Inter-comparison of satellite sensor land surface phenology and ground phenology in Europe

Land surface phenology (LSP) and ground phenology (GP) are both important sources of information for monitoring terrestrial ecosystem responses to climate changes. Each measures different vegetation phenological stages and has different sources of uncertainties, which make comparison in absolute terms challenging, and therefore, there has been limited attempts to evaluate the complementary nature of both measures. However, both LSP and GP are climate driven and therefore should exhibit similar interannual variation. LSP obtained from the whole time series of Medium-Resolution Imaging Spectrometer data was compared to thousands of deciduous tree ground phenology records of the Pan European Phenology network (PEP725). Correlations observed between the interannual time series of the satellite sensor estimates of phenology and PEP725 records revealed a close agreement (especially for Betula Pendula and Fagus Sylvatica species). In particular, 90% of the statistically significant correlations between LSP and GP were positive (mean R2 = 0.77). A large spatiotemporal correlation was observed between the dates of the start of season (end of season) from space and leaf unfolding (autumn coloring) at the ground (pseudo R2 of 0.70 (0.71)) through the application of nonlinear multivariate models, providing, for the first time, the ability to predict accurately the date of leaf unfolding (autumn coloring) across Europe (root-mean-square error of 5.97 days (6.75 days) over 365 days)

Perennial snow and ice variations (2000–2008) in the Arctic circumpolar land area from satellite observations

Perennial snow and ice (PSI) extent is an important parameter of mountain environments with regard to its involvement in the hydrological cycle and the surface energy budget. We investigated interannual variations of PSI in nine mountain regions of interest (ROI) between 2000 and 2008. For that purpose, a novel MODIS data set processed at the Canada Centre for Remote Sensing at 250 m spatial resolution was utilized. The extent of PSI exhibited significant interannual variations, with coefficients of variation ranging from 5% to 81% depending on the ROI. A strong negative relationship was found between PSI and positive degree‐days (threshold 0°C) during the summer months in most ROIs, with linear correlation coefficients (r) being as low as r = −0.90. In the European Alps and Scandinavia, PSI extent was significantly correlated with annual net glacier mass balances, with r = 0.91 and r = 0.85, respectively, suggesting that MODIS‐derived PSI extent may be used as an indicator of net glacier mass balances. Validation of PSI extent in two land surface classifications for the years 2000 and 2005, GLC‐2000 and Globcover, revealed significant discrepancies of up to 129% for both classifications. With regard to the importance of such classifications for land surface parameterizations in climate and land surface process models, this is a potential source of error to be investigated in future studies. The results presented here provide an interesting insight into variations of PSI in several ROIs and are instrumental for our understanding of sensitive mountain regions in the context of global climate change assessment

A new world malaria map: Plasmodium falciparum endemicity in 2010

Background: transmission intensity affects almost all aspects of malaria epidemiology and the impact of malaria on human populations. Maps of transmission intensity are necessary to identify populations at different levels of risk and to evaluate objectively options for disease control. To remain relevant operationally, such maps must be updated frequently. Following the first global effort to map Plasmodium falciparum malaria endemicity in 2007, this paper describes the generation of a new world map for the year 2010. This analysis is extended to provide the first global estimates of two other metrics of transmission intensity for P. falciparum that underpin contemporary questions in malaria control: the entomological inoculation rate (PfEIR) and the basic reproductive number (PfR). Methods: annual parasite incidence data for 13,449 administrative units in 43 endemic countries were sourced to define the spatial limits of P. falciparum transmission in 2010 and 22,212 P. falciparum parasite rate (PfPR) surveys were used in a model-based geostatistical (MBG) prediction to create a continuous contemporary surface of malaria endemicity within these limits. A suite of transmission models were developed that link PfPR to PfEIR and PfR and these were fitted to field data. These models were combined with the PfPR map to create new global predictions of PfEIR and PfR. All output maps included measured uncertainty. Results: an estimated 1.13 and 1.44 billion people worldwide were at risk of unstable and stable P. falciparum malaria, respectively. The majority of the endemic world was predicted with a median PfEIR of less than one and a median PfRc of less than two. Values of either metric exceeding 10 were almost exclusive to Africa. The uncertainty described in both PfEIR and PfR was substantial in regions of intense transmission. Conclusions: the year 2010 has a particular significance as an evaluation milestone for malaria global health policy. The maps presented here contribute to a rational basis for control and elimination decisions and can serve as a baseline assessment as the global health community looks ahead to the next series of milestones targeted at 20

