Remote Sensing Applied to Ecosystem Management

  • Henry M. Lachowski
  • Vicky C. Johnson


Ecosystems are complex and dynamic; often, they support many diverse and competing demands. (1996) states that “in simple terms, the ecosystem concept states that the earth operates as a series of interrelated systems within which all components are linked, so that a change in any one component may bring about some corresponding changes in other components and in the operation of the whole system.” Managing ecosystems requires that we look at numerous phenomena and deal with information and analyses at multiple scales, whether geographic or temporal. The human dimension also must not be neglected. Effective management of ecosystems requires access to current and consistent geospatial information that can be shared by resource managers and the public. Geospatial information describing our land and natural resources comes from many sources and is most effective when stored in a geospatial database and used in a geographic information system (GIS). Information on the location and condition of current vegetation patterns is one of the key elements in ecosystem management. Remotely sensed data are primary sources for mapping vegetation. Furthermore, comparing images acquired several days, or several years, apart can assist in determining changes over time.


Geographic Information System Remote Sensing Advance Very High Resolution Radiometer Advance Very High Resolution Radiometer Ecosystem Management 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Achard, F.; Estreguil, C. 1995. Forest classification of Southeast Asia using NOAA AVHRR data. Remote Sens. Environ.54(3): 198–208.CrossRefGoogle Scholar
  2. Agee, J. K.; Johnson, D. R. 1988. Introduction to ecosystem management. In: Agee, J. K.; Johnson, D. R., eds. Ecosystem management for parks and wilderness. Seattle: University of Washington Press: 3–14.Google Scholar
  3. Atkinson, P. M.; Curran, P. J. 1997. Choosing an appropriate spatial resolution for remote sensing investigation. Photogramm. Eng. Remote Sensing63(12): 1345–1351.Google Scholar
  4. Avers, P. E.; Cleland, D. T.; McNab, W. H.; Jensen, M. E.; Bailey, R. G.; King, T.; Goudey, C. B. 1993. National hierarchical framework of ecological units. Washington DC: U.S. Dept. Agric., For. Serv.Google Scholar
  5. Avery, T. E.; Berlin, G. L. 1992. Fundamentals of remote sensing and airphoto interpretation, 5th ed. Minneapolis, MN: Burgess Publishing Co.Google Scholar
  6. Bailey, R. G. 1996. Ecosystem geography. New York: Springer-Verlag.CrossRefGoogle Scholar
  7. Bobbe, T.; McKean, J. 1995. Evaluation of a digital camera system for natural resource management. Earth Observation Magazine:45–48.Google Scholar
  8. Bobbe, T.; Reed, D.; Schramek, J. 1993. Georeferenced airborne video imagery. J. For.91(8):34–37.Google Scholar
  9. Boudreau, S. L.; Maus, P. A. 1996. An ecological approach to assess vegetation change after large scale fires on the Payette National Forest. In: Greer, J. D., ed. Proceedings of the sixth biennial Forest Service Conference on remote sensing. Denver, CO: Amer. Soc. Photogramm. Remote Sensing: 330–339.Google Scholar
  10. Brand, D. G. 2001. Criteria and indicators for the conservation and sustainable management of forests: progress to date and future directions. New Zealand J. For. An online version of the paper is located at: Scholar
  11. Campbell, J. B. 1996. Introduction to remote sensing, 2nd ed. New York: Guilford Press.Google Scholar
  12. Cohen, W. B.; Spies, T. A. 1992. Estimating structural attributes of Douglas-fir/western hemlock forest stands from Landsat and SPOT imagery. Remote Sens. Environ.41(1): 1–17.CrossRefGoogle Scholar
