Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
Canopy height is a key variable in tropical forest functioning and for regional carbon inventories. We investigate the spatial structure of the canopy height of a tropical forest, its relationship with environmental physical covariates, and the implication for tropical forest height variation mapping. Making use of high-resolution maps of LiDAR-derived Digital Canopy Model (DCM) and environmental covariates from a Digital Elevation Model (DEM) acquired over 30,000 ha of tropical forest in French Guiana, we first show that forest canopy height is spatially correlated up to 2500 m. Forest canopy height is significantly associated with environmental variables, but the degree of correlation varies strongly with pixel resolution. On the whole, bottomland forests generally have lower canopy heights than hillslope or hilltop forests. However, this global picture is very noisy at local scale likely because of the endogenous gap-phase forest dynamic processes. Forest canopy height has been predictively mapped across a pixel resolution going from 6 m to 384 m mimicking a low resolution case of 3 points·km −2. Results of canopy height mapping indicated that the error for spatial model with environment effects decrease from 8.7 m to 0.91 m, depending of the pixel resolution. Results suggest that, outside the calibration plots, the contribution of environment in shaping the global canopy height distribution is quite limited. This prevents accurate canopy height mapping based only on environmental information, and suggests that precise canopy height maps, for local management purposes, can only be obtained with direct LiDAR monitoring.
ISSN: 2072-4292

hal-01590726
https://hal-univ-guyane.archives-ouvertes.fr/hal-01590726
https://hal-univ-guyane.archives-ouvertes.fr/hal-01590726/document
https://hal-univ-guyane.archives-ouvertes.fr/hal-01590726/file/remotesensing-08-00747.pdf
DOI : 10.3390/rs8090747

Éditeur(s) : HAL CCSD
Résumé : French Guiana is a region located on the Guiana Shield in South America. Is presents at the same time a high originality in biodiversity, a good state of conservation and a strong correlation to Amazonian carbon stocks. Knowledge on evolution of the forest structure of French Guiana is however still insufficient to bring an optimal management at territory scale. Availability as never seen before of remote sensing data at high spatial resolution such as LiDAR, on large coverage, offers the opportunity to assess the forest resource in French Guiana. The work done during this thesis aim mostly to study the structure and the variations of the canopy height, the factors determining its variability and the consequences of forest logging activities. The data used during this thesis come from an airborne LiDAR and field data from Office National des Forêts (ONF). This thesis manuscript is divided in three parts. The first part introduces the problem and sets the objectives of the thesis. The second part takes the so-called "article" form, that is, the different chapters that compose it are scientific articles published or in the course of publication, it is composed of three studies (I) A study on the gaps detection and environmental determinants on the gap sizes distribution in natural forest: How to characterize the gap size distribution and are there environmental determinants that influence the statistical distribution of gap size? This article has been submitted in Biogeoscience. (ii) A study of the control of the environment on the canopy height structure and variations at different spatial resolutions. Which spatial scales are relevant for analyzing the environmental determinants of canopy height changes in tropical forests from remote sensing data? How can the environment help or not map the height of the canopy in the rainforest? This article was accepted for publication in Remote Sensing. (iii) a third study where we quantified the effects of Reduced Impact Logging (RIL) on the structure and variation of canopy height. What are the effects of logging on canopy height variations and gap size distribution? This article will be submitted for publication in a Forest Ecology and Management journal. In the last part, after a synthesis of the main results, we propose a conclusion.
La Guyane française est une région située sur le Plateau des Guyanes en Amérique du sud. Elle présente à la fois une grande originalité en termes de biodiversité, un bon état de conservation et une forte contribution au stock de carbone Amazonien. Les connaissances sur l’évolution de la structure forestière de la Guyane française restent cependant insuffisantes pour offrir une gestion optimale à l’échelle du territoire. La disponibilité sans précédent de données de télédétection à très haute résolution spatiale telles que le LiDAR , sur de larges couvertures nous offre l'opportunité d’évaluer la ressource forestière de la Guyane Française. Les travaux réalisés dans le cadre de cette thèse visent prioritairement à étudier la structure et les variations de la hauteur de canopée, les facteurs déterminant sa variabilité ainsi que les conséquences des activités d’exploitation forestière. Les données utilisées au cours de la thèse proviennent d’un LiDAR aéroporté et des données de terrains de l'Office National des Forêts (ONF). Ce manuscrit de thèse est divisé en trois grandes parties. La première partie introduit la problématique et fixe les objectifs de la thèse. La seconde partie prend la forme dite « sur article », c'est-à-dire que les différents chapitres qui la compose sont des articles scientifiques publiés ou en cours de publication, elle est composée de trois études. (i) Une étude concernant la détection des trouées et des déterminants environnementaux sur la distribution de tailles des trouées en forêt non exploitée : Comment caractériser la distribution de taille des trouées et est-ce qu’il y a des déterminants environnementaux qui influencent la distribution statistique de taille de trouées? Cet article à été soumis dans Biogeoscience. (ii) Une étude du contrôle de l'environnement sur la structure et les variations de la hauteur de canopée à différentes résolutions spatiales. Quelles échelles spatiales sont pertinentes pour analyser les déterminants environnementaux des variations de hauteur de la canopée en forêt tropicale à partir des données de télédétection ? Comment l’environnement peut aider, ou pas, à cartographier la hauteur de la canopée en forêt tropicale ? Cet article à été accepté pour publication dans Remote Sensing. (iii) Une troisième étude où nous avons quantifié les effets de l’exploitation à faible impact (EFI) sur la structure et la variation de la hauteur de canopée. Quelles sont les effets de l’exploitation forestière sur les variations de hauteur de canopée et sur la distribution de taille des trouées ? Cet article sera soumis pour publication dans une revue scientifique (Forest Ecology and Management). Dans la dernière partie, après une synthèse des principaux résultats, nous proposons des pistes de développement.
https://tel.archives-ouvertes.fr/tel-01527553

