Recipient Organization
UNIVERSITY OF WASHINGTON
4333 BROOKLYN AVE NE
SEATTLE,WA 98195
Performing Department
Bioresource Science and Engineering
Non Technical Summary
Airborne laser scanning (LIDAR) data can be used to characterize vegetation structure across large land areas. Data layers derived directly from the point cloud provide information describing vegetation size, density, and spatial distribution. When combined with appropriate ground plots, descriptive statistics computed from the point cloud can be used to model and predict forest inventory variables and structure (Strunk 2008, Means et al. 2000). The primary purpose of this project is to process LIDAR point data and canopy surfaces to derive descriptive statistics for land managed by the Bureau of Land Management (BLM) in Oregon covered by LIDAR data acquired by the Oregon Department of Geology and Mineral Industries (DOGAMI) and to use previously computed statistics and ground measurements for plots on the Deschutes National Forest to develop models describing forest inventory variables. This project will benefit the PNW by processing LIDAR data acquired by DOGAMI for areas owned by the Bureau of Land Management in Oregon to produce raster geographic information systems (GIS) layers containing descriptive statistics. This project will specifically address ways to structure the processing workflow for large-area acquisitions and test this workflow in a production environment. In addition, this project will develop models relating the LIDAR metrics to forest inventory data collected on the Deschutes National Forest and use these models to map the variables across the entire National Forest. The Rural Technology Initiative (RTI) at the University of Washington's School of Forest Resources (SFR), is a leader in the development and application of new methods for applying emerging technologies in forest management and monitoring, particularly, the use of geospatial analysis and modeling tools. Through this project, RTI will assist PNW in the application of the FUSION/LDV LIDAR processing software developed by PNW to large-area LIDAR acquisitions in Oregon.
Animal Health Component
(N/A)
Research Effort Categories
Basic
(N/A)
Applied
(N/A)
Developmental
(N/A)
Goals / Objectives
The primary purpose of this project is to process LIDAR point data and canopy surfaces to derive descriptive statistics for land managed by the Bureau of Land Management (BLM) in Oregon covered by LIDAR data acquired by the Oregon Department of Geology and Mineral Industries (DOGAMI) and to use previously computed statistics and ground measurements for plots on the Deschutes National Forest to develop models describing forest inventory variables. Specific objectives include: 1. Develop procedures to organize and process LIDAR data covering areas managed by the BLM in Oregon to produce canopy height models and compute descriptive statistics for the canopy surface and LIDAR point cloud. 2. Develop models relating LIDAR metrics to forest inventory variables for the Deschutes National Forest. 3. Use the models to map forest inventory variables across the Deschutes National Forest (portions of the forest included in the 2009 and 2010 LIDAR acquisitions). 4. Assess the accuracy of the mapped results using independent data (stand exams from the National Forest and photo-interpreted information from other USDA Forest Service Pacific Northwest Research Station (PNW) scientists). Specific objectives will be accomplished through collaboration between the PNW, the Rural Technology Initiative (RTI) at the University of Washington's School of Forest Resources (SFR), and an additional scientist at UW-SFR (Dr. Van Kane).
Project Methods
We will: 1. Collaborate with the USDA Forest Service Pacific Northwest Research Station (PNW) in the preparation and execution of a mutually acceptable implementation plan. 2. Process the LIDAR data acquired by the Oregon Department of Geology and Mineral Industries (DOGAMI) to produce descriptive metrics for the point cloud and canopy surface. 3. Develop models to relate LIDAR descriptive metrics (previous computed by PNW) to forest inventory variables for the Deschutes National Forest. 4. Use the predictive models to map forest inventory variables across the areas of the Deschutes National Forest covered by LIDAR data. 5. Assess the mapping results using independent stand exam data for selected stands. 6. Co-author publication of the modeling results as appropriate with respective participation by individual scientists on their particular subjects.