  13. Collins, J. B.; Woodcock, C. E. 1997. An assessment of several linear change detection techniques for mapping forest mortality using multitemporal Landsat TM data. Remote Sens. Environ.56(l):66–77.Google Scholar
  14. Congalton, R.; Green, K. 1998. Assessing the accuracy of remotely sensed data, principles and practices. Boca Raton, FL: Lewis Publishers.CrossRefGoogle Scholar
  15. Congalton, R.; Green, K.; Teply, J. 1993. Mapping old growth forests on national forest and park lands in the Pacific Northwest from remotely sensed data. Photogramm. Eng. Remote Sensing59(4):529–535.Google Scholar
  16. Cristofani, A. 1996. The earth in balance-maintaining Brazil’s biodiversity. GPS World7(6):20–30.Google Scholar
  17. Edwards, T. C.; Moisen, G. G.; Cutler, D. R. 1998. Assessing map accuracy in a remotely sensed, ecoregion-scale cover map. Remote Sens. Environ.63(l):73–83.CrossRefGoogle Scholar
  18. Federal Geographic Data Committee. 1997a. Content Standard for digital geospatial data. Washington, DC: The Committee.Google Scholar
  19. Federal Geographic Data Committee. 1997b. National vegetation classification system. FGDC-STD-OO5. Reston, VA: The Committee.Google Scholar
  20. Flood, M.; Gutelius, B. 1997. Commercial implications of topographie terrain mapping using scanning airborne laser radar. Photogramm. Eng. Remote Sensing 63(4):327–329, 363-366.Google Scholar
  21. French, W. D., publisher. 1997. Photogramm. Eng. Remote Sensing 63(7).Google Scholar
  22. GIS Core Data Team. 1997. Recommended core data Standards for GIS application, report to the Ecosystem Management Corporate Team and Inter-Regional Ecosystem Management Coordinating Group. Washington, DC: U.S. Department of Agriculture, Forest-Service.Google Scholar
  23. Grumbine, R. E. 1994. What is ecosystem management? Conserv. Biol.8:27–39.CrossRefGoogle Scholar
  24. Hardwick, P.; Lachowski, H.; Griffith, R.; Parsons, A. 1998. Burned area emergency rehabilitation project: an example of successful technology transfer. In: Greer, J. D., ed. Proceedings of the seventh biennial Forest Service conference on remote sensing. Nassau Bay, TX: Amer. Soc. Photogramm. Remote Sensing: 62–71.Google Scholar
  25. Hoffer, R.; Maxwell, S.; Ochis, H. 1995. Use of radar for forestry applications. Internal report. Salt Lake City, UT: U.S. Dept. Agric., For. Serv., Remote Sensing Applications Center.Google Scholar
  26. Imhoff, M. L.; Sisk, T. D.; Milne, A.; Morgan, G.; Orr, T. 1997. Remotely sensed indicators of habitat heterogeneity: use of synthetic aperture radar in mapping vegetation structure and bird habitat. Remote Sens. Environ. 60(3):217–227.CrossRefGoogle Scholar
  27. Jensen, J.R. 1996. Introductory digital image processing. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  28. Kasischke, E. S.; French, N. H. F. 1995. Locating and estimating the areal extent of wildfires in Alaska Boreal forests using multiple-season AVHRR NDVI composite data. Remote Sens. Environ.51(2):263–275.CrossRefGoogle Scholar
  29. Kasischke, E. S.; Melack, J. M.; Dobson, M. C. 1997. The use of imaging radars for ecological assessments. Remote Sens. Environ.59(2): 141–156.CrossRefGoogle Scholar
  30. King, D. J. 1995. Airborne multispectral digital camera and video sensors: a critical review of system designs and applications. Can. J. Remote Sensing21(3):245–273.Google Scholar
  31. Knapp, K. A.; Disperati, A.; Sheng, Z. J. 1998. Evaluation and integration of a digital camera system into forest health protection programs in the Western United States, Southern Brazil, and Anhui Province, China. In: Greer, J. D., ed. Proceedings of the seventh biennial Forest Service conference on remote sensing. Nassau Bay, TX: Amer. Soc. Photogramm. Remote Sensing: 257–268.Google Scholar
