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Dr. Gregoire Vincent
AMAP Lab, IRD, Montpellier, 34000, France

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Research Keywords & Expertise

0 Biodiversity
0 Forest Ecology
0 LiDAR
0 Modelling
0 hyperspectral

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LiDAR
Biodiversity
Modelling
Neotropics
hyperspectral

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Journal article
Published: 28 May 2021 in Remote Sensing
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Optical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART (Discrete Anisotropic Radiative Transfer) to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in a complex tropical forest, and to reproduce spectral dissimilarity within and among species, as well as species discrimination based on spectral information. We focused on two factors contributing to these canopy reflectance properties: the horizontal variability in leaf optical properties (LOP) and the fraction of non-photosynthetic vegetation (NPVf). The variability in LOP was induced by changes in leaf pigment content, and defined for each pixel based on a hybrid approach combining radiative transfer modeling and spectral indices. The influence of LOP variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. We incorporated NPVf into simulations following two approaches, either considering NPVf as a part of wood area density in each voxel or using leaf brown pigments. We validated the different scenarios by comparing simulated scenes with experimental airborne imaging spectroscopy using statistical metrics, spectral dissimilarity (within crowns, within species, and among species dissimilarity) and supervised classification for species discrimination. The simulation of NPVf based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. The definition of LOP at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. Therefore, we recommend future research on forest biodiversity using physical modeling of remote-sensing data to account for LOP variability within crowns and species. Our simulation framework could contribute to better understanding of performances of species discrimination and the relationship between spectral variations and taxonomic and functional dimensions of biodiversity. This work contributes to the improved integration of physical modeling tools for applications, focusing on remotely sensed monitoring of biodiversity in complex ecosystems, for current sensors, and for the preparation of future multispectral and hyperspectral satellite missions.

ACS Style

Dav Ebengo; Florian de Boissieu; Grégoire Vincent; Christiane Weber; Jean-Baptiste Féret. Simulating Imaging Spectroscopy in Tropical Forest with 3D Radiative Transfer Modeling. Remote Sensing 2021, 13, 2120 .

AMA Style

Dav Ebengo, Florian de Boissieu, Grégoire Vincent, Christiane Weber, Jean-Baptiste Féret. Simulating Imaging Spectroscopy in Tropical Forest with 3D Radiative Transfer Modeling. Remote Sensing. 2021; 13 (11):2120.

Chicago/Turabian Style

Dav Ebengo; Florian de Boissieu; Grégoire Vincent; Christiane Weber; Jean-Baptiste Féret. 2021. "Simulating Imaging Spectroscopy in Tropical Forest with 3D Radiative Transfer Modeling." Remote Sensing 13, no. 11: 2120.

Preprint
Published: 23 April 2021
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Optical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in complex tropical forest, and to reproduce spectral dissimilarity within and among species, as well as species discrimination based on spectral information. We focused on two factors contributing to these canopy reflectance properties: the horizontal variability in leaf optical properties (LOP) and the fraction of non-photosynthetic vegetation (NPVf). The variability in LOP was induced by changes in leaf pigment content, and defined for each pixel based on a hybrid approach combining radiative transfer modeling and spectral indices. The influence of LOP variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. We incorporated NPVf into simulations following two approaches, either considering NPVf as a part of wood area density in each voxel or using leaf brown pigments. We validated the different scenarios by comparing simulated scenes with experimental airborne imaging spectroscopy using statistical metrics, spectral dissimilarity (within crowns, within species, and among species dissimilarity) and supervised classification for species discrimination. The simulation of NPVf based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. The definition of LOP at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. Our simulation framework could contribute to better understand performances for species discrimination and relationship between spectral variations and taxonomic and functional dimensions of biodiversity.

ACS Style

Dav M. Ebengo; Florian de Boissieu; Gregoire Vincent; Christiane Weber; Jean-Baptiste Féret. Simulating Imaging Spectroscopy in Tropical Forest with 3D Radiative Transfer Modeling. 2021, 1 .

AMA Style

Dav M. Ebengo, Florian de Boissieu, Gregoire Vincent, Christiane Weber, Jean-Baptiste Féret. Simulating Imaging Spectroscopy in Tropical Forest with 3D Radiative Transfer Modeling. . 2021; ():1.

Chicago/Turabian Style

Dav M. Ebengo; Florian de Boissieu; Gregoire Vincent; Christiane Weber; Jean-Baptiste Féret. 2021. "Simulating Imaging Spectroscopy in Tropical Forest with 3D Radiative Transfer Modeling." , no. : 1.

Preprint content
Published: 04 March 2021
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Tropical forests are integral to the global carbon, water and energy budgets. However, the magnitude of matter and energy fluxes are poorly resolved both spatially and temporally, and the driving underlying mechanisms by which they occur remain unclear poorly described. Specifically, the diversity of foliar phenological patterns and there influence forest fluxes in the tropics has not been properly studied. As a result of these knowledge gaps, dynamic global vegetation models (DGVMs) consistently fail to exhibit observed productivity dynamics and climate-vegetation feedbacks. These shortcomings prevent reliable predictions on the fate and role of tropical forests under changing climate conditions from being made.

Working at perminant tropical forest fieldsites in French Guiana, we demonstrate that biweekly scans with UAV mounted LiDAR and multispecral sensors can observe subtle phenological changes of individual trees across novel spatial scales. We explore the intra- and inter-species variation in phenological behavoirs and link these dynamics to in-situ flux measurements.

ACS Style

James Ball; Gregoire Vincent; Nicolas Barbier; Ilona Clocher. Dycrypting tropical forest phenology with coupled remote sensing and field observation. 2021, 1 .

