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Title: MAPPING ASHE JUNIPER INFESTATIONS USING AIRBORNE HYPERSPECTRAL IMAGERY

Author
item Yang, Chenghai
item Everitt, James
item JOHNSON, HYRUM - USDA-ARS, TEMPLE, TX
item DAVIS, MICHAEL - 6204-05-00

Submitted to: Biannual Workshop in Color Photography and Videography in Resource
Publication Type: Proceedings
Publication Acceptance Date: 10/15/2005
Publication Date: 3/15/2006
Citation: Yang, C., Everitt, J.H., Johnson, H.B., Davis, M.R. 2006. Mapping Ashe juniper infestations using airborne hyperspectral imagery. In: Proceedings of the 20th Biennial Workshop on Aerial Photography, Videography, and High Resolution Digital Imagery for Resource Assessment, Bethesda, Maryland. 2006 CDROM.

Interpretive Summary: Ashe juniper is a noxious, evergreen shrub or small tree that invades rangelands in central Texas. This study evaluated airborne hyperspectral imagery and different image classification techniques for mapping ashe juniper infestations. Image analysis and ground verification showed that ashe juniper could be distinguished from associated woody and herbaceous plant species. These results indicate that airborne hyperspectral imagery in conjunction with image processing techniques can be a useful tool for mapping ashe juniper infestations.

Technical Abstract: Ashe juniper (Juniperus ashei Buchholz) in excessive coverage reduces forage production, interferes with livestock management, and degrades watersheds and wildlife habitat in rangelands. The objective of this study was to evaluate airborne hyperspectral imagery and different image classification techniques for mapping ashe juniper infestations. Hyperspectral imagery with 128 bands in the visible to near-infrared region of the spectrum was acquired from two ashe juniper infested sites in central Texas. Five classification methods, including minimum distance, Mahalanobis distance, maximum likelihood, spectral angle mapper (SAM), and binary encoding, were applied to the hyperspectral imagery for distinguishing ashe juniper from associated plant species (mixed woody species and mixed herbaceous species) and other cover types (bare soil and water). Accuracy assessment showed that overall accuracy varied from 70% for binary encoding to 96% for maximum likelihood for site 1 and from 58% for binary encoding to 93% for minimum distance for site 2. Producers' and users' accuracy values for ashe juniper ranged from 55% to 100% for site 1 and 48% to 98% for site 2. All the classification methods, except binary encoding, were able to identify ashe juniper, even though minimum distance and maximum likelihood provided best overall and class accuracy. These results indicate that airborne hyperspectral imagery, incorporated with image classification techniques, can be a useful tool for mapping ashe juniper infestations.