Browsing by Author "Saroj K. Barik"

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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) Mukunda Dev Behera; J.S.R. Krishna; Somnath Paramanik; Shubham Kumar; Soumit K. Behera; Sonik Anto; Shiv Naresh Singh; Anil Kumar Verma; Saroj K. Barik; Manas Ranjan Mohanta; Sudam Charan Sahu; Chockalingam Jeganathan; Prashant K. Srivastava; Biswajeet Pradhan
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.
Nanocurcumin Potently Inhibits SARS-CoV-2 Spike Protein-Induced Cytokine Storm by Deactivation of MAPK/NF-κB Signaling in Epithelial Cells
(American Chemical Society, 2022) Vivek K. Sharma; None Prateeksha; Shailendra P. Singh; Brahma N. Singh; Chandana V. Rao; Saroj K. Barik
Interleukin-mediated deep cytokine storm, an aggressive inflammatory response to SARS-CoV-2 virus infection in COVID-19 patients, is correlated directly with lung injury, multi-organ failure, and poor prognosis of severe COVID-19 patients. Curcumin (CUR), a phenolic antioxidant compound obtained from turmeric (Curcuma longa L.), is well-known for its strong anti-inflammatory activity. However, its in vivo efficacy is constrained due to poor bioavailability. Herein, we report that CUR-encapsulated polysaccharide nanoparticles (CUR-PS-NPs) potently inhibit the release of cytokines, chemokines, and growth factors associated with damage of SARS-CoV-2 spike protein (CoV2-SP)-stimulated liver Huh7.5 and lung A549 epithelial cells. Treatment with CUR-PS-NPs effectively attenuated the interaction of ACE2 and CoV2-SP. The effects of CUR-PS-NPs were linked to reduced NF-κB/MAPK signaling which in turn decreased CoV2-SP-mediated phosphorylation of p38 MAPK, p42/44 MAPK, and p65/NF-κB as well as nuclear p65/NF-κB expression. The findings of the study strongly indicate that organic NPs of CUR can be used to control hyper-inflammatory responses and prevent lung and liver injuries associated with CoV2-SP-mediated cytokine storm. © 2022 American Chemical Society
Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data Using Machine Learning over India
(MDPI, 2022) Sujit M. Ghosh; Mukunda D. Behera; Subham Kumar; Pulakesh Das; Ambadipudi J. Prakash; Prasad K. Bhaskaran; Parth S. Roy; Saroj K. Barik; Chockalingam Jeganathan; Prashant K. Srivastava; Soumit K. Behera
Forest canopy height estimates, at a regional scale, help understand the forest carbon storage, ecosystem processes, the development of forest management and the restoration policies to mitigate global climate change, etc. The recent availability of the NASA’s Global Ecosystem Dynamics Investigation (GEDI) LiDAR data has opened up new avenues to assess the plant canopy height at a footprint level. Here, we present a novel approach using the random forest (RF) for the wall-to-wall canopy height estimation over India’s forests (i.e., evergreen forest, deciduous forest, mixed forest, plantation, and shrubland) by employing the high-resolution top-of-the-atmosphere (TOA) reflectance and vegetation indices, the synthetic aperture radar (SAR) backscatters, the topography and tree canopy density, as the proxy variables. The variable importance plot indicated that the SAR backscatters, tree canopy density and the topography are the most influential height predictors. 33.15% of India’s forest cover demonstrated the canopy height <10 m, while 44.51% accounted for 10–20 m and 22.34% of forests demonstrated a higher canopy height (>20 m). This study advocates the importance and use of GEDI data for estimating the canopy height, preferably in data-deficit mountainous regions, where most of India’s natural forest vegetation exists. © 2022 by the authors.