Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
This study aimed to compare the relative survival of patients with invasive breast cancer between women from French Guiana (a French territory in South America) and metropolitan France. No study hadever compared survival of breast cancer on the basis of immigrant status in France. Our study underlined that access to care for migrants is challenging whichwgenerates health inequalities. Background The prognosis of breast cancer in French Guiana is worse than in France with 23 deaths per 100 incident cases against 17 per 100 in metropolitan France. This study aimed to compare relative survival of patients with invasive breast cancer (IBC) between women from French Guiana and metropolitan France and to determine risk factors influencing breast cancer survival in French Guiana.Materials and methods Data were collected from the Cancer registry of French Guiana. We comparedthe relative survival of women with IBC between French Guiana and metropolitan France. We used Cox's proportional hazard regression to evaluate the effect of prognostic factors on cancer-specific mortality in French Guiana. Results We included all 269 cases of IBC in women diagnosed in French Guiana between 2003 and 2009. The overall 5-year relative survival rate of patients with IBC was 79% in French Guiana and 86% in metropolitan France. The place of birth (foreign country versus French territory), the tumor stage at the time of diagnosis, the mode of diagnosis (symptoms versus screening), the presence of hormone receptors in the tumor and the histologic type were the variables associated with survival differences. None of the other study variables were significantly associated with prognosis. Conclusion Access to care for migrants is challenging,which leads to health inequalities. Early detection through prevention programs is crucial to increaseIBC survival notably for foreign-born patients.
ISSN: 1526-8209

hal-01288657
https://hal.archives-ouvertes.fr/hal-01288657
https://hal.archives-ouvertes.fr/hal-01288657/document
https://hal.archives-ouvertes.fr/hal-01288657/file/2016%2C%20Roue%20-%20Predictive%20factors%20of%20the%20survival%20of%20women%20with%20invasive%20breast%20cancer%20in%20French%20Guiana.pdf

Éditeur(s) : HAL CCSD Wiley
Résumé : International audience
While the effects of deforestation and habitat fragmentation on parasite prevalence or richness are well investigated, host-parasite networks are still understudied despite their importance in understanding the mechanisms of these major disturbances. Because fragmentation may negatively impact species occupancy, abundance and co-occurrence, we predict a link between spatiotemporal changes in habitat and the architecture of host-parasite networks. For this, we used an extensive data set on 16 rodent species and 29 helminth species from seven localities of South-East Asia. We analysed the effects of rapid deforestation on connectance and modularity of helminth-parasite networks. We estimated both the degree of fragmentation and the rate of deforestation through the development of land uses and their changes through the last 20 to 30years in order to take into account the dynamics of habitat fragmentation in our statistical analyses. We found that rapid fragmentation does not affect helminth species richness per se but impacts host-parasite interactions as the rodent-helminth network becomes less connected and more modular. Our results suggest that parasite sharing among host species may become more difficult to maintain with the increase of habitat disturbance.
ISSN: 0021-8790

hal-01355307
https://hal.archives-ouvertes.fr/hal-01355307
DOI : 10.1111/1365-2656.12368
IRD : fdi:010064927

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
Addressing data quality issues in information systems remains a challenging task. Many approaches only tackle this issue at the extract, transform and load steps. Here we define a comprehensive method to gain greater insight into data quality characteristics within data warehouse. Our novel architecture was implemented for an hydroecological case study where massive French watercourse sampling data are collected. The method models and makes effective use of spatial, thematic and temporal accuracy, consistency and completeness for multidimensional data in order to offer analysts a “data quality” oriented framework. The results obtained in experiments carried out on the Saône River dataset demonstrated the relevance of our approach
ISSN: 0098-3004

hal-01223918
https://hal.archives-ouvertes.fr/hal-01223918
https://hal.archives-ouvertes.fr/hal-01223918/document
https://hal.archives-ouvertes.fr/hal-01223918/file/final-cogeo.pdf
DOI : 10.1016/j.cageo.2015.09.012

Éditeur(s) : HAL CCSD Pôle de Recherche pour l'Organisation et la diffusion de l'Information Géographique
Résumé : International audience
The Amazon has recently experienced a dramatic decline in deforestation coupled with economic development. However, this process is uneven. While large and medium-sized producers are intensifying and diversifying their production systems in the already cleared areas, thus apparently abandoning expansion strategies at the moment, family farming remains aloof from this decoupling between production and deforestation. In this article, the commitments of public authorities and sectors, their effects on deforestation and inequalities of adaptation are analyzed at the scale of the Legal Amazon and in a municipality considered as a model because involved in a Município Verde initiative: Paragominas, in the Eastern Amazon.
L’Amazonie a amorcé depuis quelques années une baisse de la déforestation couplée à un développement économique. Le processus est cependant inégal. Si les grands et moyens producteurs font évoluer leurs systèmes de production vers l’intensification et la diversification dans les espaces déjà ouverts, délaissant en apparence les stratégies d’expansion, l’agriculture familiale reste à l’écart du découplage entre production et déforestation. Dans cet article, les engagements des pouvoirs publics et des filières, leurs effets en matière de déforestation et les inégalités d’adaptation sont analysés à l’échelle de l’Amazonie Légale et à celle d’une municipalité considérée comme un modèle car à l’initiative d’une démarche de Município Verde : Paragominas, en Amazonie orientale.
ISSN: 1963-1197

halshs-01609039
https://halshs.archives-ouvertes.fr/halshs-01609039
DOI : 10.4000/echogeo.15035

Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
LiDAR (Light Detection And Ranging) remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and above ground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution.In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data (e.g. geology, slope, vegetation indices, etc.). The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of random forest regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on the precision of canopy height estimates, six subsets were derived from the initial airborne LiDAR dataset with flight line spacing of 5, 10, 20, 30, 40 and 50 km (corresponding to 0.29, 0.11, 0.08, 0.05, 0.04, and 0.03 points/km² respectively). Results indicated that using the regression-kriging approach achieved a precision of 1.8 m on the canopy height map with flight line spacing of 5 km and achieved an average RMSE of 4.8m for the configuration for the 50 km flight line spacing.
ISSN: 2072-4292

hal-01361343
https://hal.archives-ouvertes.fr/hal-01361343
https://hal.archives-ouvertes.fr/hal-01361343/document
https://hal.archives-ouvertes.fr/hal-01361343/file/mt2016-pub00048036.pdf
DOI : 10.3390/rs8030240

Éditeur(s) : HAL CCSD
Résumé : CIRAD, Montpellier, Perspective
In the Brazilian Amazon, degraded forests dominate the landscapes on the agricultural frontiers. This region is now facing a major challenge: halting degradation and sustainably managing these forests. Today, degraded forests represent a class of forest in their own right. They can nevertheless play a key role in combating climate change, and can also help to improve the ecological functioning of the different territories. Implementing public policies with the twin objectives of reducing degradation and promoting these forests implies strong support from research. In this Perspective, we focus on four research priorities: developing methods to characterise and monitor degraded forests; drafting specific forest management plans; understanding the role played by all social actors; and supporting policies at the territorial level.
En Amazonie brésilienne, les forêts dégradées dominent les paysages des fronts pionniers. Un grand défi attend désormais cette région : stopper la dégradation et gérer durablement ces forêts. Les forêts dégradées représentent aujourd’hui une catégorie de forêt à part entière. Elles peuvent néanmoins jouer un rôle majeur pour lutter contre le changement climatique. Elles peuvent aussi contribuer à un meilleur fonctionnement écologique des territoires. Développer des politiques publiques visant le double objectif de réduire la dégradation et de valoriser ces forêts implique un appui fort de la recherche. Dans ce Perspective, nous mettons l’accent sur quatre priorités de recherche : développer des méthodes de caractérisation et de suivi des forêts dégradées, élaborer des plans d’aménagement spécifiques, comprendre le rôle joué par tous les acteurs sociaux et accompagner les politiques à l’échelle territoriale.
Na Amazônia brasileira, as florestas degradadas dominam as paisagens das frentes pioneiras. Atualmente, esta região enfrenta um grande desafio: parar a degradação e fazer a gestão sustentável dessas florestas. As florestas degradadas são atualmente parte integrante de uma categoria de florestas. No entanto, elas podem desempenhar um papel importante na luta contra as mudanças climáticas e podem contribuir para um melhor funcionamento ecológico dos territórios. Desenvolver políticas públicas com o duplo objetivo de reduzir a degradação e melhorar a condição dessas florestas implica um forte apoio da pesquisa. Neste Perspective, nós focalizamos em quatro prioridades de pesquisa: desenvolver os métodos de caracterização e monitoramento das florestas degradadas, elaborar planos específicos de ordenamento do território, compreender o papel desempenhado por todos os atores sociais e acompanhar as politicas em escala territorial.
https://hal.archives-ouvertes.fr/hal-01537653

Droits : http://creativecommons.org/licenses/by-nc-sa/
hal-01537653
https://hal.archives-ouvertes.fr/hal-01537653
https://hal.archives-ouvertes.fr/hal-01537653/document
https://hal.archives-ouvertes.fr/hal-01537653/file/Persp40_LBLanc%20FR.pdf
https://hal.archives-ouvertes.fr/hal-01537653/file/Persp40_LBLanc%20PORT.pdf

Éditeur(s) : HAL CCSD
Résumé : IGARSS 2015, Milan, ITA, 26-/07/2015 - 31/07/2015
International audience
LiDAR remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and aboveground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution.In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data. The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of RF regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset.
Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International
Milan, Italy

hal-01244677
https://hal.archives-ouvertes.fr/hal-01244677
https://hal.archives-ouvertes.fr/hal-01244677/document
https://hal.archives-ouvertes.fr/hal-01244677/file/mt2015-pub00044297.pdf
DOI : 10.1109/IGARSS.2015.7326729

