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Over the last 20 years, there has been a surge of interest in the use of reflectance data collected using satellites and aerial vehicles to monitor vegetation diversity. One methodological option to monitor these systems involves developing empirical relationships between spectral heterogeneity in space (spectral variation) and plant or habitat diversity. This approach is commonly termed the ‘Spectral Variation Hypothesis’. Although increasingly used, it is controversial and can be unreliable in some contexts. Here, we review the literature and apply three-level meta-analytical models to assess the test results of the hypothesis across studies using several moderating variables relating to the botanical and spectral sampling strategies and the types of sites evaluated. We focus on the literature relating to grasslands, which are less well studied compared to forests and are likely to require separate treatments due to their dynamic phenology and the taxonomic complexity of their canopies on a small scale. Across studies, the results suggest an overall positive relationship between spectral variation and species diversity (mean correlation coefficient = 0.36). However, high levels of both within-study and between-study heterogeneity were found. Whether data was collected at the leaf or canopy level had the most impact on the mean effect size, with leaf-level studies displaying a stronger relationship compared to canopy-level studies. We highlight the challenges facing the synthesis of these kinds of experiments, the lack of studies carried out in arid or tropical systems and the need for scalable, multitemporal assessments to resolve the controversy in this field.

According to the Spectral Variation Hypothesis (SVH), spectral variance has the potential to predict taxonomic composition in grasslands over time. However, in previous studies the relationship has been found to be unstable. We hypothesise that the diversity of phenological stages is also a driver of spectral variance and could act to confound the species signal. To test this concept, intra-annual repeat spectral and botanical sampling was performed at the quadrat scale at two grassland sites, one displaying high species diversity and the other low species diversity. Six botanical metrics were used, three taxonomy based and three phenology based. Using uni-temporal linear permutation models, we found that the SVH only held at the high diversity site and only for certain metrics and at particular time points. We also tested the seasonal influence of phenological stage dominance, alongside the taxonomic and phenological diversity metrics on spectral variance using linear mixed models. A term of percentage mature leaves, alongside an interaction term of percentage mature leaves and species diversity, explained 15-25% of the model variances, depending on the spectral region used. These results indicate that the dominant canopy phenology stage is a confounding variable when examining the spectral variance-species diversity relationship. We emphasise the challenges that exist in tracking species or phenology-based metrics in grasslands using spectral variance but encourage further research that contextualises spectral variance data within seasonal plant development alongside other canopy structural and leaf traits.

Hyperspectral leaf-level reflectance data may enable the creation of taxonomic inventories and diversity assessments of grasslands, but little is known about the stability of species-specific spectral classes and discrimination models over the course of a growing season. Here, we present a cross-seasonal dataset of seventeen species that are common to a temperate, dry and nutrient-poor calcareous grassland, which spans thirteen sampling dates, a week apart, during the spring and summer months. By using a classification model that incorporated waveband selection (a sparse partial least squares discriminant analysis), most species could be classified, irrespective of the sampling date. However, between 42 and 95% of the available spectral information was required to obtain these results, depending on the date and model run. Feature selection was consistent across time for 70 out of 720 wavebands and reflectance around 1410 nm, representing water features, contributed the most to the discrimination. Model transferability was higher between neighbouring sampling dates and improved after the “green-up” period. Some species were consistently easy to classify, irrespective of time point, when using up to six latent variables, which represented about 99% of the total spectral variance, whereas other species required many latent variables, which represented very small spectral differences. We concluded that it did seem possible to create reliable taxonomic inventories for combinations of certain grassland species, irrespective of sampling date, and that the reason for this could lie in their distinctive morphological and/or biochemical leaf traits. Model transferability, however, was limited across dates and cross-seasonal sampling that captures leaf development would probably be necessary to create a predictive framework for the taxonomic monitoring of grasslands. In addition, most variance in the leaf reflectance within this system was driven by a subset of species and thi

