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): S.P. Healey; Z. Yang; W.B. Cohen; D.J. Pierce
    Date: 2006
    Source: Remote Sensing of the Environment. 101: 115-126
    Publication Series: Scientific Journal (JRNL)
    PDF: View PDF  (2.44 MB)

    Description

    Although partial harvests are common in many forest types globally, there has been little assessment of the potential to map the intensity of these harvests using Landsat data. We modeled basal area removal and percentage cover change in a study area in central Washington (northwestern USA) using biennial Landsat imagery and reference data from historical aerial photos and a system of inventory plots. First, we assessed the correlation of Landsat spectral bands and associated indices with measured levels of forest removal. The variables most closely associated with forest removal were the shortwave infrared (SWIR) bands (5 and 7) and those strongly influenced by SWIR reflectance (particularly Tasseled Cap Wetness, and the Disturbance Index). The band and indices associated with near-inked reflectance band 4, Tasseled Cap Greenness, and the Normalized Difference Vegetation Index) were only weakly correlated with degree of forest removal. Two regression-based methods of estimating forest loss were tested. The first, termed "state model differencing" (SMD), involves creating a model representing the relationship between inventory data from any date and corresponding, cross-normalized spectral data. This "state model" is then applied to imagery from two dates, with the difference between the two estimates taken as estimated change. The second approach, which we called "direct change modeling" (DCM), involves modeling forest structure changes as a single term using remeasured inventory data and spectral differences from corresponding image pairs. In a leave-one-out cross-validation process, DCM-derived estimates of harvest intensity had lower root mean square errors than SMD for both relative basal area change and relative cover change. The higher measured accuracy of DCM in this project must be weighed against several operational advantages of SMD relating to less restrictive reference data requirements and more specific resultant estimates of change.

    Publication Notes

    • You may send email to pnw_pnwpubs@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

    Healey, S.P.; Yang, Z.; Cohen, W.B.; Pierce, D.J. 2006. Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data. Remote Sensing of the Environment. 101: 115-126

    Keywords

    Change detection, partial harvest, Landsat

    Related Search


    XML: View XML
Show More
Show Fewer
Jump to Top of Page
https://www.fs.usda.gov/treesearch/pubs/29351