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A review of remote sensing of invasive weeds and example of the early detection of spotted knapweed (Centaurea maculosa) and babysbreath (Gypsophila paniculata) with a hyperspectral sensor

Published online by Cambridge University Press:  20 January 2017

Lawrence W. Lass ,
Timothy S. Prather ,
Nancy F. Glenn ,
Keith T. Weber ,
Jacob T. Mundt  and
Jeffery Pettingill
Timothy S. Prather
Affiliation:
Department of Plant, Soil, and Entomological Sciences, University of Idaho, Moscow, ID 83844-2339
Nancy F. Glenn
Affiliation:
Department of Geosciences, Idaho State University-Boise Center, Boise, ID 83713
Keith T. Weber
Affiliation:
GIS Training and Research Center, Idaho State University, Pocatello, ID, 83209-8130
Jacob T. Mundt
Affiliation:
Department of Geosciences, Idaho State University-Boise Center, Boise, ID 83713
Jeffery Pettingill
Affiliation:
Bonneville County Weed Department, Idaho Falls, ID 83402
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Abstract

Remote sensing technology is a tool for detecting invasive species affecting forest, rangeland, and pasture environments. This article provides a review of the technology, and algorithms used to process remotely sensed data when detecting weeds and a working example of the detection of spotted knapweed and babysbreath with a hyperspectral sensor. Spotted knapweed and babysbreath frequently invade semiarid rangeland and irrigated pastures of the western United States. Ground surveys to identify the extent of invasive species infestations should be more efficient with the use of classified images from remotely sensed data because dispersal of an invasive plant may have occurred before the discovery or treatment of an infestation. Remote sensing data were classified to determine if infestations of spotted knapweed and babysbreath were detectable in Swan Valley near Idaho Falls, ID. Hyperspectral images at 2-m spatial resolution and 400- to 953-nm spectral resolution with 12-nm increments were used to identify locations of spotted knapweed and babysbreath. Images were classified using the spectral angle mapper (SAM) algorithm at 1, 2, 3, 4, 5, and 10° angles. Ground validation of the classified images established that 57% of known spotted knapweed infestations and 97% of known babysbreath infestations were identified through the use of hyperspectral imagery and the SAM algorithm.

Keywords

Babysbreath, Gypsophila paniculata L. GYPPAspotted knapweed, Centaurea maculosa Lam. CENMAHyperspectral sensorimaging spectrometerinvasive plant detection

Information

Type
Symposium
Information
Weed Science , Volume 53 , Issue 2 , April 2005 , pp. 242 - 251
Copyright
Copyright © Weed Science Society of America 

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