Hyperspectral leaf-level reflectance data may enable the creation of taxonomic inventories and diversity assessments of grasslands, but little is known about the stability of species-specific spectral classes and discrimination models over the course of a growing season. Here, we present a cross-seasonal dataset of seventeen species that are common to a temperate, dry and nutrient-poor calcareous grassland, which spans thirteen sampling dates, a week apart, during the spring and summer months. By using a classification model that incorporated waveband selection (a sparse partial least squares discriminant analysis), most species could be classified, irrespective of the sampling date. However, between 42 and 95% of the available spectral information was required to obtain these results, depending on the date and model run. Feature selection was consistent across time for 70 out of 720 wavebands and reflectance around 1410 nm, representing water features, contributed the most to the discrimination. Model transferability was higher between neighbouring sampling dates and improved after the “green-up” period. Some species were consistently easy to classify, irrespective of time point, when using up to six latent variables, which represented about 99% of the total spectral variance, whereas other species required many latent variables, which represented very small spectral differences. We concluded that it did seem possible to create reliable taxonomic inventories for combinations of certain grassland species, irrespective of sampling date, and that the reason for this could lie in their distinctive morphological and/or biochemical leaf traits. Model transferability, however, was limited across dates and cross-seasonal sampling that captures leaf development would probably be necessary to create a predictive framework for the taxonomic monitoring of grasslands. In addition, most variance in the leaf reflectance within this system was driven by a subset of species and thi

According to the Spectral Variation Hypothesis (SVH), spectral variance has the potential to predict taxonomic composition in grasslands over time. However, in previous studies the relationship has been found to be unstable. We hypothesise that the diversity of phenological stages is also a driver of spectral variance and could act to confound the species signal. To test this concept, intra-annual repeat spectral and botanical sampling was performed at the quadrat scale at two grassland sites, one displaying high species diversity and the other low species diversity. Six botanical metrics were used, three taxonomy based and three phenology based. Using uni-temporal linear permutation models, we found that the SVH only held at the high diversity site and only for certain metrics and at particular time points. We also tested the seasonal influence of phenological stage dominance, alongside the taxonomic and phenological diversity metrics on spectral variance using linear mixed models. A term of percentage mature leaves, alongside an interaction term of percentage mature leaves and species diversity, explained 15-25% of the model variances, depending on the spectral region used. These results indicate that the dominant canopy phenology stage is a confounding variable when examining the spectral variance-species diversity relationship. We emphasise the challenges that exist in tracking species or phenology-based metrics in grasslands using spectral variance but encourage further research that contextualises spectral variance data within seasonal plant development alongside other canopy structural and leaf traits.

Farmland birds have declined throughout Europe over recent decades. Many farmland songbirds are associated with linear woody features on field boundaries, such as hedgerows and tree lines. Previous studies have assessed songbird associations with specific hedgerow and tree characteristics, and their landscape context, but large-scale assessments have been limited by difficulties in mapping linear woody networks over large extents, particularly their height structure. We used a high-resolution lidar model of the complete network of linear woody features in southwest England (9,424 km2), summarising linear feature lengths by height class. Associations were tested between heights of linear woody features and the abundance of 22 farmland birds, using bird survey data summarised for 1446 near-contiguous tetrads, and a weighted version of the phi coefficient of association. Land cover mapping defined tetrads as grassland, mixed or arable farmland. Results showed that the linear woody network was dominated by features corresponding to managed hedgerows (1.5-2.9 m tall, 42-47% of the network by land cover type), followed by tree lines (≥ 6.0 m, 28-35%). All songbird species had statistically significant, but weak, associations with combinations of land cover and height class of linear woody features, although land cover appeared to be the dominant factor. Many species showed more positive associations with linear woody features on arable farmland than on grassland, particularly for taller hedgerows and tree lines. The results suggest that land-use diversification may benefit some farmland songbirds, such as introducing pockets of arable farming in landscapes dominated by intensively managed grassland. Diverse heights in the linear woody network, incorporating tall hedgerows and trees, would also likely benefit a range of songbird species. The study demonstrates the significant potential of lidar in characterising the structure of linear woody features at the landscape scale

Farmland birds have declined throughout Europe over recent decades. Many farmland songbirds are associated with linear woody features on field boundaries, such as hedgerows and tree lines. Previous studies have assessed songbird associations with specific hedgerow and tree characteristics, and their landscape context, but large-scale assessments have been limited by difficulties in mapping linear woody networks over large extents, particularly their height structure. We used a high-resolution lidar model of the complete network of linear woody features in southwest England (9,424 km2), summarising linear feature lengths by height class. Associations were tested between heights of linear woody features and the abundance of 22 farmland birds, using bird survey data summarised for 1446 near-contiguous tetrads, and a weighted version of the phi coefficient of association. Land cover mapping defined tetrads as grassland, mixed or arable farmland. Results showed that the linear woody network was dominated by features corresponding to managed hedgerows (1.5-2.9 m tall, 42-47% of the network by land cover type), followed by tree lines (≥ 6.0 m, 28-35%). All songbird species had statistically significant, but weak, associations with combinations of land cover and height class of linear woody features, although land cover appeared to be the dominant factor. Many species showed more positive associations with linear woody features on arable farmland than on grassland, particularly for taller hedgerows and tree lines. The results suggest that land-use diversification may benefit some farmland songbirds, such as introducing pockets of arable farming in landscapes dominated by intensively managed grassland. Diverse heights in the linear woody network, incorporating tall hedgerows and trees, would also likely benefit a range of songbird species. The study demonstrates the significant potential of lidar in characterising the structure of linear woody features at the landscape scale

