Browsing by Subject "lidar"

Now showing 1 - 3 of 3
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    Digital Surface Model, Minnesota (2006-2012)
    (2015-06-26) Brink, Christopher; Gosack, Benjamin; Kne, Len; Luo, Yuanyuan; Martin, Christopher; McDonald, Molly; Moore, Michael; Munsch, Andrew; Palka, Stephen; Piernot, Devon; Thiede, Dan; Xie, Yiquan; Walz, Andrew; lenkne@umn.edu; Kne, Len
    A 1m resolution digital surface model that was generated from raw lidar data. This dataset was an intermediate product of a process to model potential solar insolation for the state of Minnesota.
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    Solar Insolation, Minnesota (2006-2012)
    (2015-09-24) Brink, Christopher; Gosack, Benjamin; Kne, Len; Luo, Yuanyuan; Martin, Christopher; McDonald, Molly; Moore, Michael; Munsch, Andrew; Palka, Stephen; Piernot, Devon; Thiede, Dan; Xie, Yiquan; Walz, Andrew; lenkne@umn.edu; Kne, Len
    The Minnesota Solar Suitability Analysis attempts to provide solar insolation analysis for the entire State of Minnesota. As far as we are aware, it is the only project of its scale in existence; similar studies have been limited to metro areas or focus on rooftop insolation. The project's existence is feasible because of statewide, freely available aerial lidar coverage. And the commitment of the team to work long hours on this unfunded project. The project finds itself at the intersection of renewable energy, big data analysis, geospatial technology, and open data availability. This data provides a measure of incedent solar radiation as it is intercepted by the earth surface, or features (such as vegetation and buildings) standing above the earth surface. The data is intended to be used to assess the suitability of a site for solar panel (photovoltaic cell) installations.
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    Using Single-Photon Lidar and Multispectral Imagery for Enhancing Forest Inventories
    (2019-10) Allen, Benjamin
    Modern forest management requires balancing multiple uses and management objectives, including timber production, wildlife habitat, and carbon sequestration. Forest inventories provide essential information for forest management decisions at a variety of spatial scales, including data about wood volume and the prevalence of various species. Traditional forest inventory systems rely primarily upon field data and design-based statistical estimators. These methods can provide unbiased estimates of inventory variables, albeit at a significant financial cost which limits the accuracy of the resulting data. Remote sensing technologies such as lidar and aerial photography have been used along with alternative statistical estimators to improve inventory accuracy and allow for spatially explicit maps of inventory data to be created. This research explored potential efficiency gains from the use of single-photon lidar and fall color aerial photography in a study area in northern Minnesota, USA. Remote sensing and field data combined in a model-assisted inferential framework were found to deliver relative efficiencies of approximately three for wood volume, with slightly lower values for basal area. Greater efficiency gains were found in coniferous-dominated forests than deciduous forests. The potential of these technologies to identify individual tree species and forest types was also examined. Classification between deciduous and coniferous-dominated forests provided overall classification accuracies of nearly 90% regardless of the classification algorithm used. By contrast, predictions of dominant species produced poor accuracy. Further research is needed to determine the economically optimal combination of remote sensing technologies for operational forest inventories.