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    Author(s): Steven P. NormanWilliam W. Hargrove; Joseph P. Spruce; William M. Christie; Sean W. Schroeder
    Date: 2013
    Source: Gen. Tech. Rep. SRS-GTR-180. Asheville, NC: USDA-Forest Service, Southern Research Station. 30 p.
    Publication Series: General Technical Report (GTR)
    Station: Southern Research Station
    PDF: Download Publication  (7.73 MB)


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    Satellite-based remote sensing can assist forest managers with their need to recognize disturbances and track recovery. Despite the long standing availability of raw imagery, the systematic delivery of spatially continuous, ready-to-use, processed products has evaded us until recently. The web-based ForWarn system moves us a step forward by generating forest change maps at high frequency in a format that is usable to forest managers, planners, and the public. The ForWarn system shows change in the Normalized Difference Vegetation Index derived from moderate resolution imagery according to a range of baseline normals. Expectations of normal derive from previously observed changes in seasonal leaf phenology; this adjustment is critical for forests dominated by deciduous vegetation that vary in greenness through the year. After these seasonal adjustments are made behind the scene, the remaining forest change that ForWarn users see may result from an array of climatic and disturbance causes. These include insects and disease, wildland fire, wind, hail, human development, drought, or variation in the timing of spring and fall. This publication outlines the data and methods that underlie this technology, and provides examples that illustrate selected capabilities of this system for coarse-scale forest monitoring.

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    Norman, Steven P.; Hargrove, William W.; Spruce, Joseph P.; Christie, William M.; Schroeder, Sean W. 2013. Highlights of satellite-based forest change recognition and tracking using the ForWarn System. Gen. Tech. Rep. SRS-GTR-180. Asheville, NC: USDA-Forest Service, Southern Research Station. 30 p.


    Disturbance, monitoring, phenology, recovery, remote sensing

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