Role of remote sensing for land-use and land-cover change modeling

Remote Sensing of Land Use and Land Cover: Principles and Applications

Volumn , Issue , 2016, Pages 225-239

  Sohl, Terry ^a   Sleeter, Benjamin ^a  

^a U S GEOLOGICAL SURVEY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LAND USE;

LAND USE AND LAND COVER CHANGE;

REMOTE SENSING;

EID: 84929842294     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (100)

1. Agarwal, C., Green, G.M., Grove, J.M., Evans, T.P., and Schweik, C.M. 2002. A review and assessment of land-use change models: Dynamics of space, time, and human choice. General Technical Report NE-297. Newton Square, Pennsylvania, U.S. Department of Agriculture, Forest Service, Northeastern Research Station. 61 pp.
2. Akbari, H., Shea Rose, L., and Taha, H. 2003. Analyzing the land cover of an urban environment using highresolution orthophotos. Landscape and Urban Planning, 63, 1-14.
3. Al-Bakri, J.T. and Taylor, J.C. 2003. Application of NOAA AVHRR for monitoring vegetation conditions and biomass in Jordan. Journal of Arid Environments, 54, 579-593.
4. Alcamo, J., Leemans, R., and Kreileman, E. 1998. Global Change Scenarios of the 21st Century. Results from the IMAGE 2.1 Model. London: Pergamon & Elseviers Science, 296 pp.
5. Alcamo, J. and Henrichs, T. 2008. Towards guidelines for environmental scenario analysis. In J. Alcamo (Ed.), Environmental Futures: The Practice of Environmental Scenario Analysis (Chapter 2). Amsterdam: Elsevier.
6. Bong, D.B.L., Lai, K.C., and Joseph, A. 2009. Automatic road network recognition and extraction for urban planning. International Journal of Engineering and Applied Sciences, 5, 54-59.
7. Brown, D.G., Goovaerts, P., Burnicki, A., and Li, M.Y. 2002. Stochastic simulation of land-cover change using geostatistics and generalized additive models. Photogrammetric Engineering and Remote Sensing, 68(10), 1051-1061.
8. Brown, D.G., Page, S., Riolo, R., Zellner, M., and Rand, W. 2005. Path dependence and the validation of agent-based spatial models of land use. International Journal of Geographical Information Science, 19, 153-174.
9. Brown, J., Wardlow, B.D., Maxwell, S., Pervez, S., and Callahan, K. 2008a. National irrigated lands mapping via an automated remote sensing-based methodology. In 88th Annual Meeting, American Meteorological Society. January 20-24, 2008, New Orleans, Louisiana.
10. Brown, J.F., Wardlow, B.D., Tadesse, T., Hayes, M.H., and Reed, B.C. 2008b. The vegetation drought response index (VegDRI): A new integrated approach for monitoring drought stress in vegetation. GIScience and Remote Sensing, 45(1), 16-46.
11. Büttner, G., Feranec, G., and Jaffrain, G. 2002. CORINE land cover update 2000. Technical guidelines. EEA Technical Report No 89. Available at: http://reports.eea.europa.eu/technical_report_2002_89/en
12. Carpenter, S., Pingali, P., Bennett, E., and Zurek, M. (Eds.). 2005. Ecosystems and Human Well-Being, Volume 2, Scenarios. Oxford: Island Press, pp. 145-172.
13. Castella, J.C. and Verburg, P.H. 2007. Combination of process-oriented and pattern-oriented models of land-use change in a mountain area of Vietnam. Ecological Modelling, 202, 410-420.
14. Chen, H. and Pontius, Jr., R.G. 2010. Diagnostic tools to evaluate a spatial land change projection along a gradient of an explanatory variable. Landscape Ecology, 25, 1319-1331.
15. Chowdhury, R.R. 2006. Driving forces of tropical deforestation: The role of remote sensing and spatial models. Singapore Journal of Tropical Geography, 27, 82-101.
