Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

International audience—Remote sensing is one of the most common ways to extract relevant information about the Earth and our environment. Remote sensing acquisitions can be done by both active (synthetic aperture radar, LiDAR) and passive (optical and thermal range, multispectral and hyperspectral) devices. According to the sensor, a variety of information about the Earth's surface can be obtained. The data acquired by these sensors can provide information about the structure (optical, synthetic aperture radar), elevation (LiDAR) and material content (multi and hyperspectral) of the objects in the image. Once considered together their comple-mentarity can be helpful for characterizing land use (urban analysis, precision agriculture), damage detection (e.g., in natural disasters such as floods, hurricanes, earthquakes, oil-spills in seas), and give insights to potential exploitation of resources (oil fields, minerals). In addition, repeated acquisitions of a scene at different times allows one to monitor natural resources and environmental variables (vegetation phenology, snow cover), anthropological effects (urban sprawl, deforestation), climate changes (desertification, coastal erosion) among others. In this paper, we sketch the current opportunities and challenges related to the exploitation of multimodal data for Earth observation. This is done by leveraging the outcomes of the Data Fusion contests, organized by the IEEE Geoscience and Remote Sensing Society since 2006. We will report on the outcomes of these contests, presenting the multimodal sets of data made available to the community each year, the targeted applications and an analysis of the submitted methods and results: How was multimodality considered and integrated in the processing chain? What were the improvements/new opportunities offered by the fusion? What were the objectives to be addressed and the reported solutions? And from this, what will be the next challenges

HCN emission from translucent gas and UV-illuminated cloud edges revealed by wide-field IRAM 30m maps of Orion B GMC: Revisiting its role as tracer of the dense gas reservoir for star formation

We present 5 deg^2 (~250 pc^2) HCN, HNC, HCO+, and CO J=1-0 maps of the Orion B GMC, complemented with existing wide-field [CI] 492 GHz maps, as well as new pointed observations of rotationally excited HCN, HNC, H13CN, and HN13C lines. We detect anomalous HCN J=1-0 hyperfine structure line emission almost everywhere in the cloud. About 70% of the total HCN J=1-0 luminosity arises from gas at A_V < 8 mag. The HCN/CO J=1-0 line intensity ratio shows a bimodal behavior with an inflection point at A_V < 3 mag typical of translucent gas and UV-illuminated cloud edges. We find that most of the HCN J=1-0 emission arises from extended gas with n(H2) < 10^4 cm^-3, even lower density gas if the ionization fraction is > 10^-5 and electron excitation dominates. This result explains the low-A_V branch of the HCN/CO J=1-0 intensity ratio distribution. Indeed, the highest HCN/CO ratios (~0.1) at A_V < 3 mag correspond to regions of high [CI] 492 GHz/CO J=1-0 intensity ratios (>1) characteristic of low-density PDRs. Enhanced FUV radiation favors the formation and excitation of HCN on large scales, not only in dense star-forming clumps. The low surface brightness HCN and HCO+ J=1-0 emission scale with I_FIR (a proxy of the stellar FUV radiation field) in a similar way. Together with CO J=1-0, these lines respond to increasing I_FIR up to G0~20. On the other hand, the bright HCN J=1-0 emission from dense gas in star-forming clumps weakly responds to I_FIR once the FUV radiation field becomes too intense (G0>1500). The different power law scalings (produced by different chemistries, densities, and line excitation regimes) in a single but spatially resolved GMC resemble the variety of Kennicutt-Schmidt law indexes found in galaxy averages. As a corollary for extragalactic studies, we conclude that high HCN/CO J=1-0 line intensity ratios do not always imply the presence of dense gas.Comment: accepted for publication in A&A. 24 pages, 18 figures, plus Appendix. Abridged Abstract. English language not edite

