Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): Jennifer L. R. Jensen; Karen S. Humes; Lee A. Vierling; Andrew T. Hudak
    Date: 2008
    Source: Remote Sensing of the Environment. 112: 3947-3957.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: View PDF  (469.76 KB)

    Description

    Leaf area index (LAI) is a key forest structural characteristic that serves as a primary control for exchanges of mass and energy within a vegetated ecosystem. Most previous attempts to estimate LAI from remotely sensed data have relied on empirical relationships between field-measured observations and various spectral vegetation indices (SVIs) derived from optical imagery or the inversion of canopy radiative transfer models. However, as biomass within an ecosystem increases, accurate LAI estimates are difficult to quantify. Here we use lidar data in conjunction with SPOT5-derived spectral vegetation indices (SVIs) to examine the extent to which integration of both lidar and spectral datasets can estimate specific LAI quantities over a broad range of conifer forest stands in the northern Rocky Mountains. Our results show that SPOT5-derived SVIs performed poorly across our study areas, explaining less than 50% of variation in observed LAI, while lidar-only models account for a significant amount of variation across the two study areas located in northern Idaho; the St. Joe Woodlands (R2=0.86; RMSE=0.76) and the Nez Perce Reservation (R2=0.69; RMSE=0.61). Further, we found that LAI models derived from lidar metrics were only incrementally improved with the inclusion of SPOT 5- derived SVIs; increases in R2 ranged from 0.02­0.04, though model RMSE values decreased for most models (0-11.76% decrease). Significant lidar-only models tended to utilize a common set of predictor variables such as canopy percentile heights and percentile height differences, percent canopy cover metrics, and covariates that described lidar height distributional parameters. All integrated lidar-SPOT 5 models included textural measures of the visible wavelengths (e.g. green and red reflectance). Due to the limited amount of LAI model improvement when adding SPOT 5 metrics to lidar data, we conclude that lidar data alone can provide superior estimates of LAI for our study areas.

    Publication Notes

    • You may send email to rmrspubrequest@fs.fed.us 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.

    Citation

    Jensen, Jennifer L. R.; Humes, Karen S.; Vierling, Lee A.; Hudak, Andrew T. 2008. Discrete return lidar-based prediction of leaf area index in two conifer forests. Remote Sensing of the Environment. 112: 3947-3957.

    Keywords

    Lidar, Leaf area index (LAI), SPOT, integration

    Related Search


    XML: View XML
Show More
Show Fewer
Jump to Top of Page