mirage   mirage   mirage

Examination of airborne discrete-return lidar in prediction and identification of unique forest attributes

DSpace/Manakin Repository

Show simple item record

dc.contributor.advisor Boston, Kevin D.
dc.creator Wing, Brian M.
dc.date.accessioned 2012-06-29T18:07:23Z
dc.date.available 2012-06-29T18:07:23Z
dc.date.copyright 2012-06-08
dc.date.issued 2012-06-08
dc.identifier.uri http://hdl.handle.net/1957/30360
dc.description Graduation date: 2013 en_US
dc.description.abstract Airborne discrete-return lidar is an active remote sensing technology capable of obtaining accurate, fine-resolution three-dimensional measurements over large areas. Discrete-return lidar data produce three-dimensional object characterizations in the form of point clouds defined by precise x, y and z coordinates. The data also provide intensity values for each point that help quantify the reflectance and surface properties of intersected objects. These data features have proven to be useful for the characterization of many important forest attributes, such as standing tree biomass, height, density, and canopy cover, with new applications for the data currently accelerating. This dissertation explores three new applications for airborne discrete-return lidar data. The first application uses lidar-derived metrics to predict understory vegetation cover, which has been a difficult metric to predict using traditional explanatory variables. A new airborne lidar-derived metric, understory lidar cover density, created by filtering understory lidar points using intensity values, increased the coefficient of variation (R²) from non-lidar understory vegetation cover estimation models from 0.2-0.45 to 0.7-0.8. The method presented in this chapter provides the ability to accurately quantify understory vegetation cover (± 22%) at fine spatial resolutions over entire landscapes within the interior ponderosa pine forest type. In the second application, a new method for quantifying and locating snags using airborne discrete-return lidar is presented. The importance of snags in forest ecosystems and the inherent difficulties associated with their quantification has been well documented. A new semi-automated method using both 2D and 3D local-area lidar point filters focused on individual point spatial location and intensity information is used to identify points associated with snags and eliminate points associated with live trees. The end result is a stem map of individual snags across the landscape with height estimates for each snag. The overall detection rate for snags DBH ≥ 38 cm was 70.6% (standard error: ± 2.7%), with low commission error rates. This information can be used to: analyze the spatial distribution of snags over entire landscapes, provide a better understanding of wildlife snag use dynamics, create accurate snag density estimates, and assess achievement and usefulness of snag stocking standard requirements. In the third application, live above-ground biomass prediction models are created using three separate sets of lidar-derived metrics. Models are then compared using both model selection statistics and cross-validation. The three sets of lidar-derived metrics used in the study were: 1) a 'traditional' set created using the entire plot point cloud, 2) a 'live-tree' set created using a plot point cloud where points associated with dead trees were removed, and 3) a 'vegetation-intensity' set created using a plot point cloud containing points meeting predetermined intensity value criteria. The models using live-tree lidar-derived metrics produced the best results, reducing prediction variability by 4.3% over the traditional set in plots containing filtered dead tree points. The methods developed and presented for all three applications displayed promise in prediction or identification of unique forest attributes, improving our ability to quantify and characterize understory vegetation cover, snags, and live above ground biomass. This information can be used to provide useful information for forest management decisions and improve our understanding of forest ecosystem dynamics. Intensity information was useful for filtering point clouds and identifying lidar points associated with unique forest attributes (e.g., understory components, live and dead trees). These intensity filtering methods provide an enhanced framework for analyzing airborne lidar data in forest ecosystem applications. en_US
dc.language.iso en_US en_US
dc.relation Forest Explorer en_US
dc.subject Airborne lidar en_US
dc.subject Point cloud local-area intensity filtration en_US
dc.subject Understory vegetation en_US
dc.subject Snag detection & quantification en_US
dc.subject Standing tree biomass en_US
dc.subject Intensity en_US
dc.subject Beta regression en_US
dc.subject Weighted regression en_US
dc.subject Log-linear regression en_US
dc.subject.lcsh Optical radar en_US
dc.subject.lcsh Forest biomass -- Remote sensing en_US
dc.subject.lcsh Understory plants -- Remote sensing en_US
dc.subject.lcsh Snags (Forestry) -- Remote sensing en_US
dc.title Examination of airborne discrete-return lidar in prediction and identification of unique forest attributes en_US
dc.type Thesis/Dissertation en_US
dc.degree.name Doctor of Philosophy (Ph. D.) in Forest Engineering en_US
dc.degree.level Doctoral en_US
dc.degree.discipline Forestry en_US
dc.degree.grantor Oregon State University en_US
dc.contributor.committeemember Ritchie, Martin W.
dc.contributor.committeemember Cohen, Warren B.
dc.contributor.committeemember Gitelman, Alix
dc.contributor.committeemember Olsen, Michael J.
dc.description.peerreview no en_us


The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record

Search ScholarsArchive@OSU


Advanced Search

Browse

My Account

Statistics