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Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data.Author(s): S.P. Healey; Z. Yang; W.B. Cohen; D.J. Pierce
Source: Remote Sensing of the Environment. 101: 115-126
Publication Series: Scientific Journal (JRNL)
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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 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.
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CitationHealey, 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
KeywordsChange detection, partial harvest, Landsat
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