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Background: Ongoing climate and Earth's atmosphere changes create profound effect on distribution and composition of forest, as well as on the fauna that depends on forest. The Sentinel-2A satellite data eases the mapping of Leaf Chlorophyll Content (LCC) at higher spatial and temporal resolution. In the present study, the temporal dimension of LCC was evaluated as an indicator of plant stress. LCC was retrieved using the inversion of the radiative transfer model based on an arti cial neural network. The data used for Spatio-temporal modelling of LCC was Landsat data. Result: From the Sentinel imagery derived vegetation indices, it was found that the narrowband indices having high correlation with LCC were pigment speci c simple ratio and normalized difference index (45) (R 2 > 0.7; p < 0.001) centred at 665 nm, 705 nm, and 740 nm. Landsat 8 infrared percentage vegetation index had a strong relationship with LCC (R 2 =0.8). The Spatio-temporal (1997 to 2017) plant stress were detected using changes in LCC through an equation of correlation. The negative changes and deterioration of LCC were seen in the forest during the year 1997 to 20I7(rate =-1.2 µgcm-2 year-1) showing higher rate of forest health decline. Conclusion: The 33% of plant stress increased currently in the protected forest mainly because of anthropogenic in uences. These vast decline in the chlorophyll gives rise to various photosynthetic vulnerabilities in forest ecosystem and indirectly affects human including wildlife.

The described protocol represents a safe and feasible concept for the induction of clinical and immunological responses. The application of a peptide cocktail-derived from different antigens as a novel treatment modality is supposed to allow for the genetic and biologic heterogeneity of PCa.

Abstract:The continuously increasing demand of accurate quantitative high quality information on land surface properties will be faced by a new generation of environmental Earth observation (EO) missions. One current example, associated with a high potential to contribute to those demands, is the multi-spectral ESA Sentinel-2 (S2) system. The present study focuses on the evaluation of spectral information content needed for crop leaf area index (LAI) mapping in view of the future sensors. Data from a field campaign were used to determine the optimal spectral sampling from available S2 bands applying inversion of a radiative transfer model (PROSAIL) with look-up table (LUT) and artificial neural network (ANN) approaches. Overall LAI estimation performance of the proposed LUT approach (LUT N50 ) was comparable in terms of retrieval performances with a tested and approved ANN method. Employing seven-and eight-band combinations, the LUT N50 approach obtained LAI RMSE of 0.53 and normalized LAI RMSE of 0.12, which was comparable to the results of the ANN. However, the LUT N50 method showed a higher robustness and insensitivity to different band settings. Most frequently selected wavebands were located in near infrared and red edge spectral regions. In conclusion, our results emphasize the potential benefits of the Sentinel-2 mission for agricultural applications.

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