16. Claggett, P.R., Jantz, C.A., Goetz, S.J., and Bisland, C. 2004. Assessing development pressure in the Chesapeake Bay Watershed: An evaluation of two land-use change models. Environmental Monitoring and Assessment, 94, 129-146.
17. Coppedge, B.R., Engle, D.M., and Fuhlendorf, S.D. 2007. Markov models of land cover dynamics in a southern Great Plains grassland region. Landscape Ecology, 22, 1383-1393.
18. Costanza, R. 1989. Model goodness of fit: A multiple resolution procedure. Ecological Modelling, 47, 199-215.
19. Cots-Folch, R., Aitkenhead, M.J., and Martinez-Casasnovas, J.A. 2007. Mapping land cover from detailed aerial photography data using textural and neural network analysis. International Journal of Remote Sensing, 28(7), 1625-1642.
20. Crews, K.A. and Walsh, S.J. 2009. Remote sensing and the social sciences. In T. Warner, M.D. Nellis, and G.M. Foody (Eds.), The Sage Handbook of Remote Sensing (pp. 437-445). London: Sage.
21. Dobson, J.E., Bright, E.A., Coleman, P.R., Durfee, R.C., and Worley, B.A. 2000. LandScan: A global population database for estimation populations at risk. Photogrammetric Engineering and Remote Sensing, 66(7), 849-857.
22. Elvidge, C.D., Baugh, K.E., Kihn, E.A., Kroehl, H.W., and Davis, E.R. 1997. Mapping city lights with night-time data from the DMSP Operational Linescan System. Photogrammetric Engineering and Remote Sensing, 57(11), 1453-1463.
23. Furby, S.L. 2002. Land cover change: Specification for remote sensing analysis. National Carbon Accounting System Technical Report No. 9, Australian Greenhouse Office. Available at: http://pandora.nla.gov.au/pan/102841/20090717-1556/www.climatechange.gov.au/ncas/reports/pubs/tr09final.pdf
24. Gallant, A.L., Loveland, T.R., Sohl, T.L., and Napton, D.E. 2004. Using an ecoregion framework to analyze land-cover and land-use dynamics. Environmental Management, 34, s89-s110.
25. Gerard, F., Petit, S., Smith, G., Thomson, A., Brown, N., Manchester, S., Wadsworth, R., et al. 2010. Land cover change in Europe between 1950 and 2000 determined employing aerial photography. Progress in Physical Geography, 34(2), 183-205.
26. Geoghegan, J., Pritchard, L., Ogneva-Himmelberger, Y., Chowdhury, R.R., Sanderson, S., and Turner II, B.L. 1998. "Socializing the pixel" and "pixelizing the social" in land-use and land-cover change. In D. Liverman, E. Moran, R.R. Rindfuss, and P.C. Stern (Eds.), People and Pixels: Linking Remote Sensing and Social Science (Chapter 3, pp. 51-69). Washington, D.C.: National Academy Press.
27. Gu, Y., Hunt, E., Wardlow, B., Basara, J., Brown, J.F., and Verdin, J.P. 2008. Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data. Geophysical Research Letters, 35, 5.
28. Hall, C.A.S., Tian, H., Pontius, G., and Cornell, J. 1995. Modelling spatial and temporal patterns of tropical land use change. Journal of Biogeography, 22, 753-757.
29. Heistermann, M., Muller, C., and Ronneberger, K. 2006. Land in sight? Achievements, deficits, and potentials of continental to global scale land-use modeling. Agriculture, Ecosystems, and Environment, 114, 141-158.
30. Herold, M., Latham, J.S., Di Gregorio, A., and Schmullius, C.C. 2006. Evolving standards in land cover characterization. Journal of Land Use Science, 1, 157-168.
31. Heymann, Y., Steenmans, Ch., Croissille, G., and Bossard, M. 1994. CORINE Land Cover. Technical Guide. EUR12585 Luxembourg: Office for Official Publications of the European Communities.
32. Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., Herold, N., McKerrow, A., VanDriel, J.N., and Wickham, J. 2007. Completion of the 2001 national land cover database for the conterminous United States. Photogrammetric Engineering and Remote Sensing, 73(4), 337-341.
