Automatic detection of shrub location, crown area, and cover using spatial wavelet analysis and aerial photography

Characterizing shrub-steppe rangeland condition often requires fine-scale measurement of individual plants across broad areas. Advances in remote sensing to develop improved algorithms to census and monitor individual rangeland plants using image data are important for improving the efficiency with which these critical areas are monitored. Here, we performed and evaluated the first test of spatial wavelet analysis (SWA) to automatically detect the location and crown diameter of individuals of two species of shrubs (Artemisia tridentata and Purshia tridentata). Additionally, we quantified the aggregated cover of these shrubs at the plot scale. High spatial resolution (0.25 and 1 m) multispectral aerial imagery and field-based vegetation measurements were collected in both spring and fall 2005. We found that image- and field-based measures of individual shrubs and their crown areas were highly correlated in the fall imagery (r = 0.89). Image-based SWA prediction of shrub cover at the plot level correlated better with field-based measures (r = 0.91) than did a traditional, image texture-based measure (r = 0.71). Analyses of imagery acquired in spring resulted in poorer relationships due to the decreased phenological contrast between shrubs and understory grasses in spring relative to fall. Statistical equivalence tests demonstrated that individual shrub crown areas derived from field data and SWA were statistically equivalent and not biased, but the SWA- and field-based assessments of plot-level cover were not statistically equivalent. These results represent progress towards developing automatic methods to analyze shrubs at the landscape scale using remotely sensed imagery.