Burt, Andrew Philip; (2017) New 3D measurements of forest structure. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Regular assessment of the state and change of the world’s forests is essential because of the range of climate and ecosystem services they provide. Earth observation efforts responsible for monitoring the above-ground biomass (AGB) of the world’s forests, a frequently used proxy for forest state, rely on calibration from a network of field plots. In lieu of impracticable direct measurement of AGB at these plots, estimates are ob- tained through empirical allometric correlations that relate simple measurements of tree structure, to mass. Throughout tropical forest, few pan-tropical allometric models exist because of the limited availability of calibration data. In this thesis, the best available pan-tropical allometric dataset is used to demonstrate that the statistical method employed in the construction of all widely used models, log-transformed ordinary least squares linear regression, is inappropriate. Alternatively, a new non-linear model is proposed where uncertainties are derived from non-parametric methods. These uncertainties are shown to exceed 75 % and 25 % of AGB at the tree- and plot-scale respectively. This leads to the conclusion that AGB estimates of large swathes of tropical forest are statistically indistinguishable from one another when inferred through pan-tropical allometry. These results highlight the need to introduce alternative methods. The primary objective of this thesis is to demonstrate that new 3D measurements of forest structure from terrestrial laser scanning (TLS) can provide accurate non-destructive estimates of AGB. Rich point clouds have been acquired across multiple forest types. Novel algorithms are developed and applied here to retrieve tree-scale AGB via volume estimation on a wide-scale. It is shown these new methods can estimate tree- and plot-scale AGB to within 23.3% and 7.9% of the respective direct measurement. It is shown these TLS-derived estimates of AGB, are, on average, 17.9 % larger than their allometric-derived counterparts. Allometric methods may thus significantly underestimate tropical forest carbon stocks.

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