Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (4 results)

  • Article
  • Peer Reviewed
Berkeley Research Impact Initiative (BRII) (2014)
We investigated the relationship between LiDAR and ladder fuels in the northern Sierra Nevada, California USA. Ladder fuels are often targeted in hazardous fuel reduction treatments due to their role in propagating fire from the forest floor to tree crowns. Despite their importance, ladder fuels are difficult to quantify. One common approach is to calculate canopy base height, but this has many potential sources of error. LiDAR may be a way forward to better characterize ladder fuels, but has only been used to address this question peripherally and in only a few instances. After establishing that landscape fuel treatments reduced canopy and ladder fuels at our site, we tested which LiDAR-derived metrics best differentiated treated from untreated areas. The percent cover between 2 and 4 m had the most explanatory power to distinguish treated from untreated pixels across a range of spatial scales. When compared to independent plot-based measures of ladder fuel classes, this metric differentiated between high and low levels of ladder fuels. These findings point to several immediate applications for land managers and suggest new avenues of study that could lead to possible improvements in the way that we model wildfire behavior across forested landscapes in the US.
    • Thesis
    • Peer Reviewed
    UC Berkeley Electronic Theses and Dissertations (2016)

    Forest and fire ecology have long utilized remote sensing datasets to learn more about landscapes. Advances in gps spatial accuracy, GIS software capabilities, computing power, and remote sensing technology and software, as well as increases in the spatial and temporal resolution of remote sensing products, have made remote sensing a critical component of forest and fire ecology. Aerial light detection and ranging (LiDAR) is a fast-growing active remote-sensing technology that can be mined for detailed structural information about forests. These data are utilized in the fields of hydrology, forest ecology, silviculture, wildland fire ecology, wildlife ecology, and habitat modeling. LiDAR coverage has also become increasingly common, yet still contains much untapped potential.

    Despite widespread research that derived copious valuable metrics from aerial LiDAR, few of these metrics are available to managers due to a significant knowledge and software barrier for LiDAR processing. Even when LiDAR is utilized to derive more complex metrics by scientists and LiDAR experts, metrics are often predictions of plot-based data across the landscape. While these metrics are useful, LiDAR can offer so much more. Because it holds information about forest structure in 3 dimensions, new metrics can be derived that capture the full complexity of forest structure.

    I explore ways in which managers can use the plot network and data layers already available to them to derive large tree density, a metric that is critical for habitat modeling for many species, including the California spotted owl. I also explore the utility of LiDAR for estimating ladder fuels that carry fire from the ground into the canopy. Because there was no reliable method for quantifying these fuels, I also developed a plot-based methodology to collect these data. My dissertation work aims to increase LiDAR accessibility to managers and to develop new ways to use LiDAR to solve old problems. While there is much more work to be done, I am excited to share my work with LiDAR experts and forest managers, and hope that my findings improve the way we use LiDAR, the way we manage forests, and the way that we model and manage for wildland fire.

      Cover page: Not seeing the forest for the points: Novel LiDAR metrics elucidate forest structure and increase LiDAR usability by managers
      • Article
      • Peer Reviewed
      Berkeley Research Impact Initiative (BRII) (2014)
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2016)
        Large trees are important to a wide variety of wildlife, including many species of conservation concern, such as the California spotted owl (Strix occidentalis occidentalis). Light detection and ranging (LiDAR) has been successfully utilized to identify the density of large-diameter trees, either by segmenting the LiDAR point cloud into individual trees, or by building regression models between variables extracted from the LiDAR point cloud and field data. Neither of these methods is easily accessible for most land managers due to the reliance on specialized software, and much available LiDAR data are being underutilized due to the steep learning curve required for advanced processing using these programs. This study derived a simple, yet effective method for estimating the density of large-stemmed trees from the LiDAR canopy height model, a standard raster product derived from the LiDAR point cloud that is often delivered with the LiDAR and is easy to process by personnel trained in geographic information systems (GIS). Ground plots needed to be large (1 ha) to build a robust model, but the spatial accuracy of plot center was less crucial to model accuracy. We also showed that predicted large tree density is positively linked to California spotted owl nest sites.
          Cover page: Accessible light detection and ranging: estimating large tree density for habitat identification
          Top