Skip to Main Content
Application of two regression-based methods to estimate the effects harvest on forest structure using Landsat dataAuthor(s): Sean P. Healey; Zhiqiang Yang; Warren B. Cohen; D. John Pierce
Source: Remote Sensing of Environment. 101: 115-116.
Publication Series: Miscellaneous Publication
PDF: View PDF (470 B)
DescriptionAlthough 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 percent 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-infrared 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 re-measured 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.
- 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.
CitationHealey, Sean P.; Yang, Zhiqiang; Cohen, Warren B.; Pierce, D. John. 2006. Application of two regression-based methods to estimate the effects harvest on forest structure using Landsat data. Remote Sensing of Environment. 101: 115-116.
Keywordschange detection, partial harvest, Landsat
- Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data.
- Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression
- Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data
XML: View XML