Land-cover change monitoring with classification trees using Landsat TM and ancillary data

Photogrammetric Engineering and Remote Sensing

Volumn 69, Issue 7, 2003, Pages 793-804

  Rogan, John ^a,b   Miller, Jennifer ^a,c   Stow, Doug ^a   Franklin, Janet ^a,d   Levien, Lisa ^e   Fischer, Chris ^f  

^a SAN DIEGO STATE UNIVERSITY   (United States)

^b CLARK UNIVERSITY   (United States)

^c WEST VIRGINIA UNIVERSITY   (United States)

^d SAN DIEGO STATE UNIVERSITY   (United States)

^e USDA FOREST SERVICE   (United States)

^f California Department of Forestry and Fire Protection   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; FORESTRY; MONITORING; SATELLITE COMMUNICATION SYSTEMS;

CLASSIFICATION TREES;

REMOTE SENSING;

ENVIRONMENTAL MONITORING; LAND COVER; LANDSAT THEMATIC MAPPER; MAPPING; REMOTE SENSING; VEGETATION COVER;

CALIFORNIA; SAN DIEGO; UNITED STATES;

EID: 0038606532     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.69.7.793     Document Type: Article

References (67)

1. Abuelgasim, A.A., W. Ross, S. Gopal, and C. Woodcock, 1999. Change detection using adaptive fuzzy neural networks: Environmental damage assessment after the Gulf War, Remote Sensing of Environment, 70:208-223.
2. Ahern, F.J., A. Janetos, and E. Langham, 1998. Global observation of forest cover: One component of CEOS' integrated global observing strategy, Proceedings of the 27th International Symposium on Remote Sensing of Environment, 08-12 June, Tromso, Norway, pp. 73-80.
3. Anderson, J.R., E. Hardy, J. Roach, and R. Witmer, 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data, U.S. Geological Survey Professional Paper 964, Reston, VA: U.S. Geological Survey, 28 p.
4. Bauer, M.E., T. Burk, A. Ek, P. Coppin, S. Lime, T. Walsh, D. Walters, W. Befort, and D. Heinzen, 1994. Satellite inventory of Minnesota forest resources, Photogrammetric Engineering & Remote Sensing, 60:287-298.
5. Borak, J.S., and A.H. Strahler, 1999. Feature selection and land-cover classification of a MODIS-like data set for a semiarid environment, International Journal of Remote Sensing, 20(5):919-938.
6. Borak, J.S., E. Lambin, and A. Strahler, 2000. The use of temporal metrics for landcover change detection at coarse spatial scales, International Journal of Remote Sensing, 21(6&7):1415-1432.
7. Breiman, L., J. Friedman, R. Olshen, and C. Stone, 1984. Classification and Regression Trees, The Wadsworth Statistics and Probability Series, Wadsworth International Group, Belmont, California, 356 p.
8. Brown, J.F., T. Loveland, J. Merchant, B. Reed, and D. Ohlen, 1993. Using multisource data in global land characterization: Concepts, requirements, and methods, Photogrammetric Engineering & Remote Sensing, 59(6):977-987.
9. Chavez, P.S., Jr., 1989. Radiometric calibration of Landsat Thematic Mapper multispectral images, Photogrammetric Engineering & Remote Sensing, 55(9):1285-1294.
10. Clark, L.A., and D. Pregibon, 1992. Tree-based models, Statistical Models in S (T.J. Hastie, editor), Wadsworth, Pacific Grove, California, pp. 377-419.
11. Collins, J.B., and C.E. Woodcock, 1996. An assessment of several linear change detection techniques for mapping forest mortality using multitemporal Landsat TM data, Remote Sensing of Environment, 56:66-77.
12. Congalton, R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data, Remote Sensing of Environment, 37:35-46.
13. Congalton, R.G., and R.A. Mead, 1983. A quantitative method to test for consistency and correctness, Photogrammetric Engineering & Remote Sensing, 49:69-74.
14. Coppin, P., K. Nackaerts, L. Queen, and K. Brewer, 2001. Operational monitoring of green biomass change for forest management, Photogrammetric Engineering & Remote Sensing, 67(5):603-611.
