Get reliable rangeland science

Leafy Spurge (Euphorbia esula) Classification Performance Using Hyperspectral and Multispectral Sensors
Author
Mitchell, Jessica J.
Glenn, Nancy F.
Publisher
Society for Range Management
Publication Year
2009-01-01
Body

Two demonstration sites in southeast Idaho were used to extend the scope of remote sensing of leafy spurge research toward investigating coarser scale detection limits. Hyperspectral images were obtained to produce baseline leafy spurge maps, from which spatially and/or spectrally degraded images were subsequently derived for comparative purposes with Landsat 5 Thematic Mapper (TM). The baseline presence/absence maps had an overall accuracy of 67% at the Spencer study site and 85% at the Medicine Lodge study site. Unexpectedly high-accuracy results were produced from the images that were spectrally degraded to the bandwidths of Landsat 5 TM, which suggests that high spectral resolution is not critical to leafy spurge detection. However, a classification using a Landsat 5 TM image indicates that the sensor is inadequate for regional distribution monitoring. The differences in results between the actual and degraded images suggest that a sensor with comparable resolutions but improved instrumentation (e.g., signal to noise) may offer an alternative to hyperspectral data for mapping leafy spurge at regional scales.  The Rangeland Ecology & Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact lbry-journals@email.arizona.edu for further information. Migrated from OJS platform August 2020

Language
en
Resource Type
Text
Document Type
Journal Issue/Article
Digital Object Identifier (DOI)
10.2111/08-100
Additional Information
Mitchell, J. J., & Glenn, N. F. (2009). Leafy spurge (Euphorbia esula) classification performance using hyperspectral and multispectral sensors. Rangeland Ecology & Management, 62(1), 16-27.
ISSN
0022-409X
OAI Identifier
oai:repository.arizona.edu:10150/642999
Journal Volume
62
Journal Number
1
Journal Pages
16-27
Collection
Rangeland Ecology & Management (REM)
Journal Name
Rangeland Ecology & Management
Keywords
detection limits
HyMap
invasive weeds
mixture-turned matched filtering
remote sensing