NNT : 2016YANE0006
tel-01527553
https://tel.archives-ouvertes.fr/tel-01527553
https://tel.archives-ouvertes.fr/tel-01527553/document
https://tel.archives-ouvertes.fr/tel-01527553/file/these-youven-goulamoussene2.pdf

Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
LiDAR (Light Detection And Ranging) remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and above ground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution.In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data (e.g. geology, slope, vegetation indices, etc.). The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of random forest regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on the precision of canopy height estimates, six subsets were derived from the initial airborne LiDAR dataset with flight line spacing of 5, 10, 20, 30, 40 and 50 km (corresponding to 0.29, 0.11, 0.08, 0.05, 0.04, and 0.03 points/km² respectively). Results indicated that using the regression-kriging approach achieved a precision of 1.8 m on the canopy height map with flight line spacing of 5 km and achieved an average RMSE of 4.8m for the configuration for the 50 km flight line spacing.
ISSN: 2072-4292

hal-01361343
https://hal.archives-ouvertes.fr/hal-01361343
https://hal.archives-ouvertes.fr/hal-01361343/document
https://hal.archives-ouvertes.fr/hal-01361343/file/mt2016-pub00048036.pdf
DOI : 10.3390/rs8030240

Éditeur(s) : HAL CCSD
Résumé : IGARSS 2015, Milan, ITA, 26-/07/2015 - 31/07/2015
International audience
LiDAR remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and aboveground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution.In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data. The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of RF regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset.
Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International
Milan, Italy

hal-01244677
https://hal.archives-ouvertes.fr/hal-01244677
https://hal.archives-ouvertes.fr/hal-01244677/document
https://hal.archives-ouvertes.fr/hal-01244677/file/mt2015-pub00044297.pdf
DOI : 10.1109/IGARSS.2015.7326729

Éditeur(s) : HAL CCSD
Résumé : International audience
The aboveground biomass density (AGBD) or aboveground carbon density is mapped for the rainforest of the Basse-Terre Island (Guadeloupe, French West Indies). This mapping is the first devoted to the rainforest of the Lesser Antilles.Mapping AGBD needs the successive realization of two steps. At first, AGBD was estimated in studied forest plots where trees were individually measured; secondly these local estimations were extended through multiple regression including remote sensing LiDAR measurements.Due to the structural specificity of the Caribbean-islands forests, applying general allometry relationship overestimates tree above-ground biomass. Yet we take advantage of pantropical database (Chave et al. 2014) by adopting a suitable statistical procedure: bayesian mixed decision rule.Despite the reduced canopy height, generally less than 35 m, Caribbean lowland rainforests have a high biomass, in every respect comparable to that of continental lowland forests. Such peculiarity is achieved through increased tree density that compensate for lower canopy height.
CAS
Deshaies, Guadeloupe

hal-01514241
https://hal.archives-ouvertes.fr/hal-01514241