Villeneuve-la-Rivière, France
Villeneuve-la-Rivière, France

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Tran A.,CIRAD - Agricultural Research for Development | L'Ambert G.,EID Mediterranee | Lacour G.,EID Mediterranee | Lacour G.,Catholic University of Leuven | And 12 more authors.
International Journal of Environmental Research and Public Health | Year: 2013

The mosquito Aedes (Stegomyia) albopictus (Skuse) (Diptera: Culicidae) is an invasive species which has colonized Southern Europe in the last two decades. As it is a competent vector for several arboviruses, its spread is of increasing public health concern, and there is a need for appropriate monitoring tools. In this paper, we have developed a modelling approach to predict mosquito abundance over time, and identify the main determinants of mosquito population dynamics. The model is temperature- and rainfall-driven, takes into account egg diapause during unfavourable periods, and was used to model the population dynamics of Ae. albopictus in the French Riviera since 2008 Entomological collections of egg stage from six locations in Nice conurbation were used for model validation. We performed a sensitivity analysis to identify the key parameters of the mosquito population dynamics. Results showed that the model correctly predicted entomological field data (Pearson r correlation coefficient values range from 0.73 to 0.93). The model's main control points were related to adult's mortality rates, the carrying capacity in pupae of the environment, and the beginning of the unfavourable period. The proposed model can be efficiently used as a tool to predict Ae. albopictus population dynamics, and to assess the efficiency of different control strategies. © 2013 by the authors; licensee MDPI, Basel, Switzerland.


Baghdadi N.,IRSTEA | Camus P.,IRSTEA | Beaugendre N.,SIRS | Issa O.M.,University of Reims Champagne Ardenne | And 5 more authors.
Remote Sensing | Year: 2011

The objective of this study is to validate an approach based on the change detection in multitemporal TerraSAR images (X-band) for mapping soil moisture in the Sahelian area. In situ measurements were carried out simultaneously with TerraSAR-X acquisitions on two study sites in Niger. The results show the need for comparing the difference between the rainy season image and a reference image acquired in the dry season. The use of two images enables a reduction of the roughness effects. The soils of plateaus covered with erosion crusts are dry throughout the year while the fallows show more significant moisture during the rainy season. The accuracy on the estimate of soil moisture is about 2.3% (RMSE) in comparison with in situ moisture contents. © 2011 by the authors.


Sannier C.,SIRS | McRoberts R.E.,U.S. Department of Agriculture | Fichet L.-V.,SIRS | Makaga E.M.K.,British Petroleum
Remote Sensing of Environment | Year: 2014

Forest cover maps were produced for the Gabonese Agency for Space Studies and Observations (AGEOS) for 1990, 2000 and 2010 for an area of approximately 102,000km2 corresponding to 38% of the total area of Gabon and representative of the range of human pressure on forest resources. The maps were constructed using a combination of a semi-automated classification procedure and manual enhancements to ensure the greatest possible accuracy. A two-stage area frame sampling approach was adopted to collect reference data for assessing the accuracy of the forest cover maps and to estimate proportion forest cover and net proportion deforestation. A total of 251 2×2km segments or primary sample units (PSUs) were visually interpreted by a team of photo-interpreters independently from the map production team to produce a reference dataset representing about 1% of the study area. Paired observations were extracted from the forest cover map and the reference data for a random selection of 50 secondary sample units (SSUs) in the form of pixels within each PSU. Overall map accuracies were greater than 95%. PSU and SSU outputs were used to estimate proportion forest cover and net proportion deforestation using both direct expansion and model-assisted regression (MAR) estimators. All proportion forest cover estimates were similar, but the variances of the MAR estimates were smaller than variances for the direct expansion estimates by factors as great as 50. In addition, SSU-level estimates had standard errors slightly greater than those of PSU-level estimates, but the differences were small, particularly when auxiliary variables were obtained from forest cover maps. Therefore, a two-stage sampling approach was justified for collecting a reliable forest cover reference dataset for estimating proportion forest cover area and net proportion deforestation. Finally, despite large overall map accuracies, net proportion deforestation estimates obtained from the maps alone can be misleading as indicated by the finding that the MAR estimates, which included adjustment for bias estimates, were twice the non-adjusted map estimates for the periods 1990-2000 and 1990-2010. The results confirmed the expected generally small level of net deforestation for Gabon. However, loss of forest cover appears to have almost stopped in the last 10years. One explanation could be the creation of national parks and the implementation of forest concession management plans from 2000 onward, but this should be further explored. © 2013 Elsevier Inc.


Lefebvre A.,SIRS | Picand P.-A.,SIRS | Sannier C.,SIRS
2015 Joint Urban Remote Sensing Event, JURSE 2015 | Year: 2015

In the framework of the Urban Atlas 2012 production, this paper investigated a set of generative models (Maximum likelihood, k-means) and discriminative models (k Nearest Neighbors, Support Vector Machine and Neural Network) to extract urban-tree cover at a European scale. Based on SPOT-5 images and a training on a large coarse resolution dataset, this study tested the performance of these algorithms on 3 cities regarding their geographical location, urban morphology and acquisition dates. Result reveals that discriminative models are more robust than generative ones. It shows that overall accuracy varies from 75% for the k-means classifier to 85% for the neural network. It also shows that neural networks provide the most balanced results (ratio between commission and omission errors) leading to be most suitable algorithm to process different cities with heterogeneous data. © 2015 IEEE.