AMA Style

James Ball, Gregoire Vincent, Nicolas Barbier, Ilona Clocher. Dycrypting tropical forest phenology with coupled remote sensing and field observation. . 2021; ():1.

Chicago/Turabian Style

James Ball; Gregoire Vincent; Nicolas Barbier; Ilona Clocher. 2021. "Dycrypting tropical forest phenology with coupled remote sensing and field observation." , no. : 1.

Preprint content
Published: 03 March 2021
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Repeat airborne LiDAR data provides a unique opportunity to study tree mortality at the landscape scale. We use maps of canopy height derived from repeat LiDAR (two or more scans collected a few years apart) to detect changes in forest structure. Visually, the most obvious changes are caused by large treefall events, which are difficult to study using field plots due to their rarity. While repeat LiDAR data provides exciting new possibilities, validation is a challenge, since we cannot easily determine how many trees have died and we may miss trees which are dead but still standing. I will discuss our progress so far, studying large-tree mortality rates across multiple countries and forest types.

ACS Style

Toby Jackson; Matheus Nunes; Grégoire Vincent; David Coomes. Tracking tree mortality across sites with repeat LiDAR data. 2021, 1 .

AMA Style

Toby Jackson, Matheus Nunes, Grégoire Vincent, David Coomes. Tracking tree mortality across sites with repeat LiDAR data. . 2021; ():1.

Chicago/Turabian Style

Toby Jackson; Matheus Nunes; Grégoire Vincent; David Coomes. 2021. "Tracking tree mortality across sites with repeat LiDAR data." , no. : 1.

Article
Published: 29 May 2020
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Selective logging, fragmentation, and understory fires directly degrade forest structure and composition. However, studies addressing the effects of forest degradation on carbon, water, and energy cycles are scarce. Here, we integrate field observations and high-resolution remote sensing from airborne lidar to provide realistic initial conditions to the Ecosystem Demography Model (ED–2.2) and investigate how disturbances from forest degradation affect gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H). We used forest structural information retrieved from airborne lidar samples (13,500 ha) and calibrated with 817 inventory plots (0.25 ha) across precipitation and degradation gradients in the Eastern Amazon as initial conditions to ED-2.2 model. Our results show that the magnitude and seasonality of fluxes were modulated by changes in forest structure caused by degradation. During the dry season and under typical conditions, severely degraded forests (biomass loss ≥ 66%) experienced water-stress with declines in ET (up to 34%) and GPP (up to 35%), and increases of H (up to 43%) and daily mean ground temperatures (up to 6.5°C) relative to intact forests. In contrast, the relative impact of forest degradation on energy, water, and carbon cycles markedly diminishes under extreme, multi-year droughts, as a consequence of severe stress experienced by intact forests. Our results highlight that the water and energy cycles in the Amazon are not only driven by climate and deforestation, but also the past disturbance and changes of forest structure from degradation, suggesting a much broader influence of human land use activities on the tropical ecosystems.

ACS Style

Marcos Longo; Sassan Saatchi; Michael Keller; Kevin W. Bowman; Antonio Ferraz; Paul R Moorcroft; Douglas Morton; Damien Bonal; Paulo Brando; Benoît Burban; Géraldine Derroire; Maiza Nara Dos-Santos; Victoria Meyer; Scott Saleska; Susan Trumbore; Grégoire Vincent. Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests. 2020, 1 .

AMA Style

Marcos Longo, Sassan Saatchi, Michael Keller, Kevin W. Bowman, Antonio Ferraz, Paul R Moorcroft, Douglas Morton, Damien Bonal, Paulo Brando, Benoît Burban, Géraldine Derroire, Maiza Nara Dos-Santos, Victoria Meyer, Scott Saleska, Susan Trumbore, Grégoire Vincent. Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests. . 2020; ():1.

Chicago/Turabian Style

Marcos Longo; Sassan Saatchi; Michael Keller; Kevin W. Bowman; Antonio Ferraz; Paul R Moorcroft; Douglas Morton; Damien Bonal; Paulo Brando; Benoît Burban; Géraldine Derroire; Maiza Nara Dos-Santos; Victoria Meyer; Scott Saleska; Susan Trumbore; Grégoire Vincent. 2020. "Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests." , no. : 1.

Journal article
Published: 15 May 2020 in Remote Sensing
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Tropical forests have exceptional floristic diversity, but their characterization remains incomplete, in part due to the resource intensity of in-situ assessments. Remote sensing technologies can provide valuable, cost-effective, large-scale insights. This study investigates the combined use of airborne LiDAR and imaging spectroscopy to map tree species at landscape scale in French Guiana. Binary classifiers were developed for each of 20 species using linear discriminant analysis (LDA), regularized discriminant analysis (RDA) and logistic regression (LR). Complementing visible and near infrared (VNIR) spectral bands with short wave infrared (SWIR) bands improved the mean average classification accuracy of the target species from 56.1% to 79.6%. Increasing the number of non-focal species decreased the success rate of target species identification. Classification performance was not significantly affected by impurity rates (confusion between assigned classes) in the non-focal class (up to 5% of bias), provided that an adequate criterion was used for adjusting threshold probability assignment. A limited number of crowns (30 crowns) in each species class was sufficient to retrieve correct labels effectively. Overall canopy area of target species was strongly correlated to their basal area over 118 ha at 1.5 ha resolution, indicating that operational application of the method is a realistic prospect (R2 = 0.75 for six major commercial tree species).

