Quantifying uncertainty and confusion in land change analyses: a case study from central Mexico using MODIS data

References

• Agarwal, C., G. M. Green, J. M. Grove, T. P. Evans, and C. M. Schweik. 2002. A Review and Assessment of Land-Use Change Models: Dynamics of Space, Time, and Human Choice. Newton Square, PA: US Department of Agriculture, Forest Service, Northeastern Research Station. http://www.treesearch.fs.fed.us/pubs/5027
   Google Scholar
• Aguirre Jimenez, A. A., and F. M. Martinez. 2007. “Analisis de La Aplicacion de La Politica Hidraulica En Mexico En El Periodo 2001–2006. Caso Region Lerma-Santiago-Pacifico.” V. Congreso Europeo Ceisal de Latinoamericanistas. Bruselas 2007, Brussels, April 11–14.
   Google Scholar
• Alvarez, R., R. Bonifaz, R. S. Lunetta, C. García, G. Gómez, R. Castro, A. Bernal, and A. L. Cabrera. 2003. “Multitemporal Land-Cover Classification of Mexico Using Landsat MSS Imagery.” International Journal of Remote Sensing 24 (12) (January): 2501–2514. doi:10.1080/01431160210153066.
   Web of Science ®Google Scholar
• Appendini, K., and D. Liverman. 1994. “Agricultural Policy, Climate Change and Food Security in Mexico.” Food Policy 19 (2): 149–164. doi:10.1016/0306-9192(94)90067-1.
   Web of Science ®Google Scholar
• Borak, J. S., E. F. Lambin, and A. H. Strahler. 2000. “The Use of Temporal Metrics for Land Cover Change Detection at Coarse Spatial Scales.” International Journal of Remote Sensing 21 (6–7): 1415–1432. doi:10.1080/014311600210245.
   Web of Science ®Google Scholar
• Bradley, B. A., and J. Mustard. 2005. “Identifying Land Cover Variability Distinct from Land Cover Change: Cheatgrass in the Great Basin.” Remote Sensing of Environment 94 (2) (January): 204–213. doi:10.1016/j.rse.2004.08.016.
   Web of Science ®Google Scholar
• Brower, L. P., G. Castilleja, A. Peralta, J. Lopez-Garcia, L. Bojorquez-Tapia, S. Diaz, D. Melgarejo, and M. Missrie. 2002. “Quantitative Changes in Forest Quality in a Principal Overwintering Area of the Monarch Butterfly in Mexico, 1971–1999.” Conservation Biology 16 (2): 346–359. doi:10.1046/j.1523-1739.2002.00572.x.
   Web of Science ®Google Scholar
• Brown, K. M. M., G. M. M. Foody, and P. M. Atkinson. 2009. “Estimating per-Pixel Thematic Uncertainty in Remote Sensing Classifications.” International Journal of Remote Sensing 30 (1) (January): 209–229. doi:10.1080/01431160802290568.
   Web of Science ®Google Scholar
• Byrne, G. F., P. F. Crapper, and K. K. Mayo. 1980. “Monitoring Land-Cover Change by Principal Component Analysis of Multitemporal Landsat Data.” Remote Sensing of Environment 10 (3): 175–184. doi:10.1016/0034-4257(80)90021-8.
   Web of Science ®Google Scholar
• Chrisman, N. R. R. 1991. “The Error Component in Spatial Data.” In Geographical Information Systems. Principles and Applications. Volume 1: Principles, edited by D. J. Maguire, M. F. Goodchild and D. W. Rhind, 165–174. Harlow: Longman Scientific and Technical. http://gis.depaul.edu/shwang/teaching/geog458/Cr1991.pdf
   Google Scholar
• Cihlar, J. 2000. “Land Cover Mapping of Large Areas from Satellites: Status and Research Priorities.” International Journal of Remote Sensing 21 (6–7) (April): 1093–1114. doi:10.1080/014311600210092.
   Web of Science ®Google Scholar
• Congalton, R. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Remote Sensing of Environment 37 (1) (July): 35–46. doi:10.1016/0034-4257(91)90048-B.