The leaf phenology (i.e. the seasonality of leaf amount and leaf demography) of ecosystems can be characterized through the use of Earth observation data using a variety of different approaches. The most common approach is to derive time series of vegetation indices (VIs) which are related to the temporal evolution of FPAR, LAI and GPP or alternatively used to derive phenology metrics that quantify the growing season. The product presented here shows a map of average ‘amplitude’ (i.e. maximum minus minimum) of annual cycles observed in MODIS‐derived NDVI and EVI from 2000 to 2013 for Meso‐ and South America. It is a robust determination of the amplitude of annual cycles of vegetation greenness derived from a Lomb–Scargle spectral analysis of unevenly spaced data. VI time series pre‐processing was used to eliminate measurement outliers, and the outputs of the spectral analysis were screened for statistically significant annual signals. Amplitude maps provide an indication of net ecosystem phenology since the satellite observations integrate the greenness variations across the plant individuals within each pixel. The average amplitude values can be interpreted as indicating the degree to which the leaf life cycles of individual plants and species are synchronized. Areas without statistically significant annual variations in greenness may still consist of individuals that show a well‐defined annual leaf phenology. In such cases, the timing of the phenology events will vary strongly within the year between individuals. Alternatively, such areas may consist mainly of plants with leaf turnover strategies that maintain a constant canopy of leaves of different ages. Comparison with in situ observations confirms our interpretation of the average amplitude measure. VI amplitude interpreted as leaf life cycle synchrony can support model evaluation by informing on the likely leaf turn over rates and seasonal variation in ecosystem leaf age distribution.

The leaf phenology (i.e. the seasonality of leaf amount and leaf demography) of ecosystems can be characterized through the use of Earth observation data using a variety of different approaches. The most common approach is to derive time series of vegetation indices (VIs) which are related to the temporal evolution of FPAR, LAI and GPP or alternatively used to derive phenology metrics that quantify the growing season. The product presented here shows a map of average ‘amplitude’ (i.e. maximum minus minimum) of annual cycles observed in MODIS‐derived NDVI and EVI from 2000 to 2013 for Meso‐ and South America. It is a robust determination of the amplitude of annual cycles of vegetation greenness derived from a Lomb–Scargle spectral analysis of unevenly spaced data. VI time series pre‐processing was used to eliminate measurement outliers, and the outputs of the spectral analysis were screened for statistically significant annual signals. Amplitude maps provide an indication of net ecosystem phenology since the satellite observations integrate the greenness variations across the plant individuals within each pixel. The average amplitude values can be interpreted as indicating the degree to which the leaf life cycles of individual plants and species are synchronized. Areas without statistically significant annual variations in greenness may still consist of individuals that show a well‐defined annual leaf phenology. In such cases, the timing of the phenology events will vary strongly within the year between individuals. Alternatively, such areas may consist mainly of plants with leaf turnover strategies that maintain a constant canopy of leaves of different ages. Comparison with in situ observations confirms our interpretation of the average amplitude measure. VI amplitude interpreted as leaf life cycle synchrony can support model evaluation by informing on the likely leaf turn over rates and seasonal variation in ecosystem leaf age distribution.

Recent work has shown that leaf traits and spectral properties change through time and/or seasonally as leaves age. Current field and hyperspectral methods used to estimate canopy leaf traits could, therefore, be significantly biased by variation in leaf age. To explore the magnitude of this effect, we used a phenological dataset comprised of leaves of different leaf age groups -developmental, mature, senescent and mixed-age- from canopy and emergent tropical trees in southern Peru. We tested the performance of partial least squares regression models developed from these different age groups when predicting traits for leaves of different ages on both a mass and area basis. Overall, area-based models outperformed mass-based models with a striking improvement in prediction observed for area-based leaf carbon (Carea) estimates. We observed trait-specific age effects in all mass-based models while area-based models displayed age effects in mixed-age leaf groups for Parea and Narea. Spectral coefficients and variable importance in projection (VIPs) also reflected age effects. Both mass- and area-based models for all five leaf traits displayed age/temporal sensitivity when we tested their ability to predict the traits of leaves of other age groups. Importantly, mass-based mature models displayed the worst overall performance when predicting the traits of leaves from other age groups. These results indicate that the widely adopted approach of using fully expanded mature leaves to calibrate models that estimate remotely-sensed tree canopy traits introduces error that can bias results depending on the phenological stage of canopy leaves. To achieve temporally stable models, spectroscopic studies should consider producing area-based estimates as well as calibrating models with leaves of different age groups as they present themselves through the growing season. We discuss the implications of this for surveys of canopies with synchronised and unsynchronised leaf phenology.