ACS Style

Anthony Laybros; Mélaine Aubry-Kientz; Jean-Baptiste Féret; Caroline Bedeau; Olivier Brunaux; Géraldine Derroire; Grégoire Vincent. Quantitative Airborne Inventories in Dense Tropical Forest Using Imaging Spectroscopy. Remote Sensing 2020, 12, 1577 .

AMA Style

Anthony Laybros, Mélaine Aubry-Kientz, Jean-Baptiste Féret, Caroline Bedeau, Olivier Brunaux, Géraldine Derroire, Grégoire Vincent. Quantitative Airborne Inventories in Dense Tropical Forest Using Imaging Spectroscopy. Remote Sensing. 2020; 12 (10):1577.

Chicago/Turabian Style

Anthony Laybros; Mélaine Aubry-Kientz; Jean-Baptiste Féret; Caroline Bedeau; Olivier Brunaux; Géraldine Derroire; Grégoire Vincent. 2020. "Quantitative Airborne Inventories in Dense Tropical Forest Using Imaging Spectroscopy." Remote Sensing 12, no. 10: 1577.

Journal article
Published: 07 May 2019 in Remote Sensing
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Tropical forest canopies are comprised of tree crowns of multiple species varying in shape and height, and ground inventories do not usually reliably describe their structure. Airborne laser scanning data can be used to characterize these individual crowns, but analytical tools developed for boreal or temperate forests may require to be adjusted before they can be applied to tropical environments. Therefore, we compared results from six different segmentation methods applied to six plots (39 ha) from a study site in French Guiana. We measured the overlap of automatically segmented crowns projection with selected crowns manually delineated on high-resolution photography. We also evaluated the goodness of fit following automatic matching with field inventory data using a model linking tree diameter to tree crown width. The different methods tested in this benchmark segmented highly different numbers of crowns having different characteristics. Segmentation methods based on the point cloud (AMS3D and Graph-Cut) globally outperformed methods based on the Canopy Height Models, especially for small crowns; the AMS3D method outperformed the other methods tested for the overlap analysis, and AMS3D and Graph-Cut performed the best for the automatic matching validation. Nevertheless, other methods based on the Canopy Height Model performed better for very large emergent crowns. The dense foliage of tropical moist forests prevents sufficient point densities in the understory to segment subcanopy trees accurately, regardless of the segmentation method.

ACS Style

Mélaine Aubry-Kientz; Raphaël Dutrieux; Antonio Ferraz; Sassan Saatchi; Hamid Hamraz; Jonathan Williams; David Coomes; Alexandre Piboule; Grégoire Vincent. A Comparative Assessment of the Performance of Individual Tree Crowns Delineation Algorithms from ALS Data in Tropical Forests. Remote Sensing 2019, 11, 1086 .

AMA Style

Mélaine Aubry-Kientz, Raphaël Dutrieux, Antonio Ferraz, Sassan Saatchi, Hamid Hamraz, Jonathan Williams, David Coomes, Alexandre Piboule, Grégoire Vincent. A Comparative Assessment of the Performance of Individual Tree Crowns Delineation Algorithms from ALS Data in Tropical Forests. Remote Sensing. 2019; 11 (9):1086.

Chicago/Turabian Style

Mélaine Aubry-Kientz; Raphaël Dutrieux; Antonio Ferraz; Sassan Saatchi; Hamid Hamraz; Jonathan Williams; David Coomes; Alexandre Piboule; Grégoire Vincent. 2019. "A Comparative Assessment of the Performance of Individual Tree Crowns Delineation Algorithms from ALS Data in Tropical Forests." Remote Sensing 11, no. 9: 1086.

Journal article
Published: 16 April 2019 in Sustainability
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Agroforestry, the intentional integration of trees with crops and/or livestock, can lead to multiple economic and ecological benefits compared to trees and crops/livestock grown separately. Field experimentation has been the primary approach to understanding the tree–crop interactions inherent in agroforestry. However, the number of field experiments has been limited by slow tree maturation and difficulty in obtaining consistent funding. Models have the potential to overcome these hurdles and rapidly advance understanding of agroforestry systems. Hi-sAFe is a mechanistic, biophysical model designed to explore the interactions within agroforestry systems that mix trees with crops. The model couples the pre-existing STICS crop model to a new tree model that includes several plasticity mechanisms responsive to tree–tree and tree–crop competition for light, water, and nitrogen. Monoculture crop and tree systems can also be simulated, enabling calculation of the land equivalent ratio. The model’s 3D and spatially explicit form is key for accurately representing many competition and facilitation processes. Hi-sAFe is a novel tool for exploring agroforestry designs (e.g., tree spacing, crop type, tree row orientation), management strategies (e.g., thinning, branch pruning, root pruning, fertilization, irrigation), and responses to environmental variation (e.g., latitude, climate change, soil depth, soil structure and fertility, fluctuating water table). By improving our understanding of the complex interactions within agroforestry systems, Hi-sAFe can ultimately facilitate adoption of agroforestry as a sustainable land-use practice.

ACS Style

Christian Dupraz; Kevin Wolz; Isabelle Lecomte; Grégoire Talbot; Grégoire Vincent; Rachmat Mulia; François Bussière; Harry Ozier-Lafontaine; Sitraka Andrianarisoa; Nick Jackson; Gerry Lawson; Nicolas Dones; Hervé Sinoquet; Betha Lusiana; Degi Harja; Susy Domenicano; Francesco Reyes; Marie Gosme; Meine Van Noordwijk. Hi-sAFe: A 3D Agroforestry Model for Integrating Dynamic Tree–Crop Interactions. Sustainability 2019, 11, 2293 .

