Document Type

Article

Publication Date

2008

Abstract

Direct estimation of aboveground biomass with spectral reflectance data has proven challenging for high biomass forests of the Pacific Northwestern United States. We present an alternative modeling strategy which uses Landsat’s spatial, spectral and temporal characteristics to predict live forest carbon through integration of stand age and site index maps and locally calibrated Chapman-Richards curves. Predictions from the curve-fit model were evaluated at the local and landscape scales using two periods of field inventory data. At the pixel-level, the curve-fit model had large positive bias statistics and at the landscape scale over-predicted study area carbon for both inventory periods. Despite the over-estimation, the change in forest carbon estimated by the curve-fit model was well within the standard error of the inventory estimates. In addition to validating the curve-fit models carbon predictions we used Landsat data to evaluate the degree to which the field inventory plots captured the forest conditions of the study area. Landsat-based frequency histograms revealed the systematic sample of inventory plots effectively captured the broad range of forest conditions found in the study area, whereas stand age trajectories revealed a temporally punctuated shift in land- use which was not spectrally detected by the inventory sample.

Publication Title

Journal of Applied Remote Sensing

Volume

2

Issue

023519 (9 May 2008)

Required Publisher's Statement

© 2008 Society of Photo-Optical Instrumentation Engineers [DOI: 10.1117/1.2937821] Received 21 Dec 2007; accepted 5 May 2008; published 9 May 2008 [CCC: 19313195/2008/$25.00] Journal of Applied Remote Sensing, Vol. 2, 023519 (2008)

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