15. DeFries, R.S., and J. Chan, 2000. Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data, Remote Sensing of Environment, 74:503-515.
16. Ferrell, G.T., and W. Otrosina, 1996. Differential susceptibility of white fir provenances to the fir engraver and its fungal symbiont in northern California, Dynamics of Forest Herbivory: Quest for Pattern and Principle (W.J. Mattson, P. Niemela, and M. Roosi, editors), GTR-NC-183, U.S. Forest Service, St. Paul, Minnesota, pp. 187-191.
17. Fitzgerald, R.W., and B.G. Lees, 1994. Assessing the classification accuracy of multisource remote sensing data, Remote Sensing of Environment, 41:1-25.
18. Franklin, J., 1995. Predictive vegetation mapping: Geographic modeling of biospatial patterns in relation to environmental gradients, Progress in Physical Geography, 19(4):494-519.
19. _, 1998. Predicting the distribution of shrub species in southern California from climate and terrain-derived variables, Journal of Vegetation Science, 9:733-748.
20. Friedl, M.A., and C.E. Brodley, 1997. Decision tree classification of landcover from remotely sensed data, Remote Sensing of Environment, 61:399-408.
21. Friedl, M.A., C.E. Brodley, and A.H. Strahler, 1999. Maximizing landcover classification accuracies produced by decision trees at continental to global scales, IEEE Transactions on Geoscience and Remote Sensing, 37(2):969-977.
22. Friedl, M.A., C. Woodcock, S. Gopal, D. Muchoney, A. Strahler, and C. Barker-Schaaf, 2000. A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data, International Journal of Remote Sensing, 21(5):1073-1077.
23. Gao, J., 2002. Integration of GPS with remote sensing and GIS: Reality and prospect, Photogrammetric Engineering & Remote Sensing, 68(5):447-453.
24. Gopal, S., and C.E. Woodcock, 1996. Remote sensing of forest change using artificial neural networks, IEEE Transactions on Geoscience and Remote Sensing, 34(2):398-404.
25. Gutman, G., 2002. NASA Land Cover/Land Use Change Program, Proceedings of the Third International Symposium on Remote Sensing of Urban Areas, 11-13 June, Istanbul, Turkey, pp. 11-13.
26. Hansen, M.C., R. Defries, J. Townshend, and R. Sohlberg, 2000. Global land cover classification at 1km spatial resolution using a classification tree approach, International Journal of Remote Sensing, 21(6 & 7):1331-1364.
27. Huang, X., and J.R. Jensen, 1997. A machine-learning approach to automated knowledge-base building for remote sensing image analysis with GIS data, Photogrammetric Engineering & Remote Sensing, 63(10):1185-1194.
28. Hughes, G.F., 1968. On the mean accuracy of statistical pattern recognizers, IEEE Transactions on Information Theory, 14(1):55-63.
29. Hutchinson, C.F., 1982. Technique for combining Landsat and ancillary data for digital classification improvement, Photogrammetric Engineering & Remote Sensing, 48(1):123-130.
30. Jensen, J.R., 1996. Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice-Hall, Upper Saddle River, New Jersey, 318 p.
31. Justice, C.O., and J.R.G. Townshend, 1981. Integrating ground data with remote sensing, Terrain Analysis and Remote Sensing (J.R.G. Townshend, editor), Allen and Unwin, Boston, pp. 38-58.
32. Lambin, E.F., 1998. Modeling and monitoring land-cover change processes in tropical regions, Progress in Physical Geography, 21(3):375-393.
33. Lawrence, R.L., and A. Wright, 2001. Rule-based classification systems using classification and regression tree (CART) analysis, Photogrammetric Engineering & Remote Sensing, 67(10):1137-1142.
