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    Author(s): Todd Kennaway; Eileen Helmer; Michael Lefsky; Thomas Brandeis; Kirk Sherrill
    Date: 2009
    Source: Journal of Applied Remote Sensing. 2: 023551. 1-27.
    Publication Series: Scientific Journal (JRNL)
    Station: Southern Research Station
    PDF: Download Publication  (5.4 MB)


    Current information on land cover, forest type and forest structure for the Virgin Islands is critical to land managers and researachers for accurate forest inverntory and ecological monitoring. In this study, we use cloud free image mosaics of panchromatic sharpened Landsat ETM+ images and decision tree classification software to map land cover and forest type for the Virgin Islands, illustrating a low cost, repeatable mapping approach. Also, we test if coarse-resolution discrete lidar data that are often collected in conjunction with digital orthophotos are useful for mapping forest structural attributes. This approach addresses the factors that affect vegetation distsribution and structure by testing if environmental variables can improve regression models of forest height and biomass derived from lidar data. The overall accuracy of the 29 forest and nonforest classes is 72%, while most the forest types are classified with greater than 70% accuracy. Due to the larage point spacing of this lidar dataset, it is most appropriate for height measurements of dominant and co-dominant trees (R2 = 70%) due to its inability to accurately represent forest understory. Above ground biomass per hectare is estimated by its direct relationship with plot canopy height (R2 = 0.72%).

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    Kennaway, Todd A.; Helmer, Eileen H.; Lefsky, Michael A.; Brandeis, Thomas A..; Sherrill, Kirk R. 2009. Mapping land cover and estimating forest structure using satellite imagery and coarse resolution lidar in the Virgin Islands. 2008 Society of Photo-Optical Instrumentation Engineers. Journal of Applied Remote Sensing. 2: 023551. 1-27.


    Land cover, decision tree software, discrete idar, forest structure, regression modeling, Virgin Islands

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