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23 results on '"Véga, Cédric"'

1. How reliable are remote sensing maps calibrated over large areas? A matter of scale?

Author

Luigui, Andrey Ramirez, Renaud, Jean-Pierre, and Vega, Cédric

Subjects
Statistics - Applications
Abstract

Remote sensing data are increasingly available and frequently used to produce forest attributes maps. The sampling strategy of the calibration plots may directly affect predictions and map qualities. The aim of this manuscript is to evaluate models transferability at different spatial scales according to the sampling efforts and the calibration domain of these models. Forest inventory plots from locals and regionals networks were used to calibrate randomForest (RF) models for stand basal area predictions. Auxiliary data from ALS flights and a Sentinel-2 image were used. Model transferability was assessed by comparing models developed over a given area and applied elsewhere. Performances were measured in terms of precision (RMSE and bias), coefficient of determination (R2) and the proportion of extrapolated predictions. Regional networks were also thinned to evaluate the effect of sampling efforts on models' performances. Local models showed large bias and extrapolation issues when applied elsewhere. Local issues of regional models were also observed, raising transferability and extrapolation concerns. An increase in sampling efforts was shown to reduce extrapolation issues. The outcoming results of this study underline the importance of considering models' validity domain while producing forest attribute maps, since their transferability is of crucial importance from a forest management perspective.

Published
2024

2. High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach

Author

Schwartz, Martin, Ciais, Philippe, Ottlé, Catherine, De Truchis, Aurelien, Vega, Cedric, Fayad, Ibrahim, Brandt, Martin, Fensholt, Rasmus, Baghdadi, Nicolas, Morneau, François, Morin, David, Guyon, Dominique, Dayau, Sylvia, and Wigneron, Jean-Pierre

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map over the "Landes de Gascogne" forest in France, a large maritime pine plantation of 13,000 km$^2$ with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-net models based on a combination of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each instrument in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole "Landes de Gascogne" forest area for 2020 with a mean absolute error of 2.02 m on the Test dataset. The best predictions were obtained using all available satellite layers from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region., Comment: 39 pages, 16 figures + supplementary contents

Published
2022
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12. Unit-level Small Area Estimation of Forest Inventory with GEDI Auxiliary Information

Author

Zhang, Shaohui, Véga, Cédric, Bouriaud, Olivier, Durrieu, Sylvie, Renaud, Jean-Pierre, University of Eastern Finland, Institut National de l'Information Géographique et Forestière [IGN] (IGN), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Office National des Forêts (ONF)

Subjects
[SDE]Environmental Sciences
Abstract

International audience; National Forest Inventories (NFIs) play an important role in understanding the state of forests at thenational and regional levels. Forest inventory for small territorial areas, such as municipalities, is alsoimportant for decision-makers. However, information is relatively limited at this level. As a result,developing small area estimation (SAE) approaches has gained increasing popularity in the field offorest inventory. It enables prediction of forest attributes for sub-populations using regression modelsbased on auxiliary data commonly derived from remote sensing techniques over an area of interest (AOI).It has been reported that SAE can improve the precision of forest inventory without increasing costs(Mandallaz, Breschan and Hill 2013) and may produce reliable predictions of forest attributes locally,even when field plots are not available (Rao 2014).Tomppo (2006) is a pioneer in the use of auxiliary data for multi-source forest inventory. Previously,common sources of auxiliary data often came from satellite-based imagery (McRoberts et al. 2007),digital aerial photogrammetry (Breidenbach et al. 2018), and airborne laser scanning (Magnussen et al.2014). NASA’s newly-launched Global Ecosystem Dynamics Investigation (GEDI) is a full waveformLiDAR instrument aboard the International Space Station (ISS). Its products consist of footprintmeasurements projected to cover 4% of the global land surface by the end of its mission (Dubayah et al.2020). This will provide an unprecedented opportunity to systematically collect samples of forestinformation that can be used in SAE on a large scale.The objective of this study is to explore the possibility of using GEDI auxiliary data to improve theaccuracy of forest inventory for a large natural area in central France (Sologne), as well as for smallersub-areas defined by French administrative boundaries (departments). The results will then be comparedagainst estimates obtained from simple random sampling (SRS), to assess the efficiency of the auxiliarydata.

