Integrating high resolution remote sensing, GIS and fuzzy set theory for identifying susceptibility areas of forest insect infestations

International Journal of Remote Sensing

Volumn 26, Issue 21, 2005, Pages 4809-4828

  Bone, C ^a   Dragicevic, S ^a   Roberts, A ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY SETS; GEOGRAPHIC INFORMATION SYSTEMS; INSECT CONTROL; MEMBERSHIP FUNCTIONS; VEGETATION;

FOREST INSECT INFESTATION; GEOSPATIAL DATASET; HIGH RESOLUTION REMOTE SENSING;

REMOTE SENSING;

DATA SET; DIGITAL MAPPING; FORESTRY; FUZZY MATHEMATICS; GIS; IMAGE ANALYSIS; NUMERICAL MODEL; REMOTE SENSING;

COLEOPTERA; DENDROCTONUS PONDEROSAE; INSECTA; PINUS MUGO;

EID: 33745082857     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431160500239180     Document Type: Review

References (45)

1. Amman, G. D. and Baker, B. H. (1972) Mountain pine beetle influence on lodgepole pine stand structure. Journal of Forestry, 70, pp. 204-209.
2. Bezdek, J. C. and Ehrlich, R. and Full, W. (1984) FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences, 10, pp. 191-203.
3. Brandtberg, T. (2002) Individual tree-based species classification in high spatial resolution aerial images of forests using fuzzy sets. Fuzzy Sets and Systems, 132, pp. 371-387.
4. Brown, D. G. (1998) Mapping historical forest types in Baraga County Michigan, USA as fuzzy sets. Plant Ecology, 134, pp. 97-111.
5. Burrough, P. A. (1989) Fuzzy mathematical methods for soil survey and land evaluation. Journal of Soil Science, 40, pp. 477-492.
6. Burrough, P. A.(1996) Natural objects with intermediate boundaries.. In Geographic Objects with Intermediate Boundaries. (pp. 177-188). London: Taylor and Francis.
7. Burrough, P. A. and MacMillan, R. A. and Deursen, W. V. (1992) Fuzzy classification methods for determining land suitability from soil profile observations and topography. Journal of Soil Science, 43, pp. 193-210.
8. Cevik, E. and Topal, T. (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental Geology, 44, pp. 949-962.
9. Davidson, D. A. and Theocharopoulos, S. P. and Bloksma, R. J. (1994) A land evaluation project in Greece using GIS and based on Boolean and fuzzy set methodologies. International Journal of Geographical Information Systems, 8, pp. 369-384.
10. Dragicevic, S. and Marceau, D. J. (2000) A fuzzy set approach for modelling time in GIS. International Journal of Geographical Information Science, 14, pp. 225-245.
11. Fisher, P.(1999). In Models of Uncertainty in Spatial Data. Geographic Information Systems: Principles and Applications.. New York: John Wiley.
12. Foody, G. M. and Atkinson, P.(2002). In Uncertainty in Remote Sensing and GIS.. Chichester: John Wiley.
13. Franklin, S. E. and Wulder, M. A. and Skakun, R. S. and Carroll, A. L. (2003) Mountain pine beetle red-attack damage classification using stratified Landsat TM data in British Columbia, Canada. Photogrammetric Engineering and Remote Sensing, 69, pp. 283-288.
14. Gimbarzevsky, P. and Dawson, A. F. and Sickle, G. A. V.(1992) Assessment of aerial photographs and multi-spectral scanner imagery for measuring mountain pine beetle damage.. In. Information Report BC-X-333, Canadian Forestry Centre, Victoria, British Columbia
15. Hindmarch, T. D. and Reid, M. L. (2001) Forest thinning affects reproduction in pine engravers (Coleoptera: Scolytidae) breeding in felled lodgepole pine trees. Environmental Entomology, 30, pp. 919-924.
16. Lowell, K. (1996) Modelling heterogeneity and change in natural forests. Geomatica, 50, pp. 425-440.
17. Lowell, K. and Gold, C. (1995) Using a fuzzy surface-based cartographic representation to decrease digitizing efforts to natural phenomena. Cartography and Geography Information Systems, 22, pp. 222-231.
18. Lucieer, A. and Kraak, M.-J. (2004) Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty. International Journal of Geographical Information Science, 18, pp. 491-512.
19. Luther, J. E. and Franklin, S. E. and Hudak, J. and Meades, J. P. (1997) Forecasting the susceptibility and vulnerability of balsam fir stands to insect defoliation with Landsat Thematic Mapper data. Remote Sensing of Environment, 59, pp. 77-91.
20. Mata, S. A. and Schmid, J. M. and Oakes, R. D.(2003) Growth of lodgepole pine stands and its relation to mountain pine beetle susceptibility.. In. Research Paper RMRS-RP-42, USDA Forest Service, Washington, DC
21. McClave, J. T. and Sincich, T.(2000). In Statistics.. Upper Saddle River NJ: Prentice-Hall.
22. Mitchell, R. G. and Preisler, H. K. (1991) Analysis of spatial patterns of lodgepole pine attacked by outbreak populations of the mountain pine-beetle. Forest Science, 37, pp. 1390-1408.