Gas kinematics around filamentary structures in the Orion B cloud

Context. Understanding the initial properties of star-forming material and how they affect the star formation process is key. From an observational point of view, the feedback from young high-mass stars on future star formation properties is still poorly constrained. Aims. In the framework of the IRAM 30m ORION-B large program, we obtained observations of the translucent (2 ≤ AV &lt; 6 mag) and moderately dense gas (6 ≤ AV &lt; 15 mag), which we used to analyze the kinematics over a field of 5 deg2 around the filamentary structures. Methods. We used the Regularized Optimization for Hyper-Spectral Analysis (ROHSA) algorithm to decompose and de-noise the C 18 O(1−0) and 13CO(1−0) signals by taking the spatial coherence of the emission into account. We produced gas column density and mean velocity maps to estimate the relative orientation of their spatial gradients. Results. We identified three cloud velocity layers at different systemic velocities and extracted the filaments in each velocity layer. The filaments are preferentially located in regions of low centroid velocity gradients. By comparing the relative orientation between the column density and velocity gradients of each layer from the ORION-B observations and synthetic observations from 3D kinematic toy models, we distinguish two types of behavior in the dynamics around filaments: (i) radial flows perpendicular to the filament axis that can be either inflows (increasing the filament mass) or outflows and (ii) longitudinal flows along the filament axis. The former case is seen in the Orion B data, while the latter is not identified. We have also identified asymmetrical flow patterns, usually associated with filaments located at the edge of an H II region. Conclusions. This is the first observational study to highlight feedback from H II regions on filament formation and, thus, on star formation in the Orion B cloud. This simple statistical method can be used for any molecular cloud to obtain coherent information on the kinematics

HCN emission from translucent gas and UV-illuminated cloud edges revealed by wide-field IRAM 30m maps of Orion B GMC: Revisiting its role as tracer of the dense gas reservoir for star formation

35 pags., 28 figs., 14 tabs.We present 5 deg^2 (~250 pc^2) HCN, HNC, HCO+, and CO J=1-0 maps of the Orion B GMC, complemented with existing wide-field [CI] 492 GHz maps, as well as new pointed observations of rotationally excited HCN, HNC, H13CN, and HN13C lines. We detect anomalous HCN J=1-0 hyperfine structure line emission almost everywhere in the cloud. About 70% of the total HCN J=1-0 luminosity arises from gas at A_V < 8 mag. The HCN/CO J=1-0 line intensity ratio shows a bimodal behavior with an inflection point at A_V < 3 mag typical of translucent gas and UV-illuminated cloud edges. We find that most of the HCN J=1-0 emission arises from extended gas with n(H2) ~< 10^4 cm^-3, even lower density gas if the ionization fraction is > 10^-5 and electron excitation dominates. This result explains the low-A_V branch of the HCN/CO J=1-0 intensity ratio distribution. Indeed, the highest HCN/CO ratios (~0.1) at A_V < 3 mag correspond to regions of high [CI] 492 GHz/CO J=1-0 intensity ratios (>1) characteristic of low-density PDRs. Enhanced FUV radiation favors the formation and excitation of HCN on large scales, not only in dense star-forming clumps. The low surface brightness HCN and HCO+ J=1-0 emission scale with I_FIR (a proxy of the stellar FUV radiation field) in a similar way. Together with CO J=1-0, these lines respond to increasing I_FIR up to G0~20. On the other hand, the bright HCN J=1-0 emission from dense gas in star-forming clumps weakly responds to I_FIR once the FUV radiation field becomes too intense (G0>1500). The different power law scalings (produced by different chemistries, densities, and line excitation regimes) in a single but spatially resolved GMC resemble the variety of Kennicutt-Schmidt law indexes found in galaxy averages. As a corollary for extragalactic studies, we conclude that high HCN/CO J=1-0 line intensity ratios do not always imply the presence of dense gas.M.G.S.M. and J.R.G. thank the Spanish MICINN for funding support under grant PID2019-106110GB-I00. This work was supported by the French Agence Nationale de la Recherche through the DAOISM grant ANR-21-CE31–0010, and by the Programme National “Physique et Chimie du Milieu Interstellaire” (PCMI) of CNRS/INSU with INC/INP, co-funded by CEA and CNES. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).Peer reviewe