33. Ingram, J.C. and Dawson, T.P. 2005. Climate change impacts and vegetation response on the island of Madagascar. Philosophical Transactions of the Royal Society, 363, 55-59.
34. Irwin, E.G. and Geoghegan, J. 2001. Theory, data, methods: Developing spatially explicit economic models of land-use change. Agriculture, Ecosystems and Environment, 85, 7-23.
35. Jansson, T., Bakker, M.M., Boitier, B., Fougeyrolla, A., Helming, J., van Meijl, H., and Verkerk, PJ. 2008. Linking models for land-use analysis: Experiences from the SENSOR project. In 12th Congress of the European Association of Agricultural Economists. EAAE 2008, Ghent, Belgium.
36. Kok, K. and Winograd, M. 2002. Modelling land-use change for Central America, with special reference to the impact of hurricane Mitch. Ecological Modelling, 149, 53-69.
37. Leadley, P., Pereira, H.M., Alkemade, R., Fernandez-Manjarres, J.F., Proenca, V., Scharlemann, J.P.W., and Walpole, M.J. 2010. Biodiversity scenarios: Projections of 21st century change in biodiversity and associated ecosystem services. Secretariat of the Convention on Biological Diversity, Montreal. Technical Series no. 50, 132 pp.
38. Lin, X., Liu, Z., Zhang, J., and Shen, J., 2009. Combining multiple algorithms for road network tracking from multiple source remotely sensed imagery: A practical system and performance evaluation. Sensors, 9, 1237-1258.
39. Lim, K., Treitz, P., Wulder, M., St-Onge, B., and Flood, M. 2003. Lidar remote sensing of forest structure. Progress in Physical Geography, 27(1), 88-106.
40. Liu, W.T. and Kogan, F.N. 1996. Monitoring regional drought using the Vegetation Condition Index. International Journal of Remote Sensing, 17(14), 2761-2782.
41. Loveland, T.R., Sohl, T.L., Stehman, S.V., Gallant, A.L., Sayler, K.L., and Napton, D.E. 2002. A strategy for estimating the rates of recent United States land-cover changes. Photogrammetric Engineering and Remote Sensing, 68(10), 1091-1099.
42. Marschner, F.J. 1950. Major land uses in the United States [map, scale 1:5, 000, 000]: U.S. Dept. of Agriculture, Agricultural Research Service.
43. Marcucci, D.J. 2000. Landscape history as a planning tool. Landscape and Urban Planning, 49, 67-81.
44. Matthews, R.B. 2006. The People and Landscape Model (PALM): Towards full integration of human decisionmaking and biophysical simulation models. Ecological Modeling, 194(4), 329-343.
45. Matthews, R.B., Gilbert, N.G., Roach, A., Polhill, J.G., and Gotts, N.M. 2007. Agent-based land-use models: A review of applications. Landscape Ecology, 22, 1447-1459.
46. McCracken, S.D., Brondizio, E.S., Nelson, D., Moran, E.F., Siqueria, A.D., and Rodriguez-Pedraza, C. 1999. Remote sensing and GIS at farm property level: Demography and deforestation in the Brazilian Amazon. Photogrammetric Engineering and Remote Sensing, 65(11), 1311-1320.
47. McGarigal, K., Cushman, S.A., Neel, M.C., and Ene, E. 2002. FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at: http://www.umass.edu/landeco/research/fragstats/fragstats.html
48. McNoleg, O. 2003. An account of the origins of conceptual models of geographic space. Computers, Environment and Urban Systems, 27(1), 1-3.
49. Means, J.E., Acker, S.A., Fitt, B.J., Renslow, M., Emerson, L., and Hendrix, C.J. 2000. Predicting forest stand characteristics with airborne scanning lidar. Photogrammetric Engineering and Remote Sensing, 66(11), 1367-1371.