Recent work has shown that leaf traits and spectral properties change through time and/or seasonally as leaves age. Current field and hyperspectral methods used to estimate canopy leaf traits could, therefore, be significantly biased by variation in leaf age. To explore the magnitude of this effect, we used a phenological dataset comprised of leaves of different leaf age groups -developmental, mature, senescent and mixed-age- from canopy and emergent tropical trees in southern Peru. We tested the performance of partial least squares regression models developed from these different age groups when predicting traits for leaves of different ages on both a mass and area basis. Overall, area-based models outperformed mass-based models with a striking improvement in prediction observed for area-based leaf carbon (Carea) estimates. We observed trait-specific age effects in all mass-based models while area-based models displayed age effects in mixed-age leaf groups for Parea and Narea. Spectral coefficients and variable importance in projection (VIPs) also reflected age effects. Both mass- and area-based models for all five leaf traits displayed age/temporal sensitivity when we tested their ability to predict the traits of leaves of other age groups. Importantly, mass based mature models displayed the worst overall performance when predicting the traits of leaves from other age groups. These results indicate that the widely adopted approach of using fully expanded mature leaves to calibrate models that estimate remotely-sensed tree canopy traits introduces error that can bias results depending on the phenological stage of canopy leaves. To achieve temporally stable models, spectroscopic studies should consider producing area-based estimates as well as calibrating models with leaves of different age groups as they present themselves through the growing season. We discuss the implications of this for surveys of canopies with synchronised and unsynchronised leaf phenology.

Recent work has shown that leaf traits and spectral properties change through time and/or seasonally as leaves age. Current field and hyperspectral methods used to estimate canopy leaf traits could, therefore, be significantly biased by variation in leaf age. To explore the magnitude of this effect, we used a phenological dataset comprised of leaves of different leaf age groups -developmental, mature, senescent and mixed-age- from canopy and emergent tropical trees in southern Peru. We tested the performance of partial least squares regression models developed from these different age groups when predicting traits for leaves of different ages on both a mass and area basis. Overall, area-based models outperformed mass-based models with a striking improvement in prediction observed for area-based leaf carbon (Carea) estimates. We observed trait-specific age effects in all mass-based models while area-based models displayed age effects in mixed-age leaf groups for Parea and Narea. Spectral coefficients and variable importance in projection (VIPs) also reflected age effects. Both mass- and area-based models for all five leaf traits displayed age/temporal sensitivity when we tested their ability to predict the traits of leaves of other age groups. Importantly, mass-based mature models displayed the worst overall performance when predicting the traits of leaves from other age groups. These results indicate that the widely adopted approach of using fully expanded mature leaves to calibrate models that estimate remotely-sensed tree canopy traits introduces error that can bias results depending on the phenological stage of canopy leaves. To achieve temporally stable models, spectroscopic studies should consider producing area-based estimates as well as calibrating models with leaves of different age groups as they present themselves through the growing season. We discuss the implications of this for surveys of canopies with synchronised and unsynchronised leaf phenology.

Recent work has shown that leaf traits and spectral properties change through time and/or seasonally as leaves age. Current field and hyperspectral methods used to estimate canopy leaf traits could, therefore, be significantly biased by variation in leaf age. To explore the magnitude of this effect, we used a phenological dataset comprised of leaves of different leaf age groups -developmental, mature, senescent and mixed-age- from canopy and emergent tropical trees in southern Peru. We tested the performance of partial least squares regression models developed from these different age groups when predicting traits for leaves of different ages on both a mass and area basis. Overall, area-based models outperformed mass-based models with a striking improvement in prediction observed for area-based leaf carbon (Carea) estimates. We observed trait-specific age effects in all mass-based models while area-based models displayed age effects in mixed-age leaf groups for Parea and Narea. Spectral coefficients and variable importance in projection (VIPs) also reflected age effects. Both mass- and area-based models for all five leaf traits displayed age/temporal sensitivity when we tested their ability to predict the traits of leaves of other age groups. Importantly, mass based mature models displayed the worst overall performance when predicting the traits of leaves from other age groups. These results indicate that the widely adopted approach of using fully expanded mature leaves to calibrate models that estimate remotely-sensed tree canopy traits introduces error that can bias results depending on the phenological stage of canopy leaves. To achieve temporally stable models, spectroscopic studies should consider producing area-based estimates as well as calibrating models with leaves of different age groups as they present themselves through the growing season. We discuss the implications of this for surveys of canopies with synchronised and unsynchronised leaf phenology.

Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. We explore leaf reflectance as a tool to monitor leaf age and develop a spectra-based partial least squares regression (PLSR) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (Pmass ) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (Nmass ) and carbon (Cmass ) contents showed monotonic but tree-specific age responses. We observed large age-related variation in leaf spectra across trees. A spectra-based model was more accurate in predicting leaf age (R2 = 0.86; percent root mean square error (%RMSE) = 33) compared with trait-based models using single (R2 = 0.07-0.73; %RMSE = 7-38) and multiple (R2 = 0.76; %RMSE = 28) predictors. Spectra- and trait-based models established a physiochemical basis for the spectral age model. Vegetation indices (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (PRI) were all age-dependent. This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing.

• Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. • We explore leaf reflectance as a tool to monitor leaf age and develop a spectra-based partial least squares regression (PLSR) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. • Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (Pmass) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (Nmass) and carbon (Cmass) contents showed monotonic but tree-specific age responses. We observed large age-related variation in leaf spectra across trees. A spectra-based model was more accurate in predicting leaf age (R2 = 0.86; percent root mean square error (%RMSE) = 33) compared with trait-based models using single (R2 = 0.07–0.73; %RMSE = 7–38) and multiple (R2 = 0.76; %RMSE = 28) predictors. Spectra- and trait-based models established a physiochemical basis for the spectral age model. Vegetation indices (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (PRI) were all age-dependent. • This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing.

Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. We explore leaf reflectance as a tool to monitor leaf age and develop a spectra-based partial least squares regression (PLSR) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (Pmass ) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (Nmass ) and carbon (Cmass ) contents showed monotonic but tree-specific age responses. We observed large age-related variation in leaf spectra across trees. A spectra-based model was more accurate in predicting leaf age (R2  = 0.86; percent root mean square error (%RMSE) = 33) compared with trait-based models using single (R2  = 0.07-0.73; %RMSE = 7-38) and multiple (R2  = 0.76; %RMSE = 28) predictors. Spectra- and trait-based models established a physiochemical basis for the spectral age model. Vegetation indices (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (PRI) were all age-dependent. This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing.

• Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. • We explore leaf reflectance as a tool to monitor leaf age and develop a spectra-based partial least squares regression (PLSR) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. • Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (Pmass) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (Nmass) and carbon (Cmass) contents showed monotonic but tree-specific age responses. We observed large age-related variation in leaf spectra across trees. A spectra-based model was more accurate in predicting leaf age (R2 = 0.86; percent root mean square error (%RMSE) = 33) compared with trait-based models using single (R2 = 0.07–0.73; %RMSE = 7–38) and multiple (R2 = 0.76; %RMSE = 28) predictors. Spectra- and trait-based models established a physiochemical basis for the spectral age model. Vegetation indices (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (PRI) were all age-dependent. • This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing.