AMA Style

Christian Dupraz, Kevin Wolz, Isabelle Lecomte, Grégoire Talbot, Grégoire Vincent, Rachmat Mulia, François Bussière, Harry Ozier-Lafontaine, Sitraka Andrianarisoa, Nick Jackson, Gerry Lawson, Nicolas Dones, Hervé Sinoquet, Betha Lusiana, Degi Harja, Susy Domenicano, Francesco Reyes, Marie Gosme, Meine Van Noordwijk. Hi-sAFe: A 3D Agroforestry Model for Integrating Dynamic Tree–Crop Interactions. Sustainability. 2019; 11 (8):2293.

Chicago/Turabian Style

Christian Dupraz; Kevin Wolz; Isabelle Lecomte; Grégoire Talbot; Grégoire Vincent; Rachmat Mulia; François Bussière; Harry Ozier-Lafontaine; Sitraka Andrianarisoa; Nick Jackson; Gerry Lawson; Nicolas Dones; Hervé Sinoquet; Betha Lusiana; Degi Harja; Susy Domenicano; Francesco Reyes; Marie Gosme; Meine Van Noordwijk. 2019. "Hi-sAFe: A 3D Agroforestry Model for Integrating Dynamic Tree–Crop Interactions." Sustainability 11, no. 8: 2293.

Journal article
Published: 02 April 2019 in Remote Sensing
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Imaging spectroscopy is a promising tool for airborne tree species recognition in hyper-diverse tropical canopies. However, its widespread application is limited by the signal sensitivity to acquisition parameters, which may require new training data in every new area of application. This study explores how various pre-processing steps may improve species discrimination and species recognition under different operational settings. In the first experiment, a classifier was trained and applied on imaging spectroscopy data acquired on a single date, while in a second experiment, the classifier was trained on data from one date and applied to species identification on data from a different date. A radiative transfer model based on atmospheric compensation was applied with special focus on the automatic retrieval of aerosol amounts. The impact of spatial or spectral filtering and normalisation was explored as an alternative to atmospheric correction. A pixel-wise classification was performed with a linear discriminant analysis trained on individual tree crowns identified at the species level. Tree species were then identified at the crown scale based on a majority vote rule. Atmospheric corrections did not outperform simple statistical processing (i.e., filtering and normalisation) when training and testing sets were taken from the same flight date. However, atmospheric corrections became necessary for reliable species recognition when different dates were considered. Shadow masking improved species classification results in all cases. Single date classification rate was 83.9% for 1297 crowns of 20 tropical species. The loss of mean accuracy observed when using training data from one date to identify species at another date in the same area was limited to 10% when atmospheric correction was applied.

ACS Style

Anthony Laybros; Daniel Schläpfer; Jean-Baptiste Féret; Laurent Descroix; Caroline Bedeau; Marie-Jose Lefevre; Grégoire Vincent. Across Date Species Detection Using Airborne Imaging Spectroscopy. Remote Sensing 2019, 11, 789 .

AMA Style

Anthony Laybros, Daniel Schläpfer, Jean-Baptiste Féret, Laurent Descroix, Caroline Bedeau, Marie-Jose Lefevre, Grégoire Vincent. Across Date Species Detection Using Airborne Imaging Spectroscopy. Remote Sensing. 2019; 11 (7):789.

Chicago/Turabian Style

Anthony Laybros; Daniel Schläpfer; Jean-Baptiste Féret; Laurent Descroix; Caroline Bedeau; Marie-Jose Lefevre; Grégoire Vincent. 2019. "Across Date Species Detection Using Airborne Imaging Spectroscopy." Remote Sensing 11, no. 7: 789.

Journal article
Published: 23 July 2018 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Tropical forests are a key component of the global carbon cycle. Yet, there are still high uncertainties in forest carbon stock and flux estimates, notably because of their spatial and temporal variability across the tropics. Several upcoming spaceborne missions have been designed to address this gap. High-quality ground data are essential for accurate calibration/validation so that spaceborne biomass missions can reach their full potential in reducing uncertainties regarding forest carbon stocks and fluxes. The BIOMASS mission, a P-band SAR satellite from the European Space Agency (ESA), aims at improving carbon stock mapping and reducing uncertainty in the carbon fluxes from deforestation, forest degradation, and regrowth. In situ activities in support of the BIOMASS mission were carried out in French Guiana and Gabon during the TropiSAR and AfriSAR campaigns. During these campaigns, airborne P-band SAR, forest inventory, and lidar data were collected over six study sites. This paper describes the methods used for forest inventory and lidar data collection and analysis, and presents resulting plot estimates and aboveground biomass maps. These reference datasets along with intermediate products (e.g., canopy height models) can be accessed through ESA's Forest Observation System and the Dryad data repository and will be useful for BIOMASS but also to other spaceborne biomass missions such as GEDI, NISAR, and Tandem-L for calibration/validation purposes. During data quality control and analysis, prospects for reducing uncertainties have been identified, and this paper finishes with a series of recommendations for future tropical forest field campaigns to better serve the remote sensing community.

ACS Style

Nicolas Labriere; Shengli Tao; Jerome Chave; Klaus Scipal; Thuy Le Toan; Katharine Abernethy; Alfonso Alonso; Nicolas Barbier; Pulcherie Bissiengou; Tania Casal; Stuart J. Davies; Antonio Ferraz; Bruno Herault; Gaelle Jaouen; Kathryn J. Jeffery; David Kenfack; Lisa Korte; Simon L. Lewis; Yadvinder Malhi; Herve R. Memiaghe; John R. Poulsen; Maxime Rejou-Mechain; Ludovic Villard; Gregoire Vincent; Lee J. T. White; Sassan Saatchi. In Situ Reference Datasets From the TropiSAR and AfriSAR Campaigns in Support of Upcoming Spaceborne Biomass Missions. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2018, 11, 3617 -3627.

