Abstract
Abstract. Forest biomass is the sum of above ground living organic material contained in trees which is expressed as dry weight per unit area. Forest biomass acts as substantial terrestrial carbon sinks, they are estimated to absorb 2.7 Petagrams of carbon per year, as such accurate estimation of forest carbon stock is very important. The estimation of biomass is also important because of its application in commercial exploitation as well as in global carbon cycle. Particularly in the latter context, the estimation of the total above-ground biomass (TAGB) with sufficient accuracy is vital in reporting the spatial and temporal state of forest under the United Nations Framework Convention on Climate Change (UNFCCC), Reducing Emissions from Deforestation in Developing Countries (REDD). In this research, tree height, DBH and crown cover were measured using field instruments. Individual ultra-high-resolution UAV images acquired using customized Visible-NIR, were georeferenced and tree crown were extracted using multi-resolution segmentation. A regression equation between field measured biomass and Crown Projection Area (CPA) was developed. The paper presents results from Barandabhar Forest of Chitwan District, Nepal. RMSE of ortho-mosaic was found to be 18 cm. While R2 value of 89% was obtained for relationship between DBH and biomass, that of 61% was attained for relationship between CPA and biomass.
Highlights
Forest biomass is the sum of above ground living organic material contained in trees which is expressed as dry weight per unit area
A custom-made Hexa-copter was used as Unmanned Aerial Vehicle (UAV) platform for image acquisition (Figure 7)
UAV based approach is the most feasible method of above ground tree biomass estimation (AGTB), as it facilitates the ground-based measurement with satellite-based measurements
Summary
Forest biomass is the sum of above ground living organic material contained in trees which is expressed as dry weight per unit area. (Hudak et al, 2012; Vaglio Laurin et al, 2014)) and optical multi and hyperspectral data (e.g., (Morel, Fisher, Malhi, 2012; Vaglio Laurin et al, 2014)) in various forest ecosystem These studies relate field-measured biomass values to train statistical or machine-learning methods in predicting biomass by remote sensing predictors, and the majority report favourably on the accuracy of their biomass predictions. The wall-to-wall estimation of forest biomass over large areas by ground-based measurements requires a dense network of inventory plots to reach good accuracies In many regions, this is infeasible due to high costs, required man power and inaccessible field situations.
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