Published
2021

14. Evaluation des ressources forestières pour la bioéconomie : quels nouveaux besoins et comment y répondre ?

Author

Hervé, Jean-Christophe, Morneau, François, Véga, Cédric, Leban, Jean-Michel, Saint-André, Laurent, Bontemps, Jean-Daniel, Institut géographique national [IGN] (IGN), Département Ecologie des Forêts, Prairies et milieux Aquatiques (DEPT EFPA), Institut National de la Recherche Agronomique (INRA), Unité de recherche Biogéochimie des Ecosystèmes Forestiers (BEF), Laboratoire d’Inventaire Forestier (LIF), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), and Institut National de l'Information Géographique et Forestière [IGN] (IGN)

Subjects
Changement climatique, Monitoring, [SDV]Life Sciences [q-bio], Biomasse, Inventaire forestier, Climate change, Biomass, Forest inventory
Abstract

Une bioéconomie fondée sur la forêt et le bois implique de concevoir de nouvelles stratégies de gestion pour simultanément adapter la forêt au changement climatique et lui permettre de répondre aux demandes accrues, en visant l’optimisation d’un bilan carbone forêt-bois intégré, tout en préservant les services écosystémiques. Les inventaires forestiers nationaux (IFN) tels qu’ils existent aujourd’hui nécessitent plusieurs innovations pour étendre leur portée : a) un inventaire plus complet incluant les branches et même le menu bois, en volume et en biomasse ; b) l’intégration du carbone dans le sol et ses variations ; c) le développement de capacités de monitoring des impacts du changement climatique, de la fertilité des sols et de la biodiversité. A travers l’exemple de l’inventaire forestier français et de quelques autres en Europe, nous montrons comment les inventaires forestiers nationaux sont devenus, grâce à une longue expérience et des innovations constantes comme le dispositif d’inventaire continu, des outils très performants de suivi détaillé des forêts pouvant aller bien au-delà de leur rôle initial. Nous présentons également des recherches en cours, dont plusieurs sont menées dans le cadre du projet H2020 DIABOLO réunissant tous les inventaires forestiers européens (réseau ENFIN) pour définir les méthodes visant à constituer un système européen d’informations forestières harmonisées, multiobjectifs et multi-sources., A forest-based bioeconomy requires designing new management strategies to simultaneously adapt forests to climate change and enable them to satisfy an increased demand for forest-based products, by targeting the optimisation of the forestry wood chain carbon budget, while preserving all other ecosystem services. National forest inventories (NFI) as they exist today require several innovations to extend their scope: a) to perform a more complete inventory including branches and even twigs, both in volume and biomass; b) to integrate soil carbon content and its variations; c) to develop capacity to monitor climate change impacts, soil fertility and biodiversity. Through the examples of the French NFI and some others in Europe, it is shown how these programs, thanks to their long experience and constant innovations such as the continuous inventory system, have become very efficient tools for detailed forest monitoring that can go far beyond their original role. It also presents ongoing research, several of which are carried out within the framework of the H2020 DIABOLO project (Distributed, Integrated and Harmonised Forest Information for Bioeconomy Outlooks) bringing together all the European National Forest Inventories (ENFIN network) with the aim of defining the methods to set up a harmonized, multipurpose and multisource European forest information system.

Published
2017

16. Bidirectional texture function of high resolution optical images of tropical forest: An approach using LiDAR hillshade simulations

Author

Barbier, Nicolas Serge, Proisy, Christophe, Sabatier, Daniel, Couteron, Pierre, Véga, Cédric, Barbier, Nicolas Serge, Proisy, Christophe, Sabatier, Daniel, Couteron, Pierre, and Véga, Cédric

