Abstract

Land managers need to rapidly assess vegetation composition and bare ground to effectively evaluate, manage, and restore shrub steppe communities that have been encroached by pinyon and juniper (P-J) trees. A major part of this process is assessing where to apply mechanical and prescribed fire treatments to reduce fuel loads and maintain or restore sagebrush steppe rangelands. Geospatial technologies, particularly remote sensing, offers an efficient option to assess rangelands across multiple spatial scales while reducing the need for ground-based sampling measurements. High-spatial resolution color-infrared imagery (0.06-m pixels) was acquired for sagebrush steppe communities invaded by P-J trees at five sites in Oregon, California, Nevada, and Utah with a Vexcel Ultra CamX digital camera in June/July 2009. In addition to untreated P-J woodlands, imagery was acquired over P-J woodlands where fuels were reduced by either prescribed fire, tree cutting, or mastication treatments. Ground measurements were simultaneously collected at each site in 2009 on 0.1-hectare subplots as part of the Sagebrush Steppe Treatment Evaluation Project (SageSTEP). We used Trimble eCognition Developer to 1) develop efficient methods to estimate land cover classes found in P-J woodlands; 2) determine the relationship between ground measurements and object-based image analysis (OBIA) land cover measurements for the following classes: trees (live, burned, cut, and masticated), shrubs, perennial herbaceous vegetation, litter (including annual species), and bare ground; and 3) evaluate eCognition rule-sets (models) across four spatial scales (subplot, site, region, and network) using untreated P-J woodland imagery. At the site scale, the overall accuracy of our thematic maps for untreated P-J woodlands was 84% with a kappa statistic of 0.80. For treatments, the overall accuracy and kappa statistic for prescribed fire was 85% and 0.81; cut and fell 82% and 0.77, and mastication 84% and 0.80, respectively, each indicating strong agreement between OBIA classification and ground measured data. Differences between mean cover estimates using OBIA and ground-measurements were not consistently higher or lower for any land cover class and when evaluated for individual sites, were within 5% of each other; all regional and network OBIA mean cover estimates were within 10% of the ground measurements. The trade-off for decreased precision over a larger area (region and network scale) may be useful to prioritize fuel-management strategies but will unlikely capture subtle shifts in understory plant communities that site and subplot spatial scales often capture. Although cover assessments from OBIA differed somewhat from ground measurements, they were accurate enough for many landscape-assessment applications such as evaluating treatment success and assessing the spatial distribution of fuels following fuel-reduction treatments on a site scale.

Degree

PhD

College and Department

Life Sciences; Plant and Wildlife Sciences

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2012-03-07

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd5034

Keywords

eCognition Developer, mastication, object-based image analysis, pinyon-juniper woodlands, prescribed fire, remote sensing, SageSTEP, spatial scales, tree cutting

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