   Web of Science ®Google Scholar
• Congalton, R. G., and K. Green. 2009. Assessing the Accuracy of Remotely Sensed Data. 2nd ed. Boca Raton, FL: CRC Press.
   Google Scholar
• Congalton, R. G., R. G. Oderwald, and R. A. Mead. 1983. “Assessing Landsat Classification Accuracy Using Discrete Multivariate Statistical Techniques.” Photogrammatic Engineering and Remote Sensing 49 (12): 1671–1678.
   Web of Science ®Google Scholar
• Cotler, H., M. Mazari Hiriart, and J. de Anda Sanchez. 2006. Atlas de La Cuenca Lerma-Chapala. Mexico City: Instituo Nacional de Ecologia.
   Google Scholar
• Dalton, R. 2005. “Alcohol and Science: Saving the Agave.” Nature 438 (7071): 1070–1071. doi:10.1038/4381070a.
   PubMed Web of Science ®Google Scholar
• De Anda, J., S. E. Quinones-Cisneros, R. H. French, and M. Guzmán. 1998. “Hydrologic Balance of Lake Chapala (Mexico).” Journal of the American Water Resources Association 34 (6) (December): 1319–1331. doi:10.1111/j.1752-1688.1998.tb05434.x.
   Web of Science ®Google Scholar
• De Vries, C., T. Danaher, R. Denham, P. Scarth, and S. Phinn. 2007. “An Operational Radiometric Calibration Procedure for the Landsat Sensors Based on Pseudo-Invariant Target Sites.” Remote Sensing of Environment 107 (3) (April): 414–429. doi:10.1016/j.rse.2006.09.019.
   Web of Science ®Google Scholar
• Eastman, J. R. 1991. “Change and Time Series Analysis Techniques: A Review.” Change and Time Series Analysis in GIS, edited by J. R. Eastman, and J. McKendry. Geneva: UNITAR.
   Google Scholar
• Eastman, J. R. 2009. IDRISI Geographic Information Systems Software. Worcester, MA: Clark Labs.
   Google Scholar
• Eastman, J. R. R., and M. Fulk. 1993. “Long Sequence Time Series Evaluation Using Standardized Principal Components.” Photogrammetric Engineering and Remote Sensing 59 (6): 991–996.
   Web of Science ®Google Scholar
• Eklundh, L., and A. Singh. 1993. “A Comparative Analysis of Standardised and Unstandardised Principal Components Analysis in Remote Sensing.” International Journal of Remote Sensing 14 (7): 1359–1370. doi:10.1080/01431169308953962.
   Web of Science ®Google Scholar
• Escamilla, M. 1995. “Social Participation in the Lerma-Santiago Basin: Water and Social Life Project.” International Journal of Water Resources Development 11 (4) (December): 457–466. doi:10.1080/07900629550042137.
   Google Scholar
• Estoque, R. C., and Y. Murayama. 2014. “A Geospatial Approach for Detecting and Characterizing Non-Stationarity of Land-Change Patterns and Its Potential Effect on Modeling Accuracy.” GIScience & Remote Sensing 51: 239–252. doi:10.1080/15481603.2014.908582.
   Web of Science ®Google Scholar
• Fisher, P. F. 1999. “Models of Uncertainty in Spatial Data.” In Geographical Information Systems: Principles, Techniques, Management and Applications, edited by P. Longley, M. Goodchild, D. Maguire, and D. Rhind, vol. 1, 191–205. New York: John Wiley & Sons. http://www.colorado.edu/geography/leyk/GIS1/Readings/fisher_1999.pdf
   Google Scholar
• Foody, G. M. 2004. “Thematic Map Comparison: Evaluating the Statistical Significance of Differences in Classification Accuracy.” Photogrammetric Engineering & Remote Sensing 70 (5): 627–633. doi:10.14358/PERS.70.5.627.
   Web of Science ®Google Scholar
• Foody, G. M. 2008. “Harshness in Image Classification Accuracy Assessment.” International Journal of Remote Sensing 29 (11) (June): 3137–3158. doi:10.1080/01431160701442120.
   Web of Science ®Google Scholar
• Foody, G. M. 2009a. “Sample Size Determination for Image Classification Accuracy Assessment and Comparison.” International Journal of Remote Sensing 30 (20): 5273–5291. doi:10.1080/01431160903130937.
