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Detecting the Locations of Brazilian Pepper Trees in the Everglades with a Hyperspectral Sensor

Published online by Cambridge University Press:  20 January 2017

Lawrence W. Lass  and
Timothy S. Prather
Lawrence W. Lass*
Affiliation:
Department of Plant, Soil, and Entomological Sciences, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844-2339
Timothy S. Prather
Affiliation:
Department of Plant, Soil, and Entomological Sciences, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844-2339
*
Corresponding author's E-mail: llass@uidaho.edu
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Abstract

Brazilian pepper is a small evergreen tree that forms dense colonies. It was introduced for horticultural use in the United States in the early 1800s and was widely distributed in Florida in the late 1920s. Previous remote-sensing projects to detect Brazilian pepper achieved moderate success and warranted additional research using a hyperspectral sensor. Detection with remote sensing is desirable because complete access to ground survey crews is not practical. The western half of the Everglades National Park was imaged at a 5-m spatial resolution with a hyperspectral sensor by Earth Search Sciences Inc. of Kalispell, MT, on December 12, 2000, and January 10, 2001. The sensor has 128 channels and spectral resolution between 450 and 2,500 nm. The purpose of this research was to develop spectral reflectance curves for Brazilian pepper and establish the accuracy of classified images. Classified images showed that a hyperspectral sensor could detect a “pure” Brazilian pepper pixel representing the center of an infestation but not “mixed” Brazilian pepper pixels at the sparsely populated edges. To define the sparse populations, images were classified using a spatial buffer (15- to 100-m radius) based on a low–omissional error image. A 25-m buffer reduced the amount of commissional error for Brazilian pepper in mangrove-dominated forest to 8.2% and buttonwood-dominated forest to 0%. Wider buffers did not significantly improve image accuracy when compared with the 25-m buffer distance. Results indicate that removal crews using hyperspectral images will be able to reliably find the colonies of Brazilian pepper but will not be able to use the images to find isolated scattered trees.

Keywords

Brazilian pepper (Schinus terebinthifolius Raddi) # SCITEHyperspectral sensorimaging spectrometerinvasive plant detectionwhiskbroom scannerGPS, global positioning system without differential correctionPLOS, posteriori least-squares orthogonal subspace projectionSAM, spectral angle mapperUSGS-BRD/SERP, United States Geological Survey—Biological Resource Division/Southeast Environmental Research Program
Type
Research
Information
Weed Technology , Volume 18 , Issue 2 , June 2004 , pp. 437 - 442
Copyright
Copyright © Weed Science Society of America 

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Footnotes

∗ Publication 03729 Idaho Agricultural Experiment Station Journal Series.

References

Literature Cited

Campbell, J. B. 2002. Hyperspectral remote sensing. in Campbell, J. B., ed. Introduction to Remote Sensing. 3rd ed. New York: Gulford. pp. 400–415.Google Scholar
Card, D. H. 1982. Using known map category marginal frequencies to improve estimates of thematic map accuracy. Photogramm. Eng. Remote Sens. 48:431–39.Google Scholar
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.Google Scholar
Chang, C. I. and Ren, H. 2000. An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 38:1044–1063.Google Scholar
Chang, C. I., Zhao, X., Althouse, M. L. G., and Pan, J. J. 1998. A posteriori least squares orthogonal subspace projection approach to mixed pixel classification in hyperspectral images. IEEE Trans. Geosci. Remote Sens. 36:898–912.CrossRefGoogle Scholar
Chavez, P. S. 1996. Image-based atmospheric corrections—revisited and improved. Photogramm. Eng. Remote Sens. 62:1025–1036.Google Scholar
Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37:35–46.Google Scholar
Congalton, R. G., Oderwald, R., and Mead, R. A. 1983. Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogramm. Eng. Remote Sens. 49: 1671–78.Google Scholar
Ewel, J. J., Ojima, D. S., Karl, D. A., and DeBusk, W. F. 1982. Schinus in Successional Ecosystems of Everglades National Park. Everglades National Park, FL: South Florida Research Center Rep. T-676. 141 p.Google Scholar
Ferriter, A. 1997. Brazilian Pepper Management Plan for Florida: Web page: http://www.fleppc.org. Accessed: April 15, 2004.Google Scholar
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.Google Scholar
Goodchild, M. F. and Gopal, S. eds. 1989. Accuracy of Spatial Databases. London: Taylor and Francis. P. 309.Google Scholar
Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Hiebedrecht, K. B., Shapiro, A. T., Barloom, P. J., and Goetz, A. F. H. 1993. The spatial image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 44:145–163.CrossRefGoogle Scholar
Lass, L. W., Shafii, B., Price, W. J., and Thill, D. C. 2000. Assessing agreement in multispectral images of yellow starthistle (Centaurea solstitialis) with ground truth data using a Bayesian methodology. Weed Technol. 14:539–544.Google Scholar
Lass, L. W., Thill, D. C., Shafii, B., and Prather, T. S. 2002. Detecting spotted knapweed (Centaurea maculosa) with hyperspectral remote sensing technology. Weed Technol. 16:426–432.Google Scholar
Mack, A. 1992. Vegetation analysis of a hardwood hammock in Dade County, Florida: changes since 1940. Fl. Sci. 55:258–262.Google Scholar
Morton, J. F. 1978. Brazilian pepper—its impact on people, animals and the environment. Econ. Bot. 32:353–359.Google Scholar
Mytinger, L. and Williamson, G. B. 1987. The invasion of Schinus into saline communities of Everglades National Park. Fl. Sci. 50:7–12.Google Scholar
Ren, H. and Chang, C-I. 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.Google Scholar
Settle, J. J. and Drake, N. A. 1993. Linear mixing and the estimation of ground proportions. Int. J. Remote Sens. 14:1159–1177.Google Scholar
Shafii, B., Price, W. J., Lass, L. W., and Thill, D. C. 1998. Assessing variability of agreement measures in remote sensing using a Bayesian approach. Appl. Stat. Agric. 43–54.Google Scholar
Shimabukuro, Y. E. and Smith, J. A. 1991. The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. IEEE Trans. Geosci. Remote Sens. 29:16–20.Google Scholar
Sohn, Y. and McCoy, R. M. 1997. Mapping desert shrub rangeland using spectra unmixing and modeling spectral mixtures with TM data. Photogramm. Eng. Remote Sens. 63:707–716.Google Scholar