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    Author(s): S. HealeyP. PattersonS. Urbanski
    Date: 2014
    Source: The International Forestry Review. 16(5): 196.
    Publication Series: Abstract
    Station: Rocky Mountain Research Station
    PDF: View PDF  (69.29 KB)


    Remotely sensed observations can provide unique perspective on how management and natural disturbance affect carbon stocks in forests. However, integration of these observations into formal decision support will rely upon improved uncertainty accounting. Monte Carlo (MC) simulations offer a practical, empirical method of accounting for potential remote sensing errors as maps are used as inputs in ecosystem carbon assessment. We present a generic approach for coordinating the MC alteration of map values so that specific levels of both pixel-level and map-wide systematic error may be simulated. This approach is based on constructing systems of linear equations and inequalities which incorporate results of map validation exercises. Solution of these systems provides probability functions capable of simulating different levels of error. We illustrate this approach, using error assessments calibrated by the United States (U.S.) national forest inventory data, in an assessment of the effects of wildfire and harvest on carbon storage over 20 years on a forested landscape in the western U.S. This assessment utilized the Forest Carbon Management Framework approach, which is being implemented across the U.S. National Forest System. Results showed that systematic map errors can contribute significant uncertainty in MC analysis, but that impacts of fire and harvest on landscape-level carbon storage can nevertheless be clearly identified and differentiated using remotely sensed maps.

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    Healey, S.; Patterson, P.; Urbanski, S. 2014. A systematic framework for Monte Carlo simulation of remote sensing errors map in carbon assessments. The International Forestry Review. 16(5): 196.


    Monte Carlo (MC) simulations, remote sensing, carbon stocks

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