Plasmodium falciparum Malaria Endemicity in Indonesia in 2010

BACKGROUND: Malaria control programs require a detailed understanding of the contemporary spatial distribution of infection risk to efficiently allocate resources. We used model based geostatistics (MBG) techniques to generate a contemporary map of Plasmodium falciparum malaria risk in Indonesia in 2010. METHODS: Plasmodium falciparum Annual Parasite Incidence (PfAPI) data (2006-2008) were used to map limits of P. falciparum transmission. A total of 2,581 community blood surveys of P. falciparum parasite rate (PfPR) were identified (1985-2009). After quality control, 2,516 were included into a national database of age-standardized 2-10 year old PfPR data (PfPR(2-10)) for endemicity mapping. A Bayesian MBG procedure was used to create a predicted surface of PfPR(2-10) endemicity with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population count surface. RESULTS: We estimate 132.8 million people in Indonesia, lived at risk of P. falciparum transmission in 2010. Of these, 70.3% inhabited areas of unstable transmission and 29.7% in stable transmission. Among those exposed to stable risk, the vast majority were at low risk (93.39%) with the reminder at intermediate (6.6%) and high risk (0.01%). More people in western Indonesia lived in unstable rather than stable transmission zones. In contrast, fewer people in eastern Indonesia lived in unstable versus stable transmission areas. CONCLUSION: While further feasibility assessments will be required, the immediate prospects for sustained control are good across much of the archipelago and medium term plans to transition to the pre-elimination phase are not unrealistic for P. falciparum. Endemicity in areas of Papua will clearly present the greatest challenge. This P. falciparum endemicity map allows malaria control agencies and their partners to comprehensively assess the region-specific prospects for reaching pre-elimination, monitor and evaluate the effectiveness of future strategies against this 2010 baseline and ultimately improve their evidence-based malaria control strategies

The International Limits and Population at Risk of Plasmodium vivax Transmission in 2009

Growing evidence shows that Plasmodium vivax malaria is clinically less benign than has been commonly believed. In addition, it is the most widely distributed species of human malaria and is likely to cause more illness in certain regions than the more extensively studied P. falciparum malaria. Understanding where P. vivax transmission exists and measuring the number of people who live at risk of infection is a fundamental first step to estimating the global disease toll. The aim of this paper is to generate a reliable map of the worldwide distribution of this parasite and to provide an estimate of how many people are exposed to probable infection. A geographical information system was used to map data on the presence of P. vivax infection and spatial information on climatic conditions that impede transmission (low ambient temperature and extremely arid environments) in order to delineate areas where transmission was unlikely to take place. This map was combined with population distribution data to estimate how many people live in these areas and are, therefore, exposed to risk of infection by P. vivax malaria. The results show that 2.85 billion people were exposed to some level of risk of transmission in 2009

An assessment of three candidate compositing methods for global MERIS time series

Compositing has long been used to produce complete, cloud-free images over large areas. Several compositing methods have been developed and implemented for global time series, and each method corrects for angular effects and atmospheric variations differently. This study assesses the performance of three compositing methods on real and simulated medium-resolution imaging spectrometer (MERIS) images. The three methods considered are best index slope extraction (BISE), mean compositing (MC), and the method of Hagolle et al. The optimal method was selected using two criteria, namely a qualitative examination of the temporal MERIS reduced resolution (RR) profiles, and a quantitative analysis of the noise introduced into composite images of the reflectance data time series. The latter calculation relies on the standard deviation of the normalized error based on simultaneous SPOT VEGETATION images. All three methods succeed in reproducing the long-term temporal evolution of the normalized difference vegetation index (NDVI). The BISE method, however, produces a temporal profile with more short-term variations. The other two methods produce very similar noise levels, with MC holding a small but consistent advantage. Owing to its performance and simplicity, the MC method has been selected to process global MERIS time series.La technique de compositing a longtemps été utilisée pour produire des images complètes et sans nuages au-dessus de vastes territoires. Plusieurs méthodes de compositing ont été développées et implantées pour les séries temporelles à l’échelle du globe, chacune corrigeant différemment les effets angulaires et les variations atmosphériques. La présente étude évalue la performance de trois méthodes de compositing appliquées sur des images MERIS (« medium resolution imaging spectrometer ») réelles et simulées. Les trois méthodes considérées sont la méthode BISE (« best index slope extraction »), MC (« mean compositing ») et la méthode de Hagolle et al. La méthode optimale a été choisie en utilisant deux critères: un examen qualitatif des profils temporels à résolution réduite (RR) de MERIS et une analyse quantitative du bruit introduit dans les images composites des séries temporelles de données de réflectance. Ce dernier calcul repose sur l’écart type de l’erreur normalisée basée sur les images simultanées de SPOT VEGETATION. Les trois méthodes reproduisent avec succès l’évolution temporelle à long terme du NDVI (« normalized diference vegetation index »). La méthode BISE donne toutefois un profil temporel avec plus de variations à court terme. Les deux autres méthodes produisent des niveaux de bruit très similaires, la méthode MC montrant cependant un avantage léger mais constant. En raison de sa performance et de sa simplicité, la méthode MC a été choisie pour traiter les séries temporelles de MERIS à l’échelle du globe.[Traduit par la Rédaction