Browsing by Author "Anto S."

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    Digital hemispherical photographs and Sentinel-2 multi-spectral imagery for mapping leaf area index at regional scale over a tropical deciduous forest
    (Springer, 2024) Behera M.D.; Krishna J.S.R.; Paramanik S.; Kumar S.; Behera S.K.; Anto S.; Singh S.N.; Verma A.K.; Barik S.K.; Mohanta M.R.; Sahu S.C.; Jeganathan C.; Srivastava P.K.; Pradhan B.
    The leaf area index (LAI) provides valuable input for modeling climate and ecosystem processes. However, ground-based observations are necessitated across various phenophases from dense tropical forests for a better understanding in terms of their contribution to carbon fixation. In this study, Digital Hemispherical Photography (DHP) was used for LAI observation from Similipal Biosphere Reserve, and to predict high-resolution LAI using Random Forest Machine Learning approach. Observations were taken from ninety-three Elementary sampling units (ESUs) corresponding to the beginning and end of leaf fall seasons across moist deciduous, dry deciduous, and semi-evergreen forests. LAI demonstrated high values for dry deciduous, followed by semi-evergreen and moist deciduous forests for the start of the leaf fall season, whereas moist deciduous forests demonstrated high values during the end of the leaf fall season. Satellite-based spectral reflectance bands of Sentinel-2 and vegetation indices (VIs) were used as predictor variables, wherein the band-7, band-8, band-12, enhanced vegetation index (EVI), and Red-edge based EVI were evaluated as the most dominant responsive variables for LAI estimation. Random Forest (RF) model provided good accuracy (R2 = 0.64, RMSE = 0.62) with observed DHP-based LAI. However, a comparison of RF model-based predicted LAI with global LAI products (MOD15A2H and VNP15A2H) provided a moderate correlation. Such studies demonstrate the potential of site or region-specific case studies to evaluate coarser-resolution global LAI products for possible improvement. � International Society for Tropical Ecology 2024.