Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Subject "multispectral imagery"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    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.