Introduction to LCA, interests and opportunities for the rubber supply chain

Life Cycle Assessment (LCA) is a product-oriented method to assess the environmental impacts of a product while accounting for its whole life cycle, "from the cradle to the grave". It is standardised by international norms (ISO, 2006). It was first mostly used for eco-conception in industrial productions, but has been widely spread in the agricultural sector in the last twenty years. By its holistic nature, LCA is a unique method to assess several environmental impacts while avoiding pollution trade-offs between production stages or impact categories. The most renowned impact categories are climate change or energy use, but several other impact categories can also be assessed such as eutrophication or human toxicity. With the growing awareness of the risks associated with climate change and the need to protect the environment, the design of eco-friendly production modes has become critical. Throughout the world, initiatives from both the private and public sectors promote the development of sustainable supply chains including the development of communication tools using LCA indicators. In France, a law was recently promulgated (Grenelle 1, 2009) that makes the eco-labelling based on LCA compulsory for a wide range of products such as food and pet food, automobile, clothes, electronics etc. Application of LCA to agricultural products or bio-sourced materials is not straightforward due to the variability in agricultural production systems. This variability is particularly important in the Tropics, where both pedo-climatic and socio-cultural conditions greatly vary. To account for the influence of these conditions on the field emissions and the final impacts within LCA, methodological developments are being carried out by the scientific community. Researchers at CIRAD especially focus on how to better account for tropical specificities and perennial crops within LCA (Bessou et al., 2012). They work together with several partners in France (www.elsa-lca.org) and abroad, and CIRAD is notably member of the LCA AgriFood ASIA Network (http://lca-agrifood-asia.org). Undoubtedly, there is a good opportunity for the actors in the rubber supply chain to benefit from the researches at CIRAD and the dynamism of the LCA AgriFood ASIA Network. Environmental impacts of rubber products will necessarily need to be assessed in a short to medium term, for instance because of buyers requests, and LCA has become the most commonly used method in order to compare products. As a perennial crop, not used for food products, it is crucial to assess the assets and drawbacks of rubber production in order to define best management practices and supply chain strategies to limit environmental impacts. (Résumé d'auteur

Marges urbaines, re-développement et gouvernance multi-échelles

http://www.territoires-rdd.net/pdf/Fiches%20et%20posters/28gay_2.pdfPoster, Programme D2RT 2003, Politiques territoriales et développement durable, Axe 4 : Les inégalités écologiques, Colloque de valorisation des travaux de recherches menés dans le cadre de l'APR 200

Estimation of the normalized coherency matrix through the SIRV model. Application to high resolution POLSAR data

8 pagesInternational audienceIn the context of non-Gaussian polarimetric clutter models, this paper presents an application of the recent advances in the field of Spherically Invariant Random Vectors (SIRV) modelling for coherency matrix estimation in heterogeneous clutter. The complete description of the POLSAR data set is achieved by estimating the span and the normalized coherency independently. The normalized coherency describes the polarimetric diversity, while the span indicates the total received power. The main advantages of the proposed Fixed Point estimator are that it does not require any "a priori" information about the probability density function of the texture (or span) and it can be directly applied on adaptive neighbourhoods. Interesting results are obtained when coupling this Fixed Point estimator with an adaptive spatial support based on the scalar span information. Based on the SIRV model, a new maximum likelihood distance measure is introduced for unsupervised POLSAR classification. The proposed method is tested with airborne POLSAR images provided by the RAMSES system. Results of entropy/alpha/anisotropy decomposition, followed by unsupervised classification, allow discussing the use of the normalized coherency and the span as two separate descriptors of POLSAR data sets

Real-Time Tracking with Classifiers

Two basic facts motivate this paper: (1) particle filter based trackers have become increasingly powerful in recent years, and (2) object detectors using statistical learning algorithms often work at a near real-time rate. We present the use of classifiers as likelihood observation function of a particle filter. The original resulting method is able to simultaneously recognize and track an object using only a statistical model learnt from a generic database. Our main contribution is the definition of a likelihood function which is produced directly from the outputs of a classifier. This function is an estimation of calibrated probabilities P (class|data). Parameters of the function are estimated to minimize the negative log likelihood of the training data, which is a cross-entropy error function. Since a generic statistical model is used, the tracking does not need any image based model learnt inline. Moreover, the tracking is robust to appearance variation because the statistical learning is trained with many poses, illumination conditions and instances of the object. We have implemented the method for two recent popular classifiers: (1) Support Vector Machines and (2) Adaboost. An experimental evaluation shows that the approach can be used for popular applications like pedestrian or vehicle detection and tracking. Finally, we demonstrate that an efficient implementation provides a real-time system on which only a fraction of CPU time is required to track at frame rate

KummerU clutter model for PolSAR data: Application to segmentation and classification

International audienceIn this paper, Spherically Invariant Random Vectors (SIRV) are introduced to describe the heterogeneity of the Polarimetric Synthetic Aperture Radar (PolSAR) clutter. In this context, the scalar texture parameter and the normalized covariance matrix are extracted from the PolSAR images. If the texture parameter is modeled by a Fisher Probability Density Function (PDF), the observed target scattering vector follows a KummerU PDF. This PDF is then implemented in a hierarchical segmentation algorithm. Finally, segmentation results are shown on both synthetic and real images