Lefebvre A.,SIRS | Cheval J.,Tongji University
2015 Joint Urban Remote Sensing Event, JURSE 2015 | Year: 2015

In this paper, we monitored the urban morphology of the old city of Shanghai and its former foreign concessions from 1985 to 2014. Based on a time-series of Landsat 5, 7 and 8 images, this study used Iteratively Reweighted Multivariate Alteration Detection (IRMAD) analysis to detect land-use modifications from traditional urban pattern to new constructions. Results show that urban transformation mainly started in 1995 and perpetuate at an average rate of 88 ha per year. It also brings out that about 55% of the old urban pattern was modified in 2014. A detailed interpretation highlights the development of modern high-rise buildings, roads and subway networks, green infrastructures but also the conservation of protected historical buildings. © 2015 IEEE.


Lefebvre A.,SIRS | Corpetti T.,French National Center for Scientific Research
2015 Joint Urban Remote Sensing Event, JURSE 2015 | Year: 2015

This paper is concerned with monitoring morphological changes in the Beijing inner city using remote sensing. The Beijing inner city contains a huge urban heritage composed of traditional courtyards (shiheyuans) and alleys (hutongs). Since the economic reform in 1978, Beijing as most of Chinese cities experiences a fast urbanization process and has to deal with the pressure of the real estate industry and historic preservation. Based on remote sensing images, we have developed a change detection method able to identify the construction of new high-rise buildings to the expense of the old districts. The results highlight the surface area of high-rise buildings has increased from 18% of Beijing inner city to 60% between 1966 and 2010. This study brings out both methodological and thematic interests: the efficiency of the proposed remote sensing approach to process heterogeneous remote sensing images and the Beijing urban transformation to the expense of the old districts. © 2015 IEEE.


McRoberts R.E.,U.S. Department of Agriculture | Sannier C.,SIRS
Accuracy 2014 - Proceedings of the 11th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences | Year: 2014

Tropical countries, often with remote and inaccessible forests, frequently rely on satellite image-based maps to estimate land cover change as a component of a greenhouse gas inventory. However, estimates obtained by simply aggregating map unit predictions are subject to classification error which induces bias into the estimation procedure. Further, overall map accuracy can be large and still induce bias into the process. The model-assisted estimator provides a mechanism for compensating for bias, simultaneously estimating uncertainty, and thereby complying with the IPCC good practice guidance. An example for Gabon is used for illustration purposes.


Sannier C.,SIRS | McRoberts R.E.,U.S. Department of Agriculture | Fichet L.-V.,SIRS
Remote Sensing of Environment | Year: 2016

For purposes of greenhouse gas emissions (GHG) accounting, estimation of deforestation area in tropical countries often relies on satellite remote sensing in the absence of National Forest Inventories (NFI). Gabon has recently launched a National Climate Action Plan with the intent of establishing a National Forest Monitoring System that meets the Intergovernmental Panel on Climate Change (IPCC) 2006 guidelines for the Agriculture, Forestry and Other Land Use (AFOLU) sector. The assessment of areas of forest cover and forest cover change is essential to estimate activity data, defined as areas of various categories of land use change by the IPCC guidelines.An appropriately designed probability sample can be used to estimate forest cover and net change and their associated uncertainties and express them in the form of confidence intervals at selected probability thresholds as required in the IPCC 2006 guidelines and for reporting to the United Nations Framework Convention on Climate Change (UNFCCC). However, wall-to-wall mapping is often required to provide a comprehensive assessment of forest resources and as input to land use plans for management purposes, but wall-to-wall approaches are more expensive than a sample based approach based on visual interpretation and require specialized equipment and staff. The recent release of the University of Maryland (UMD) Global Forest Change (GFC) map products could be an alternative for tropical countries wishing to develop their own wall-to-wall forest map products but without the resources to do so. Therefore, the aim of this study is to assess the feasibility of replacing national wall-to-wall forest maps with forest maps obtained from the UMD GFC initiative.A model assisted regression (MAR) estimator was applied using the combination of reference data obtained from a probability sample and forest cover and forest cover change maps either (i) produced nationally or (ii) obtained from the UMD GFC data. The resulting activity data are potentially more accurate than the SRS estimate and provide an assessment of the precision of the estimate which is not available from map accuracy indices alone. Results obtained for 2000 and 2010 for both the national and UMD GFC datasets confirm the high level of forest cover in Gabon, more than 23.5 million ha representing approximately 88.5% of the country.Although the UMD GFC dataset provides a reliable means of producing area statistics at national level combined with appropriate sample reference data, thus offering an alternative to nationally produced datasets (i) the classification errors associated with the Global dataset have non-negligible effects on both the estimate and the precision which supports the more general statement that map data should not be used alone to produce area estimates, and (ii) the maps obtained from the UMD GFC dataset require specific calibration of the tree cover percentage representing a non-negligible effort requiring specialized staff and equipment. Guidelines on how to use and further improve UMD GFC maps for national reporting are suggested. However, this additional effort would still most likely be less than the production of national based maps. © 2015 Elsevier Inc.


Lefebvre A.,SIRS | Corpetti T.,Chinese Academy of Sciences | Courty N.,IRISA
IOP Conference Series: Earth and Environmental Science | Year: 2014

This paper is concerned with morphological change analysis in the old foreign concessions of Shanghai from 1969 to 2010. To that end, we use a series of 17 Landsat TM and Landsat ETM + images on which we estimate some feature parameters. The analysis of the resulting time series enables to isolate changes from traditional constructions to new buildings or gardens. Our results show that 70% of the old urban pattern was converted in modern high-rise buildings and green spaces.

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