IntroductionLa drépanocytose est l’affection génétique la plus répandue dans le monde plus particulièrement en Afrique subsaharienne, considérée à juste titre comme l’une des régions originaires de la mutation drépanocytaire avec de fortes prévalences. Elle est reconnue comme un problème de santé majeur par la communauté internationale en raison de sa morbidité et de sa mortalité élevées. Au Mali, environ 5 à 6 milles naissances de drépanocytaires sont enregistrées par an [Diallo, 2008] et ils auront besoin d’une prise en charge médicale spécifique. Le Centre de Recherche et de Lutte contre la Drépanocytose (CRLD) situé à Bamako est le seul centre de traitement spécifique dédié à la lutte contre la drépanocytose au Mali. Il reçoit des patients drépanocytaires du Mali et de la sous région. Ces patients sont pour la plupart vus au stade de complications puisqu’il n’existe pas de programme de dépistage systématique dès la naissance et à fortiori de suivi.La transfusion sanguine intervient dans une large mesure pour la prise en charge thérapeutique de ces patients. Acte thérapeutique essentiel sur le continent Africain en raison de la fréquence d’anémies de diverses origines (certaines nécessiteront un traitement de la cause).Cette transfusion sanguine comporte des risques non négligeables en Afrique, notamment dans le domaine des agents transmissibles par le sang. Un dépistage systématique des infections par le virus de l’immunodéficience humaine (VIH), le virus de l’hépatite C (VHC), le virus de l’hépatite B (VHB) et le tréponème, agent de la syphilis, est en vigueur sur tous les dons de sang à Bamako, mais les prévalences élevées de ces agents infectieux dans la population générale dont sont issus les donneurs de sang incitent à penser que le risque transfusionnel résiduel doit être important. Dans le but de renforcer la sécurité transfusionnelle au Mali, nous avons entrepris des études prospectives et rétrospectives chez les donneurs de sang et chez les drépanocytai, Introduction Sickle cell disease is the most common genetic disorder in the world, especially in sub-Saharan Africa where sickle cell mutation with high prevalence’s is originated high. It is recognized as a major health problem by the international community due to its morbidity and mortality. In Mali, around 5-6000 newborns annually affected by sickle cell disease [Diallo, 2008] and they will require specific medical that is by the “Centre de Recherche et de Lutte contre la Drépanocytose » (CRLD) in Bamako. This is the unique Malian center where sickle cell patients from Mali and the sub-region are treated. Because of the lack of systematic screening program at birth, most of the patients are seen at the stage of complications. Blood transfusion is largely used for therapeutic management of these patients. It constitutes an essential therapeutic approach against anemia of highly prevalent in Africa various origins (some requiring also treatment of the cause). However, blood transfusion means significant risks, particularly in area of high prevalence of transfusion transmissible infectious agents. A systematic screening for viral infections like human immunodeficiency virus (HIV), hepatitis C virus (HCV), hepatitis B virus(HBV) and Treponema agent of syphilis is required for all blood donations in Bamako, but the high prevalence of these infectious agents in the general population, i.e. blood donors, suggest that the residual risk of transfusion must be important.In order to enhance blood transfusion safety in Mali, we undertook prospective and retrospective studies in the population of blood donor and sickle cell patient transfused to identify transfusion related adverse events and especially for SCD’ patients, suggest a better strategy for their follow-up. Methodology:We conducted a sery of studies which aims to determine:- The characteristics of donors, blood donation and the organization of blood transfusion centers in sub-Saharan French speaking Africa;- The s, Doctorat en Sciences biomédicales et pharmaceutiques (Médecine), info:eu-repo/semantics/nonPublished

A range of monitoring techniques including digital cameras, radiometers and satellites have the potential to provide new sources of data on vegetation dynamics, including consistent data on the spatial and temporal variability of phenological events and vegetation productivity. This paper compares four data sets collected in 2012 that capture the above-canopy and below-canopy phenology of a deciduous woodland. The longest time-series is provided by weekly below-canopy photos recorded weekly from 1998 onwards. The weekly photos are used to assess the inter-annual variability from 2004 onwards and suggest that green-up varies between 8th April and 3rd May.

A range of monitoring techniques including digital cameras, radiometers and satellites have the potential to provide new sources of data on vegetation dynamics, including consistent data on the spatial and temporal variability of phenological events and vegetation productivity. This paper compares four data sets collected in 2012 that capture the above-canopy and below-canopy phenology of a deciduous woodland. The longest time-series is provided by weekly below-canopy photos recorded weekly from 1998 onwards. The weekly photos are used to assess the inter-annual variability from 2004 onwards and suggest that green-up varies between 8th April and 3rd May.

In tropical areas, Dynamic Global Vegetation Models (DGVMs) still have deficiencies in simulating the timing of vegetation phenology. To start addressing this problem, standard Fourier-based methods are applied to aerosol screened monthly remotely sensed phenology time series (Enhanced Vegetation Index, EVI) and two major driving factors of phenology: solar radiation and precipitation (for March 2000 through December 2006 over northern South America). At 1 × 1 km scale using, power (or variance) spectra on good quality aerosol screened time series, annual cycles in EVI are detected across 58.24% of the study area, the strongest (largest amplitude) occurring in the savanna. Terra Firme forest have weak but significant annual cycles in comparison with savannas because of the heterogeneity of vegetation and nonsynchronous phenological events within 1 × 1 km scale pixels. Significant annual cycles for radiation and precipitation account for 86% and 90% of the region, respectively, with different spatial patterns to phenology. Cross-spectral analysis was used to compare separately radiation with phenology/EVI, precipitation with phenology/EVI and radiation with precipitation. Overall the majority of the Terra Firme forest appears to have radiation as the driver of phenology (either radiation is in phase or leading phenology/EVI at the annual scale). These results are in agreement with previous research, although in Acre, central and eastern Peru and northern Bolivia there is a coexistence of ‘in phase’ precipitation over Terra Firme forest. In contrast in most areas of savanna precipitation appears to be a driver and savanna areas experiencing an inverse (antiphase) relationship between radiation and phenology is consistent with inhibited grassland growth due to soil moisture limitation. The resulting maps provide a better spatial understanding of phenology–driver relationships offering a bench mark to parameterize ecological models.

