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United States Department of Agriculture

Agricultural Research Service

Research Project: MANAGEMENT TECHNOLOGIES FOR ARID RANGELANDS

Location: Range Management Research

Title: Rangeland and pasture monitoring: An approach to interpretation of high-resolution imagery focused on observer calibration for repeatability

Authors
item Duniway, Michael -
item Karl, Jason
item Schrader, Theodore
item Baquera, Noemi -
item Herrick, Jeffrey

Submitted to: Environmental Monitoring and Assessment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 30, 2011
Publication Date: July 23, 2011
Repository URL: http://handle.nal.usda.gov/10113/57243
Citation: Duniway, M., Karl, J.W., Schrader, T.S., Baquera, N., Herrick, J.E. 2012. Rangeland and pasture monitoring: An approach to interpretation of high-resolution imagery focused on observer calibration for repeatability. Environmental Monitoring and Assessment. 184(6):3789-3804.

Interpretive Summary: Standard techniques for obtaining assessment and monitoring data are important for supporting policy and management at all scales. While remote sensing techniques have shown promise for collecting plant community composition and ground cover data efficiently, more work needs to be done, to evaluate whether these techniques are sufficiently feasible, cost effective and repeatable for application in large monitoring 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 United States. Seven observers with little prior experience used the system to classify 300 points in each plot into ten cover types (grass, shrub, soil, etc.). A training and calibration system like the one we described above is critical if high resolution, remotely-sensed data are to be used in national-level surveys that collect fine-scale data on plant community composition and ground cover. An image interpretation system like the one described in this paper could help maximize the potential of expensive-to-collect field data by allowing it to serve as a validation data source for data collected via image interpretation. However, with the increasing availability of high-resolution imagery, novel procedures are needed that collect reliable data with a minimal time requirement by experts. Results presented in this paper indicate the image-interpretation system developed could help fill that resource gap by transferring expert knowledge to non-experts.

Technical Abstract: 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 United States. 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 (R2 > 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.

Last Modified: 7/31/2014