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

TitleRangeland and pasture monitoring: an approach to interpretation of high-resolution imagery focused on observer calibration for repeatability
Publication TypeJournal Article
Year of Publication2012
AuthorsDuniway M.C., Karl JW, Schrader T. S, Baquera N, Herrick JE
JournalEnvironmental Monitoring and Assessment
Volume184
Start Page3789
Pagination3789-3804
Date Published07/23/2011
Accession NumberJRN00602
ARIS Log Number270823
Keywordsaerial photography, assessment and monitoring, image interpretation, large-scale, remote sensing, repeatability
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 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.

URL/files/bibliography/12-005.pdf
DOI10.1007/s10661-011-2224-2