   Web of Science ®Google Scholar
• Foody, G. M. 2009b. “Classification Accuracy Comparison: Hypothesis Tests and the Use of Confidence Intervals in Evaluations of Difference, Equivalence and Non-Inferiority.” Remote Sensing of Environment 113 (8): 1658–1663. doi:10.1016/j.rse.2009.03.014.
   Web of Science ®Google Scholar
• Foody, G. M., N. A. Campbell, N. M. Trodd., and T. F. Wood. 1992. “Derivation and Application of Probabilistic Measures of Class Membership from the Maximum Likelihood Classification.” Photogrammetric Engineering and Remote Sensing 58 (9): 1335–1341.
   Web of Science ®Google Scholar
• Fortier, J., J. Rogan, C. E. Woodcock, and D. M. Runfola. 2011. “Utilizing Temporally Invariant Calibration Sites to Classify Multiple Dates and Types of Satellite Imagery.” Photogrammetric Engineering & Remote Sensing 77 (2): 181–189. doi:10.14358/PERS.77.2.181.
   Web of Science ®Google Scholar
• Frey, K. E., and L. C. Smith. 2007. “How Well Do We Know Northern Land Cover? Comparison of Four Global Vegetation and Wetland Products with a New Ground-Truth Database for West Siberia.” Global Biogeochemical Cycles 21 (1). doi:10.1029/2006gb002706.
   Web of Science ®Google Scholar
• Friedl, M. A., A. Strahler, X. Zhang, and J. Hodges. 2002. “The MODIS Land Cover Product: Multi-Attribute Mapping of Global Vegetation and Land Cover Properties from Time Series MODIS Data.” Geoscience and Remote 6: 3199–3201.
   Google Scholar
• Fritz, S., and L. See. 2008. “Identifying and Quantifying Uncertainty and Spatial Disagreement in the Comparison of Global Land Cover for Different Applications.” Global Change Biology 14 (5) (May): 1057–1075. doi:10.1111/j.1365-2486.2007.01519.x.
   Web of Science ®Google Scholar
• Goodchild, M. F., L. Chih-Chang, and Y. Leung. 1994. “Visualizing Fuzzy Maps.” In Visualization in GIS, edited by H. M. Hearnshaw and D. J. Unwin, 158–167. New York: Wiley.
   Google Scholar
• Goodchild, M. F., and S. Gopal. 1989. The Accuracy of Spatial Databases. London: Taylor & Francis. http://www.worldcat.org/title/accuracy-of-spatial-databases/oclc/252954557
   Google Scholar
• Gopal, S., and C. Woodcock. 1994. “Theory and Methods for Accuracy Assessment of Thematic Maps Using Fuzzy Sets.” Photogrammetric Engineering and Remote Sensing 60 (2): 181–188.
   Web of Science ®Google Scholar
• Gustafson, E. J., and G. R. Parker. 1992. “Relationships between Landcover Proportion and Indices of Landscape Spatial Pattern.” Landscape Ecology 7 (2): 101–110. doi:10.1007/BF02418941.
   Web of Science ®Google Scholar
• Gutman, G., R. Byrnes, J. Masek, S. Covington, C. Justice, S. Franks, and R. Headley. 2008. “Towards Monitoring Land-Cover and Land-Use Changes at a Global Scale: The Globla Land Survey 2005.” Photogrammatic Engineering and Remote Sensing 74 (1): 6–10.
   Web of Science ®Google Scholar
• Hansen, M. C., R. S. Defries, J. R. G. Townshend, and R. Sohlberg. 2000. “Global Land Cover Classification at 1 km Spatial Resolution Using a Classification Tree Approach.” International Journal of Remote Sensing 21 (6–7) (April): 1331–1364. doi:10.1080/014311600210209.
   Web of Science ®Google Scholar
• Herold, M., P. Mayaux, C. E. Woodcock, A. Baccini, and C. Schmullius. 2008. “Some Challenges in Global Land Cover Mapping: An Assessment of Agreement and Accuracy in Existing 1 Km Datasets.” Remote Sensing of Environment 112 (5): 2538–2556. doi:10.1016/j.rse.2007.11.013.