34. Levien, L.M., C. Fischer, P. Roffers, B. Maurizi, J. Suero, and X. Huang, 1999. A machine-learning approach to change detection using multi-scale imagery, Proceedings of ASPRS Annual Conference, 17-21 May, Portland, Oregon, (American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland) 1:22. CD-ROM.
35. Levien, L.M., C. Fischer, P. Roffers, B. Maurizi, and J. Suero, 2002. Monitoring Land Cover Changes in California: Northeastern California Project Area, USDA Technical Report, U.S. Forest Service, Sacramento, California, 165 p.
36. Lins, K.S., and R.L. Kleckner, 1996. Land cover mapping: An overview and history of the concepts, Gap Analysis: A Landscape Approach to Biodiversity Planning (J.M. Scott, T.H. Tear, and F. Davis, editors), American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland, pp. 57-65.
37. Liu, W., S. Gopal, and C. Woodcock, 2001. Spatial data mining for classification, visualization and interpretation with ARTMAP neural networks, Data Mining for Scientific and Engineering Applications (R.L. Grossman, C. Kamath, P. Kegelmeyer, V. Kumar, and R.R. Namburu, editors), Kluwer Academic Publishers, Dordrecht, Netherlands, 628 p.
38. Loveland, T.R., and D.M. Shaw, 1996. Multiresolution land characterization: Building collaborative partnerships, Gap Analysis: A Landscape Approach to Biodiversity Planning, Proceedings of the ASPRS/GAP Symposium (J.M. Scott, T. Tear, and F. Davis, editors), 27 February - 02 March, Charlotte, North Corolina (National Biological Service, Moscow, Idaho), pp. 83-89.
39. Macleod, R.D., and R.G. Congalton, 1998. A quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data, Photogrammetric Engineering & Remote Sensing, 64(3):207-216.
40. Malerba, D., F. Esposito, A. Lanza, and F.A. Lisi, 2001. Machine learning for information extraction from topographic maps, Geographic Data Mining and Knowledge Discovery (H.J. Miller and J. Han, editors), Taylor and Francis, Inc., New York, N.Y., pp. 291-314.
41. McIver, D.K., and M.A. Friedl, 2002. Using prior probabilities in decision-tree classification of remotely sensed data, Remote Sensing of Environment, 81:253-261.
42. Miller, J., and J. Franklin, 2002. Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence, Ecological Modelling, 157:227-247.
43. Muchoney, D., and A. Strahler, 2002. Regional vegetation mapping and direct land surface parametrization from remotely sensed and site data, Remote Sensing of Environment, 23:1125-1142.
44. Pax-Lenney, M., C.E. Woodcock, S.A. Macomber, S. Gopal, and C. Song, 2001. Forest mapping with a generalized classifier and Landsat TM data, Remote Sensing of Environment, 77:241-276.
45. Pontius, R.G., Jr., 2000. Quantification error versus location error in comparison of categorical maps, Photogrammetric Engineering & Remote Sensing, 66(8):1011-1016.
46. Radeloff, V.C., D. Mladenoff, and M. Boyce, 2000. Effects of interacting disturbances on landscape patterns: budworm defoliation and salvage logging, Ecological Applications, 10(1):233-247.
47. Riano, D., E. Chuvieco, S. Ustin, R. Zomer, P. Dennison, D. Roberts, and J. Salas, 2002. Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains, Remote Sensing of Environment, 79:60-71.
48. Ricchetti, E., 2000. Multispectral satellite image and ancillary data for geological classification, Photogrammetric Engineering & Remote Sensing, 66(4):429-435.
49. Richards, J.A., 1996. Classifier performance and map accuracy, Remote Sensing of Environment, 57:161-166.
50. Roberts, D.A., G.T. Batista, J. Pereira, E. Waller, and B. Nelson, 1998. Change identification using multitemporal spectral mixture analysis: Applications in Eastern Amazonia, Remote Sensing Change Detection: Environmental Monitoring Applications and Methods (C. Elvidge and R. Lunetta, editors), Ann Arbor Press, Ann Arbor, Michigan, pp. 137-161.
