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Service Description: A number of factors contribute to overall risk of water quality impacts related to timber harvest. Here, we summarize a variety of physical and hydro-geologic characteristics related to each of our watershed sample units, which may contribute to the potential for sediment and nutrient contribution from harvest sites to surface waters. These characteristics are best viewed as baseline conditions which could influence potential impacts to water quality in the absence of FMG implementation. These factors range from percent forest cover within a watershed to soil erosivity, and include slope, concentration potential for runoff, land use, distance to surface hydrology, and a variety of other factors. It is critical to keep in mind that our summaries of the resulting models tend to average conditions across the entire watershed sample unit, while certain conditions may actually be concentrated along stream corridors, steep and narrow valleys, glacial moraine edges, or other physical features present in the watersheds. The series of maps resulting from this analysis shows these factors on a landscape level. Most factors are derived from map layers created at a 30 meter resolution (e.g. pixel size on the ground). Hence, our background erosivity risk model also describes these factors at the 30 meter scale.
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Comments: This normalized digital surface model (nDSM) was part of a suite of datasets produced during a forest inventory enhancement research project, funded by the Environment and Natural Resources Trust Fund in 2016. The nDSM was produced at 1-m spatial resolution using high density lidar point cloud LAS files and USFS FUSION software. The source lidar was collected in October 2017 using single photon lidar (SPL; green laser) by Quantum Spatial Inc (QSI). There were on average 27.8 points per square meter. An nDSM is a height above ground model, where every pixel represents the elevation of the highest lidar return among all lidar returns within the grid cell. The production of the nDSM involved two basic steps: 1) filtering ground returns (i.e., points that sit on top of the bare-earth) to create a ground surface model, and 2) subtracting the ground elevations from all other above ground lidar return elevations. Any elevation values less than zero and above 50-m were not included. The information displayed here, summarizing the above ground height of a pixel, is even more useful with the picture. Accompanied by this lidar was four band aerial photography, also available by Web Mapping Service on the Minnesota Geospatial Commons. Having both height and visual information available, analysts and managers can strategically plan with data-driven results
Subject: Normalized Digital Surface Model (nDSM) of part of Cass County
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Keywords: erosivity,risk,hydro
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