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

Authors: Karl, Jason; MCCORD, SARAH - New Mexico State University; HADLEY, BRIAN - Bureau Of Land Management

Submitted to: Ecological Indicators
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/6/2016
Publication Date: 1/20/2017
Publication URL: https://handle.nal.usda.gov/10113/5575670
Citation: Karl, J.W., McCord, S.E., Hadley, B.C. 2017. A comparison of cover calculation techniques for relating point-intercept vegetation sampling to remote sensing imagery. Ecological Indicators. 73:156-165.

Interpretive Summary: Maps of the distribution and amount of vegetation derived from satellite and aerial imaging sensors are an important source of data for natural resource management. Developing these maps requires high-quality data on vegetation conditions that is most often measured in the field. There are, however, different methods for calculating vegetation indicators such as percent cover from field data, and the usage of one method versus another is not standardized and often not clearly stated in published studies. It is common practice for remote sensing specialists to prefer "top-hit" vegetation information where information on plant occurrences in lower canopy layers is ignored under the idea that an image "sees" only the top canopy layers. In contrast, most resource managers use all plant occurrence information in decision making. These two techniques ("top-hit" versus "any-hit" vegetatin cover) can produce different results and confound the use and interpretation of vegetation data for different purposes. 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 high resolution satellite imagery and top-hit and any-hit methods of calculating vegetation cover in study areas managed by the Bureau of Land Management in northwestern Colorado and northern California. Top-hit cover values were similar to any-hit values for shrubs which are typically encountered in the top canopy. Top-hit values for indicators commonly occurring in lower canopy layers (e.g., forbs, grasses) were on average much lower than any-hit values. We found model predictions of any-hit cover to have higher performance for all six vegetation indicators, although the difference between any-hit and top-hit for some indicators was often minimal. Given that there was consistently better or very similar performance between the two methods of calculating the indicators, we see no benefit from using special top-hit cover indicators for remote sensing projects.

Technical Abstract: 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.