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Research objectives

The general objective of this thesis is to use information on the structure and spatial organization of canopy trees to improve our ability to model forest AGB from field and RS data.
Our analyses are restricted to two approaches that were deemed promising: the pantropical approach for the estimation of AGB at the tree and plot level (i.e. from field data) and the canopy texture approach for the detection and extrapolation of field-derived AGB estimations via RS data.
A first part of this work seeks to increase our understanding of the pantropical model error and propose ways to mitigate this error. In particular, the compound predictor variable of the pantropical model (!+  » #  » $) does not allow capturing between-tree variations in relative crown dimensions, while crown allometries varies between species, along tree ontogeny and environmental gradients (e.g. Banin et al., 2012; Cannell, 1984; Poorter et al., 2006). We thus:
(i) Assess the contribution of crown mass variation to the pantropical model error, either at the tree level or when propagated to the plot level;
(ii) Propose a new operational strategy to explicitly take crown mass variation into account in pantropical AGB models.
We further used the predictions of the MTE on branch scaling properties as a point-of-entry to investigate the relevance of the power model form in AGB allometries. Specifically, we:
(iii) Test whether large trees branching structure conform to the predictions of the MTE.
A second part of the thesis focuses on assessing and improving the potential of a canopy texture-based RS method (FOTO) to retrieve tropical forest AGB. Here, a major objective was to:
(iv) Stabilize the texture-structure relationship across contrasted forest types from different regions of the world.

A pantropical approach

This thesis is based on two types of field data: (i) destructive measurements at the tree level and (ii) forest inventories at plot level. In this section, I briefly explain where the data I assembled came from and what the datasets are composed of.

Study areas and datasets

Central Africa

Core datasets of this work (at both tree- and plot-level) come from about five years of field data collection campaigns in central Africa (Cameroon, Gabon, Democratic Republic of Congo) carried out by the Institut de Recherche pour le Développement (IRD) in collaboration with the Ecole Normale Supérieure of Yaoundé I (LaBosystE, ENS, Université de Yaoundé I), the Missouri Botanical Garden (MBG) and the Université Libre de Kisangani (UniKis). During the two years preceding my thesis and during the thesis itself, I participated to the establishment of nearly 80 1-ha forest inventory plots, the bulk of which being located in south-eastern Cameroon (c. 50 %, left panel in Figure 1-3). Forests in this region have been described as a transitional type between evergreen and deciduous forests (Letouzey, 1985) and expand across the borders of neighboring countries. From a structural point of view, these forests can be described as forests mosaics that notably include patches of mixed, closed-canopy, semi-deciduous stands, open-canopy Marantaceae stands and monodominant Gilbertiodendron dewevrei stands. The diversity of stands structural profiles, from which contrasted canopy textures emerge (illustrated in Figure 1-3), justifies our interest in this region. Forests of south-eastern Cameroon are also particularly rich in tall trees and stock relatively high biomass densities (Fayolle et al., 2016), and several logging companies are established in the region. Through a collaboration with the Alpicam company, we assembled a large destructive dataset on trees dimensions and AGB (77 trees).

Additional study areas

In the scope of this thesis, I investigate broad biophysical relationships, be it at the tree level in biomass allometry models or at the stand levels in canopy texture-based AGB models. In order to increase the robustness of the results, I compiled additional data from the literature, collaborating institutions or peer researchers (right panel on Figure 1-3).
The sets of 1 ha forest inventories from IRD and collaborators field work in central Africa were complemented with 28 1-ha plots from a forest-savanna mosaic in Republic Democratic of Congo (from Bastin et al., 2014), a 50 ha plots located in the Atlantic evergreen forests of western Cameroon (from Chuyong et al., 2004), 15 and 22 1-ha plots in evergreen and semi-deciduous forests of the Western Ghats of India (from Ploton et al., 2011 and Pargal et al., submitted) and 16 plots covering 85 ha of evergreen forests at Paracou, French Guiana (from Vincent et al., 2012). In total, texture analyses were based on 279 ha of forest inventory distributed on 3 continents.
To the 77 trees destructively sampled in south-eastern Cameroon, I added 132 trees from a semi-deciduous forest of the same region (from Fayolle et al., 2013), 99 trees sampled in a semi-deciduous forest of Gabon (from Ngomanda et al., 2014), 29 trees sampled in an evergreen forest of Ghana (from Henry et al., 2010), 285 trees sampled in a dry-to-wet forest types gradient in Madagascar (from Vieilledent et al., 2012) and 51 trees sampled in an evergreen forest of Peru (from Goodman et al., 2014). The total destructive dataset (n=673) thus comprises trees from 6 sites distributed in five tropical countries (right panel in Figure 1-3).
Figure 1-3. Distribution of datasets across the tropics. Dots and triangles represent tree-level destructive datasets and field plot inventories, respectively. Red color indicates that data have been collected by IRD. Blue color indicates that data were compiled from literature, collaborating institutions or shared by the peer researchers.
Figure 1-4. VHSR satellite image (GeoEye sensor) covering a typical forest mosaic from semi-deciduous forests of south-eastern Cameroon. Patches of Gilbertiodendron dewevrei (black square), mixed closed-canopy stands (red square) and open-canopy Marantaceae stands closely co-occur.

