With the growing recognition that effective action on climate change will require a combination of emissions reductions and carbon sequestration, protecting, enhancing and restoring natural carbon sinks have become political priorities. Mangrove forests are considered some of the most carbon-dense ecosystems in the world with most of the carbon stored in the soil. In order for mangrove forests to be included in climate mitigation efforts, knowledge of the spatial distribution of mangrove soil carbon stocks are critical. Current global estimates do not capture enough of the finer scale variability that would be required to inform local decisions on siting protection and restoration projects. To close this knowledge gap, we have compiled a large georeferenced database of mangrove soil carbon measurements and developed a novel machine-learning based statistical model of the distribution of carbon density using spatially comprehensive data at a 30 m resolution. This model, which included a prior estimate of soil carbon from the global SoilGrids 250 m model, was able to capture 63% of the vertical and horizontal variability in soil organic carbon density (RMSE of 10.9 kg m −3 ). Of the local variables, total suspended sediment load and Landsat imagery were the most important variable explaining soil carbon density. Projecting this model across the global mangrove forest distribution for the year 2000 yielded an estimate of 6.4 Pg C for the top meter of soil with an 86-729 Mg C ha −1 range across all pixels. By utilizing remotely-sensed mangrove forest cover change data, loss of soil carbon due to mangrove habitat loss between 2000 and 2015 was 30-122 Tg C with >75% of this loss attributable to Indonesia, Malaysia and Myanmar. The resulting map products

The Sundarbans (21830 0 -22830 0 N and 89800 0 -89855 0 E) is the largest mangrove forest in the world. Forests are very important for sequestering atmospheric carbon and mangroves are amongst the most efficient in carbon sequestration. This study presents the estimation of ecosystem carbon (aboveand belowground) stock in the Sundarbans using a large scale data sets collected from systematic grid samples throughout the forest. The variation of carbon stock in different vegetation types and in different salinity zones in Sundarbans was investigated. The relationships between carbon stock and different vegetation functional attributes (basal area, mean tree height, crown coverage etc.) were also investigated. The amount of carbon stored varied significantly among vegetation types, salinity zones and vegetation functional attributes (P \ 0.05). Sundri (Heritiera fomes) dominated forest types store more ecosystem carbon (360.1 ± 22.71 Mg C ha -1 ) than other vegetation types. The fresh water zone shows the highest ecosystem carbon stock (336.09 ± 14.74 Mg C ha -1 ) followed by moderate and strong salinity zones.Salinity was found to enhance belowground carbon stock as revealed by the lowest proportion of belowground carbon stock (57.2 %) with respect to ecosystem carbon in fresh water zone and by the highest (71.9 %) in strong salinity zone. The results also reveal that no matter whether the mangroves are tall or dwarf, a significant amount of carbon is stored into the sediment. The vegetation attributes (basal area and mean tree height) of the dominant mangrove species in each vegetation type were identified as the key indicator of ecosystem carbon stock. We recommended some generalized regression equations to predict ecosystem carbon stock from basal area or mean tree height.

The conservation of ecosystems and their biodiversity has numerous co-benefits, both for local societies and for humankind worldwide. While the co-benefit of climate change mitigation through so called blue carbon storage in coastal ecosystems has raised increasing interest in mangroves, the relevance of multifaceted biodiversity as a driver of carbon storage remains unclear. Sediment salinity, taxonomic diversity, functional diversity and functional distinctiveness together explain 69%, 69%, 27% and 61% of the variation in above- and belowground plant biomass carbon, sediment organic carbon and total ecosystem carbon storage, respectively, in the Sundarbans Reserved Forest. Functional distinctiveness had the strongest explanatory power for carbon storage, indicating that blue carbon in mangroves is driven by the functional composition of diverse tree assemblages. Protecting and restoring mangrove biodiversity with site-specific dominant species and other species of contrasting functional traits would have the co-benefit of maximizing their capacity for climate change mitigation through increased carbon storage.

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Kitchen waste (KW) can be utilized to produce biogas due to its high biodegradability, calorific value and nutritive value to microbes, which will reduce our dependency on fossil fuels. The research work was conducted to investigate the production ability of biogas as an alternative energy from KW with co-digestion of cow manure (CM) through anaerobic digestion (AD). Firstly, three digesters were prepared to observe the individual degradation rate of KW, CM and co-digested KW with CM at room temperature (25°C~30° C) and at temperature of 37°C (mesophilic digestion) respectively and observed the degradation rate for co-digested KW with CM was higher than KW and CM alone. Secondly, three digesters were constructed to observe the effect of alkalinity at temperature 37° C and loading rate 200 gm/L. Three alkali (NaOH) doses 1.0%, 1.5% and 2.0% on wet matter basis of kitchen waste were applied to improve biodegradability and biogas production. The highest degradation rate was 6.8 ml/gm which was obtained from 1.5% NaOH and also observed that biogas production was almost doubled from treated KW than untreated KW. Finally, a portable biogas reactor was fabricated for pilot-scale biogas production which included an agitator and heating system. This reactor was operated at both 37° C and room temperature at a loading rate of 200 gm/L and observed that the digestion rate was faster at 37°C than room temperature. The prime object of this work was to investigate the prospect of kitchen waste for biogas production and ultimate protection of environment from the bad effect of methane gas that would be produced by uncontrolled anaerobic digestion.

Recent developments of remote sensing techniques which can capture both the structure and function of the ecosystem provide a more representative view of the landscape. These unique Earth observations were used to help improve traditional forestry surveys by providing species‐specific land cover classes for mangrove forests in the Sundarbans East Wildlife Sanctuary. By combining optical data from WorldView2 (WV2; 2 m pixel) and a canopy height model derived using radar data from TanDEM‐X (TDX; 12 m pixel), we identified nine mangrove and five non‐mangrove classes by following an Iterative Self‐Organizing Data Analysis Algorithm. Three dominant mangrove species accounted for nearly 50% of the sanctuary. Heritieria fomes disproportionately covered the largest area at 43%, overturning previous field‐based estimates of Excoecaria agallocha dominance. E. agallocha and Sonneratia apetala, covered 3% and 1.47% of the sanctuary, respectively. Four mixed species classes were also identified with clear vegetation zonation patterns that trended toward species homogeneity with increasing distance from shore. The overall land cover accuracy (WV2: 89.33%; WV2‐TDX: 89.89%), the Kappa Coefficient (WV2: 0.88; WV2‐TDX: 0.89) and change statistics between WV2 and WV2‐TDX land cover classifications indicate that the WV2 imagery can separate mangrove community types without structural data. The combination of the land cover classifications and the canopy height model indicated that H. fomes were not only the most dominant forest but also, on average, the tallest (12.3 m) among the other eight mangrove types. Our large‐scale mapping with high resolution optical and radar platforms can capture subtle changes in mangrove vegetation and canopy structural gradients more accurately and be used to monitor biodiversity changes and Aichi Biodiversity Targets and Indicators, which would contribute to biodiversity policy updating.

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