Mapping regional distribution of a single tree species: Whitebark pine in the Greater Yellowstone Ecosystem

Sensors

Volumn 8, Issue 8, 2008, Pages 4983-4994

  Landenburger, Lisa ^a   Lawrence, Rick L ^a   Podruzny, Shannon ^b   Schwartz, Charles C ^b  

^a MONTANA STATE UNIVERSITY   (United States)

^b U S GEOLOGICAL SURVEY   (United States)

Author keywords

Boosting; Classification trees; Landsat; Remote sensing; See5; Yellowstone

Indexed keywords

BOOSTING; CLASSIFICATION TREES; LANDSAT; SEE5; YELLOWSTONES;

GEOGRAPHICAL DISTRIBUTION; MAPPING; REMOTE SENSING; SATELLITE IMAGERY; TREES (MATHEMATICS);

POPULATION DISTRIBUTION;

EID: 50849123033     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s8084983     Document Type: Article

References (23)

1. Lanner, R.M.; Gilbert, B.K. Nutritive value of whitebark pine seeds, and the questions of their variable dormancy. In Proceedings international workshop on subalpine stone pines and their environment: the status of our knowledge. Schmidt, W.C., Holtmeier, F.K., Eds.; U.S. Forest Service General Technical Report INT-GTR-309, 1994; pp. 206-211.
2. Tomback, D.F.; Kendall, K.C. Biodiversity losses: the downward spiral. In Whitebark pine communities: ecology and restoration. Tomback, D.F., Arno, S.F., Keane, R.E., Eds.; Island Press: Washington, DC, 2001; pp. 243-262.
3. th Edition; John Wiley & Sons, Inc.: New York, 2004; p.574.
4. Redmond, R.L.; Winne, J.C.; Fisher, C. Existing vegetation and land cover of west-central Montana: Final Report. Unpublished report submitted to U.S. Department of Agriculture Forest Service; Wildlife Spatial Analysis Lab, University of Montana, Missoula, Montana, 2001.
5. Keane, R.E.; Morgan, P.; Menakis, J.P. Landscape assessment of the decline of whitebark pine (Pinus albicaulis) in the Bob Marshall Wilderness Complex, Montana, USA. Northwest Sci. 1994, 68, 213-229.
6. Huang, C.; Wylie, B.; Homer, C.; Yang, L.; Zylstra, G. Derivation of a tasseled cap transformation based on Landsat 7 at-satellite reflectance. Int. J. Remote Sen. 2002, 23, 1741-1748.
7. Lawrence, R.; Wright, A. Rule-based classification systems using classification and regression tree (CART) analysis. Photogramm. Eng. Rem. Sens. 2001, 67, 1137-1142.
8. Brieman, L.; Friedman, J.H.; Olshen, R.A.; Stone, C.J. In Classification and Regression Trees. Wadsworth International Group: Belmont, California, 1984.
9. Huang, C.; Yang, L.; Wylie, B.; Homer, C. A strategy for estimating tree canopy density using Landsat 7 ETM+ and high resolution images over large areas. In Proceedings of the Third International Conference on Geospatial Information in Agriculture and Forestry, Denver, Colorado, 5-7 November, 2001; CD-ROM 1 disk.
10. Quinlan, J.R. In C4.5 programs for machine learning; Morgan Kaufmann Publishers: San Mateo, California, 1993.
11. Freund, Y.; Schapire, R.E. A short introduction to boosting. J. Jpn. Soc. Artif. Intel. 1999, 14, 771-780.
12. Schapire, R.E. Theoretical views of boosting and applications. In Proceedings of algorithmic learning theory, 10th International Conference, Tokyo, Japan, 1999.
13. Lawrence, R.; Burn, A.; Powell, S.; Zambon, M. Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote Sens Environ. 2004, 90, 331-336.
14. Parmenter, A.W.; Hansen, A.; Kennedy, R.; Cohen, W.; Langner, U.; Lawrence, R.; Maxwell, B.; Gallant, A.; Aspinall, R. Land use and land cover change in the Greater Yellowstone Ecosystem: 1975-1995. Ecol. Appl. 2003, 13, 687-703.
15. Cromley, R.G. A comparison of optimal classification strategies for choroplethic displays of spatially aggregated data. Int. J. Geogr. Inf. Syst. 1996, 10, 405-424.
16. Lawrence, R.L.; Hurst, R.; Weaver, T.; Aspinall, R. Mapping prairie pothole communities with multitemporal IKONOS satellite imagery. Photogramm. Eng. Rem. Sens. 2006, 72, 169-174.
17. Arno, S.F.; Hoff, R.J. Silvics of whitebark pine (Pinus albicaulis). U.S. Department of Agriculture, General Technical Report INT-253: Ogden, Utah, 1989.
18. Hansen-Bristow, K.; Montagne, C.; Schmid, G. Geology, geomorphology, and soils within whitebark pine ecosystems. In Proceedings -Symposium on whitebark pine ecosystems: ecology and management of a high-mountain resource. U.S. Department of Agriculture, General Technical Report INT-270: Ogden, Utah, 1990; pp. 62-71.
19. Boyce, M.S.; Blanchard, B.M.; Knight, R.R.; Servheen, C. Population viability for grizzly bears: a critical review. Int. Assoc. Bear Res. Manage Monogr. Ser. 4, 2001
20. U.S. Fish and Wildlife Service (USFWS). Grizzly bear recovery plan. U.S. Fish and Wildlife Service: Missoula, Montana, USA, 1993.
21. Weaver J.; Escano, R.; Mattson, D.; Puchlerz, T.; Despain, D. A cumulative effects model for grizzly bear management in the Yellowstone Ecosystem. In Proceedings-Grizzly Bear Habitat Symposium, General Technical Report, 1985 Apr 30-1985 May 2; Contraras, G.P., Evans, K.E., Eds.; U.S.D.A, Forest Service, Intermountain Research Station: Missoula, MT, Ogden, UT: 1986; pp. 234-246.
22. Dixon, B.G. Cumulative effects modeling for grizzly bears in the Greater Yellowstone Ecosystem. M.S. Thesis, Montana State University, Bozeman, Montana, USA, 1997.
23. U.S. Fish and Wildlife Service (USFWS). Final Conservation Strategy for the grizzly bear in the Yellowstone Ecosystem. U.S. Fish and Wildlife Service: Missoula, Montana, USA, 2003.