   Web of Science ®Google Scholar
• Herold, M., C. E. Woodcock, A. Gregorio, P. Mayaux, A. S. Belward, J. Latham, and C. C. Schmullius. 2006. “A Joint Initiative for Harmonization and Validation of Land Cover Datasets.” IEEE Transactions on Geoscience and Remote Sensing 44 (7): 1719–1727. doi:10.1109/TGRS.2006.871219.
   Web of Science ®Google Scholar
• Iiames, J. S., R. G. Conglaton, and R. S. Lunetta. 2013. “Analyst Variation Associated with Land Cover Image Classification of Landsat ETM + Data for the Assessment of Coarse Spatial Resolution Regional/Global Land Cover Products.” GIScience and Remote Sensing 50: 604–622.
   Web of Science ®Google Scholar
• INE. 2003. “Direccion de Manejo Integral de Cuencas Hidricas: Diagnostico Bio-Fisico Y Socio-Economico de La Cuenca Lerma-Chapala.” Edited by Instituto Nacional de Ecologia. www.agua.org.mx/
   Google Scholar
• INEGI. 2005. “Conteo 2005 (Institutio Nacional De Estadistica Y Geografia).” 2010. Accessed February 21, 2010. http://www.inegi.org.mx/
   Google Scholar
• Ingebritsen, S. E., and R. J. P. Lyon. 1985. “Principal Components Analysis of Multitemporal Image Pairs.” International Journal of Remote Sensing 6 (5) (May): 687–696. doi:10.1080/01431168508948491.
   Web of Science ®Google Scholar
• Jansen, L. J. M., G. Carrai, L. Morandini, P. O. Cerutti, and A. Spisni. 2006. “Analysis of the Spatio-Temporal and Semantic Aspects of Land-Cover/Use Change Dynamics 1991–2001 in Albania at National and District Levels.” Environmental Monitoring and Assessment 119 (1–3): 107–136. doi:10.1007/s10661-005-9013-8.
   PubMed Web of Science ®Google Scholar
• Justice, C. O., J. R. G. Townshend, B. N. Holben, and C. J. Tucker. 1985. “Analysis of the Phenology of Global Vegetation Using Meteorological Satellite Data.” International Journal of Remote Sensing 6 (8): 1271–1318. doi:10.1080/01431168508948281.
   Web of Science ®Google Scholar
• Justice, C. O., J. R. G. Townshend, E. F. Vermote, E. Masuoka, R. E. Wolfe, N. Saleous, D. P. Roy, and J. T. Morisette. 2002. “An Overview of MODIS Land Data Processing and Product Status.” Remote Sensing of Environment 83 (1–2): 3–15. doi:10.1016/S0034-4257(02)00084-6.
   Web of Science ®Google Scholar
• Klooster, D. 2003. “Forest Transitions in Mexico: Institutions and Forests in a Globalized Countryside*.” Professional Geographer 55 (2): 227–237.
   Web of Science ®Google Scholar
• Knight, J. F. 2002. Accuracy Assessment of Thematic Maps Using Inter-Class Spectral Distances. Raleigh, NC: North Carolina State University. http://adsabs.harvard.edu/abs/2002PhDT….175K
   Google Scholar
• Krishnaswamy, J., K. S. Bawa, K. N. Ganeshaiah, and M. C. Kiran. 2009. “Quantifying and Mapping Biodiversity and Ecosystem Services: Utility of a Multi-Season NDVI Based Mahalanobis Distance Surrogate.” Remote Sensing of Environment 113 (4): 857–867. doi:10.1016/j.rse.2008.12.011.
   Web of Science ®Google Scholar
• Lambin, E. F. II, B. L. Turner, H. Geist, S. Agbola, A. Angelsen, J. W. Bruce, O. Coomes, et al. 2001. “The Causes of Land-Use and Land-Cover Change: Moving Beyond the Myths.” Global Environmental Change: Human and Policy Dimensions 11: 261–269. doi:10.1016/S0959-3780(01)00007-3.
   Web of Science ®Google Scholar
• Leyk, S., R. Boesch, and R. Weibel. 2005. “A Conceptual Framework for Uncertainty Investigation in Map-Based Land Cover Change Modelling.” Transactions in GIS 9 (3): 291–322. doi:10.1111/tgis.2005.9.issue-3.