50. Mertens, B. and Lambin, E. 1999. Modelling land cover dynamics: Integration of fine-scale land cover data with landscape attributes. International Journal of Applied Earth Observation and Geoinformation, 1(1), 48-52.
51. Millette, T.L., Tuladhar, A.R., Kasperson, R.E., and Turner II, B.L. 1995. The use and limits of remote sensing for analyzing environmental and social change in the Himalayan Middle Mountains of Nepal. Global Environmental Change, 5(4), 367-380.
52. Moreira, E., Costa, S., Aguiar, A.P., Camara, G., and Carneiro, T. 2009. Dynamical coupling of multiscale land change models. Landscape Ecology, 24, 1183-1194.
53. Muller, M.R. and Middleton, J. 1994. A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2), 151-157.
54. Nakicenovic, N., Alcamo, J., Davis, G., De Vrfies, B., Fenhann, J., Gaffin, S., Gregory, K., et al. 2000. Special Report on Emissions Scenarios, IPCC Special Reports, Cambridge University Press, Cambridge, 599 pp.
55. Njoku, E.G., Wilson, W.J., Yueh, S.H., Dinardo, S.J., Li, F.K., Jackson, T.J., Lakshmi, V., and Bolten, J. 2002. Observations of soil moisture using a passive and active low-frequency microwave airborne sensor during SGP99. IEEE Transactions on Geoscience and Remote Sensing, 40(12), 2659-2673.
56. Omernik, J.M. 1987. Ecoregions of the conterminous United States. Annals of the Association of American Geographers, 77, 118-125.
57. Ozah, A.P., Adesina, F.A., and Dami, A. 2010. A deterministic cellular automata model for simulating rural land use dynamics: A case study of Lake Chad basin. ISPRS Archive Vol. XXXVIII, Part 4-8-2-W9, Core Spatial Databases-Updating, Maintenance, and Services-From Theory to Practice, Haifa, Israel, 2010.
58. Parker, D.C., Berger, T., and Manson, S.M. (Eds.). 2002. Agent-based models of land-use and land-cover change. Report and review of an international workshop, Irvine.
59. Parker, D., Brown, D., Polhill, J.G., Manson, S.M., and Deadman, P. 2006. Illustrating a new 'conceptual design pattern' for agent-based models and land use via five case studies: the MR POTATOHEAD framework. In A.L. Paredes and C. H. Iglesias (Eds.), Agent-based Modelling in Natural Resource Management (pp. 29-62). Valladolid, Spain: Universidad de Valladolid.
60. Peters, A.J., Walter-Shea, E.A., Ji, L., Vina, A., Hayes, M., and Svoboda, M.D. 2002. Drought monitoring with NDVI-based standardized vegetation index. Photogrammetric Engineering and Remote Sensing, 68(1), 71-75.
61. Petit, C., Scudder, T., and Lambin, E. 2001. Quantifying processes of land-cover change by remote sensing: Resettlement and rapid land-cover changes in south-eastern Zambia. International Journal of Remote Sensing, 22(17), 3435-3456.
62. Pijanowski, B.C., Brown, D.G., Shellito, B.A., and Manik, G.A. 2002. Using neural networks and GIS to forecast land use change: A land transformation model. Computers, Environment, and Urban Systems, 26, 553-575.
63. Pontius, Jr., R.G., Cornell, J.D., and Hall, C.A.S. 2001. Modeling the spatial pattern of land-use change with GEOMOD2: Application and validation for Costa Rica. Agriculture, Ecosystems and Environment, 85, 191-203.
64. Pontius, Jr., R.G. and Petrova, S.H. 2010. Assessing a predictive model of land change using uncertain data. Environmental Modelling and Software, 25, 299-309.
65. Ray, D.K. and Pijanowski, B.C. 2010. A backcast land use change model to generate past land use maps: Application and validation at the Muskegon River watershed of Michigan, USA. Journal of Land Use Science, 5(1), 1-29.
66. Ray, D.K., Duckles, J.M., and Pijanowski, B.C. 2010. The impact of future land use scenarios on runoff volumes in the Muskegon River watershed. Environmental Management, 46(3), 351-366.
