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    Remotely sensed images including LANDSAT, SPOT, NAIP orthoimagery, and LiDAR and relevant processing tools can be used to predict plant stomatal conductance (gs), leaf area index (LAI), and canopy temperature, vegetation density, albedo, and soil moisture using vegetation indices like normalized difference vegetation index (NDVI) or soil adjusted vegetation index (SAVI) developed with near infrared (NIR) and red bands. In this study, we present results of those analyses for two study sites with different plant species: 1) a managed loblolly pine (Pinus taeda L.) forest in coastal North Carolina for canopy temperature and gs and 2) a managed Blueberry (Vaccinium corymbosum) orchard within a natural forest in coastal Georgia (Z-Blu orchard) for the LAI. An Object Based Image Analysis (OBIA) technique was employed on the Z-Blu orchard to distinguish the forest species and establish their correlation with LAI using ground-truthing. Similarly, we used OBIA technique for the forest speciation on Turkey Creek watershed at Francis Marion National Forest site in coastal South Carolina with ground-truthing. Both classified images yielded 80% classification accuracy based on field verifications. Similarly, >90% correlation was obtained for the LAI map developed for Z-Blu orchard site plant speciation. However, for the NC pine site, the correlations were poor, with R2 values of 0.33 and 0.26 for gs v/s Landsat Middle Infrared (MIR) and gs v/s Landsat Thermal Infrared TIR models, respectively. This study on advanced image processing approach for forest speciation and ET parameters prediction/estimation can be a basis for similar other studies in the region.

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    Panda, S.; Amatya,D.M.; Hoogenboom, G. 2014. Stomatal conductance, canopy temperature, and leaf area index estimation using remote sensing and OBIA techniques. Journal of Spatial Hydrology. 12(1): 24 p.


    ET, NDVI, SAVI, OBIA-segmentation, loblolly pine, blueberry

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