23. Moeck, H. A. and Simmons, C. S. (1991) Primary attraction of mountain pine beetle, Dendroctonus poderosae Hopk. (Coleoptera: Scolytidae), to bolts of lodgepole pine. Canadian Entomologist, 123, pp. 299-304.
24. Perkins, D. L. and Roberts, D. W. (2003) Predictive models of whitebark pine mortality from mountain pine beetle. Forest Ecology and Management, 174, pp. 495-510.
25. Preisler, H. K. and Mitchell, R. G. (1993) Colonization patterns of the mountain pine beetle in thinned and unthinned lodgepole pine stands. Forest Science, 39, pp. 528-545.
26. Riel, W. G. and Fall, A. and Shore, T. L. and Safranyik, L.(2004) A spatio-temporal simulation of mountain pine beetle impacts on the landscape.. In Mountain Pine Beetle Symposium: Challenges and Solutions. (pp. 106-113). Natural Resources Canada, Canadian Forestry Services, Pacific Forestry Centre, Information Report BC-X-399, Kelowna, British Columbia
27. Roberts, A. and Dragicevic, S. and Northrup, J. and Wolf, S. and Li, Y. and Coburn, C.(2003) Mountain pine beetle detection and monitoring: remote sensing evaluations.. In. BC Forest Innovation Investment Operational Research Report with reference to Recipient Agreement R2003-0205. Available online at: Fii.forrex.org/reports/R2003-205.pdf (accessed 6 September 2005)
28. Robinson, V. B. (2003) A perspective on the fundamentals of fuzzy sets and their use in geographical information systems. Transactions in GIS, 7, pp. 3-30.
29. Safranyik, L.(1988) Mountain pine beetle: Biology overview.. In Symposium on the Management of Lodgepole Pine to Minimize Losses to the Mountain Pine Beetle.. Kelispell, USA, 9-13 July 2003, pp. 9-13 (US Department of Agriculture)
30. Safranyik, L. and Barclay, H. and Thomson, A. and Riel, W. G.(1999a) A population dynamics model for the mountain pine beetle, Dendroctonus Ponderosae Hopk.. In. Coleoptera: Scolytidae. Information Report BC-X-326, Pacific Forestry Centre, Victoria, British Columbia
31. Safranyik, L. and Shore, T. L. and Linton, D. A. (1999b) Attack by bark beetles (Coleoptera: Scolytidae) following spacing of mature lodgepole fine (Pinaceae) stands. Canadian Entomologist, 131, pp. 671-685.
32. Safranyik, L. and Silversides, L. H. and McMullen, L. H. and Linton, D. A. (1989) An empirical approach to modeling the local dispersal of the mountain pine beetle (Dendroctonus ponderosae Hopk.) (Col., Scolytidae) in relation to sources of attraction, wind direction and speed. Journal of Applied Entomology, 108, pp. 498-511.
33. Sarkar, S. and Kanungo, D. P. (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Engineering and Remote Sensing, 70, pp. 617-625.
34. Shore, T. L. and Safranyik, L.(1992) Susceptibility and risk rating systems for the mountain pine beetle in lodgepole pine stands.. In. Information Report BC-X-336, Pacific Forestry Centre, Victoria, British Columbia
35. Shore, T. L. and Safranyik, L. and Lemieux, J. P. (2000) Susceptibility of lodgepole pine stands to the mountain pine beetle: Testing of a rating system. Canadian Journal of Forest Research - Revue Canadienne De Recherche Forestiere, 30, pp. 44-49.
36. Skakun, R. S. and Wulder, M. A. and Franklin, S. E. (2003) Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage. Remote Sensing of Environment, 86, pp. 433-443.
37. Thomson, A.(1991) Simulation of mountain pine beetle (Dendroctonus ponderosae Hopkins) spread and control in British Columbia.. In. Information Report BC-X-329, Pacific Forestry Centre, Victoria, British Columbia
38. Van Westen, C. J. (2000) The modelling of landslide hazards using GIS. Surveys in Geophysics, 21, pp. 241-255.
39. Wang, F. (1990) Improving remote-sensing image analysis through fuzzy information representation. Photogrammetric Engineering and Remote Sensing, 56, pp. 1163-1169.
40. Wang, F. and Hall, G. B. (1996) Fuzzy representation of geographical boundaries in GIS. International Journal of Geographical Information Systems, 10, pp. 573-590.
41. Wulder, M. A. and Dymond, D.(2004) Remote sensing technologies for mountain pine beetle survey.. In Mountain Pine Beetle Symposium. (pp. 146-153). Natural Resources Canada, Canadian Forestry Services, Pacific Forestry Centre, Information Report BC-X-399, Kelowna, British Columbia
42. Zadeh, L. A. (1965) Fuzzy sets. Information and Control, 8, pp. 338-353.
43. Zadeh, L. A. (1972) A fuzzy-set-theoretic interpretation of linguistic hedges. Journal of Cybernetics, 2, pp. 4-34.
44. Zhang, J. and Foody, G. M. (1998) A fuzzy classification method of sub-urban land cover from remotely sensed imagery. International Journal of Remote Sensing, 19, pp. 2721-2738.
45. Zhang, J. and Goodchild, M. F.(2002). In Uncertainty in Geographical Information.. London: Taylor and Francis.