   Google Scholar
• López, E., G. Bocco, M. Mendoza, and E. Duhau. 2001. “Predicting Land-Cover and Land-Use Change in the Urban Fringe: A Case in Morelia City, Mexico.” Landscape and Urban Planning 55 (4) (August): 271–285. doi:10.1016/S0169-2046(01)00160-8.
   Web of Science ®Google Scholar
• Loveland, T. R. R., B. C. C. Reed, J. F. F. Brown, D. O. Ohlen, Z. Zhu, L. Yang, and J. W. Merchant. 2000. “Development of a Global Land Cover Characteristics Database and IGBP DISCover from 1 Km AVHRR Data.” International Journal of Remote Sensing 21 (6–7) (April): 1303–1330. doi:10.1080/014311600210191.
   Web of Science ®Google Scholar
• Loveland, T. R. R., Z. Zhiliang, D. O. Ohlen, Z. L. Zhu, J. F. Brown, B. C. Reed, and L. M. Yang. 1999. “An Analysis of the IGBP Global Land-Cover Characterization Process.” Photogrammetric Engineering and Remote Sensing 65 (September): 1021–1032.
   Web of Science ®Google Scholar
• Lunetta, R. S., R. Alvarez, C. M. Edmonds, J. G. Lyon, C. D. Elvidge, R. Bonifaz, and C. Garcia. 2002. “NALC/Mexico Land-Cover Mapping Results: Implications for Assessing Landscape Condition.” International Journal of Remote Sensing 23 (16) (August): 3129–3148. doi:10.1080/01431160110071888.
   Web of Science ®Google Scholar
• Mas, J.-F., A. Velázquez, J. Díaz-Gallegos, R. Mayorgasaucedo, C. Alcántara, G. Bocco, R. Castro, T. Fernández, and A. Pérez-Vega. 2004. “Assessing Land Use/cover Changes: A Nationwide Multidate Spatial Database for Mexico.” International Journal of Applied Earth Observation and Geoinformation 5 (4) (October): 249–261. doi:10.1016/j.jag.2004.06.002.
   Google Scholar
• Mas, J. F., A. Velazquez, J. L. Palacio-Prieto, G. Bocco, A. Peralta, J. Prado, and A. Velázquez. 2002. “Assessing Forest Resources in Mexico- Wall-to-Wall Land Use/cover Mapping.” PE & RS 68 (10). http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=A0247163AH
   PubMed Web of Science ®Google Scholar
• McIver, D. K., and M. A. Friedl. 2002. “Estimating Pixel-Scale Land Cover Classification Confidence Using Nonparametric Machine Learning Methods.” IEEE Transactions on Geoscience and Remote Sensing 39 (9): 1959–1968. doi:10.1109/36.951086.
   Web of Science ®Google Scholar
• Pontius, R. G., and M. L. Cheuk. 2006. “A Generalized Cross‐tabulation Matrix to Compare Soft‐classified Maps at Multiple Resolutions.” International Journal of Geographical Information Science 20 (1) (January): 1–30. doi:10.1080/13658810500391024.
   Web of Science ®Google Scholar
• Pontius, R. G. Jr. 2000. “Quantification Error Versus Location Error in Comparison of Categorical Maps.” Photogrammetric Engineering and Remote Sensing 66 (8): 1011–1016.
   Web of Science ®Google Scholar
• Powell, R. L., N. Matzke, C. De Souza, M. Clark, I. Numata, L. L. Hess, and D. A. Roberts. 2004. “Sources of Error in Accuracy Assessment of Thematic Land-Cover Maps in the Brazilian Amazon.” Remote Sensing of Environment 90 (2): 221–234. doi:10.1016/j.rse.2003.12.007.
   Web of Science ®Google Scholar
• Richards, J. A. 1984. “Thematic Mapping from Multitemporal Image Data Using the Principal Components Transformation.” Remote Sensing of Environment 16 (1): 35–46. doi:10.1016/0034-4257(84)90025-7.