In tropical areas, Dynamic Global Vegetation Models (DGVMs) still have deficiencies in simulating the timing of vegetation phenology. To start addressing this problem, standard Fourier-based methods are applied to aerosol screened monthly remotely sensed phenology time series (Enhanced Vegetation Index, EVI) and two major driving factors of phenology: solar radiation and precipitation (for March 2000 through December 2006 over northern South America). At 1 × 1 km scale using, power (or variance) spectra on good quality aerosol screened time series, annual cycles in EVI are detected across 58.24% of the study area, the strongest (largest amplitude) occurring in the savanna. Terra Firme forest have weak but significant annual cycles in comparison with savannas because of the heterogeneity of vegetation and nonsynchronous phenological events within 1 × 1 km scale pixels. Significant annual cycles for radiation and precipitation account for 86% and 90% of the region, respectively, with different spatial patterns to phenology. Cross-spectral analysis was used to compare separately radiation with phenology/EVI, precipitation with phenology/EVI and radiation with precipitation. Overall the majority of the Terra Firme forest appears to have radiation as the driver of phenology (either radiation is in phase or leading phenology/EVI at the annual scale). These results are in agreement with previous research, although in Acre, central and eastern Peru and northern Bolivia there is a coexistence of ‘in phase’ precipitation over Terra Firme forest. In contrast in most areas of savanna precipitation appears to be a driver and savanna areas experiencing an inverse (antiphase) relationship between radiation and phenology is consistent with inhibited grassland growth due to soil moisture limitation. The resulting maps provide a better spatial understanding of phenology–driver relationships offering a bench mark to parameterize ecological models.

The aim of this project is the development of a noninvasive technique based on LED multispectral imaging (MSI) for monitoring the conservation state of the Dead Sea Scrolls (DSS) collection. It is well-known that changes in the parchment reflectance drive the transition of the scrolls from legible to illegible. Capitalizing on this fact, we will use spectral imaging to detect changes in the reflectance before they become visible to the human eye. The technique uses multivariate analysis and statistical process control theory. The present study was carried out on a "sample" parchment of calfskin. The monitoring of the surface of a commercial modern parchment aged consecutively for 2 h and 6 h at 80 degrees C and 50% relative humidity (ASTM) was performed at the Imaging Lab of the Library of Congress (Washington, DC, U.S.A.). MSI is here carried out in the vis-NIR range limited to 1 mu m, with a number of bands of 13 and bandwidths that range from about 10 nm in UV to 40 nm in IR. Results showed that we could detect and locate changing pixels, on the basis of reflectance changes, after only a few "hours" of aging

A new approach for monitoring the state of conservation of cultural heritage objects surfaces is being developed. The technique utilizes multi-spectral imaging. multivariate analysis and statistical process control theory for the automatic detection of a possible deterioration process, its localization and identification, and the wavelengths most sensitive to detecting this before the human eye can detect the damage or potential degradation changes occur. A series of virtual degradation analyses were performed on images of parchment in order to test the proposed algorithm in controlled conditions. The spectral image of a Dead Sea Scroll (DSS) parchment, IAA (Israel Antiquities Authority) inventory plate # 279, 4Q501 Apocryphal Lamentations B, taken during the 2008 Pilot of the DSS Digitization Project, was chosen for the simulation.

