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Journal of Applied Remote Sensing

Mapping giant reed with QuickBird imagery in the Mexican portion of the Rio Grande Basin
Author(s): Chenghai Yang; John A. Goolsby; James H. Everitt
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Paper Abstract

Giant reed (Arundo donax L.) is a perennial invasive weed that presents a severe threat to agroecosystems and riparian areas in the Texas and Mexican portions of the Rio Grande Basin. The objective of this study was to use QuickBird satellite imagery to map giant reed infestations and estimate infested areas in the Mexican portion of the Rio Grande Basin (MPRGB). Intensive ground surveys were conducted and global positioning system (GPS) points were collected to document the locations along major tributaries and other infested areas in the MPRGB. To map the scattered infestations, QuickBird image scenes covering a total area of 7190 square km are needed. In this study, 40 archived and newly-tasked QuickBird image scenes acquired from 2002 to 2007 and covering a total area of 3252 square km were used. These images were classified using a maximum likelihood classifier and the areas for giant reed and water in each scene were determined. The total giant reed-infested area determined from the 40 images was 2160 ha. Since the 40 images constituted about 45.2% of the total area to be imaged, the total infested area in the MPRGB was estimated to be 4775 ha. Based on the images acquired along the tributaries, the ratio of giant reed-infested area to water area was 1.6 ha/ha and the ratio of giant reed-infested area to river length was 4.6 ha/km. Although actual infested areas are larger than the estimated areas because some of the infested areas may have not been counted during the ground surveys, this study provides the first and accurate estimates of giant reed infestations in the MPRGB. These results will be useful for both land owners and government agencies for the management and control of this invasive weed.

Paper Details

Date Published: 1 May 2009
PDF: 12 pages
J. Appl. Remote Sens. 3(1) 033530 doi: 10.1117/1.3148866
Published in: Journal of Applied Remote Sensing Volume 3, Issue 1
Show Author Affiliations
Chenghai Yang, USDA Agricultural Research Service (United States)
John A. Goolsby, USDA Agricultural Research Service (United States)
James H. Everitt, USDA Agricultural Research Service (United States)


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