A comparison of cover calculation techniques for relating point-intercept vegetation sampling to remote sensing imagery

• Published in: Ecological indicators 2017-02, Vol.73, p.156-165
• Main Authors: Karl, Jason W., McCord, Sarah E., Hadley, Brian C.
• Format: Article
• Language: English
• Subjects: Beta regression; Method comparison; Monitoring; Point-intercept sampling; Remote sensing; Vegetation cover
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2016.09.034

•Use of plant cover in remote sensing is complicated due to many ways cover is calculated.•We model vegetation cover from RapidEye imagery using two cover calculation methods.•Cover indicators using intercept data from all canopy layers gave lower model RMSE.•Indicator meaning and model performance should influence choice of calculation method. 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 (α