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.
University of Minnesota M.S. thesis. October 2019. Major: Natural Resources Science and Management. Advisor: Joseph Knight. 1 computer file (PDF); v, , 47 pages.
Using Single-Photon Lidar and Multispectral Imagery for Enhancing Forest Inventories.
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