AMA Style

Nicolas Labriere, Shengli Tao, Jerome Chave, Klaus Scipal, Thuy Le Toan, Katharine Abernethy, Alfonso Alonso, Nicolas Barbier, Pulcherie Bissiengou, Tania Casal, Stuart J. Davies, Antonio Ferraz, Bruno Herault, Gaelle Jaouen, Kathryn J. Jeffery, David Kenfack, Lisa Korte, Simon L. Lewis, Yadvinder Malhi, Herve R. Memiaghe, John R. Poulsen, Maxime Rejou-Mechain, Ludovic Villard, Gregoire Vincent, Lee J. T. White, Sassan Saatchi. In Situ Reference Datasets From the TropiSAR and AfriSAR Campaigns in Support of Upcoming Spaceborne Biomass Missions. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018; 11 (10):3617-3627.

Chicago/Turabian Style

Nicolas Labriere; Shengli Tao; Jerome Chave; Klaus Scipal; Thuy Le Toan; Katharine Abernethy; Alfonso Alonso; Nicolas Barbier; Pulcherie Bissiengou; Tania Casal; Stuart J. Davies; Antonio Ferraz; Bruno Herault; Gaelle Jaouen; Kathryn J. Jeffery; David Kenfack; Lisa Korte; Simon L. Lewis; Yadvinder Malhi; Herve R. Memiaghe; John R. Poulsen; Maxime Rejou-Mechain; Ludovic Villard; Gregoire Vincent; Lee J. T. White; Sassan Saatchi. 2018. "In Situ Reference Datasets From the TropiSAR and AfriSAR Campaigns in Support of Upcoming Spaceborne Biomass Missions." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 10: 3617-3627.

Journal article
Published: 08 June 2018 in Biogeosciences
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Large tropical trees store significant amounts of carbon in woody components and their distribution plays an important role in forest carbon stocks and dynamics. Here, we explore the properties of a new lidar-derived index, the large tree canopy area (LCA) defined as the area occupied by canopy above a reference height. We hypothesize that this simple measure of forest structure representing the crown area of large canopy trees could consistently explain the landscape variations in forest volume and aboveground biomass (AGB) across a range of climate and edaphic conditions. To test this hypothesis, we assembled a unique dataset of high-resolution airborne light detection and ranging (lidar) and ground inventory data in nine undisturbed old-growth Neotropical forests, of which four had plots large enough (1 ha) to calibrate our model. We found that the LCA for trees greater than 27 m (∼ 25–30 m) in height and at least 100 m2 crown size in a unit area (1 ha), explains more than 75 % of total forest volume variations, irrespective of the forest biogeographic conditions. When weighted by average wood density of the stand, LCA can be used as an unbiased estimator of AGB across sites (R2 = 0.78, RMSE = 46.02 Mg ha−1, bias = −0.63 Mg ha−1). Unlike other lidar-derived metrics with complex nonlinear relations to biomass, the relationship between LCA and AGB is linear and remains unique across forest types. A comparison with tree inventories across the study sites indicates that LCA correlates best with the crown area (or basal area) of trees with diameter greater than 50 cm. The spatial invariance of the LCA–AGB relationship across the Neotropics suggests a remarkable regularity of forest structure across the landscape and a new technique for systematic monitoring of large trees for their contribution to AGB and changes associated with selective logging, tree mortality and other types of tropical forest disturbance and dynamics.

ACS Style

Victoria Meyer; Sassan Saatchi; David B. Clark; Michael Keller; Grégoire Vincent; António Ferraz; Fernando Espírito-Santo; Marcus V. N. D'oliveira; Dahlia Kaki; Jérôme Chave. Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes. Biogeosciences 2018, 15, 3377 -3390.

AMA Style

Victoria Meyer, Sassan Saatchi, David B. Clark, Michael Keller, Grégoire Vincent, António Ferraz, Fernando Espírito-Santo, Marcus V. N. D'oliveira, Dahlia Kaki, Jérôme Chave. Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes. Biogeosciences. 2018; 15 (11):3377-3390.

Chicago/Turabian Style

Victoria Meyer; Sassan Saatchi; David B. Clark; Michael Keller; Grégoire Vincent; António Ferraz; Fernando Espírito-Santo; Marcus V. N. D'oliveira; Dahlia Kaki; Jérôme Chave. 2018. "Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes." Biogeosciences 15, no. 11: 3377-3390.

Journal article
Published: 20 April 2018 in Remote Sensing
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Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass (AGB) maps of mangrove forests. AGB was estimated in 34 small plots scatted over a 50 km2 mangrove forest in Rio de Janeiro, Brazil. Plot AGB was computed using either species-specific or non-species-specific allometric models. A total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors (Auto-PLS)) were tested to predict the recorded AGB. The models developed using species-specific allometric models were distinctly more accurate (R2(calibration) = 0.89, R2(validation) = 0.80, root-mean-square error (RMSE, calibration) = 11.20 t·ha−1, and RMSE(validation) = 14.80 t·ha−1). The use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. It was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the AGB in mangrove forests. Conversely this study underlines the potential bias affecting the estimates of AGB in other forested landscapes where only non-species-specific allometric equations are available.

ACS Style

Francisca Rocha De Souza Pereira; Milton Kampel; Mário Luiz Gomes Soares; Gustavo Calderucio Duque Estrada; Cristina Bentz; Gregoire Vincent. Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data. Remote Sensing 2018, 10, 637 .

