Statistical mechanics of two-dimensional vortices and stellar systems

The formation of large-scale vortices is an intriguing phenomenon in two-dimensional turbulence. Such organization is observed in large-scale oceanic or atmospheric flows, and can be reproduced in laboratory experiments and numerical simulations. A general explanation of this organization was first proposed by Onsager (1949) by considering the statistical mechanics for a set of point vortices in two-dimensional hydrodynamics. Similarly, the structure and the organization of stellar systems (globular clusters, elliptical galaxies,...) in astrophysics can be understood by developing a statistical mechanics for a system of particles in gravitational interaction as initiated by Chandrasekhar (1942). These statistical mechanics turn out to be relatively similar and present the same difficulties due to the unshielded long-range nature of the interaction. This analogy concerns not only the equilibrium states, i.e. the formation of large-scale structures, but also the relaxation towards equilibrium and the statistics of fluctuations. We will discuss these analogies in detail and also point out the specificities of each system.Comment: Chapter of the forthcoming "Lecture Notes in Physics" volume: ``Dynamics and Thermodynamics of Systems with Long Range Interactions'', T. Dauxois, S. Ruffo, E. Arimondo, M. Wilkens Eds., Lecture Notes in Physics Vol. 602, Springer (2002

Fusion of hyperspectral imaging and LiDAR for forest monitoring

Effective strategies for forest characterization and monitoring are important to support sustainable management. Recent advances in remote sensing, like hyperspectral and LiDAR sensors, provide valuable information to describe forests at stand, plot, and tree level. Hyperspectral imaging contains meaningful reflectance attributes of plants or spectral traits, while LiDAR data offer alternatives for analyzing structural properties of canopy. The fusion of these two data sources can improve forest characterization. The method to use for the data fusion should be chosen according to the variables to predict. This work presents a literature review on the integration of hyperspectral imaging and LiDAR data by considering applications related to forest monitoring. Although different authors propose a variety of taxonomies for data fusion, we classified our reviewed methods according to three levels of fusion: low level or observation level, medium level or feature level, and high level or decision level. This review examines the relationship between the three levels of fusion and the methods used in each considered approac

Segmentation 3D des arbres d'un peuplement forestier par fusion de données lidar aériennes et hyperspectrales.

International audienceAccess to data with high spatial and spectral resolution is becoming more widespread and makes it possible to consider new applications for monitoring forest ecosystems. In particular, it is possible to consider studies of an entire stand but at the tree level. However, this raises questions about the joint use of data from different sensors such as LiDAR and hyperspectral imagers. This study presents a fusion methodology between high-density LiDAR data (45 pts/m² minimum) and VNIR hyperspectral images (HI) - (80 cm spatial resolution) acquired on french Alpine forests along an altitude gradient. The objective is to extract the main architectural characteristics of each individual tree and in particular the dimensions of crowns knowing the species. The methodology is based on the integration of HI and LiDAR data at different levels of fusion. First species are identified using the reflectance attributes contained in HI and the LiDAR canopy model, and then the 3D point cloud is segmented based on the allometric characteristics of the species. The integration of this additional information together with the segmentation algorithm provides an essential association strategy for LiDAR points located in the lower part of the canopy. Finally, the main dimensions of the crowns and associated trees are extracted from the 3D segmentation

30 ans de recherche et transfert sur les forêts à fonction de protection contre le risque de chute de bloc en France et en Europe

International audienceMountain forests are multifunctional ecosystems but an efficient and sustainable forest management and land use strategy have to be based on the definition of priority functions. This could only be done if efficient knowledge and decision support systems are developed in order to identify, qualify, quantify and prioritize the different forest ecosystems services. Since 30 years now, the team Protection-Ecological Engineering -Restoration (PEER) of the Irstea regional research center of Grenoble is working on these actions for valorizing the forest rockfall protection ecosystem service in French and European rockfall mitigation and protection policies. The data and knowledge acquired through the development of innovative experimental protocols ( real size experiments) has allowed the development of effective models (1, 2 and 3D rockfall trajectories models integrating the effect of forest stands, tree biomechanical behavior model based on the discrete element method, forest resources and dendrometrical parameters mapping with LiDAR data, dendrogeomorphological analysis), silviculture guidelines and forest policies. This presentation will summarize the main milestone and results of these 30 years of research but also the scientific and applied perspectives for the future

Fusion de données LiDAR et hyperspectrales pour la gestion forestière - une revue

International audienceAccording to the Intergovernmental Panel on Climate Change (IPCC), forests represent an essential source of all carbon stocks in vegetation for maintaining life conditions of many organisms in the terrestrial biosphere. The utilization of strategies for forest characterization and monitoring, plays an imperative role to develop a proper sustainable management. Current research in the field is focused on sensor potentiality and data processing. Recent advances in remote sensing afford valuable information to describe forests at tree level. On the one hand, hyperspectral images contain meaningful reflectance attributes of plants or spectral traits. On the other hand, LiDAR data offers alternatives for analyzing structural properties of canopy. A convenient selection of fusion methods provide better and more robust estimation of the variable of interest. This work presents a literature review for the integration of hyperspectral images and LiDAR data by considering applications related to forestry monitoring. Although different authors propose a variety of taxonomies for data fusion, we classified our reviewed methods according to three levels of fusion based on data processing: Low level or observation level, medium level or feature level, and high level or decision level. Fusion at observation level preserves most of the original information from both modalities by handling data at the same spatial dimension. Canopy Height Model (CHM) is the most used two-dimensional representation of LiDAR point cloud for the registration with hyperspectral images. Fusion at feature level seeks to complement information by exploiting the original data. The most relevant features extracted from hyperspectral or LiDAR data are statistical, morphological, structural, vegetation indexes, textural, among others. Some of these feature descriptors are stacked to be fused at higher level, or these are normalized to be integrated through methods of dimension reduction or feature selection. Fusion at decision level is directly associated to the forestry application and implies tasks of thresholding, segmentation, classification, or regression analysis. This review examines a relationship between the three levels of fusion and the methods used in each considered approach. The most important applications listed in this work are oriented to individual tree crown delineation, tree specie classification, landcovermaps, aboveground biomass estimation, and biophysical parameters