Apprentissage non supervisé pour la détection d'arbres par scanner laser aéroporté

International audienceNumerous methods have been proposed for the detection of single trees in airborne laser scanning (ALS) data. Most of them are highly dependent on the initial settings of the algorithm, as parameters (e.g. smoothing of the digital canopy height model) will affect the overall detection performance, and more particularly the trade-off between omission and commission errors. To tackle this issue, the use of prior information about the forest stand is possible when ground truth for the area is available. Alternatively, adaptive parametrization in the course of the detection procedure requires more complex algorithms which might have trouble when processing large areas. In this article a procedure for automated parametrization is presented. It is based on the unsupervised training of the detection algorithm with reference forest plots including coregistered field and ALS data. The local maxima filtering algorithm is adopted as it is simple and fast. The training step consists in evaluating the detection performance of the algorithm on the reference plots for several parameter combinations. Detection quality is evaluated as a trade-off between the number of correctly detected trees and the number of false detections. When trees are to be detected in a newly surveyed area, two possibilities for algorithm parametrization are compared. The first option is to use the parameter combination that is the more robust when used on the training set (“average” setting). The second option is to use the combination that yields the best detection on the ALS point cloud from the training set that best resembles the new data. The matching criterion is based on the Fourier spectrum of the canopy height model computed from the point cloud. 26 forest plots located in seven different ALS surveys of mountainous areas are used to test the workflow. Plots have a minimum area of 0.25 ha and represent various stand structures and tree species. To compare the two parametrization options and evaluate their sensitivity to the training set size (number of reference plots), a cross validation procedure based on repetitive sampling of the training set among the available 26 plots is performed. Results show that for training sets with less than 15 plots, the “average” setting performs better, whereas with training sets larger than 20, the matching procedure yields better detection performance. This method for unsupervised training is quite flexible as it can be used with any detection algorithm that requires initial parametrization. Moreover, the detection performance criterion can be modified in order to reflect the end-user preference regarding detection results. This study is an example of how single tree methods can benefit from an area-based analysis. Further work should investigate whether metrics usually computed for area-based methods (e.g. height quantiles) could also improve the point cloud matching

Flexible and Dynamic Control of Network QoS in Grid environments: the QoSINUS approach.

International audienceGrids rely on a complex interconnection of IP domains that may exhibit changing performance characteristics and may offer different quality of service (QoS) facilities. We examine the case of a biomedical application distributed over a grid and show it may suffer from uncontrolled communication performance. Then we present the QoSINUS service that dynamically allocates the network resources to Grid flows in order to match their specific QoS requirements under different load conditions. The aim of this approach is to optimize the end to end performances the heterogeneous mix of grid flows get from the network to enhance the individual application's performance as well as the overall grid infrastructure performance and utilization level. The QoSINUS service is based on the programmable network approach that offers flexibility, evolutivity and enables dynamic adaptation to network load variations. Finally, results of QoSINUS experiments conducted in the context of the eToile french grid testbed based on the high speed and DiffServ capable research network infrastructure, VTHD, are presented

Régression par séparateurs à vaste marge pour l'estimation de paramètres forestiers avec des données LiDAR aéroporté

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SEDYVINInternational audienceEstimation of forest stand parameters from airborne laser scanning data relies on the selection of laser metrics sets and numerous field plots for model calibration. In mountainous areas, forest is highly heterogeneous and field data collection labour-intensive hence the need for robust prediction methods. The aim of this paper is to compare stand parameters prediction accuracies of support vector machines regression and multiple regression models. Sensitivity of these techniques to the number and type of laser metrics, and use of dimension reduction techniques such as principal component and independent component analyses are also tested. Results show that support vector regression was less accurate but more stable than multiple regression for the prediction of forest parameters