67. Reeves, M.C., Winslow, J.C., and Running, S.W. 2001. Mapping weekly rangeland vegetation productivity using MODIS algorithms. Journal of Range Management, 54, A90-A105.
68. Rindfuss, R.R. and Stern, P.C., 1998. Linking remote sensing and social science: The need and challenges. In D. Liverman, E.F. Moran, R.R. Rindfuss, and P.C. Stern (Eds.), People and Pixels (pp. 1-27). Washington, DC: National Academy Press.
69. Rindfuss, R.R., Walsh, S.J., Turner II, B.L., Fox, J., and Mishra, V. 2004. Developing a science of land change: Challenges and methodological issues. Proceedings of the National Academy of Sciences of the USA, 101(39), 13976-13981.
70. Rollins, M.G. and Frame, C.K. (Tech. Eds.). 2006. The LANDFIRE Prototype Project: Nationally consistent and locally relevant geospatial data for wildland fire management. Gen. Tech. Rep. RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 416 p.
71. Sala, O.E., Chapin, III, F.S., Armesto, J.J., Berlow, E., Bloomfield, J., Dirzo, R., Huber-Sanwald, E., et al. 2000. Global biodiversity scenarios for the year 2100. Science, 287(5459), 1770-1774.
72. Sellers, PJ., Meeson, B.W., Hall, F.G., Asrar, G., Murphy, R.E., Schiffer, R.A., Bretherton, F.P., et al. 1995. Remote sensing of land surface for studies of global change: Models-Algorithms-Experiments. Remote Sensing of Environment, 51, 3-26.
73. Silva, E.A. and Clarke, K.C. 2002. Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems, 26, 525-552.
74. Silva, M.P.S., Camara, G., Escada, M.I.S., and De Souza, R.C.M. 2008. Remote-sensing image mining: Detecting agents of land-use change in tropical forest areas. International Journal of Remote Sensing, 29(16), 4803-4822.
75. Sleeter, B. M., Wilson, T., Soulard, C., and Liu, J. 2010. Estimation of late 20th century landscape change in California. Environmental Monitoring and Assessment, 173(1), 251.
76. Sohl, T.L., Sayler, K.L., Drummond, M.A., and Loveland, T.R. 2007. The FORE-SCE model: A practical approach for projecting land cover change using scenario-based modeling. Journal of Land Use Science, 2(2), 103-126.
77. Sohl, T.L. and Sayler, K.L. 2008. Using the FORE-SCE model to project land-cover change in the southeastern United States. Ecological Modelling, 219, 49-65.
78. Sohl, T.L., Loveland, T.R., Sleeter, B.M., Sayler, K.L., and Barnes, C.A. 2010. Addressing foundational elements of regional land-use change forecasting. Landscape Ecology, 25, 233-247.
79. Stow, D.A., Hope, A., McGuire, D., Verbyla, D., Gamon, J., Huemmrich, F., Houston, S., et al. 2004. Remote sensing of vegetation and land-cover change in Arctic tundra ecosystems. Remote Sensing of Environment, 89, 281-308.
80. Strengers, B., Leemans, R., Eickhout, B., de Vries, B., and Bouwman, L. 2004. The land-use projections and resulting emissions in the IPCC SRES scenarios as simulated by the IMAGE 2.2 model. GeoJournal, 61, 381-393.
81. Tang, Z., Engel, B.A., Pijanowski, B.C., and Lim, K.J. 2005. Forecasting land use change and its environmental impact at a watershed scale. Journal of Environmental Management, 76, 35-45.
82. Tang, J., Wang, L., and Yao, Z. 2007. Spatio-temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm. International Journal of Remote Sensing, 15(10), 3255-3271.
83. Tayyebi, A., Delavar, M.R., Saeedi, S., Amini, J., and Alinia, H. 2008. Monitoring land use change by multitemporal landsat remote sensing imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII, Part B7, Beijing.