51. Rogan, J., and S. Yool, 2001. Mapping fire-induced vegetation depletion in the Peloncillo Mountains, Arizona and New Mexico, International Journal of Remote Sensing, 22(16):3101-3121.
52. Rogan, J., J. Franklin, D. Stow, L. Levien, and C. Fischer, 2001. Toward operational monitoring of forest cover change in California using multitemporal remote sensing data, Proceedings of the International Geoscience and Remote Sensing Symposium (IGARRS, 01), 09-13 July, Sydney, Australia, CD-ROM.
53. Rogan, J., J. Franklin, and D. Roberts, 2002a. A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery, Remote Sensing of Environment, 80(1):143-156.
54. Rogan, J., J. Miller, D. Stow, J. Franklin, L. Levien, and C. Fischer, 2002b. Operational detection of changes in forest and shrub cover in California using multitemporal Landsat data, Proceedings of RS2002, 08-12 April, San Diego, California, CD-ROM.
55. Scott, A., and E. Soja, (editors), 1998. The City: Los Angeles and Urban Theory at the End of the Twentieth Century, University of California Press, Los Angeles, California, 492 p.
56. Seto, K.C., C. Woodcock, C. Song, X. Huang, J. Lu, and R. Kaufmann, 2002. Monitoring land-use change in the Pearl River Delta using Landsat TM, International Journal of Remote Sensing, 23(10):1985-2004.
57. Singh, A., 1989. Digital change detection techniques using remotely sensed data, International Journal of Remote Sensing, 10(6):989-1003.
58. Skole, D.L., and C. Tucker, 1993. Tropical deforestation and habitat fragmentation in the Amazon: Satellite data from 1978 and 1988, Science, 260:1905-1910.
59. Song, C., C. Woodcock, K. Seto, M.P. Lenney, and S. Macomber, 2001. Classification and change detection using Landsat TM data: When and how to correct atmospheric effects, Remote Sensing of Environment, 75:230-244.
60. Stephenson, J.R., and G. Calcarone, 1999. Southern California Mountains and Foothills Assessment: Habitat and Species Conservation Issues, General Technical Report GTR-PSW-172, Pacific Southwest Research Station, Forest Service, U.S. Department of Agriculture, Albany, California, 402 p.
61. Stow, D., 1995. Monitoring ecosystem response to global change: Multitemporal remote sensing analyses, Anticipated Effects of a Changing Global Environment in Mediterranean Type Ecosystems (J. Moreno and W. Oechel, editors), Springer-Verlag, New York, N.Y., pp. 254-286.
62. Stow, D.A., L. Tinney, and J. Estes, 1980. Deriving land use/land cover change statistics from Landsat: A study of prime agricultural land, Proceedings of the 14th International Symposium on Remote Sensing of the Environment, Ann Arbor, (ERIM), pp. 1227-1237.
63. Stow, D., D. Collins, and D. McKinsey, 1990. Land use change detection based on multi-date imagery from different satellite sensor systems, Geocarto International, 5(3):1-12.
64. Strahler, A.H., J. Franklin, C. Woodcock, and T. Logan, 1981. FOCIS: A Forest Classification and Inventory System Using Landsat and Digital Terrain Data, Report NFAP/255, Nationwide Forestry Application Program, U.S. Department of Agriculture, Houston, Texas, 60 p.
65. Trietz, P., and P. Howarth, 2000. Integrating spectral, spatial and terrain variables for forest ecosystem classification, Photogrammetric Engineering & Remote Sensing, 66(3):305-317.
66. USFS, 2001. CaIVeg Geobook, USDA Forest Service, Region 5 Remote Sensing Lab, Sacramento, California, CD-ROM.
67. Vogelmann, J.E., T. Sohl, and S. Howard, 1998. Regional characterization of land cover using multiple sources of data, Photogrammetric Engineering & Remote Sensing, 64(1):45-47.