   Web of Science ®Google Scholar
• Rogan, J., and J. Miller. 2006. “Integrating GIS and Remotely Sensed Data for Mapping Forest Disturbance and Change.” In Understanding Forest Disturbance and Spatial Pattern: Remote Sensing and GIS Approaches, edited by M. A. Wulder and S. E. Franklin. Boca Raton, FL: CRC Press.
   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 (E2): 2659–2672.
   Web of Science ®Google Scholar
• Sanderson, S. E. 1986. The Transformation of Mexican Agriculture: International Structure and the Politics of Rural Change. Princeton, NJ: Princeton University.
   Google Scholar
• Sangermano, F. 2009. “Inferential Monitoring of Global Change Impact on Biodiversity through Remote Sensing and Species Distribution Modeling” (May). http://adsabs.harvard.edu/abs/2009PhDT….90S
   Google Scholar
• Saura, S. 2002. “Effects of Minimum Mapping Unit on Land Cover Data Spatial Configuration and Composition.” International Journal of Remote Sensing 23 (22) (November): 4853–4880. doi:10.1080/01431160110114493.
   Web of Science ®Google Scholar
• Schaaf, C. B., F. Gao, A. H. Strahler, W. Lucht, X. W. Li, T. Tsang, N. C. Strugnell, et al. 2002. “First Operational BRDF, Albedo Nadir Reflectance Products from MODIS.” Remote Sensing of Environment 83 (1–2): 135–148. doi:10.1016/S0034-4257(02)00091-3.
   Web of Science ®Google Scholar
• Schmook, B., R. Dickson, F. Sangermano, J. M. Vadjunec, J. R. Eastman, and J. Rogan. 2011. “A Step-Wise Land-Cover Classification of the Tropical Forests of the Southern Yucatán, Mexico.” International Journal of Remote Sensing 32 (4): 1139–1164. doi:10.1080/01431160903527413.
   Web of Science ®Google Scholar
• Song, C., C. E. E. Woodcock, K. C. Seto, M. P. P. Lenney, and S. A. Macomber. 2001. “Classification and Change Detection Using Landsat TM Data–When and How to Correct Atmospheric Effects?” Remote Sensing of Environment 75 (2): 230–244. doi:10.1016/S0034-4257(00)00169-3.
   Web of Science ®Google Scholar
• Strahler, A. H., C. E. Woodcock, and J. A. Smith. 1986. “On the Nature of Models in Remote Sensing.” Remote Sensing of Environment 20: 121–139. doi:10.1016/0034-4257(86)90018-0.
   Web of Science ®Google Scholar
• Styles, B. T. 1993. “Genus Pinus: A Mexican Purview.” In Biological Diversity of Mexico: Origins and Distribution, edited by T. P. Ramamoorthy, R. Bye, A. Lot, and J. Fa, 397–420. Oxford: Oxford University Press.
   Google Scholar
• Tan, B., C. E. Woodcock, J. Hu, P. Zhang, M. Ozdogan, D. Huang, W. Yang, Y. Knyazikhin, and R. B. Myneni. 2006. “The Impact of Gridding Artifacts on the Local Spatial Properties of MODIS Data: Implications for Validation, Compositing, and Band-to-Band Registration across resolutions. (Report).” Remote Sensing of Environment 105 (2): 98–114. doi:10.1016/j.rse.2006.06.008.
   Web of Science ®Google Scholar
• Torres-Vera, M. A., R. M. Prol-Ledesma, and D. Garcia-Lopez. 2009. “Three Decades of Land Use Variations in Mexico City.” International Journal of Remote Sensing 30 (1) (January): 117–138. doi:10.1080/01431160802261163.
   Web of Science ®Google Scholar
• Townshend, J. R. G., C. Huang, S. N. V. Kalluri, R. S. Defries, S. Liang, and K. Yang. 2000. “Beware of per-Pixel Characterization of Land Cover.” International Journal of Remote Sensing 21 (4): 839–843. doi:10.1080/014311600210641.
   Web of Science ®Google Scholar
• Townshend, J. R. G., C. Justice, W. Li, C. Gurney, and J. McManus. 1991. “Global Land Cover Classification by Remote Sensing: Present Capabilities and Future Possibilities.” Remote Sensing of Environment 35 (2–3): 243–255. doi:10.1016/0034-4257(91)90016-Y.