  32. Lachowski, H.; Wirth, T.; Maus, P.; Powell, J.; Suzuki, K.; McNamara, J.; Riordan, P.; Brahman, R. 1996. Monitoring aspen decline using remote sensing and GIS, Gravelly Mountain landscape, Montana. Internal Project Report. Salt Lake City, UT: U.S. Dept. Agric., For. Serv. Remote Sensing Applications Center.Google Scholar
  33. Lachowski, H.; Hardwick, P.; Griffith, R.; Parsons, A.; Warbington, R. 1997. Faster, better data for burned watersheds needing emergency rehab. J. For. 95(6): 4–8.Google Scholar
  34. Lambin, E. F. 1996. Change detection at multiple temporal scales: seasonal and annual variations in landscape variables. Photogramm. Eng. Remote Sensing 62(8):931–938.Google Scholar
  35. Lefsky, M. A.; Harding, D.; Cohen, W. B.; Parker, G.; Shugurt, H. H. 1999. Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA. Remote Sens. Environ.67(l):83–98.CrossRefGoogle Scholar
  36. Lillesand, T. M.; Kiefer. R. W. 1994. Remote sensing and image interpretation, 3rd ed. New York: John Wiley & Sons.Google Scholar
  37. Loveland, T. R.; Shaw, D. M. 1996. Multiresolution landscape characterization: building collaborative partnerships. In: Scott, J. M.; Tear, T. H.; Davis, F. W., eds. Gap analysis: a landscape approach to biodiversity planning. Bethesda, MD: Amer. Soc. Photogramm. Remote Sensing: 83–89.Google Scholar
  38. Lunetta, R.; Lyon, J. G.; Guindon, B.; Elvidge, C. D. 1998. North American landscape characterization dataset development and data fusion issues. Photogramm. Eng. Remote Sensing 64(8):821–829.Google Scholar
  39. Lyon, J. G.; Yuan, D.; Lunetta, R. S.; Elvidge, C. D. 1998. A change detection experiment using vegetation indices. Photogramm. Eng. Remote Sensing64(2): 143–150.Google Scholar
  40. Malone, C. R. 1998. The federal ecosystem management initiative in the United States. In: Lemons, J.; Good-land, R.; Westra, L., eds. Environmental stability: case studies on the prospects of science and ethics. Dordrecht, The Netherlands: Kluwer Academic Publishers.Google Scholar
  41. Means, J. E.; Acker, S. A.; Harding, D. J.; Blair, J. B.; Lefsky, M. A.; Cohen, W. B.; Harmon, M. E.; McKee, W. A. 1999. Use of large-footprint scanning airborne lidar to estimate forest stand characteristics in the western Cascades of Oregon. Remote Sens. Environ. 67(3):298–308.CrossRefGoogle Scholar
  42. Michener, W. K.; Houhoulis, P. F. 1997. Detection of vegetation changes associated with extensive flooding in a forested ecosystem. Photogramm. Eng. Remote Sensing 63(12):1363–1374.Google Scholar
  43. Montreal Process Working Group. 1994. Montreal Process Working Group[on line]. Available: (http://www.dpie, Scholar
  44. Muchoney, D. M.; Haack, B. N. 1994. Change detection for monitoring forest defoliation. Photogramm. Eng. Remote Sensing 60(10):1243–1251.Google Scholar
  45. Nelson, R. F. 1983. Detecting forest canopy change due to insect activity using Landsat MSS. Photogramm. Eng. Remote Sensing 49(9):1303–1314.Google Scholar
  46. Powell, D. S.; Faulkner, J. L.; Darr, D. R.; Zhu, Z.; Mac-Cleery, D. W. 1993. Forest resources of the United States, 1992. Gen. Tech. Rep. RM-234. Fort Collins, CO: U.S. Dept. Agric., For. Serv., Rocky Mt. For. Range Exp. Sta.Google Scholar
  47. Pozo, D.; Olmo, F. J.; Alados-Arboledas, L. 1997. Fire detection and growth monitoring using a multitemporal technique on AVHRR mid-infrared and thermal channels. Remote Sens. Environ.60(2): 11–120.CrossRefGoogle Scholar