AMA Style

Francisca Rocha De Souza Pereira, Milton Kampel, Mário Luiz Gomes Soares, Gustavo Calderucio Duque Estrada, Cristina Bentz, Gregoire Vincent. Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data. Remote Sensing. 2018; 10 (4):637.

Chicago/Turabian Style

Francisca Rocha De Souza Pereira; Milton Kampel; Mário Luiz Gomes Soares; Gustavo Calderucio Duque Estrada; Cristina Bentz; Gregoire Vincent. 2018. "Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data." Remote Sensing 10, no. 4: 637.

Author correction
Published: 12 April 2018 in Scientific Reports
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A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

ACS Style

Stéphane Guitet; Daniel Sabatier; Olivier Brunaux; Pierre Couteron; Thomas Denis; Vincent Freycon; Sophie Gonzalez; Bruno Herault; Gaëlle Jaouen; Jean-François Molino; Raphaël Pélissier; Cécile Richard-Hansen; Gregoire Vincent. Author Correction: Disturbance Regimes Drive The Diversity of Regional Floristic Pools Across Guianan Rainforest Landscapes. Scientific Reports 2018, 8, 6125 .

AMA Style

Stéphane Guitet, Daniel Sabatier, Olivier Brunaux, Pierre Couteron, Thomas Denis, Vincent Freycon, Sophie Gonzalez, Bruno Herault, Gaëlle Jaouen, Jean-François Molino, Raphaël Pélissier, Cécile Richard-Hansen, Gregoire Vincent. Author Correction: Disturbance Regimes Drive The Diversity of Regional Floristic Pools Across Guianan Rainforest Landscapes. Scientific Reports. 2018; 8 (1):6125.

Chicago/Turabian Style

Stéphane Guitet; Daniel Sabatier; Olivier Brunaux; Pierre Couteron; Thomas Denis; Vincent Freycon; Sophie Gonzalez; Bruno Herault; Gaëlle Jaouen; Jean-François Molino; Raphaël Pélissier; Cécile Richard-Hansen; Gregoire Vincent. 2018. "Author Correction: Disturbance Regimes Drive The Diversity of Regional Floristic Pools Across Guianan Rainforest Landscapes." Scientific Reports 8, no. 1: 6125.

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Published: 05 January 2018
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Victoria Meyer; Sassan Saatchi; David B. Clark; Michael Keller; Grégoire Vincent; António Ferraz; Fernando Espírito-Santo; Marcus V. N. D'oliveira; Dahlia Kaki; Jérôme Chave. Supplementary material to "Canopy Area of Large Trees Explains Aboveground Biomass Variations across Nine Neotropical Forest Landscapes". 2018, 1 .

AMA Style

Victoria Meyer, Sassan Saatchi, David B. Clark, Michael Keller, Grégoire Vincent, António Ferraz, Fernando Espírito-Santo, Marcus V. N. D'oliveira, Dahlia Kaki, Jérôme Chave. Supplementary material to "Canopy Area of Large Trees Explains Aboveground Biomass Variations across Nine Neotropical Forest Landscapes". . 2018; ():1.

Chicago/Turabian Style

Victoria Meyer; Sassan Saatchi; David B. Clark; Michael Keller; Grégoire Vincent; António Ferraz; Fernando Espírito-Santo; Marcus V. N. D'oliveira; Dahlia Kaki; Jérôme Chave. 2018. "Supplementary material to "Canopy Area of Large Trees Explains Aboveground Biomass Variations across Nine Neotropical Forest Landscapes"." , no. : 1.

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Large tropical trees store significant amounts of carbon in woody components and their distribution plays an important role in forest carbon stocks and dynamics. Here, we explore the properties of a new Lidar derived index, large tree canopy area (LCA) defined as the area occupied by canopy above a reference height. We hypothesize that this simple measure of forest structure representing the crown area of large canopy trees could consistently explain the landscape variations of forest volume and aboveground biomass (AGB) across a range of climate and edaphic conditions. To test this hypothesis, we assembled a unique dataset of high-resolution airborne Light Detection and Ranging (Lidar) and ground inventory data in nine undisturbed old growth Neotropical forests. We found that the LCA for trees greater than 27 m (~ 25–30 m) in height and at least 100 m2 crown size in a unit area (1 ha), explains more than 75 % of total forest volume variations, irrespective of the forest biogeographic conditions. When weighted by average wood density of the stand, LCA can be used as an unbiased estimator of AGB across all sites (R2 = 0.78, RMSE = 46.02 Mg ha−1, bias = 0.76 Mg ha−1). Unlike other Lidar derived metrics with complex nonlinear relations to biomass, the relationship between LCA and AGB is linear. A comparison with tree inventories across the study sites indicates that LCA correlates best with the crown area (or basal area) of trees with diameter > 50 cm. The spatial invariance of the LCA–AGB relationship across the Neotropics suggests a remarkable regularity of forest structure across the landscape and a new technique for systematic monitoring of large trees for their contribution to AGB and changes associated with selective logging, tree mortality, and other types of forest disturbance and dynamics.

ACS Style

Victoria Meyer; Sassan Saatchi; David B. Clark; Michael Keller; Grégoire Vincent; António Ferraz; Fernando Espírito-Santo; Marcus V. N. D'oliveira; Dahlia Kaki; Jérôme Chave. Canopy Area of Large Trees Explains Aboveground Biomass Variations across Nine Neotropical Forest Landscapes. 2018, 2018, 1 -38.

