Contribution du sol dans la réflectance proche infrarouge de la forêt tropicale sur images SPOT

Cinq scènes SPOT situées sur les plateaux forestiers du Sud Cameroun entre 2 et 5° de latitude Nord, ont été utilisées pour étudier les variations de la luminance en fonction de la nature et de la densité du couvert forestier. Une analyse détaillée du canal proche infrarouge prenant en compte la géométrie d'acquisition, la date de prise de vue et les caractéristiques spatiales de la surface, met en évidence des variations de luminance qui ne peuvent s'expliquer que par des variations d'humidité de la surface du sol. Ainsi, la contribution du sol à la luminance totale enregistrée par le satellite SPOT sur une zone de forêt dense semble établie. (Résumé d'auteur

Temporal and spatial assessment of four satellite rainfall estimates over French Guiana and North Brazil

Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance

Formalizing spatiotemporal knowledge in remote sensing applications to improve image interpretation

Technological tools allow the generation of large volumes of data. For example satellite images aid in the study of spatiotemporal phenomena in a range of disciplines such as urban planning environmental sciences and health care. Thus remote-sensing experts must handle various and complex image sets for their interpretations. The GIS community has undertaken significant work in describing spatiotemporal features and standard specifications nowadays provide design foundations for GIS software and spatial databases. We argue that this spatiotemporal knowledge and expertise would provide invaluable support for the field of image interpretation. As a result we propose a high level conceptual framework based on existing and standardized approaches offering enough modularity and adaptability to represent the various dimensions of spatiotemporal knowledge

Water level fluctuations in the Congo basin derived from ENVISAT satellite altimetry

In the Congo Basin, the elevated vulnerability of food security and the water supply implies that sustainable development strategies must incorporate the effects of climate change on hydrological regimes. However, the lack of observational hydro-climatic data over the past decades strongly limits the number of studies investigating the effects of climate change in the Congo Basin. We present the largest altimetry-based dataset of water levels ever constituted over the entire Congo Basin. This dataset of water levels illuminates the hydrological regimes of various tributaries of the Congo River. A total of 140 water level time series are extracted using ENVISAT altimetry over the period of 2003 to 2009. To improve the understanding of the physical phenomena dominating the region, we perform a K-means cluster analysis of the altimeter-derived river level height variations to identify groups of hydrologically similar catchments. This analysis reveals nine distinct hydrological regions. The proposed regionalization scheme is validated and therefore considered reliable for estimating monthly water level variations in the Congo Basin. This result confirms the potential of satellite altimetry in monitoring spatio-temporal water level variations as a promising and unprecedented means for improved representation of the hydrologic characteristics in large ungauged river basins

Mapping a knowledge-based malaria hazard index related to landscape using remote sensing : application to the cross-border area between French Guiana and Brazil

Malaria remains one of the most common vector-borne diseases in the world and the definition of novel control strategies can benefit from the modeling of transmission processes. However, data-driven models are often difficult to build, as data are very often incomplete, heterogeneous in nature and in quality, and/or biased. In this context, a knowledge-based approach is proposed to build a robust and general landscape-based hazard index for malaria transmission that is tailored to the Amazonian region. A partial knowledge-based model of the risk of malaria transmission in the Amazonian region, based on landscape features and extracted from a systematic literature review, was used. Spatialization of the model was obtained by generating land use and land cover maps of the cross-border area between French Guiana and Brazil, followed by computing and combining landscape metrics to build a set of normalized landscape-based hazard indices. An empirical selection of the best index was performed by comparing the indices in terms of adequacy with the knowledge-based model, intelligibility and correlation with P. falciparum incidence rates. The selected index is easy to interpret and successfully represents the current knowledge about the role played by landscape patterns in malaria transmission within the study area. It was significantly associated with P. falciparum incidence rates, using the Pearson and Spearman correlation coefficients (up to 0.79 and 0.75, respectively; p-value <0.001), and the linear regression coefficient of determination (reaching 0.63; p-values <0.001). This study establishes a spatial knowledge-driven, landscape-based hazard malaria index using remote sensing that can be easily produced on a regular basis and might be useful for malaria prediction, surveillance, and control

Recommended β-lactam regimens are inadequate in septic patients treated with continuous renal replacement therapy

Introduction: Sepsis is responsible for important alterations in the pharmacokinetics of antibiotics. Continuous renal replacement therapy (CRRT), which is commonly used in septic patients, may further contribute to pharmacokinetic changes. Current recommendations for antibiotic doses during CRRT combine data obtained from heterogeneous patient populations in which different CRRT devices and techniques have been used. We studied whether these recommendations met optimal pharmacokinetic criteria for broad-spectrum antibiotic levels in septic shock patients undergoing CRRT.Methods: This open, prospective study enrolled consecutive patients treated with CRRT and receiving either meropenem (MEM), piperacillin-tazobactam (TZP), cefepime (FEP) or ceftazidime (CAZ). Serum concentrations of these antibiotics were determined by high-performance liquid chromatography from samples taken before (t = 0) and 1, 2, 5, and 6 or 12 hours (depending on the β-lactam regimen) after the administration of each antibiotic. Series of measurements were separated into those taken during the early phase ( 48 hours).Results: A total of 69 series of serum samples were obtained in 53 patients (MEM, n = 17; TZP, n = 16; FEP, n = 8; CAZ, n = 12). Serum concentrations remained above four times the minimal inhibitory concentration for Pseudomonas spp. for the recommended time in 81% of patients treated with MEM, in 71% with TZP, in 53% with CAZ and in 0% with FEP. Accumulation after 48 hours of treatment was significant only for MEM.Conclusions: In septic patients receiving CRRT, recommended doses of β-lactams for Pseudomonas aeruginosa are adequate for MEM but not for TZP, FEP and CAZ; for these latter drugs, higher doses and/or extended infusions should be used to optimise serum concentrations. © 2011 Seyler et al. licensee BioMed Central Ltd.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Correction of interferometric and vegetation biases in the SRTMGL1 spaceborne DEM with hydrological conditioning towards improved hydrodynamics modeling in the Amazon Basin

In the Amazon basin, the recently released SRTM Global 1 arc-second (SRTMGL1) remains the best topographic information for hydrological and hydrodynamic modeling purposes. However, its accuracy is hindered by errors, partly due to vegetation, leading to erroneous simulations. Previous efforts to remove the vegetation signal either did not account for its spatial variability or relied on a single assumed percentage of penetration of the SRTM signal. Here, we propose a systematic approach over an Amazonian floodplain to remove the vegetation signal, addressing its heterogeneity by combining estimates of vegetation height and a land cover map. We improve this approach by interpolating the first results with drainage network, field and altimetry data to obtain a hydrological conditioned DEM. The averaged interferometric and vegetation biases over the forest zone were found to be -2.0 m and 7.4 m, respectively. Comparing the original and corrected DEM, vertical validation against Ground Control Points shows a RMSE reduction of 64%. Flood extent accuracy, controlled against Landsat and JERS-1 images, stresses improvements in low and high water periods (+24% and +18%, respectively). This study also highlights that a ground truth drainage network, as a unique input during the interpolation, achieves reasonable results in terms of flood extent and hydrological characteristics