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Analysis of biomass dynamics derived from spectral trajectories.Author(s): S.L. Powell; W.B. Cohen; R.E. Kennedy
Source: In: Proceedings of the Eleventh Forest Service remote sensing application conference. Bethesda, MA: American Society for Photogrammetry and Remote Sensing
Publication Series: Scientific Journal (JRNL)
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DescriptionQuantification of the rates and variability of forest disturbance and regrowth at continental scales remains a critical challenge. We have undertaken an effort to address these issues, through funding from NASA, by coordinating research efforts between the USDA Forest Service Forest Inventory and Analysis (FIA) and the North American Carbon Program (NACP). We present preliminary results of our approach to analyze 20+ years of biennial Landsat data, and translate change trajectories from spectral space into biomass change trajectories. We have implemented an empirical modeling approach to interpret spectral change as biomass change. To demonstrate our approach, we compare disturbance and regrowth trajectories in two distinct regions of the United States. We anticipate that the outcome of this study will prove beneficial to both the FIA program for tracking spatially explicit estimates of forest disturbance and regrowth, and to the NACP for quantification of the consequences of these rates for the carbon cycle.
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CitationPowell, S.L.; Cohen, W.B.; Kennedy, R.E. 2007. Analysis of biomass dynamics derived from spectral trajectories. In: Proceedings of the Eleventh Forest Service remote sensing application conference. Bethesda, MA: American Society for Photogrammetry and Remote Sensing
KeywordsLandsat, biomass(trees), disturbance, regrowth, FIA
- Use of Landsat-based monitoring of forest change to sample and assess the role of disturbance and regrowth in the carbon cycle at continental scales
- Using FIA and landsat observations to improve the spatial and temporal resolution of forest carbon estimates
- Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches
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