Open Access Mini Review

Non-Conventional Methods as a New Alternative for the Estimation of Terrestrial Biomass and Carbon Sequestered

Basam Dahy1, Salem Issa1*, Taoufik Ksiksi2 and Nazmi Saleous3

1Department of Geosciences, UAEU, UAE

2Department of Biology, UAEU, UAE

3Department of Geography, UAEU, UAE

Corresponding Author

Received Date: December 16, 2019;  Published Date: December 18, 2019


A practical definition of forest biomass is “the total amount of aboveground living organic matter in trees expressed as oven-dry tons per unit area”. Terrestrial carbon sequestration is the process of storing atmospheric CO2 as carbon in the stems, roots of plants and in soil. Estimating of biomass can give an idea of the amount of carbon dioxide (CO2) that can be removed from the atmosphere by forests and plantations. Mapping, measuring and monitoring of biomass are now central issues due to the importance of biomass estimation in many ecology-related research areas. The estimation of biomass is a challenging task, especially in areas with both complex stands and varying environmental conditions, requiring accurate and consistent measurement methods. To efficiently and effectively estimate terrestrial biomass amount, it is important to have detailed knowledge of its distribution, abundance, and quality. Non-conventional approaches such as remote sensing (RS) and Geographic Information Systems (GIS) technologies offer the mean to enable rapid assessment of terrestrial biomass over large areas relatively quickly and at low cost. Indeed, RS and GIS have great potential in current estimation, future prediction and management of terrestrial carbon. In this regard, the deployment of an integrated RS / GIS solution for precision carbon management has high potential significance. This paper provides a mini review of biomass and carbon sequestered assessment using innovative non-conventional methods as compared to traditional conventional methods.

Keywords: Allometric equations; Soil organic carbon; Terrestrial carbon; Remote sensing; GIS

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