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Title: AUTOMATED DIGITAL COVER ANALYSIS OF VERY-LARGE SCALE AERIAL (VLSA) RANGELAND IMAGES

Author
item Cox, Samuel
item Booth, D
item JOHNSON, D - OREGON STATE UNIVERSITY

Submitted to: Society for Range Management Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 2/2/2003
Publication Date: 2/2/2003
Citation: COX, S.E., BOOTH, D.T., JOHNSON, D. AUTOMATED DIGITAL COVER ANALYSIS OF VERY-LARGE SCALE AERIAL (VLSA) RANGELAND IMAGES. SOCIETY FOR RANGE MANAGEMENT MEETING ABSTRACTS. 2003.

Interpretive Summary:

Technical Abstract: Vegetation cover and bare ground are key indicators of rangeland health. However, cover and bare ground assessments are often confounded by subjective evaluations, resulting in reduced measurement repeatability and questionable accuracy. Measurement methods, such as the point frame and line-intercept, rely on human decision-making that varies among individuals, and are affected by heat, light, wind and worker fatigue. A new method for automatically measuring vegetation cover uses image analysis software in conjunction with VLSA photographs. Digital and film aerial images were collected automatically by a low flying ultralight-type airplane (214 kg empty weight) utilizing GPS to follow a predetermined flight grid. The cover calculation was performed quickly and precisely from VLSA images by a computer using `Vegmeasurement', a software program developed at Oregon State University. Bare ground measurements in a saltbush / grass community in south-central Wyoming obtained using this new method gave an average value not different from that obtained by a traditional point-frame technique. The new analysis was based on over 170 aerially-photographed plots, a sample size far greater than that feasible by traditional ground data collection methods. Further testing is needed but the early results indicate that VLSA can indeed be a useful tool to collect data for accurate cover analysis. The advantages of this approach include time savings, easier access to remote areas, increased data collection capacity and reduction of personal bias and subjectivity.