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Master Thesis

Estimation de la biomasse forestière du Brésil à partir des données LiDAR

Abstract : Tropical forests are considered important stocks of carbon and biodiversity. LiDAR is a technology that quantifies forest biomass in large areas. The estimation of aboveground biomass depends on the estimation of height which is itself influenced by the density of points acquired by aerial LiDAR. The mathematical model of this study evaluates the accuracy of estimating the maximum height of the canopy according to point density. This essay shows that the bias in height estimation is less in tropical forest than in boreal forest in northern Canada. This is certainly due to the different shape of the canopy, since the forest in northern Canada contains triangular conifers, while the Brazilian rainforest presents a rather flat canopy. This study then shows the effect of height estimation on forest biomass in Brazil. The underestimation is on average 50 Mg / ha for density data of 1 point / m², but from 3 points / m² the underestimation is almost negligible. However, the bias of aboveground biomass estimation does not appear to be important on low density data. Therefore, this study shows that it is not necessary to acquire high-density data on the Atlantic forest of Brazil for a biomass study based on height estimation.
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Master Thesis
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Submitted on : Tuesday, October 6, 2020 - 2:03:41 PM
Last modification on : Wednesday, February 3, 2021 - 3:13:02 AM


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  • HAL Id : dumas-02959002, version 1



Vivien Scholkopf. Estimation de la biomasse forestière du Brésil à partir des données LiDAR. Sciences de l'ingénieur [physics]. 2019. ⟨dumas-02959002⟩



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