Hamilton, R.; Megown, K.; Lachowski, H.; Campbell, R. 2006. Mapping Russian olive: using remote sensing to map an invasive tree. RSAC-0087-RPT1. Salt Lake City, UT: U.S. Department of Agriculture Forest Service, Remote Sensing Application Center. 7 p.With funding from the Remote Sensing Steering Committee, a pilot project was initiated to develop a cost-effective method for mapping Russian olive (Elaeagnus angustifolia L.), an invasive tree species, from scanned large-scale aerial photographs. A study area was established along a riparian zone within a semiarid region of the Fishlake National Forest, located in central Utah. Two scales of natural color aerial photographs (1:4,000 and 1:12,000) were evaluated as part of the project. Feature Analyst, an extension for ArcGIS and several image processing software packages, was used to map the invasive tree. Overall, Feature Analyst located Russian olive (RO) throughout the imagery with a relatively high degree of accuracy. For the map derived from 1:4,000-scale photographs, the software correctly located the tree in 85 percent of all 4-by-4 meter transect cells where Russian olive was actually present. However, smaller trees were sometimes missed and the size of trees and groups of trees were frequently underestimated. The map derived from 1:4,000-scale photographs was only slightly more accurate than the map derived from 1:12,000-scale photographs, suggesting that the smaller scale photography may be adequate for mapping Russian olive.
HighlightsFour crop growth modules in RZWQM2 were calibrated for four sugarbeet rotation sequences.Sugarbeet following wheat had a slightly higher yield (3% to 6.5%).Moldboard plow increased sugarbeet yield by 1% to 2%.The difference in N losses under different crop rotations and tillage operations was negligible.Abstract. Sugarbeet (Beta vulgaris) is considered to be one of the most viable alternatives to corn for biofuel production as it may be qualified as the feedstock for advanced biofuels (reducing greenhouse gas emission by 50%) under the Energy Independence and Security Act (EISA) of 2007. Because sugarbeet production is affected by crop rotation and tillage through optimal use of soil water and nutrients, simulation of these effects will help in making proper management decisions. In this study, the CSM-CERES-Beet, CSM-CERES-Maize, CROPSIM-Wheat, and CROPGRO-Soybean models included in the RZWQM2 were calibrated against experimental field data of crop yield, soil water, and soil nitrate from the North Dakota State University Carrington Research Extension Center from 2014 to 2016. The models performed reasonably well in simulating crop yield, soil water, and nitrate (rRMSE = 0.055 to 2.773, d = 0.541 to 0.997). Simulation results identified a non-significant effect of crop rotation on sugarbeet yield, although sugarbeets following wheat resulted in 3% to 6.5% higher yields compared to other crops. Net mineralization and N uptake rates were slightly higher when sugarbeets followed wheat compared to the other crops. Seasonal N and water mass balances also showed lower N and water stresses when sugarbeets followed wheat. The effects of tillage operations on sugarbeet yield were also non-significant. The difference in the N losses to runoff and drainage from the sugarbeet fields under different crop rotations and tillage operations was negligible. As sugarbeet production may be expanded into nontraditional planting areas in the Red River Valley due to potential demand for biofuel production, our findings will help to assess the associated environmental impacts and identify suitable crop rotations and management scenarios in the region. Keywords: Biofuel, Crop rotation, RZWQM2, Sugarbeet, Tillage.
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