sensing of Environment 59: 472–489. doi:10.1016/S0034-4257(96)00122-8.
   Web of Science ®Google Scholar
• US Census Bureau. 2014. “National Population Projections.” http://www.census.gov/population/projections/data/national/
   Google Scholar
• Vauclin, M., S. R. Vieira, G. Vachaud, and D. R. Nielsen. 1983. “The Use of Cokriging with Limited Field Soil Observations.” Soil Science Society of America Journal 47: 175–184. doi:10.2136/sssaj1983.03615995004700020001x.
   Web of Science ®Google Scholar
• Wu, C. 2004. “Normalized Spectral Mixture Analysis for Monitoring Urban Composition Using ETM+ Imagery.” Remote Sensing of Environment 93: 480–492. doi:10.1016/j.rse.2004.08.003.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Wu, C., and A. T. Murray. 2005. “A Cokriging Method for Estimating Population Density in Urban Areas.” Computers, Environment and Urban Systems 29: 558–579. doi:10.1016/j.compenvurbsys.2005.01.006.
   Google Scholar
• Yang, F., B. Matsushita, T. Fukushima, and W. Yang. 2012. “Temporal Mixture Analysis for Estimating Impervious Surface Area from Multi-Temporal MODIS NDVI Data in Japan.” ISPRS Journal of Photogrammetry and Remote Sensing 72: 90–98. doi:10.1016/j.isprsjprs.2012.05.016.
   Web of Science ®Google Scholar
• Zhang, J., B. Rivard, A. Sanchez-Azofeifa, and K. Castro-Esau. 2006. “Intra and Inter-Class Spectral Variability of Tropical Tree Species at La Selva, Costa Rica: Implications for Species Identification Using HYDICE Imagery.” Remote Sensing of Environment 105: 129–141. doi:10.1016/j.rse.2006.06.010.
   Web of Science ®Google Scholar
• Zhang, J. K., B. Rivard, and A. Sanchez-Azofeifa. 2004. “Derivative Spectral Unmixing of Hyperspectral Data Applied to Mixtures of Lichen and Rock.” IEEE Transactions on Geoscience and Remote Sensing 42: 1934–1940. doi:10.1109/TGRS.2004.832239.
   Web of Science ®Google Scholar
• Zhang, J. K., B. Rivard, and A. Sanchez-Azofeifa. 2005. “Spectral Unmixing of Normalized Reflectance Data for the Deconvolution of Lichen and Rock Mixtures.” Remote Sensing of Environment 95: 57–66. doi:10.1016/j.rse.2004.11.019.
   Web of Science ®Google Scholar