AMA Style

Victoria Meyer, Sassan Saatchi, David B. Clark, Michael Keller, Grégoire Vincent, António Ferraz, Fernando Espírito-Santo, Marcus V. N. D'oliveira, Dahlia Kaki, Jérôme Chave. Canopy Area of Large Trees Explains Aboveground Biomass Variations across Nine Neotropical Forest Landscapes. . 2018; 2018 ():1-38.

Chicago/Turabian Style

Victoria Meyer; Sassan Saatchi; David B. Clark; Michael Keller; Grégoire Vincent; António Ferraz; Fernando Espírito-Santo; Marcus V. N. D'oliveira; Dahlia Kaki; Jérôme Chave. 2018. "Canopy Area of Large Trees Explains Aboveground Biomass Variations across Nine Neotropical Forest Landscapes." 2018, no. : 1-38.

Journal article
Published: 30 May 2016 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Recent studies have questioned the applicability of satellite-derived vegetation indices (VIs) for evaluating phenological variation in tropical forests, due to potential artifacts caused by the bidirectional reflectance distribution function (BRDF). For nadir-normalized data, BRDF will be driven principally by intraannual variation in solar elevation. Where areas lying on the same latitude are under similar solar elevation “regimes,” if the observed variation in VIs is indeed driven by BRDF, then different regions at the same latitude should display identical VI variations. That hypothesis was tested by comparing VI data for tropical evergreen forests in three zones north of the equator (the Guianas, central Africa, and northern Borneo). Enhanced vegetation index, the fraction of green vegetation cover, and leaf area index (LAI) from MODIS and SPOT VEGETATION ultimately showed that VI trends for the regions differ greatly. The trend for Borneo's forests is generally flat over the 12 years studied, while data for the Guianas and central Africa both exhibit strong but distinct seasonal patterns. Correlation analyses indicate that the VI trends between zones are neither strongly correlated to each other nor to variation in solar elevation (except in central Africa), suggesting that the observed variation in the VIs is not driven by BRDF. In contrast, regression analysis indicated that for the Guianas and central Africa, VI variation was most explained by variation in environmental factors, but not atmospheric effects, suggesting seasonally driven phenology.

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Emil A. Cherrington; Nicolas Barbier; Pierre Ploton; Gregoire Vincent; Daniel Sabatier; Uta Berger; Raphael Pelissier. Equatorial Forests Display Distinct Trends in Phenological Variation: A Time-Series Analysis of Vegetation Index Data from Three Continents. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2016, 9, 3505 -3511.

AMA Style

Emil A. Cherrington, Nicolas Barbier, Pierre Ploton, Gregoire Vincent, Daniel Sabatier, Uta Berger, Raphael Pelissier. Equatorial Forests Display Distinct Trends in Phenological Variation: A Time-Series Analysis of Vegetation Index Data from Three Continents. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2016; 9 (8):3505-3511.

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Emil A. Cherrington; Nicolas Barbier; Pierre Ploton; Gregoire Vincent; Daniel Sabatier; Uta Berger; Raphael Pelissier. 2016. "Equatorial Forests Display Distinct Trends in Phenological Variation: A Time-Series Analysis of Vegetation Index Data from Three Continents." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 8: 3505-3511.

Journal article
Published: 11 March 2014 in Oecologia
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Airborne laser scanning provides continuous coverage mapping of forest canopy height and thereby is a powerful tool to scale-up above-ground biomass (AGB) estimates from stand to landscape. A critical first step is the selection of the plot variables which can be related to light detection and ranging (LiDAR) statistics. A universal approach was previously proposed which combines local and regional estimates of basal area (BA) and wood density with LiDAR-derived canopy height to map carbon at a regional scale (Asner et al. in Oecologia 168:1147–1160, 2012). Here we explore the contribution of stem diameter distribution, specific wood density and height-diameter (H–D) allometry to forest stand AGB and propose an alternative model. By applying the new model to a large tropical forest data set we show that an appropriate choice of input variables is essential to minimize prediction error of stand AGB which will propagate at larger scale. Stem number (N) and average stem cross-sectional area should be used instead of BA when scaling from tree to plot. Stand quadratic mean diameter above the census threshold diameter size should be preferred over stand mean diameter as it reduces the prediction error of stand AGB by a factor of ten. Wood density should be weighted by stem volume per species instead of BA. LiDAR-derived statistics should prove useful for estimating local H-D allometries as well as mapping N and the mean quadratic diameter above 10 cm at the landscape level. Prior stratification into forest types is likely to improve both estimation procedures significantly and is considered the foremost current challenge.

ACS Style

Grégoire Vincent; Daniel Sabatier; Ervan Rutishauser. Revisiting a universal airborne light detection and ranging approach for tropical forest carbon mapping: scaling-up from tree to stand to landscape. Oecologia 2014, 175, 439 -443.

AMA Style

Grégoire Vincent, Daniel Sabatier, Ervan Rutishauser. Revisiting a universal airborne light detection and ranging approach for tropical forest carbon mapping: scaling-up from tree to stand to landscape. Oecologia. 2014; 175 (2):439-443.

Chicago/Turabian Style

Grégoire Vincent; Daniel Sabatier; Ervan Rutishauser. 2014. "Revisiting a universal airborne light detection and ranging approach for tropical forest carbon mapping: scaling-up from tree to stand to landscape." Oecologia 175, no. 2: 439-443.

