Rangeland and pasture monitoring: an approach to interpretation of high-resolution imagery focused on observer calibration for repeatability
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Collection of standardized assessment and monitoring data is critically important for supporting policy and management at local to continental scales. Remote sensing techniques, including image interpretation, have shown promise for collecting plant community composition and ground cover data efficiently. More work needs to be done, however, evaluating whether these techniques are sufficiently feasible, cost-effective, and repeatable to be applied in large programs. The goal of this study was to design and test an image-interpretation approach for collecting plant community composition and ground cover data appropriate for local and continental-scale assessment and monitoring of grassland, shrubland, savanna, and pasture ecosystems. We developed a geographic information system image-interpretation tool that uses points classified by experts to calibrate observers, including point-by-point training and quantitative quality control limits. To test this approach, field data and high-resolution imagery (∼3 cm ground sampling distance) were collected concurrently at 54 plots located around the USA. Seven observers with little prior experience used the system to classify 300 points in each plot into ten cover types (grass, shrub, soil, etc.). Good agreement among observers was achieved, with little detectable bias and low variability among observers (coefficient of variation in most plots <0.5). There was a predictable relationship between field and image-interpreter data (R 2 > 0.9), suggesting regression-based adjustments can be used to relate image and field data. This approach could extend the utility of expensive-to-collect field data by allowing it to serve as a validation data source for data collected via image interpretation.
KeywordsRemote sensing Image interpretation Aerial photography Repeatability Assessment and monitoring Large-scale
This study was supported by the USDA-ARS, USDA-NRCS Conservation Effects Assessment Project (CEAP), and the National Park Service Lake Mead National Recreation Area and Mojave Desert Network. We would like to thank Michelle Mattocks for her assistance with field data collection and expert classifications, the NMSU students and other Jornada staff who contributed to this project, and the assistance we received from our collaborators including Melvin George, Neil McDougald, Paul Garner, George Poole, Chris Roberts, Toshi Yoshida, Jeanne Taylor, Joe Chigbrow, John Wilford, Mark Murdock, Lance Kosberg, Jeff Gonet, Sarah Goslee, Frank Stoltzfus, and Lloyd Rietz. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the US Government.
- Elzinga, C. L., Salzer, D. W., & Willoughby, J. W. (1998). Measuring and monitoring plant populations. Denver, CO, US Department of the Interior, Bureau of Land Management. National Applied Resource Sciences Center. BLM/RS/ST-98/005 + 1730.Google Scholar
- Herrick, J. E., Van Zee, J. W., Havstad, K. M., Burkett, L. M., & Whitford, W. G. (2005). Monitoring Manual for Grassland, Shrubland and Savanna Ecosystems. Volume I: Quick Start. Tucson, AZ: The University of Arizona.Google Scholar
- Holthausen, R. , R. Czaplewski , D. DeLorenzo , G. Hayward , W. Kessler , P. N. Manley et al. (2005). Strategies for monitoring terrestrial animals and habitats. Gen. Tech. Rep. RMRS-GTR-161. Fort Collins, CO US Department of Agriculture, Forest Service, Rocky Mountain Research Station. 34 p.Google Scholar
- Hunt, E. R., Jr., Everitt, J. H., Ritchie, J. C., et al. (2003). Applications and research using remote sensing for rangeland management. Photogrammetric Engineering and Remote Sensing, 69(6), 675–693.Google Scholar
- Laliberte, A. S., Herrick, J. E., Rango, A., & Winters, C. (2010). Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring. Photogrammetric Engineering and Remote Sensing, 76(6), 661–672.Google Scholar
- MacKinnon, W. C., Toevs, G., Spurrier, C., & Taylor, J. J. (2011). BLM Core Terrestrial Indicators and Methods. Washington, D.C: Bureau of Land Management.Google Scholar
- National Research Council (NRC). (1994). Rangeland Health: New Ways to Classify, Inventory and Monitor Rangelands. Washington: National Academy Press.Google Scholar
- Stevens, D. L., Jr. (2006). Spatial properties of design-based versus model-based approaches to environmental sampling. 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Lisbon, Portugal.Google Scholar
- Thompson, S. K. (1992). Sampling. New York: John Wiley and Sons, Inc.Google Scholar
- United States Department of Agriculture- Natural Resources Conservation Service (USDA-NRCS) (2006). Major land resource area (MLRA). Land resource regions and major land resource areas of the United States, the Caribbean, and the Pacific Basin. U.S. Department of Agriculture Handbook 296. http://soils.usda.gov/survey/geography/mlra/. Accessed 12 November 2010.
- United States Department of Agriculture- Natural Resources Conservation Service (USDA-NRCS) (2007). National Resources Inventory—Handbook of instructions for rangeland field study data collection. http://www.ncgc.nrcs.usda.gov/products/nri/range/2007range.html. Accessed 12 November, 2010.
- United States Department of Agriculture- Natural Resources Conservation Service (USDA-NRCS) (2010). National Resources Inventory—Rangeland resource assessment. http://www.nrcs.usda.gov/technical/nri/rangeland/. Accessed 12 November 2010.