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

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

Modelling Of Non-Stationary Spatial Structure By Parametric Radial Basis Deformations

. Spatial environmental processes often exhibit non-stationarity. Sampson and Guttorp (1992) model the dispersion D(x; y) = E(Z(x) \Gamma Z(y)) 2 of a non-stationary spatial process Z(x) by a bijective deformation of the geographic coordinate x so that the spatial dispersion structure can be considered stationary and isotropic in terms of a new spatial coordinate system. The model can be defined as follows D(x; y) = fl ` (kf(y) \Gamma f(x)k) where f represents a bijective transformation and fl ` a stationary and isotropic variogram function with parameters `. The non-parametric family of thin-plate splines was used to compute the deformation with two drawbacks: (i) bijection condition is not ensured (ii) the fitting of the model can be a challenging numerical problem. To avoid these disadvantages we propose to use a parametric family of bijective functions we call Radial Basis Deformations. Bijection is ensured by a constraint on parameters and deformation f is a composition of a s..

Modelling Of Non-Stationary Spatial Structure Using Parametric Radial Basis Deformations

. Spatial environmental processes often exhibit non-stationarity. Modelling the dispersion d(x; y) = E(Z(x) \Gamma Z(y)) 2 , (x; y) 2 IR 2 \Theta IR 2 , of a nonstationary spatial process Z(x) has been proposed in the last decade. It consists of deforming bijectively the geographic coordinate x so that the spatial dispersion structure can be considered stationary and isotropic in terms of a new spatial coordinate system. The model is d(x; y) = fl fi (kf(y) \Gamma f(x)k) where f represents a bijective transformation and fl fi a stationary and isotropic variogram function with parameters fi. The non-parametric family of thin-plate splines was used to compute the deformation with two drawbacks: (i) bijection condition is not ensured (ii) the fitting of the model can be a challenging numerical problem. To avoid these disadvantages we propose to use a parametric family of bijective functions we call Radial Basis Deformations. Bijection is ensured by a constraint on parameters and defo..