Skip to Main Content
A comparison of accuracy and cost of LiDAR versus stand exam data for landscape management on the Malheur National ForestAuthor(s): Susan Hummel; A. T. Hudak; E. H. Uebler; M. J. Falkowski; K. A. Megown
Source: Journal of Forestry. July/August: 267-273.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
PDF: View PDF (367.26 KB)
DescriptionForesters are increasingly interested in remote sensing data because they provide an overview of landscape conditions, which is impractical with field sample data alone. Light Detection and Ranging (LiDAR) provides exceptional spatial detail of forest structure, but difficulties in processing LiDAR data have limited their application beyond the research community. Another obstacle to operational use of LiDAR data has been the high cost of data collection. Our objectives in this study were to summarize, at the stand level, both LiDAR- and Landsat (satellite)-based predictions of some common structural and volume attributes and to compare the cost of obtaining such summaries with those obtained through traditional stand exams. We found that the accuracy and cost of a LiDAR-based inventory summarized at the stand level was comparable to traditional stand exams for structural attributes. However, the LiDAR data were able to provide information across a much larger area than the stand exams alone.
- You may send email to firstname.lastname@example.org to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- 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.
CitationHummel, Susan; Hudak, A. T.; Uebler, E. H.; Falkowski, M. J.; Megown, K. A. 2011. A comparison of accuracy and cost of LiDAR versus stand exam data for landscape management on the Malheur National Forest. Journal of Forestry. July/August: 267-273.
Keywordssilviculture, forest management, LiDAR, inventory, stand exams
- Predicting plot basal area and tree density in mixed-conifer forest from lidar and Advanced Land Imager (ALI) data
- Aggregating pixel-level basal area predictions derived from LiDAR data to industrial forest stands in North-Central Idaho
- Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys
XML: View XML