Journal article
Published: 12 July 2013 in Trees
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While theoretical allometric models postulate universal scaling exponents, empirical relationships between tree dimensions show marked variability that reflects changes in the biomass allocation pattern. As growth of the various tree compartments may be controlled by different functions, it is hypothesized that they may respond differently to factors of variation, resulting in variable tree morphologies and potentially in trade-offs between allometric relationships. We explore the variability of tree stem and crown allometries using a dataset of 1,729 trees located in an undisturbed wet evergreen forest of the Western Ghats, India. We specifically test whether species adult stature, terrain slope, tree size and crown light exposure affect the relationships between stem diameter and stem height (stem allometry), and between stem diameter and crown width, crown area and crown volume (crown allometries). Results show that both stem and crown allometries are subject to variations in relation to both endogenous (tree size, species adult stature) and exogenous (terrain slope, crown light exposure) factors. Stem allometry appears to be more affected by these factors than are crown allometries, including the stem diameter–crown volume relationship, which proved to be particularly stable. Our results support the idea that height is a prevailing adjustment factor for a tree facing variable growth (notably light) conditions, while stem diameter–crown volume allometry responds more to internal metabolic constraints. We ultimately discuss the various sources of variability in the stem and crown allometries of tropical trees that likely play an important role in forest community dynamics.

ACS Style

Cécile Antin; Raphaël Pélissier; Grégoire Vincent; Pierre Couteron. Crown allometries are less responsive than stem allometry to tree size and habitat variations in an Indian monsoon forest. Trees 2013, 27, 1485 -1495.

AMA Style

Cécile Antin, Raphaël Pélissier, Grégoire Vincent, Pierre Couteron. Crown allometries are less responsive than stem allometry to tree size and habitat variations in an Indian monsoon forest. Trees. 2013; 27 (5):1485-1495.

Chicago/Turabian Style

Cécile Antin; Raphaël Pélissier; Grégoire Vincent; Pierre Couteron. 2013. "Crown allometries are less responsive than stem allometry to tree size and habitat variations in an Indian monsoon forest." Trees 27, no. 5: 1485-1495.

Journal article
Published: 01 December 2012 in BOIS & FORETS DES TROPIQUES
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Une opération de balayage laser aéroporté à haute densité a permis de modéliser la hauteur du couvert forestier d'un site expérimental en forêt néotropicale (à Paracou en Guyane française). La hauteur des arbres individuels a été calculée par segmentation manuelle des houppiers sur le modèle numérique de canopée et extraction de la hauteur maximale locale du couvert forestier. Trois cent quatrevingt- seize estimations de hauteur d'arbres dominants ou émergents ont été mises en relation avec les données de terrain correspondantes pour les diamètres des tiges échantillonnées sur deux placettes de hauteur moyenne différente (28,1 m et 31,3 m). Les résultats montrent une corrélation positive et très significative entre l'élancement des tiges et la hauteur moyenne du couvert à l'échelle des placettes. La même corrélation apparaît à l'échelle des peuplements des trois essences suffisamment échantillonnées. Il est possible de conclure qu'une stratification selon la hauteur du couvert est à recommander dans le calcul de relations allométriques afin d'éviter les biais dans les estimations de biomasse aérienne.

ACS Style

Gregoire Vincent; F. Caron; Damien Sabatier; Lilian Blanc. L'imagerie LiDAR montre que les forêts les plus hautes comportent des tiges plus élancées. BOIS & FORETS DES TROPIQUES 2012, 314, 51 -56.

AMA Style

Gregoire Vincent, F. Caron, Damien Sabatier, Lilian Blanc. L'imagerie LiDAR montre que les forêts les plus hautes comportent des tiges plus élancées. BOIS & FORETS DES TROPIQUES. 2012; 314 (314):51-56.

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Gregoire Vincent; F. Caron; Damien Sabatier; Lilian Blanc. 2012. "L'imagerie LiDAR montre que les forêts les plus hautes comportent des tiges plus élancées." BOIS & FORETS DES TROPIQUES 314, no. 314: 51-56.

Journal article
Published: 31 October 2012 in Remote Sensing of Environment
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We predict stand basal area (BA) from small footprint LiDAR data in 129 one-ha tropical forest plots across four sites in French Guiana and encompassing a great diversity of forest structures resulting from natural (soil and geological substrate) and anthropogenic effects (unlogged and logged forests). We use predictors extracted from the Canopy Height Model to compare models of varying complexity: single or multiple regressions and nested models that predict BA by independent estimates of stem density and quadratic mean diameter. Direct multiple regression was the most accurate, giving a 9.6% Root Mean Squared Error of Prediction (RMSEP). The magnitude of the various errors introduced during the data collection stage is evaluated and their contribution to MSEP is analyzed. It was found that these errors accounted for less than 10% of model MSEP, suggesting that there is considerable scope for model improvement. Although site-specific models showed lower MSEP than global models, stratification by site may not be the optimal solution. The key to future improvement would appear to lie in a stratification that captures variations in relations between LiDAR and forest structure.

ACS Style

G. Vincent; Daniel Sabatier; Lilian Blanc; J. Chave; E. Weissenbacher; Raphaël Pélissier; E. Fonty; Jean-François Molino; P. Couteron. Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure. Remote Sensing of Environment 2012, 125, 23 -33.

AMA Style

G. Vincent, Daniel Sabatier, Lilian Blanc, J. Chave, E. Weissenbacher, Raphaël Pélissier, E. Fonty, Jean-François Molino, P. Couteron. Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure. Remote Sensing of Environment. 2012; 125 ():23-33.

Chicago/Turabian Style

G. Vincent; Daniel Sabatier; Lilian Blanc; J. Chave; E. Weissenbacher; Raphaël Pélissier; E. Fonty; Jean-François Molino; P. Couteron. 2012. "Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure." Remote Sensing of Environment 125, no. : 23-33.