   Web of Science ®Google Scholar
• Treitz, P., and J. Rogan. 2004. “Remote Sensing for Mapping and Monitoring Land-Cover and Land-Use Change–an Introduction.” Progress in Planning 61 (4): 269–279. doi:10.1016/S0305-9006(03)00064-3.
   Google Scholar
• Turner, M. G. 1989. “Landscape Ecology: The Effect of Pattern on Process.” Annual Review of Ecology and Systematics 20 (1) (November): 171–197. doi:10.1146/annurev.es.20.110189.001131.
   Google Scholar
• Van Der Wel, F. J. M., L. C. Van Der Gaag, and B. G. H. Gorte. 1998. “Visual Exploration of Uncertainty in Remote-Sensing Classification.” Computers & Geosciences 24 (4): 335–343. doi:10.1016/S0098-3004(97)00120-9.
   Web of Science ®Google Scholar
• Velázquez, A., E. Durán, I. Ramı́rez, J.-F. Mas, G. Bocco, G. Ramı́rez, and J. L. Palacio. 2003. “Land Use-Cover Change Processes in Highly Biodiverse Areas: The Case of Oaxaca, Mexico.” Global Environmental Change-Human and Policy Dimensions 13 (3): 175–184. doi:10.1016/S0959-3780(03)00035-9.
   Web of Science ®Google Scholar
• Verbyla, D. L., and S. H. Boles. 2000. “Bias in Land Cover Change Estimates due to Misregistration.” International Journal of Remote Sensing 21 (18): 3553–3560. doi:10.1080/014311600750037570.
   Web of Science ®Google Scholar
• Wessels, K. J., R. S. De Fries, J. Dempewolf, L. O. Anderson, A. J. Hansen, S. L. Powell, and E. F. Moran. 2004. “Mapping Regional Land Cover with MODIS Data for Biological Conservation: Examples from the Greater Yellowstone Ecosystem, USA and Pará State, Brazil.” Remote Sensing of Environment 92: 67–83. doi:10.1016/j.rse.2004.05.002.
   Web of Science ®Google Scholar
• West, R. C., and J. P. Augelli. 1976. “Physical Patterns of Middle America.” In Middle America, Its Lands and Peoples, 22–57. Englewood Cliffs, NJ: Prentice Hall.
   Google Scholar
• Wolfe, R. E., M. Nishihama, A. J. Fleig, J. A. Kuyper, D. P. Roy, J. C. Storey, and F. S. Patt. 2002. “Achieving Sub-Pixel Geolocation Accuracy in Support of MODIS Land Science.” Remote Sensing of Environment 83 (1–2): 31–49. doi:10.1016/S0034-4257(02)00085-8.
   Web of Science ®Google Scholar
• Woodcock, C. E., and A. H. Strahler. 1987. “The Factor of Scale in Remote Sensing.” Remote Sensing of Environment 21 (3): 311–332. doi:10.1016/0034-4257(87)90015-0.
   Web of Science ®Google Scholar
• Wulder, M. A., J. C. White, S. N. Goward, J. G. Masek, J. R. Irons, M. Herold, W. B. Cohen, T. R. Loveland, and C. E. Woodcock. 2008. “Landsat Continuity: Issues and Opportunities for Land Cover Monitoring.” Remote Sensing of Environment 112 (3): 955–969. doi:10.1016/j.rse.2007.07.004.
   Web of Science ®Google Scholar
• Young, S. S., and R. Harris. 2005. “Changing Patterns of Global-Scale Vegetation Photosynthesis, 1982–1999.” International Journal of Remote Sensing 26 (20): 4537–4563. doi:10.1080/01431160500239198.
   Web of Science ®Google Scholar
• Zhang, X., M. A. Friedl, C. B. Schaaf, A. H. Strahler, J. C. F. Hodges, F. Gao, B. C. Reed, and A. Huete. 2003. “Monitoring Vegetation Phenology Using MODIS.” Remote Sensing of Environment 84 (3): 471–475. doi:10.1016/S0034-4257(02)00135-9.
   Web of Science ®Google Scholar