Detecting the locations of brazilian pepper trees in the everglades with a hyperspectral sensor

Weed Technology

Volumn 18, Issue 2, 2004, Pages 437-442

  Lass, Lawrence W ^b   Prather, Timothy S ^a  

^a UNIVERSITY OF IDAHO   (United States)

^b NONE

Author keywords

Hyperspectral sensor; Imaging spectrometer; Invasive plant detection; Whiskbroom scanner

Indexed keywords

SCHINUS TEREBINTHIFOLIUS;

EID: 15744396477     PISSN: 0890037X     EISSN: None     Source Type: Journal    
DOI: 10.1614/WT-03-174R     Document Type: Article

References (24)

1. Campbell, J. B. 2002. Hyperspectral remote sensing. In J. B. Campbell, ed. Introduction to Remote Sensing. 3rd ed. New York: Gulford. Pp. 400 415.
2. Card, D. H. 1982. Using known map category marginal frequencies to improve estimates of thematic map accuracy. Photogramm. Eng. Remote Sens. 48:431-39.
3. Chang, C. I. 1999. Least squares error theory for linear mixing problems with mixed pixel classification for hyperspectral imagery. Recent Res. Dev. Opt. Eng. 2:214-268.
4. Chang, C. I. and H. Ren. 2000. An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 38:10441063.
5. Chang, C. I., X. Zhao, M.L.G. Althouse, and J. J. Pan. 1998. A posteriori least squares orthogonal subspace projection approach to mixed pixel classification in hyperspectral images. IEEE Trans. Geosci. Remote Sens. 36:898-912.
6. Chavez, P. S. 1996. Image-based atmospheric corrections revisited and improved. Photogramm. Eng. Remote Sens. 62:1025-1036.
7. Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37:35-46.
8. Congalton, R. G., R. Oderwald, and R. A. Mead. 1983. Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogramm. Eng. Remote Sens. 49:1671-78.
9. Ewel, J. J., D. S. Ojima, D. A. Karl, and W. F. DeBusk. 1982. Schinus in Successional Ecosystems of Everglades National Park. Everglades National Park, FL: South Florida Research Center Rep. T-676. 141 p.
10. Ferriter, A. 1997. Brazilian Pepper Management Plan for Florida: Web page: http://www.fleppc.org. Accessed: April 15, 2004.
11. Forster, B. C. 1984. Derivation of atmospheric correction procedures for LANDSAT MSS with particular reference to urban data. Int. J. Remote Sens. 5:799-817.
12. Goodchild, M. F. and S. Gopal, eds. 1989. Accuracy of Spatial Databases. London: Taylor and Francis. P. 309.
13. Kruse, F. A., A. B. Lefkoff, J. W. Boardman, K. B. Hiebedrecht, A. T. Shapiro, P. J. Barloom, and A.F.H. Goetz. 1993. The spatial image processing
14. system (SIPS)-interactive"visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 44:145-163.
15. Lass, L. W., B. Shafii, W. J. Price, and D. C. Thill. 2000. Assessing agreement in multispectral images of yellow starthistle (Centaurea solstitialis) with ground truth data using a Bayesian methodology. Weed Technol. 14:539 544.
16. Lass, L. W., D. C. Thill, B. Shafii, and T. S. Prather. 2002. Detecting spotted knapweed (Centaurea maculosa) with hyperspectral remote sensing technology. Weed Technol. 16:426-432.
17. Mack, A. 1992. Vegetation analysis of a hardwood hammock in Dade County, Florida: changes since 1940. Fl. Sci. 55:258-262.
18. Morton, J. F. 1978. Brazilian pepper-its impact on people, and the environment. Econ. Bot. 32:353-359.
19. Mytinger, L. and G. B. Williamson. 1987. The invasion of Schinus into saline communities of Everglades National Park. Fl. Sci. 50:7-12.
20. Ren, H. and C.-I. Chang. 1998. A computer-aided detection and classification method of concealed targets in hyperspectral imagery. In International Symposium of Geoscience and Remote Sensing '98; Seattle, WA. Pp. 1016-1018.
21. Settle, J. J. and N. A. Drake. 1993. Linear mixing and the estimation of ground proportions. Int. J. Remote Sens. 14:1159-1177.
22. Shafii, B., W. J. Price, L. W. Lass, and D. C. Thill. 1998. Assessing variability of agreement measures in remote sensing using a Bayesian approach. Appl. Stat. Agric. 43-54.
23. Shimabukuro, Y. E. and J. A. Smith. 1991. The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. IEEE Trans. Geosci. Remote Sens. 29:16-20.
24. Sohn, Y. and R. M. McCoy. 1997. Mapping desert shrub rangeland using spectra unmixing and modeling spectral mixtures with TM data. Photogramm. Eng. Remote Sens. 63:707-716.