Abstract

Quantifying and monitoring the structure and degradation of tropical forests over regional to global scales is gaining increasing scientific and societal importance. Reliable automated methods are only beginning to appear; for instance, through the recent development of textural approaches applied to high resolution optical imagery. In particular, the Fourier Transform Textural Ordination (FOTO) method shows some potential to provide non-saturating estimates of tropical forest structure, including for large scale applications. However, we need to understand more precisely how canopy structure interacts with physical signals (light) to produce a given texture, notably to assess the method's sensitivity to varying sun-view acquisition conditions. In this study, we take advantage of the detailed description of canopy topography provided by airborne small footprint LiDAR data acquired over the Paracou forest experimental station in French Guiana. Using hillshade models and a range of sun-view angles identical to the actual parameter distributions found for Quickbird™ images over the Amazon, we study noise and bias in texture estimation induced by the changing configurations. We introduce the bidirectional texture function, which summarizes these effects, and in particular the existence of a textural 'hot spot', similar to a well-known feature of bidirectional reflectance studies. For texture, this effect implies that coarseness decreases in configurations for which shadows are concealed to the observer. We also propose a method, termed partitioned standardization, that allows mitigating acquisition effects and discuss the potential for an operational use of VHR optical imagery and the FOTO method in the current context of international decisions to reduce CO2 emissions due to deforestation and forest degradation. © 2010 Elsevier Inc., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2011

17. A TREE-BASED APPROACH TO ESTIMATE WOOD VOLUME FROM LiDAR DATA: A CASE STUDY IN A PINE PLANTATION.

Author

Hamrouni, Ahmed, Véga, Cédric, Renaud, Jean-Pierre, Durrieu, Sylvie, and Bouvier, Marc

Subjects
*STANDARD deviations, *LIDAR, *CROWNS (Botany)
Abstract

We developed an-object based framework to assess individual tree volume from airborne LiDAR data in a pined-ominated forest. Individual tree crowns were extracted using a point-based segmentation algorithm and total tree volume was estimated using height and either tree or crown bounding volume information using nonlinear models. Tree-level models provided root mean squared errors (RMSE) around 30%. Scaling volume at the plot level allows to reduce RMSE by a factor 2, i.e. around 15%. This scale change may benefits from error compensation associated to segmentation involving false tree detections or tree omissions leading to crown fusions. Along with height, crown volume was found to be a good predictor of tree volume, but suffers from computational issues that may further induce variability in the models. Future work should integrate an analysis of tree neighborhood in order to improve tree- models by the use of indices reflecting competition and growth conditions. [ABSTRACT FROM AUTHOR]

Published
2015
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22. Height growth reconstruction of a boreal forest canopy over a period of 58 years using a combination of photogrammetric and lidar models

Author

Véga, Cédric and St-Onge, Benoît

Subjects
*TREE growth, *ALTITUDES, *FOREST canopies, *REMOTE sensing, *OPTICAL radar, *DIGITAL photogrammetry, *AERIAL photographs, *STEREOSCOPIC views, *VEGETATION dynamics
Abstract

Field data describing the height growth of trees or stands over several decades are very scarce. Consequently, our capacity of analyzing forest dynamics over large areas and long periods of time is somewhat limited. This study proposes a new method for retrospectively reconstructing plot-wise average dominant tree height based on a time series of high-resolution canopy height maps, termed canopy height models (CHMs). The absolute elevation of the canopy surface, or digital surface model (DSM), was first reconstructed by applying image-matching techniques to stereo-pairs of aerial photographs acquired in 1945, 1965, 1983, and 2003. The historical CHMs were then created by subtracting the bare earth elevation provided from a recent lidar survey from the DSMs. A method for estimating average dominant tree height from these historical CHMs was developed and calibrated for each photographic year. The accuracy of the resulting remote sensing height estimates was compared to age–height data reconstructed based on dendrometric measurements. The height bias of the remote sensing estimates relative to the verification data ranged from 0.52 m to 1.55 m (1.16 m on average). The corresponding root-mean-square errors varied between 1.49 m and 2.88 m (2.03 m average). Despite being slightly less accurate than historical field data, the quality of the remote sensing estimates is sufficient for many types of forest dynamics studies. The procedures for implementing this method, with the exception of the calibration phase, are entirely automated such that forest height growth curves can be reconstructed and mapped over large areas for which recent lidar data and historical photographs exist. [Copyright &y& Elsevier]

Published
2008
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23. Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data.

Author

Ravaglia, Joris, Fournier, Richard A., Bac, Alexandra, Véga, Cédric, Côté, Jean-François, Piboule, Alexandre, and Rémillard, Ulysse

Subjects
AIRBORNE lasers, OPTICAL scanners, POINT cloud, FOREST surveys, CLOUD forests, FOREST measurement, ALGORITHMS
Abstract

Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm. [ABSTRACT FROM AUTHOR]

Published
2019
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