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Blue Carbon Futures
Published in Lisamarie Windham-Myers, Stephen Crooks, Tiffany G. Troxler, A Blue Carbon Primer, 2018
Lisamarie Windham-Myers, Stephen Crooks, Tiffany Troxler
NASA-USGS Blue Carbon Monitoring System (BCMS, https://water.usgs.gov/nrp/blue-carbon/nasa-blue-cms/): From 2014 to 2018, a team of 18 scientists across U.S. institutions assessed, and sought to reduce the uncertainty in U.S. coastal carbon fluxes reported in the IPCC National Greenhouse Gas Inventory (NGGI). As seen in Chapter 16, coastal wetlands were included for the first time in the 2017 report, with an estimated 8.6 MT of CO2 sequestered due to land management. The BCMS synthesis across all U.S. coasts supported this assessment but quantified uncertainty in a spatially explicit manner, identifying key trends as well as bias and error associated with national-scale approaches to soil C stock, aboveground biomass C stocks, and mapped habitat. Holmquist et al. (2018a) found a surprisingly narrow distribution of soil carbon densities both at the surface and downcore to 1 m, among a representative set of 1,900+ in tidal wetland soil core locations across U.S. coasts (0.027 g C cm−3 + −0.0001 SE). Further, with a universally applicable algorithm at 30 m scale using remotely satellite data from LandSat and the National Agricultural Imaging Program, Byrd et al (2018) found a surprisingly similar distribution of tidal marsh aboveground biomass carbon stocks at validated sites located in six states across the U.S. coastline (Massachusetts, Maryland, Florida, Louisiana, California and Washington). Ultimately these two products (soil and biomass maps) illustrate that C stock accounting at continental scales is fairly well constrained, and not a major point of uncertainty; C stocks in the top meter of tidal wetland soil in the continental United States can be parsimoniously estimated at 270 Mg ha−1 and C stocks in aboveground vegetation are roughly 1.85 Mg ha−1. Agency-provided national scale products, however, were found to have significant biases for C accounting, with overestimation of soil C stocks (USDA-NRCS Soil Survey Geographic Database) and underestimation of tidally influenced wetlands (USFWS National Wetlands Inventory). Uncertainty assessments across all GHG accounting components point to the particular need for (i) improved assessment of methane emissions, and (ii) improved modeling of the fate of eroded soil carbon and rate of carbon dioxide emissions following wetland conversion to open water (Holmquist et al. 2018b). Numerous chapters in this book show that increasing empirical data and metadata sets, finer-scaled mapping, and model development will greatly improve the spatial extrapolation of field data into mapped products.
A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data
Published in Enterprise Information Systems, 2021
Ruei-Yuan Wang, Pao-an Lin, Jui-Yuan Chu, Yi-Huang Tao, Hsiao-Chi Ling
The carbon monitoring and management of forests are most crucial mechanisms for the regulatory control of global warming. Plants are generally regarded as a solution to the global increase in carbon dioxide, which can mitigate and adapt to climate change. Utilising reasonable and feasible control is asignificant cornerstone of sustainable development. However, from the perspective of global carbon monitoring, the key problems in Taiwan are that the terrain is very complex and the forest species are multitudinous. Thus, it is not easy to acquire accurate information for monitoring and estimation. Meanwhile, it is time-consuming and costly to use manpower for the conduction of surveys. Hereby, the methodology provided by this study is based on the characteristics of Taiwan, using RS conjugated with forest investigation ground truth to construct a feasible estimation model as a basis for management decisions.