|Title||A comparison of cover calculation techniques for relating point-intercept vegetation sampling to remote sensing imagery|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Karl J, McCord S, Hadley B|
|ARIS Log Number||321862|
Accurate and timely spatial predictions of vegetation cover from remote imagery are an important data source for natural resource management. High-quality in situ data are needed to develop and validate these products. Point-intercept sampling techniques are a common method for obtaining quantitative information on vegetation cover that have been widely implemented in a number of local and national monitoring programs. The use of point-intercept data in remote sensing projects, however, is complicated due to differences in how vegetation cover indicators can be calculated. Decisions on whether to use plant intercepts from any canopy layer (i.e., any-hit cover) or only the first plant intercept at each point (i.e., top-hit cover) can result in discrepancies in cover estimates which are used to train remotely-sensed imagery. Our objective in this paper was to explore the theory of point-intercept sampling relative to training and testing remotely-sensed imagery, and to test the strength of relationships between top-hit and any-hit methods of calculating vegetation cover and high-resolution satellite imagery in two study areas managed by the Bureau of Land Management in northwestern Colorado and northeastern California. We modeled top-hit and any-hit percent cover for six vegetation indicators from 5m-resolution RapidEye imagery using beta regression. Model performance was judged using normalized root mean-squared error (RMSE) from a 5-fold cross validation. Any-hit cover estimates were significantly higher (a < 0.05) than top-hit cover estimates for forbs and grasses in the White River study area, but only marginally higher in Northern California. Pseudo-R2 values for beta regression models of vegetation cover from RapidEye image information varied from 0.1045 to 0.7681 in White River and 0.2143 to 0.5775 in Northern California, with little pattern to whether any-hit or top-hit indicators produced better model fit. However, in all cases except for annual forbs in Northern California, normalized RMSE was lower for any-hit cover indicators indicating better model performance, though for some indicators the difference was minimal. Our results do not support the idea that top-hit cover estimates from point-intercept sampling are the most appropriate for remote sensing applications in arid and semi-arid shrub-steppe environments. In fact, having two sets of different indicators calculated from the same data may cause additional confusion in a situation where there is already considerable debate on how vegetation cover should be measured and used. Ultimately, selection of indicators to use for developing remote sensing classification or predictive models should be based first on the meaning or interpretation of the indicator, and second on how well the indicator performs in modeling applications.