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The study aimed to determine the above-ground biomass (AGB) of tropical deciduous forests in Central India using field-based techniques and spaceborne quad-pol ALOS PALSAR-2 L-band and dual-pol Sentinel 1A C-band synthetic aperture radar (SAR). Field sampling based on 73 forested grids exhibited a diverse forest composition in the Satpura Tiger Reserve (STR) with a total of 54 tree species of which 31.48% species were IUCN Red-Listed. The forests were highly diverse (H′=3.22) with a low concentration of dominance (CD = 0.06) and a quite high species evenness (E = 0.81). The sensitivity and correlation between field-based AGB and SAR backscatter in STR were highest in cross-polarization due to its higher sensitivity to the vegetation structure in both L- and C-band SAR, where L-VH based AGB estimates were found more accurate (RMSE:5.17%). The study provided vital information for setting up strategies for biodiversity conservation, enhancing forest productivity, and restoring wildlife habitats. [ABSTRACT FROM AUTHOR]

A few decades ago, hydroelectric projects were considered a clean source of energy. The present study aims to assess the biomass and soil carbon (C) stock in the hydroelectric catchment in India and further, its implications to mitigate GHG emissions. The estimation depicts the low 'C' stock in the reservoir's catchment in comparison to reservoirs located globally in the same eco-region which will ultimately result in lower GHG emission in the future. Moreover, estimated total carbon density of the forest species was found as 54.92 ± 18.07 MgC.ha−1 while mean SOC was 79.64 ± 19.32 MgC.ha−1. The average total 'C' stock of Tehri hydroelectric reservoir's catchment was estimated as 134.56 MgC.ha−1. Moreover, ratios (total flooded carbon/installed capacity) comprised between 4 and304 kgC/W from the other reservoirs reported globally. These ratios are higher than the Tehri reservoir (0.28 kgC/W) as flooded area of Tehri reservoir during impoundment was 52 km2. Thus, Tehri reservoir was found to emit less GHG in time to come. The present study helps the hydropower industries, policymakers, and environmentalists in designing the catchment area treatment plan to minimize the negative impact of GHG on climate change. [ABSTRACT FROM AUTHOR]

The hydropower reservoirs, considered as a green source of energy, are now found to emit significant quantities of greenhouse gas (GHG) to the atmosphere. This article attempts to predict the vulnerability of Tehri reservoir, India to GHG emissions using the GHG risk assessment tool (GRAT). The GRAT is verified with experimental GHG fluxes. The annual mean CO2fluxes from diffusion, bubbling, and degassing were 425.93 ± 122.50, 4.81 ± 1.33, and 7.01 ± 2.77 mg m−2d−1, whereas CH4fluxes were 23.11 ± 7.08, 4.79 ± 1.08, and 7.41 ± 4.50 mg m−2d−1, respectively, during 2011–12. The model found that Tehri reservoir emitted higher CO2and CH4(i.e., 790 mg m−2d−1and 64 mg m−2d−1, respectively) in 2011, which came within vulnerability range causing more climate change impact. By the year 2015, it would scale down to medium risks necessitating no further assessment of GHG. Significant difference between predicted and experimental GHG emission are assessed, which may be due to insufficient data, spatial and temporal variations, decomposition of flooded biomass, limitation of GRAT model, and inadequate methodology. The study reveals that GHG emission from Tehri reservoir is less than predicted by the GRAT. [ABSTRACT FROM PUBLISHER]

Unmanned Aerial Vehicle (UAV) based remote sensing is one of the modern techniques for crop management, which has been used in this study for biomass and Nitrogen (N) level estimations for Stevia rebaudiana, a medicinal crop used as an alternative to sugar as a natural sweetener. Different levels of nitrogen treatments were given to S. rebaudiana and the crops were harvested for biomass estimation. Mica sense Altum multispectral sensor on board was used for acquiring the image data of the crop. The linear regression model was used to examine the best vegetation index using K-fold cross validation approach. Excess Green Index (ExG) was identified as best vegetation index for biomass estimation (R2 = 0.7; RMSE = 23.77 g/m2; nRMSE = 29.14%), whereas Enhanced Normalized Difference Vegetation Index (ENDVI) was found as best predictor for Nitrogen (N) level estimation (R2 = 0.9; RMSE = 1.75 g/m2; nRMSE = 14.59%). [ABSTRACT FROM AUTHOR]