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2002 ARC Projects |
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The Southern Wind River Landscape: Site Conservation Planning Project |
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 Kelli Taylor Cooperator: The Nature Conservancy - Wyoming Chapter |
A coarse level risk assessment using existing geospatial data (GIS data layers coupled with Landsat)
available through The Nature Conservancy-Wyoming will help identify general areas
of risk within the southern Wind River landscape study area. This risk assessment
will not require collection and compilation of additional information and will function
as a first cut risk assessment of the three million acre southern Wind River landscape
study area. Two study sites already identified by the Nature Conservancy-Wyoming will
act as test sites for a more focused risk assessment. The first site consists of the
Red Canyon Ranch currently owned by the Nature Conservancy-Wyoming. The second site is
Green Mountain managed by the USDI Bureau of Land Management and represents a different
ecosystem than the Red Canyon Ranch. This study site has been impacted by OHV and other
uses that are representative of the environmental risks mentioned above.
Panchromatic and multispectral imagery will be used to manually and automatically identify risk features on the surface. LandSat and Aster imagery will be used to evaluate their usefulness in identifying risk features on the surface. This will help TNC-Wyoming to evaluate the ability of medium resolution imagery to detect risk features over a broad landscape. IKONOS imagery will provide an evaluation of finer resolution imagery to detect these same features. If the purchase of IKONOS imagery is not feasible, we will use aerial imagery. We anticipate that medium resolution remote sensing imagery coupled with thematic GIS layers will provide a general estimate of landscapes at risk. The utilization of finer resolution imagery over areas identified as at risk by the general estimate will allow the Nature Conservancy-Wyoming the ability to better quantify and present landscape risk to land managers.
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Establishing a Semi-Automated, Image Based Landscape Change Monitoring System |
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Chris McGinty Cooperator: Utah National Guard |
Initially, wet and dry season LandSat MSS and TM data were purchased for 1972 thru 1997
(excluding 1978). Each image was geo-rectified/referenced and radiometrically corrected using
specific atmospheric correction methods. The data were then subset to a three kilometer buffer
surrounding Camp Williams for simpler analysis and data storage purposes. Each image was then
subjected to a model, which output fractional vegetation (Fv) grids.
Following the generation of Fv grids, areas of known vegetation and historical data (previously maintained military transect locations) were buffered to a 30-meter (TM imagery/grids) and 80-meter (MSS imagery/grids) oval. These plots were then visually inspected using recent, summer 2000, one-meter digital ortho-photography to accept or reject the study areas based on areas of ubiquitous vegetation. Additionally, each site was evaluated for a possible future on-site visit. Using the spatial locations of each acceptable transect area, mean and variance calculations were generated via the Fv grids. These values are to be plotted and analyzed for change in vegetation using a steady state plot and various statistical methods.
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Using Remote Sensing information for Protein Management in Spring Wheat |
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 Dennis Wright Cooperator: Idaho Wheat Commission |
Improving grain quality can help growers increase revenue and retain customers. Remote sensing is a valuable tool to assist in managing in-season nitrogen applications during the growing season to improve grain quality. Our objective was to obtain spectral signatures of wheat under various N rates (0, 72, 180, 234 kg N ha-1) and the response to a midseason N application (54 kg N ha-1) at heading. Spectral data from satellite and aerial platforms were compared with pre-anthesis tissue samples and post-harvest grain quality. Imagery and tissue samples correlated significantly with each other with and preseason N applications (P<.0001). A second application of N at heading improved protein only marginally for wheat with sufficient N, but almost 2% in areas of stress, which could mean an increase in the selling price of up to 25%. Wheat stress identified data from satellite and aerial sensors could help growers increase revenue and decrease N over-application. |