We present a study which has used a Fourier-based time-series analysis method applied to 8 years of EO-derived observations of phenology (vegetation indices) and their potential drivers (downward shortwave radiation and precipitation). We use these data sets to test the correlation (coherency) of the phenology to the driving variables and to determine the relative timing of their seasonal cycles. This has led to a better understanding of the linkages between phenology and their driving factors. Typically Amazonian canopy vegetation has varying timing in phenology on small (sub-pixel) scales. However, at the spatial resolution of 1 km2 spectral analysis shows that the sub-pixel phenology is well synchronised for a large proportion of the Terra Firme forest. Across the whole of northern South America about 60% of the land shows a significant seasonal cycle in grassland- and forest-biomes. Within this area having seasonal cycles, 43% has phenology in-phase with radiation of which 75% is Terra Firme-type forest and 25% is mainly grassland. 37% of the area with seasonal cycles is in-phase with precipitation, of which 40% is grassland and the remainder is forest. These results are in agreement with recent research that suggests much of the Amazon over humid tropical forests may be radiation-driven. However, we also support the hypothesis that in places both radiation and precipitation are influential, but may not be completely in-phase with the phenology. We identify core areas where the phenology-radiation and phenology-precipitation relationships are most apparent. The information regarding spatial controls of phenology provides a benchmark for land-surface modellers.

We present a study which has used a Fourier-based time-series analysis method applied to 8 years of EO-derived observations of phenology (vegetation indices) and their potential drivers (downward shortwave radiation and precipitation). We use these data sets to test the correlation (coherency) of the phenology to the driving variables and to determine the relative timing of their seasonal cycles. This has led to a better understanding of the linkages between phenology and their driving factors. Typically Amazonian canopy vegetation has varying timing in phenology on small (sub-pixel) scales. However, at the spatial resolution of 1 km2 spectral analysis shows that the sub-pixel phenology is well synchronised for a large proportion of the Terra Firme forest. Across the whole of northern South America about 60% of the land shows a significant seasonal cycle in grassland- and forest-biomes. Within this area having seasonal cycles, 43% has phenology in-phase with radiation of which 75% is Terra Firme-type forest and 25% is mainly grassland. 37% of the area with seasonal cycles is in-phase with precipitation, of which 40% is grassland and the remainder is forest. These results are in agreement with recent research that suggests much of the Amazon over humid tropical forests may be radiation-driven. However, we also support the hypothesis that in places both radiation and precipitation are influential, but may not be completely in-phase with the phenology. We identify core areas where the phenology-radiation and phenology-precipitation relationships are most apparent. The information regarding spatial controls of phenology provides a benchmark for land-surface modellers.

In order to provide some answers to the much debated subject of the consequences of the Chernobyl accident, this study attempts to measure the incidence of surgically removed thyroid cancers in Belgium ten years following the explosion. The analysis was made from the hospital discharge data between 1993 and 1998. It offers the advantage of national coverage in spite of certain validity limits. The results show an increase in surgically removed thyroid cancers, which is not, however, evident in the more susceptible younger generation who were involved at the time of the accident. Furthermore, the geographic distribution of the incidence is more marked in the south of the country, unaffected by the radioactive iodine contamination of 1986, which was more prevalent in the east of the country. The study of the type of surgery involved shows a rise in the proportion of total thyroidectomies. These findings are in favour of the hypothesis of a causal effect linking the increased incidence of thyroid cancers to medical practice and surgery in particular and not to the consequence of the possible contamination.

Health records should be readily accessed by authorised persons for diagnosis, treatment and legal security purposes during patient's lifetime and thereafter for research and training purposes. Technology makes difficult the preservation of digital material, because of rapid changes in information media, hardware and software evolution, and because of the multiplicity of its location as well as the complexity of its environment. A committee in a large teaching hospital in Belgium, recommends two complementary approaches: (1) After a patient departure, all his health information should be managed by a unique organisation that would update regularly accesses to all data bases concerned by this patient. Health archives should be formatted using XML (Extensible Mark up Language) software family, and set on supports such as DVD-ROM, to be upgraded or updated when needed. This archive media is fast but not safe. (2) Computer output microfilm (COM) and scanners for non electronic data to be preserved appears to be also needed, as it is storage safe for at least 250 years and readable directly by eye. This archive media is safe but not fast.

We describe the trends over time in patient's diagnoses, case-complexity, case-severity, length of stay and mortality rate during the last 15 years in an academic division of general internal medicine (GIM). Our study shows that GIM patients presented with a broad spectrum of diseases. Moreover, important changes were observed in the patient characteristics, i.e. a progressive increase in urgent admissions, severe medical conditions and elderly population. This evolution was associated with a significant rise in case-severity and case-complexity which explains the observed increase in hospital mortality rate. These variables should be accounted for when analyzing processes and outcomes of inpatient medical care in internal medicine.

The primary question is whether the Panama Canal is vital to the security of the United States. The major issues affecting United States policy toward Panama were analyzed and the significant political, economic, and military aspects regarding the Panama Canal were reviewed in depth. (Modified author abstract)