84. Thomson, A.G., Manchester, S.J., Swetnam, R.D., Smith, G.M., Wadsworth, R.A., Petit, S., and Gerard, F.F. 2007. The use of digital aerial photography and CORINE-derived methodology for monitoring recent and historic changes in land cover near UK Natura 2000 sites for the BIOPRESS project. International Journal of Remote Sensing, 28(23), 5397-5426.
85. Valbuena, D., Verburg, P.H., Bregt, A.K., and Ligtenberg, A. 2010. An agent-based approach to model land-use at a regional scale. Landscape Ecology, 25(2), 185-199.
86. Verburg, P.H., Veldkamp, A., and Fresco, L.O. 1999a. Simulation of changes in the spatial pattern of land use in China. Applied Geography, 19, 211-233.
87. Verburg, P.H., DeKoning, G.H.J., Kok, K., Veldkamp, A., and Bouma, J. 1999b. A spatial explicit allocation procedure for modeling the pattern of land use change based upon actual land use. Ecological Modelling, 116, 45-61.
88. Verburg, P.H., Overmars, K.P., Huigen, M.G.A., de Groot, W.T., and Veldkamp, A. 2006. Analysis of the effects of land use change on protected areas in the Philippines. Applied Geography, 26, 153-173.
89. Verburg, P.H., Eickhout, B., and van Meijl, H. 2008. A multi-scale, multi-model approach for analyzing the future dynamics of European land use. The Annals of Regional Science, 42, 57-77.
90. Visser, H. and de Nijs, T. 2006. The map comparison kit. Environmental Modelling and Software, 21, 346-358.
91. Vogelmann, J.E., Howard, S.M., Yang, L., Larson, C.R., Wylie, B.K., and Van Driel, J.N. 2001. Completion of the 1990s national land cover dataset for the conterminous United States. Photogrammetric Engineering and Remote Sensing, 67, 650-662.
92. Vogelmann, J.E., Tolk, B., and Zhu, Z. 2009. Monitoring forest changes in the southwestern United States using multitemporal Landsat data. Remote Sensing of Environment, 113, 1739-1748.
93. Wallace, J., Behn, G., and Furby, S. 2006. Vegetation condition assessment and monitoring from sequences of satellite imagery. Ecological Management and Restoration, 7, S31-S36.
94. Walsh, S.J., Entwisle, B., Rindfuss, R.R., and Page, P.H. 2006. Spatial simulation modeling of land-use/land-cover change scenarios in northeastern Thailand: A cellular automata approach. Journal of Land Use Science, 1(1), 5-28.
95. Wang, Y., Mitchell, B.R., Nugranad-Marzilli, J., Bonynge, Zhou, Y., and Shriver, G. 2009. Remote sensing of land-cover change and landscape context of the National Parks: A case study of the Northeast Temperate Network. Remote Sensing of Environment, 13, 1453-1461.
96. Waterworth, R.M., Richards, G.P., Brack, C.L., and Evans, D.M.W. (2007). A generalized hybrid process-empirical model for predicting plantation forest growth. Forest Ecology and Management, 238, 231-243.
97. Wilk, J. and Hughes, D.A. 2002. Simulating the impacts of land-use and climate change on water resource availability for a large south Indian catchment. Hydrological Sciences, 47(1), 19-30.
98. Wu, Q., Li, H., Wang, R., Paulussen, J., He, Y, Wang, M., Wang, Bi., and Wang, Z. 2006. Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape and Urban Planning, 78, 322-333.
99. Wu., X., Hu, Y., He., H.S., Bu, R., Onsted, J., and Xi, F. 2008. Performance evaluation of the SLEUTH model in the Shenyang metropolitan area of northeastern China. Environmental Modeling and Assessment, 13, 1-10.
100. Xibao, X., Feng, Z., and Jianming, Z. 2006. Modeling the impacts of different policy scenarios on urban growth in Lanzhou with remote sensing and cellular automata. Geoscience and Remote Sensing Symposium 2006, IGARSS 2006, Denver, CO, 1435-1438.