Estimating live forest carbon dynamics with a Landsat-based curve-fitting approach

Todd A. Schroeder; Andrew Gray; Mark E. Harmon; David O. Wallin; Warren B. Cohen

doi:10.1117/1.2937821

1 May 2008 Estimating live forest carbon dynamics with a Landsat-based curve-fitting approach

Todd A. Schroeder, Andrew Gray, Mark E. Harmon, David O. Wallin, Warren B. Cohen

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 2, Issue 1, 023519 (May 2008). https://doi.org/10.1117/1.2937821

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 inthe study area, whereas stand age trajectories revealed a temporally punctuated shift in landuse which was not spectrally detected by the inventory sample.

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Todd A. Schroeder, Andrew Gray, Mark E. Harmon, David O. Wallin, and Warren B. Cohen "Estimating live forest carbon dynamics with a Landsat-based curve-fitting approach," Journal of Applied Remote Sensing 2(1), 023519 (1 May 2008). https://doi.org/10.1117/1.2937821

Published: 1 May 2008

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