Skip to main content
ARS Home » Research » Publications at this Location » Publication #191514
Title: MAPPING SPINY ASTER INFESTATIONS WITH QUICKBIRD IMAGERY

Author
item Everitt, James
item Yang, Chenghai

Submitted to: Geocarto International
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/15/2007
Publication Date: 12/1/2007
Citation: Everitt, J.H., Yang, C. 2007. Mapping spiny aster infestations with QuickBird imagery. Geocarto International. 22:273-283.

Interpretive Summary: Spiny aster is a troublesome perennial weed that invades rangelands on the Coastal Prairie in south and southeast Texas. QuickBird satellite imagery from two dates (June 2003 and September 2004) was evaluated for distinguishing spiny aster infestations on a south Texas rangeland area near Corpus Christi. Unsupervised and supervised image analysis techniques were used to classify false color composite images of the study site. Imagery acquired in June was superior to that obtained in September for distinguishing spiny aster. This was attributed to differences to spiny aster phenology between the two dates. Both unsupervised and supervised techniques did a good job in identifying spiny aster in the June imagery, with producer’s and user’s accuracies ranging from 82% to 93%. These results should be of interest to rangeland resource managers.

Technical Abstract: QuickBird satellite imagery acquired in June 2003 and September 2004 was evaluated for detecting the noxious weed spiny aster [Leucosyris spinosa (Benth.) Greene] on a south Texas rangeland area. A subset of each of the satellite images representing a diversity of cover types was extracted and used as a study site. The satellite imagery had a spatial resolution of 2.8 m and contained 11-bit data. Unsupervised and supervised classification techniques were used to classify false color composite (green, red, and near-infrared bands) images of the study site. Imagery acquired in June was superior to that obtained in September for distinguishing spiny aster infestations. This was attributed to differences in spiny aster phenology between the two dates. An unsupervised classification of the June image showed that spiny aster had producer’s and user’s accuracies of 90% and 93.1%, respectively, whereas a supervised classification of the June image had producer’s and user’s accuracies of 90% and 81.8%, respectively. These results indicate that high resolution satellite imagery coupled with image analysis techniques can be used successfully for detecting spiny aster infestations on rangelands.