Rangeland and pasture monitoring: an approach to interpretation of high-resolution imagery focused on observer calibration for repeatability |
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Authors: | Email author" target="_blank">Michael?C?DuniwayEmail author Jason?W?Karl Scott?Schrader Noemi?Baquera Jeffrey?E?Herrick |
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Institution: | (1) Jornada Experimental Range, United States Department of Agriculture-Agricultural Research Service (USDA-ARS), P.O. Box 30003, MSC 3JER, Las Cruces, NM 88003-8003, USA |
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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. |
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