Éditeur(s) : HAL CCSD
Résumé : 16 p.
National audience
The objective of this paper is to present an approach for experimenting territorial prospective analysis based on spatial modelling. This approach is carried out in the framework of the DESCARTES project which aims at developing a spatial simulation tool to support the design and analysis of different scenarios for land-use allocation in Reunion Island in terms of environmental services. The spatial modelling tool is composed of two complementary applications: (i) the Ocelet modelling language and its land dynamics simulation environment, and (ii) the Margouill@ platform. The first demonstrator, a model of farm land consumption by urbanization, was developed and presented during workshops in order to test the role of the spatial simulation tool in support of a collaborative innovation process among stakeholders, and to foster new research on social learning, spatial simulation of environmental services, and scale change issues.
L'objectif de cet article est de présenter une démarche de construction d'un exercice de prospective territoriale basé sur un outil de modélisation spatiale. Cette démarche est mise en œuvre dans le cadre du projet ANR DESCARTES dont l'objectif est de construire un outil de simulation cartographique pour analyser différents scénarios d'affectation de l'usage des sols à l'Ile de La Réunion, en termes de services environnementaux. La plateforme de simulation cartographique est composée de deux applications complémentaires (i) le langage de modélisation Ocelet et son environnement de simulation de paysages dynamiques, et (ii) la plateforme Margouill@. Le développement puis la présentation, en atelier, d'un premier démonstrateur sur la consommation des terres agricoles par l'urbanisation a permis de tester l'outil cartographique comme support d'un processus d'innovation collective entre les parties prenantes, et d'ouvrir de nouveaux champs de recherche sur l'analyse de la démarche par les apprentissages, la spatialisation et la simulation prospective des services écosystémiques, et la prise en compte du changement d'échelle.
Acte de la Conférence Internationale de Géomatique et d'Analyse Spatiale
Brest, France

cirad-00850243
http://hal.cirad.fr/cirad-00850243
http://hal.cirad.fr/cirad-00850243/document
http://hal.cirad.fr/cirad-00850243/file/SAGEO-Brest2013-version_finale.pdf

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (>150 Mg/ha, and >300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean >300 Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter-and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R 2 =0.54, RMSE=48.3 Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain "wall-to-wall" AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ~51 Mg/ha and R²=0.48 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values.
ISSN: 0303-2434

Droits : http://creativecommons.org/licenses/by/
hal-01381116
https://hal.archives-ouvertes.fr/hal-01381116
https://hal.archives-ouvertes.fr/hal-01381116/document
https://hal.archives-ouvertes.fr/hal-01381116/file/Fayad2016a.pdf
DOI : 10.1016/j.jag.2016.07.015

Éditeur(s) : HAL CCSD
Résumé : SilviLaser 2015, La Grande Motte, FRA, 28-/09/2015 - 30/09/2015
International audience
The study provides technical guidelines to optimize sampling design parameters in the context of the implementation of biomass predictive models from airborne lidar data. Parameters under study were those easily controlled when implementing an area-based approach, i.e. lidar pulse density and both field sampling protocols and measurements. Parameters most impacting the model accuracy were identified by conducting a one-factor- at -a-time and a global sensitivity analyses. Number and size of field plot, and plot center position accuracy were identified to be the parameters most impacting model accuracy.
Proceedings of silviLaser 2015. Durrieu S. et Véga, C. (eds)
La Grande Motte, France

hal-01286833
https://hal.archives-ouvertes.fr/hal-01286833
https://hal.archives-ouvertes.fr/hal-01286833/document
https://hal.archives-ouvertes.fr/hal-01286833/file/mt2015-pub00046522.pdf
IRSTEA : PUB00046522

Éditeur(s) : HAL CCSD BMJ Publishing Group
Résumé : International audience
OBJECTIVE:To identify local meteorological drivers of dengue fever in French Guiana, we applied an original data mining method to the available epidemiological and climatic data. Through this work, we also assessed the contribution of the data mining method to the understanding of factors associated with the dissemination of infectious diseases and their spatiotemporal spread.METHODS:We applied contextual sequential pattern extraction techniques to epidemiological and meteorological data to identify the most significant climatic factors for dengue fever, and we investigated the relevance of the extracted patterns for the early warning of dengue outbreaks in French Guiana.RESULTS:The maximum temperature, minimum relative humidity, global brilliance, and cumulative rainfall were identified as determinants of dengue outbreaks, and the precise intervals of their values and variations were quantified according to the epidemiologic context. The strongest significant correlations were observed between dengue incidence and meteorological drivers after a 4-6-week lag.DISCUSSION:We demonstrated the use of contextual sequential patterns to better understand the determinants of the spatiotemporal spread of dengue fever in French Guiana. Future work should integrate additional variables and explore the notion of neighborhood for extracting sequential patterns.CONCLUSIONS:Dengue fever remains a major public health issue in French Guiana. The development of new methods to identify such specific characteristics becomes crucial in order to better understand and control spatiotemporal transmission.Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
ISSN: 1067-5027

lirmm-01379072
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01379072
DOI : 10.1136/amiajnl-2013-002348
PUBMED : 24549761