  48. Ramsey, R. D.; Falconer, A.; Jensen, J. R. 1995. The relationship between NOAA-AVHRR NDVI and ecoregions in Utah. Remote Sens. Environ.53(3):188–198.CrossRefGoogle Scholar
  49. Rosenfeld, C. L.; Thatcher, T. 1994. On the move: GPS monitors Alaska’s Surging Bering Glacier. GPS World 5(11):18–24.Google Scholar
  50. Slaymaker, D. M.; Jones, K. M. L.; Griffin, C. R.; Finn, J. T. 1996. Mapping deciduous forests in southern New England using aerial videography and hyperclustered multi-temporal Landsat TM imagery. In: Scott, J. M.; Tear, T. H.; Davis, F. W., eds. Gap analysis: a landscape approach to biodiversity planning. Bethesda, MD: Amer. Soc. Photogramm. Remote Sensing: 87–101.Google Scholar
  51. Stehman, S. V.; Czaplewski, R. L. 1998. Design and analysis for thematic map accuracy assessment: fundamental principles. Remote Sens. Environ.64(3): 331–344.CrossRefGoogle Scholar
  52. Stoms, D.; Davis, F.; Cogan, C.; Cassidy, K. 1994. Assessing land cover map accuracy for GAP analysis [on line]. Available: ( Publications/Publications.htm).Google Scholar
  53. Thomas, J. W. 1999. Guiding principles and workshop overviews. In: Sexton, W.T.; Szaro, R.C.; Johnson, N.; Malk, A.J., eds. Ecological Stewardship: A Common Reference for Ecosystem Management. Oxford, UK: Elsevier Science.Google Scholar
  54. USDA Forest Service. 1995. Burned area emergency rehabilitation handbook.GPO FSH 2509.13. Washington DC: Govt. Print. Off.Google Scholar
  55. USDA Forest Service Regional Ecology Group. 1981. CALVEG: a classification of California vegetation. San Francisco: U.S. Dept. Agric., For. Serv., Region Five.Google Scholar
  56. USDA Forest Service Remote Sensing Advisory Team. 1998. Implementation of remote sensing for ecosystem management. Salt Lake City, UT: U.S. Dept. Agric., For. Serv., Remote Sensing Applications Center.Google Scholar
  57. Varner, V.; Lachowski, H.; Lake, L.; Anderson, B. 1998. Mapping and monitoring noxious weeds using remote sensing. In: Greer, J. D., ed. Proceedings of the seventh biennial Forest Service conference on remote sensing. Nassau Bay, TX: Amer. Soc. Photogramm. Remote Sensing: 182–193.Google Scholar
  58. Viedma, D.; Melia, J.; Segarra, D.; Garcia-Haro, J. 1997. Modeling rates of ecosystem recovery after fire using Landsat TM data. Remote Sens. Environ.61(3): 383–398.CrossRefGoogle Scholar
  59. Wirth, T.; Maus, P.; Lachowski, H.; Taylor, J.; Linquist, E. 1994. Investigative report on digital orthophoto quadrangles using data from the Superior National Forest. Internal report. Salt Lake City, UT: U.S. Dept. Agric., For. Serv., Remote Sensing Applications Center.Google Scholar
  60. Woodcock, C. E.; Strahler, A. H. 1987. The factor of scale in remote sensing. Remote Sens. Environ. 21: 311–332.CrossRefGoogle Scholar
  61. Woodcock, C. E.; Collins, J. B.; Gopal, S.; Jakabhazy, V. D.; Li, X.; Macomber, S.; Ryherd, S.; Harward, V. J.; Levitan, J.; Wu, Y.; Warbington, R. 1994. Mapping forest vegetation using Landsat TM imagery and a canopy reflectance model. Remote Sens. Environ.50: 240–254.Google Scholar
  62. Zhu, Z. 1994. Forest density mapping in the lower 48 states: a regression procedure. Research Paper SO-280. New Orleans: U.S. Dept. Agric., For. Serv., Southern For. Exp. Sta.Google Scholar
  63. Zhu, Z.; Evans, D. L. 1994. U.S. forest types and predicted percent forest cover from AVHRR data. Photogramm. Eng. Remote Sensing 60(5):525–531.Google Scholar

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© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Henry M. Lachowski
  • Vicky C. Johnson

There are no affiliations available

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