Recipient Organization
UNIVERSITY OF WASHINGTON
4333 BROOKLYN AVE NE
SEATTLE,WA 98195
Performing Department
Sustainable Resource Management
Non Technical Summary
Federal, state, and local government agencies have been actively acquiring high-density airborne light detecting and ranging data (LiDAR) for lands in Washington, Oregon and California over the last 10 years. Bare-earth surface models derived from these data have been used extensively for hydrologic analyses and to identify and map areas prone to landslide activity. However, applications of the data for characterizing vegetation have been more limited.Western forests are in a continual state of change. Insects, prolonged drought conditions, and fire have caused stress and mortality at the stand and individual tree level. High-density LiDAR provides measurements at the individual tree and branch level and may be useful for assessing and mapping tree vigor over large land areas. A small number of published studies have looked into ways to identify and characterize individual trees and their condition using LiDAR data. However, most studies have not involved large land areas, a variety of forest types, and a mix of stress-causing agents. Preliminary analyses using data from southern California indicate that LiDAR data can be used to isolate and characterize individual trees and to assess their relative vigor. However, the methods require more testing and refinement before they can be applied to produce information useful for land managers tasked with planning activities over large land areas containing a mix of forest types and conditions.In this project, LiDAR scientists will work together to develop, test, and refine methods aimed at identifying individual trees and assessing their condition. This project will utilize existing data as well as any new data that becomes available during the study period.The research conducted will use existing LiDAR data from Washington, Oregon, and California:Compare and evaluate tools and methods to identify and characterize individual trees within LiDAR point cloudsIdentify LiDAR-derived height and intensity metrics useful for distinguishing live and dead treesCompare results using data from WA, OR, and CA representing areas affected by fire-, drought-, and insect-related mortalityWork with land managers to identify analysis products and data layers that provide information useful for prioritizing and planning management activities
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
The research conducted will use existing LiDAR data from Washington, Oregon, and California:Compare and evaluate tools and methods to identify and characterize individual trees within LiDAR point cloudsIdentify LiDAR-derived height and intensity metrics useful for distinguishing live and dead treesCompare results using data from WA, OR, and CA representing areas affected by fire-, drought-, and insect-related mortalityWork with land managers to identify analysis products and data layers that provide information useful for prioritizing and planning management activities
Project Methods
In this project, LiDAR scientists will work together to develop, test, and refine methods aimed at identifying individual trees and assessing their condition.The research conducted will use existing LiDAR data from Washington, Oregon, and California:Compare and evaluate tools and methods to identify and characterize individual trees within LiDAR point cloudsIdentify LiDAR-derived height and intensity metrics useful for distinguishing live and dead treesCompare results using data from WA, OR, and CA representing areas affected by fire-, drought-, and insect-related mortalityWork with land managers to identify analysis products and data layers that provide information useful for prioritizing and planning management activities