Co-kriging of soil properties with Vis-NIR hyperspectral covariates in the Cap Bon region (Tunisia)

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Calibration procedures and first data set of Southern Ocean chlorophyll a profiles collected by elephant seals equipped with a newly developed CTD-fluorescence tags

In-situ observation of the marine environment has traditionally relied on ship-based platforms. The obvious consequence is that physical and biogeochemical properties have been dramatically undersampled, especially in the remote Southern Ocean (SO). The difficulty in obtaining in situ data represents the major limitations to our understanding, and interpretation of the coupling between physical forcing and the biogeochemical response. Southern elephant seals (Mirounga leonina) equipped with a new generation of oceanographic sensors can measure ocean structure in regions and seasons rarely observed with traditional oceanographic platforms. Over the last few years, seals have allowed for a considerable increase in temperature and salinity profiles from the SO. However we were still lacking information on the spatio-temporal variation of phytoplankton concentration. This information is critical to assess how the biological productivity of the SO, with direct consequences on the amount of CO2 "fixed" by the biological pump, will respond to global warming. In this research program, we use an innovative sampling fluorescence approach to quantify phytoplankton concentration at sea. For the first time, a low energy consumption fluorometer was added to Argos CTD-SRDL tags, and these novel instruments were deployed on 27 southern elephant seals between 25 December 2007 and the 4 February 2011. As many as 3388 fluorescence profiles associated with temperature and salinity measurements were thereby collected from a vast sector of the Southern Indian Ocean. This paper address the calibration issue of the fluorometer before being deployed on elephant seals and present the first results obtained for the Indian Sector of the Southern Ocean.This in situ system is implemented in synergy with satellite ocean colour radiometry. Satellite-derived data is limited to the surface layer and is restricted over the SO by extensive cloud cover. However, with the addition of these new tags, we're able to assess the 3 dimension distribution of phytoplankton concentration by foraging southern elephant seals. This approach reveals that for the Indian sector of the SO, the surface chlorophyll a (chl a) concentrations provided by MODIS were underestimated by a factor of the order of 2–3 compared to in situ measurements. The scientific outcomes of this program include an improved understanding of both the present state and variability in ocean biology, and the accompanying biogeochemistry, as well as the delivery of real-time and open-access data to scientists

Marine Citizen Science: Current State in Europe and New Technological Developments

Marine citizen science is emerging with promising opportunities for science, policy and public but there is still no comprehensive overview of the current state in Europe. Based on 127 projects identified for the North Sea area we estimate there might be as much as 500 marine and coastal citizen science projects running in Europe, i.e., one marine citizen science project per 85 km of coastline, with an exponential growth since 1990. Beach-based projects are more accessible and hence most popular (60% of the projects), and the mean duration of the projects is 18–20 years. Current trends, topics, organizers, aims, and types of programme in terms of participation are presented in this overview. Progress in marine citizen science is specially enabled and promoted through technological developments. Recent technological advances and best practise examples are provided here, untapping the potential of smart mobile apps, do-it-yourself (DIY) technologies, drones, and artificial intelligence (AI) web servicesVersión del edito

Spatial variation and hot-spots of district level diarrhea incidences in Ghana: 2010–2014

Background: Diarrhea is a public health menace, especially in developing countries. Knowledge of the biological and anthropogenic characteristics is abundant. However, little is known about its spatial patterns especially in developing countries like Ghana. This study aims to map and explore the spatial variation and hot-spots of district level diarrhea incidences in Ghana. Methods: Data on district level incidences of diarrhea from 2010 to 2014 were compiled together with population data. We mapped the relative risks using empirical Bayesian smoothing. The spatial scan statistics was used to detect and map spatial and space-Time clusters. Logistic regression was used to explore the relationship between space-Time clustering and urbanization strata, i.e. rural, peri-urban, and urban districts. Results: We observed substantial variation in the spatial distribution of the relative risk. There was evidence of significant spatial clusters with most of the excess incidences being long-Term with only a few being emerging clusters. Space-Time clustering was found to be more likely to occur in peri-urban districts than in rural and urban districts. Conclusion: This study has revealed that the excess incidences of diarrhea is spatially clustered with peri-urban districts showing the greatest risk of space-Time clustering. More attention should therefore be paid to diarrhea in peri-urban districts. These findings also prompt public health officials to integrate disease mapping and cluster analyses in developing location specific interventions for reducing diarrhea

A poisson regression approach for modelling spatial autocorrelation between geographically referenced observations

Abstract Background Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, omission of that autocorrelation can bias parameter estimates and yield incorrect standard error estimates. Methods We used age standardised incidence ratios (SIRs) of esophageal cancer (EC) from the Babol cancer registry from 2001 to 2005, and extracted socioeconomic indices from the Statistical Centre of Iran. The following models for SIR were used: (1) Poisson regression with agglomeration-specific nonspatial random effects; (2) Poisson regression with agglomeration-specific spatial random effects. Distance-based and neighbourhood-based autocorrelation structures were used for defining the spatial random effects and a pseudolikelihood approach was applied to estimate model parameters. The Bayesian information criterion (BIC), Akaike's information criterion (AIC) and adjusted pseudo R2, were used for model comparison. Results A Gaussian semivariogram with an effective range of 225 km best fit spatial autocorrelation in agglomeration-level EC incidence. The Moran's I index was greater than its expected value indicating systematic geographical clustering of EC. The distance-based and neighbourhood-based Poisson regression estimates were generally similar. When residual spatial dependence was modelled, point and interval estimates of covariate effects were different to those obtained from the nonspatial Poisson model. Conclusions The spatial pattern evident in the EC SIR and the observation that point estimates and standard errors differed depending on the modelling approach indicate the importance of accounting for residual spatial correlation in analyses of EC incidence in the Caspian region of Iran. Our results also illustrate that spatial smoothing must be applied with care.</p

Using landscape characteristics to define an adjusted distance metric for improving kriging interpolations

Interpolation of point measurements using geostatistical techniques such as kriging can be used to estimate values at non-sampled locations in space. Traditional geostatistics are based on the spatial autocorrelation concept that nearby things are more related than distant things. In this study, additional information was used to modify the traditional Euclidean concept of distance into an adjusted distance metric that incorporates similarity in terms of quantifiable landscape characteristics such as topography or land use. This new approach was tested by interpolating soil moisture content, pH and carbon-to-nitrogen (C:N) ratio measured in both the mineral and the organic soil layers at a field site in central Sweden. Semivariograms were created using both the traditional distance metrics and the proposed adjusted distance metrics to carry out ordinary kriging (OK) interpolations between sampling points. In addition, kriging with external drift (KED) was used to interpolate soil properties to evaluate the ability of the adjusted distance metric to incorporate secondary data into interpolations. The new adjusted distance metric typically lowered the nugget associated with the semivariogram, thereby better representing small-scale variability in the measured data compared to semivariograms based on the traditional distance metric. The pattern of the resulting kriging interpolations using KED and OK based on the adjusted distance metric were similar because they represented secondary data and, thus, enhanced small-scale variability compared to traditional distance OK. This created interpolations that agreed better with what is expected for the real-world spatial variation of the measured properties. Based on cross-validation error, OK interpolations using the adjusted distance metric better fit observed data than either OK interpolations using traditional distance or KED