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Classification of Digital Photography for Measuring Productive Ground Cover

Rotz, J. D., Abaye, A. O., Wynne, R. H., Rayburn, E. B., Scaglia, G., Phillips, R. D.
Rangeland ecology & management 2008 v.61 no.2 pp. 245-248
interspecific variation, correlation, automatic detection, image analysis, vegetation cover, forage grasses, color, remote sensing, sward, measurement, grasslands, Virginia
Productive ground cover (PGC) is often used as a measure of sward health and persistence. To measure PGC, a camera stand was constructed to provide diffuse lighting of grass swards for color digital photography; the photographs were classified into productive and nonproductive cover using Mahalanobis distance. The PGC measurement techniques were tested on a grazing experiment that used four forage types: Lakota prairie grass (Bromus catharticus Vahl.), Kentucky 31 endophyte (Neotyphodium coenophialum)-free tall fescue (Lolium arundinaceum [[Schreb.]] S. J. Darbyshire), Kentucky 31 endophyte-infected tall fescue, and Quantum (novel-endophyte) tall fescue. The accuracy of the PGC maps was assessed using a stratified subsample of 48 images, 12 from each of four productive cover classes (0%%––39%%, 40%%––59%%, 60%%––79%%, and 80%%––100%%). On each of these 48 images 100 random points were labeled by a single skilled interpreter. The PGC percentages thus derived had an 83.7%% agreement with the PGC maps. However, the percentages derived from the PGC maps were not well correlated with the PGC percentages derived from either ocular estimation (r == 0.22) or a simple digital point quadrat method (r == 0.47). This experiment highlights the potential for semiautomated classification of ground-based digital photographs for estimating PGC, though further research (including more direct comparison with established field techniques) is warranted.