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Title: USING AERIAL COLORINFRARED PHOTOGRAPHY AND QUICKBIRD SATELLITE DATA FOR MAPPING WETLAND VEGETATION

Author
item Everitt, James
item YANG, CHENGHAI - TX A&M EXP'T. STATION
item Fletcher, Reginald
item DAVIS, MICHAEL - 6204-05-00
item DRAWE, D. LYNN - WELDER WILDLIFE REFUGE

Submitted to: Geocarto International
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/13/2004
Publication Date: 12/15/2004
Citation: Everitt, J.H., Yang, C., Fletcher, R.S., Davis, M.R., Drawe, D. 2004. Using aerial color-infrared photography and Quickbird satellite data for mapping wetland vegetation. Geocarto International 19(4):15-22.

Interpretive Summary: Recently, high resolution (2.4 to 4 m) multispectral satellite imagery from commercial satellite systems has become available for remote sensing applications. A study was conducted in south Texas comparing QuickBird high resolution (2.8 m) multispectral satellite imagery to aerial color-infrared (CIR) photography for distinguishing wetland vegetation in two freshwater lakes. Results from computer classification of imagery showed that several plant species and mixtures of species could be distinguished in both types of imagery. However, higher classification accuracies were obtained with the CIR photography than with the satellite imagery. The higher accuracy of the photography was attributed to its superior spatial resolution (0.5 m). These results should be of interest to wetland resource managers who are interested in using remote sensing techniques for mapping vegetation.

Technical Abstract: Aerial color-infrared (CIR) photography and QuickBird high resolution (2.8 m) false color satellite imagery were evaluated for differentiating among wetland vegetation in two freshwater lakes on the Welder Wildlife Refuge in south Texas. Field spectral measurements made on dominant vegetation types (plant species and vegetation mixtures) showed significant differences in visible and near-infrared reflectance. Several plant species and mixtures of species could be distinguished in the aerial photos and satellite imagery. Accuracy assessments performed on computer classifications of the photos of the two lakes had overall accuracies of 84% and 87%; whereas, accuracy assessments performed on classifications of the satellite imagery of the lakes had overall accuracies of 69% and 76%. The lower accuracies of the satellite image classifications were attributed to their coarser spatial resolution.