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Sampling strategy and data description

Tree-level destructive data

Large trees exhibits the largest AGB variability at a given D and H while being insufficiently represented in biomass destructive datasets (Chave et al., 2005), probably because of the disproportionate amount of work that the biomass estimation of a large tropical canopy tree represents. To fill this gap, we exclusively targeted very large canopy trees in our field work protocol. This unique dataset, which was incorporated in the latest pantropical database (Chave et al. 2014), contains 17 out of the 30 world’s heaviest sampled tropical trees. For a typical tree, we measured the trunk diameter D, the tree height H and two perpendicular crown diameters before the tree was felled. After felling, tree biomass was estimated by combining direct measurements, indirect measurements and allometries. Direct measurements (i.e. weightings) were made on branches of varying sizes, to build an allometry relating branch diameter to biomass. We estimated the volume of the largest tree components (i.e. trunk and branches with diameter > 20 cm) by measuring diameters and lengths of approximately 2-m and 1-m long subsections along the trunk and branches, respectively. Biomass values were obtained by multiplying volumes by wood density estimates, derived from wood samples. The biomass of branches with D ≤ 20 cm was obtained with species-specific biomass allometries. A detailed description of the field protocol is provided in the supplementary material of chapter 2. An important feature of this dataset is that it contains a description of the crown geometry (from diameter and length measurements) and topology (from the identification of branching points), allowing to reconstruct the tree branching network (up to a branch diameter of c. 20 cm) in 3D and explore allometries and scaling properties at the branch level.
Destructive data compiled from other studies were obtained by direct biomass measurement, I refer the reader to the publications associated to each dataset for more details. Importantly, I only considered datasets providing enough information to distinguish trunk from crown biomass (rather than ones providing a single total tree biomass estimate).

Table of contents :

1.1 Context and challenges
1.1.1 Tropical forests and climate change
1.1.2 REDD monitoring frame of tropical forest biomass: basics and challenges
1.1.3 Remote sensing-based modelling of tropical forest biomass
1.2 Research objectives
1.3 A pantropical approach
1.3.1 Study areas and datasets
1.3.2 Sampling strategy and data description
1.4 Thesis outline
1.5 List of (co-)publications
1.6 References
2.1 Introduction
2.2 Materials and Methods
2.2.1 Biomass data
2.2.2 Forest inventory data
2.2.3 Allometric model fitting
2.2.4 Development of crown mass proxies
2.2.5 Model error evaluation
2.3 Results
2.3.1 Contribution of crown to tree mass
2.3.2 Crown mass sub-models
2.3.3 Accounting for crown mass in biomass allometric models
2.4 Discussion
2.4.1 Crown mass ratio and the reference biomass model error
2.4.2 Model error propagation depends on targeted plot structure
2.4.3 Accounting for crown mass variation in allometric models
2.5 Appendix A: Crown mass sub-models
2.5.1 Method
2.5.2 Results & Discussion
2.6 Appendix B: Plot-level error propagation
2.7 References
2.8 Supplement: Field data protocols
2.8.1 Unpublished dataset: site characteristics
2.8.2 Biomass data
2.8.3 Inventory data
3.1 Introduction
3.2 Methods
3.2.1 Sampled trees and field protocol
3.2.2 MTE model assumptions and predictions of branch scaling exponents
3.2.3 Assessing the effect of asymmetry and node morphology on species area ratio
3.3 Results
3.3.1 Does the average tree conform to branch scaling exponents and area ratio predictions?
3.3.2 Is the average tree self-similar?
3.3.3 What is the effect of species asymmetry on branch scaling exponents and area ratio? .
3.3.4 Does node morphology induce systematic differences of area ratio at the species level?
3.4 Discussion
3.4.1 Evidence of area increasing branching (R > 1)
3.4.2 Sources of variation of the node area ratio
3.4.3 Optimal tree of the MTE model vs average real trees
3.4.4 Implications of the results
3.5. Reference
3.6. Supplementary figure
4.1 Introduction
4.2 Methodological background and rationale
4.3 Results from some case studies
4.4 Limits and perspectives
4.5 Reference
5.1 Introduction
5.2 Material and Methods
5.2.1 Forest inventory data
5.2.2 Generation of 3D forest mockups
5.2.3 Simulation of canopy images
5.2.4 Real satellite images
5.2.5 Canopy texture analysis
5.2.6 Statistical analyses
5.3 Results
5.3.1 Texture analysis of virtual canopy images
5.3.2 Canopy texture – AGB models
5.3.3 Application to real satellite images
5.4 Discussion
5.4.1 Contrasted canopy texture – stand AGB relationships among sites
5.4.2 On 3D stand mockups and virtual canopy images for model calibration
5.5 Reference
5.6 Appendix
6.1 Estimation of forest AGB from field data
6.1.1 Driver(s) of pantropical model bias on large trees
6.1.2 The influence of forest structure on plot-level AGB modelling error
6.2 The influence of forest structure on the canopy texture – AGB relationship
6.3 Key thesis findings
6.4 Reference


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