Title: Building capacity for providing canopy cover and canopy height at FIA plot locations using high-resolution imagery and leaf-off LiDAR
Author: Riemann, Rachel; O'Neil-Dunne, Jarlath; Liknes, Greg C.;
Source: In: Morin, Randall S.; Liknes, Greg C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CD-ROM]: 242-247.
Publication Series: Paper (invited, offered, keynote)
Description: Tree canopy cover and canopy height information are essential for estimating volume, biomass, and carbon; defining forest cover; and characterizing wildlife habitat. The amount of tree canopy cover also influences water quality and quantity in both rural and urban settings. Tree canopy cover and canopy height are currently collected at FIA plots either in the field or by dot-grid interpretation of digital aerial imagery. These techniques can be time consuming and costly. The University of Vermont's Spatial Analysis Laboratory has developed an automated approach using Object-Based Image Analysis (OBIA) techniques for extracting canopy cover, canopy height, and land cover from readily available high resolution aerial imagery and leaf-off LiDAR. We used datasets generated by the OBIA approach for 10 different counties spread across 4 states, representing a range of conditions. Canopy cover, canopy height, and land cover information were computed for each FIA plot, at scales of 144-foot-radius (plot circle) and 3,280-foot-(1-km)-radius, and compared to FIA estimates at the plot level. Results are discussed in terms of the comparative assessment of the three canopy cover data sources (including what is missing when nonforest plot data are not available), and the prognosis for using the OBIA techniques to extract this type of information at the county and state levels. Acquiring tree canopy cover data using the OBIA approach would allow FIA to apply a consistent method for acquiring canopy cover to both visit and non-visit plots, and even potentially increase the reliability of the canopy cover data available. This approach also provided valuable data on canopy height for FIA plots not visited in the field and additional data on landscape context for all FIA plots, improving capacity to characterize and analyze forest characteristics with respect to local levels of urbanization.
Keywords: statistics, estimation, sampling, modeling, remote sensing, forest health, data integrity, environmental monitoring, cover estimation, international forest monitoring
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
- This publication may be available in hard copy. Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat. During the capture process some typographical errors may occur. Please contact Sharon Hobrla, email@example.com if you notice any errors which make this publication unusable.
XML: View XML
Riemann, Rachel; ONeil-Dunne, Jarlath; Liknes, Greg C. 2012. Building capacity for providing canopy cover and canopy height at FIA plot locations using high-resolution imagery and leaf-off LiDAR. In: Morin, Randall S.; Liknes, Greg C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CD-ROM]: 242-247.
Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility