Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing

The aim of this paper is to show that Dempster-Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. Dempster-Shafer formulation allows for consideration of unions of classes, and to represent both imprecision and uncertainty, through the definition of belief and plausibility functions. These two functions, derived from mass function, are generally chosen in a supervised way. In this paper, the authors describe an unsupervised method, based on the comparison of monosource classification results, to select the classes necessary for Dempster-Shafer evidence combination and to define their mass functions. Data fusion is then performed, discarding invalid clusters (e.g. corresponding to conflicting information) thank to an iterative process. Unsupervised multisource classification algorithm is applied to MAC-Europe'91 multisensor airborne campaign data collected over the Orgeval French site. Classification results using different combinations of sensors (TMS and AirSAR) or wavelengths (L- and C-bands) are compared. Performance of data fusion is evaluated in terms of identification of land cover types. The best results are obtained when all three data sets are used. Furthermore, some other combinations of data are tried, and their ability to discriminate between the different land cover types is quantified

Remote sensing of ocean waves by polarimetric SAR

A new method to measure ocean wave slope spectra using fully polarimetric synthetic aperture radar (POLSAR) data was developed without the need for a complex hydrodynamic modulation transform function. There is no explicit use of a hydrodynamic modulation transfer function. This function is not clearly known and is based on hydrodynamic assumptions. The method is different from those developed by Schuler and colleagues or Pottier but complements their methods. The results estimated from NASA Jet Propulsion Laboratory (JPL) Airborne Synthetic Aperture Radar (AIRSAR) C-band polarimetric SAR data show that the ocean wavelength, wave direction, and significant wave height are in agreement with buoy measurements. The proposed method can be employed by future satellite missions such as RADARSAT-2.

贵阳市大气颗粒物、雨水及土壤中重金属的研究

重金属是指比重大于或等于5.0的金属。随着城市化在全球范围内的飞速发展，以及城市人口的不断增长，客观上需要增加对城市生态环境的了解，以及研究城市生态环境与人类健康之间的相互关系。 大气沉降包括干沉降与湿沉降，大气的干、湿沉降是大气化学、环境化学和生物地球化学研究的重要内容之一，在重金属循环中扮演着重要的角色。大气沉降对土壤重金属地球化学过程有着重要影响。因此，准确把握环境中重金属的主要来源、行为及归宿，对有效控制重金属的污染、保护人类健康提供科学依据具有非常重要的意义。城市土壤是城市生态环境的重要组成部分，对城市的可持续发展有着重要意义。在城市环境中，各种各样的人类活动将大量的重金属带人城市土壤中，造成这些元素在土壤中的积累，并通过大气、水体或食物链而直接或间接地威胁着人类的健康甚至生命。 本研究以贵阳为例，对环境中的重金属进行研究。系统研究了贵阳市大气颗粒物重金属含量与形态、雨水重金属的含量与季节变化及土壤重金属含量与形态。主要结果如下： 1 贵阳市的干、湿沉降与气象因子关系密切。随温度的升高，贵阳市TSP浓度下降；相对湿度与TSP呈负相关性；风速对贵阳市区的TSP浓度的影响主要表现为扩散和稀释作用，随风速的增加，贵阳TSP浓度降低；雨水对贵阳市大气总悬浮颗粒物的有明显的清除作用。随着风速的增加，贵阳市雨水中重金属含量降低；雨量与雨水中的Cd、Cr、Cu、Pb与Zn的含量均呈一定的负相关关系，总体趋势为当降雨量较大的时候，雨水中重金属的含量较低。 2 贵阳市大气颗粒物中重金属含量有明显季节变化规律。Cd、Cr、Pb和Zn冬季的含量明显高于其它季节，而春季和夏季颗粒物中重金属含量相对较低，冬季的浓度是夏季的1.8-4.6倍。同样，贵阳市雨水中Cr、Cu、Zn的最高浓度都出现在冬季，这与贵阳市在冬季大量使用燃煤来取暖的时段相符合。 3与其它城市相比，贵阳市大气颗粒物重金属污染比较严重。同样，与地表水国家标准值相比较，贵阳市大部分样品Cd、Cu、Pb与Zn明显超过地表水国家标准，雨水中Cd、Cu、Pb与Zn的平均值分别国家标准的为2.7、2.2、6.0和1.3倍。贵阳市表层土壤已受到重金属不同程度的污染。表层土壤Cd的平均含量为0.66mg/kg，Co的平均含量为17.31mg/kg，Cr的平均含量为82.91mg/kg，Cu的平均含量为52.20mg/kg，Ni的平均含量38.76mg/kg，Pb的平均含量为71.03 mg/kg，Zn的平均含量为216.05mg/kg。土壤Cd、Co、Cr、Cu、Ni、Pb和Zn的平均含量分别我国土壤元素的背景值的6.8倍、1.36倍、1.36倍、2.31倍、1.44倍、2.73倍与2.98倍，其中以Cd和Zn 积累较明显。 4对贵阳市大气颗粒物、土壤重金属形态分别进行分析，结果表明：大气颗粒物中Cd主要以环境可迁移态存在，占61.4%；Cr和Cu主要稳定态存在，二者对环境不会造成直接影响；Pb主要以环境可迁移态、碳酸盐和氧化物结合态存在；Zn主要以环境可迁移态存在。土壤中，除Cd以外，Co、Cr、Cu、Ni、Pb和Zn元素的酸可提取态在总的元素含量中所占比例较小，均小于总量的3.0%，而Cd的酸可提取态含量为23.3%。相反，除Cd以外，其它6种金属元素的残渣态含量都比较高，Zn为96.8%，Cr为92.2%，Cu为83.4%，Co为69.2%，Ni为57.4%，Pb为41.9%。 5 贵阳市土壤剖面重金属元素的分布比较混乱，没有规律性，底土层含量仍然很高。城市土壤受到人为因素等影响，已发生严重改变。土壤中有许多不同来源物质的混入，土壤剖面经常受到人为扰动，自然土壤发生层被破坏，导致许多土壤剖面上下土层没有发生学上的联系。

Urinary naphthalene and phenanthrene as biomarkers of occupational exposure to polycyclic aromatic hydrocarbons.

OBJECTIVES: The study investigated the utility of unmetabolised naphthalene (Nap) and phenanthrene (Phe) in urine as surrogates for exposures to mixtures of polycyclic aromatic hydrocarbons (PAHs). METHODS: The report included workers exposed to diesel exhausts (low PAH exposure level, n = 39) as well as those exposed to emissions from asphalt (medium PAH exposure level, n = 26) and coke ovens (high PAH exposure level, n = 28). Levels of Nap and Phe were measured in urine from each subject using head space-solid phase microextraction and gas chromatography-mass spectrometry. Published levels of airborne Nap, Phe and other PAHs in the coke-producing and aluminium industries were also investigated. RESULTS: In post-shift urine, the highest estimated geometric mean concentrations of Nap and Phe were observed in coke-oven workers (Nap: 2490 ng/l; Phe: 975 ng/l), followed by asphalt workers (Nap: 71.5 ng/l; Phe: 54.3 ng/l), and by diesel-exposed workers (Nap: 17.7 ng/l; Phe: 3.60 ng/l). After subtracting logged background levels of Nap and Phe from the logged post-shift levels of these PAHs in urine, the resulting values (referred to as ln(adjNap) and ln(adjPhe), respectively) were significantly correlated in each group of workers (0.71 < or = Pearson r < or = 0.89), suggesting a common exposure source in each case. Surprisingly, multiple linear regression analysis of ln(adjNap) on ln(adjPhe) showed no significant effect of the source of exposure (coke ovens, asphalt and diesel exhaust) and further suggested that the ratio of urinary Nap/Phe (in natural scale) decreased with increasing exposure levels. These results were corroborated with published data for airborne Nap and Phe in the coke-producing and aluminium industries. The published air measurements also indicated that Nap and Phe levels were proportional to the levels of all combined PAHs in those industries. CONCLUSION: Levels of Nap and Phe in urine reflect airborne exposures to these compounds and are promising surrogates for occupational exposures to PAH mixtures.

Applications of finite element computer modelling and thermal infrared remote sensing to the study of geothermal anomalies

Buried heat sources can be investigated by examining thermal infrared images and comparing these with the results of theoretical models which predict the thermal anomaly a given heat source may generate. Key factors influencing surface temperature include the geometry and temperature of the heat source, the surface meteorological environment, and the thermal conductivity and anisotropy of the rock. In general, a geothermal heat flux of greater than 2% of solar insolation is required to produce a detectable thermal anomaly in a thermal infrared image. A heat source of, for example, 2-300K greater than the average surface temperature must be a t depth shallower than 50m for the detection of the anomaly in a thermal infrared image, for typical terrestrial conditions. Atmospheric factors are of critical importance. While the mean atmospheric temperature has little significance, the convection is a dominant factor, and can act to swamp the thermal signature entirely. Given a steady state heat source that produces a detectable thermal anomaly, it is possible to loosely constrain the physical properties of the heat source and surrounding rock, using the surface thermal anomaly as a basis. The success of this technique is highly dependent on the degree to which the physical properties of the host rock are known. Important parameters include the surface thermal properties and thermal conductivity of the rock. Modelling of transient thermal situations was carried out, to assess the effect of time dependant thermal fluxes. One-dimensional finite element models can be readily and accurately applied to the investigation of diurnal heat flow, as with thermal inertia models. Diurnal thermal models of environments on Earth, the Moon and Mars were carried out using finite elements and found to be consistent with published measurements. The heat flow from an injection of hot lava into a near surface lava tube was considered. While this approach was useful for study, and long term monitoring in inhospitable areas, it was found to have little hazard warning utility, as the time taken for the thermal energy to propagate to the surface in dry rock (several months) in very long. The resolution of the thermal infrared imaging system is an important factor. Presently available satellite based systems such as Landsat (resolution of 120m) are inadequate for detailed study of geothermal anomalies. Airborne systems, such as TIMS (variable resolution of 3-6m) are much more useful for discriminating small buried heat sources. Planned improvements in the resolution of satellite based systems will broaden the potential for application of the techniques developed in this thesis. It is important to note, however, that adequate spatial resolution is a necessary but not sufficient condition for successful application of these techniques.

Particulate allergens potentiate allergic asthma in mice through sustained IgE-mediated mast cell activation.

Allergic asthma is characterized by airway hyperresponsiveness, inflammation, and a cellular infiltrate dominated by eosinophils. Numerous epidemiological studies have related the exacerbation of allergic asthma with an increase in ambient inhalable particulate matter from air pollutants. This is because inhalable particles efficiently deliver airborne allergens deep into the airways, where they can aggravate allergic asthma symptoms. However, the cellular mechanisms by which inhalable particulate allergens (pAgs) potentiate asthmatic symptoms remain unknown, in part because most in vivo and in vitro studies exploring the pathogenesis of allergic asthma use soluble allergens (sAgs). Using a mouse model of allergic asthma, we found that, compared with their sAg counterparts, pAgs triggered markedly heightened airway hyperresponsiveness and pulmonary eosinophilia in allergen-sensitized mice. Mast cells (MCs) were implicated in this divergent response, as the differences in airway inflammatory responses provoked by the physical nature of the allergens were attenuated in MC-deficient mice. The pAgs were found to mediate MC-dependent responses by enhancing retention of pAg/IgE/FcεRI complexes within lipid raft–enriched, CD63(+) endocytic compartments, which prolonged IgE/FcεRI-initiated signaling and resulted in heightened cytokine responses. These results reveal how the physical attributes of allergens can co-opt MC endocytic circuitry and signaling responses to aggravate pathological responses of allergic asthma in mice.

The toxicology of climate change: environmental contaminants in a warming world.

Climate change induced by anthropogenic warming of the earth's atmosphere is a daunting problem. This review examines one of the consequences of climate change that has only recently attracted attention: namely, the effects of climate change on the environmental distribution and toxicity of chemical pollutants. A review was undertaken of the scientific literature (original research articles, reviews, government and intergovernmental reports) focusing on the interactions of toxicants with the environmental parameters, temperature, precipitation, and salinity, as altered by climate change. Three broad classes of chemical toxicants of global significance were the focus: air pollutants, persistent organic pollutants (POPs), including some organochlorine pesticides, and other classes of pesticides. Generally, increases in temperature will enhance the toxicity of contaminants and increase concentrations of tropospheric ozone regionally, but will also likely increase rates of chemical degradation. While further research is needed, climate change coupled with air pollutant exposures may have potentially serious adverse consequences for human health in urban and polluted regions. Climate change producing alterations in: food webs, lipid dynamics, ice and snow melt, and organic carbon cycling could result in increased POP levels in water, soil, and biota. There is also compelling evidence that increasing temperatures could be deleterious to pollutant-exposed wildlife. For example, elevated water temperatures may alter the biotransformation of contaminants to more bioactive metabolites and impair homeostasis. The complex interactions between climate change and pollutants may be particularly problematic for species living at the edge of their physiological tolerance range where acclimation capacity may be limited. In addition to temperature increases, regional precipitation patterns are projected to be altered with climate change. Regions subject to decreases in precipitation may experience enhanced volatilization of POPs and pesticides to the atmosphere. Reduced precipitation will also increase air pollution in urbanized regions resulting in negative health effects, which may be exacerbated by temperature increases. Regions subject to increased precipitation will have lower levels of air pollution, but will likely experience enhanced surface deposition of airborne POPs and increased run-off of pesticides. Moreover, increases in the intensity and frequency of storm events linked to climate change could lead to more severe episodes of chemical contamination of water bodies and surrounding watersheds. Changes in salinity may affect aquatic organisms as an independent stressor as well as by altering the bioavailability and in some instances increasing the toxicity of chemicals. A paramount issue will be to identify species and populations especially vulnerable to climate-pollutant interactions, in the context of the many other physical, chemical, and biological stressors that will be altered with climate change. Moreover, it will be important to predict tipping points that might trigger or accelerate synergistic interactions between climate change and contaminant exposures.

Chemical and biological applications of digital-microfluidic devices

The advent of digital microfluidic lab-on-a-chip (LoC) technology offers a platform for developing diagnostic applications with the advantages of portability, reduction of the volumes of the sample and reagents, faster analysis times, increased automation, low power consumption, compatibility with mass manufacturing, and high throughput. Moreover, digital microfluidics is being applied in other areas such as airborne chemical detection, DNA sequencing by synthesis, and tissue engineering. In most diagnostic and chemical-detection applications, a key challenge is the preparation of the analyte for presentation to the on-chip detection system. Thus, in diagnostics, raw physiological samples must be introduced onto the chip and then further processed by lysing blood cells and extracting DNA. For massively parallel DNA sequencing, sample preparation can be performed off chip, but the synthesis steps must be performed in a sequential on-chip format by automated control of buffers and nucleotides to extend the read lengths of DNA fragments. In airborne particulate-sampling applications, the sample collection from an air stream must be integrated into the LoC analytical component, which requires a collection droplet to scan an exposed impacted surface after its introduction into a closed analytical section. Finally, in tissue-engineering applications, the challenge for LoC technology is to build high-resolution (less than 10 microns) 3D tissue constructs with embedded cells and growth factors by manipulating and maintaining live cells in the chip platform. This article discusses these applications and their implementation in digital-microfluidic LoC platforms. © 2007 IEEE.

Comparative analyses of clinical and environmental populations of Cryptococcus neoformans in Botswana.

Cryptococcus neoformans var. grubii (Cng) is the most common cause of fungal meningitis, and its prevalence is highest in sub-Saharan Africa. Patients become infected by inhaling airborne spores or desiccated yeast cells from the environment, where the fungus thrives in avian droppings, trees and soil. To investigate the prevalence and population structure of Cng in southern Africa, we analysed isolates from 77 environmental samples and 64 patients. We detected significant genetic diversity among isolates and strong evidence of geographic structure at the local level. High proportions of isolates with the rare MATa allele were observed in both clinical and environmental isolates; however, the mating-type alleles were unevenly distributed among different subpopulations. Nearly equal proportions of the MATa and MATα mating types were observed among all clinical isolates and in one environmental subpopulation from the eastern part of Botswana. As previously reported, there was evidence of both clonality and recombination in different geographic areas. These results provide a foundation for subsequent genomewide association studies to identify genes and genotypes linked to pathogenicity in humans.

Space-based lidar measurements of global ocean carbon stocks

Global ocean phytoplankton biomass (C-phyto) and total particulate organic carbon (POC) stocks have largely been characterized from space using passive ocean color measurements. A space-based light detection and ranging (lidar) system can provide valuable complementary observations for C-phyto and POC assessments, with benefits including day-night sampling, observations through absorbing aerosols and thin cloud layers, and capabilities for vertical profiling through the water column. Here we use measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) to quantify global C-phyto and POC from retrievals of subsurface particulate backscatter coefficients (b(bp)). CALIOP b(bp) data compare favorably with airborne, ship-based, and passive ocean data and yield global average mixed-layer standing stocks of 0.44 Pg C for C-phyto and 1.9 Pg for POC. CALIOP-based C-phyto and POC data exhibit global distributions and seasonal variations consistent with ocean plankton ecology. Our findings support the use of spaceborne lidar measurements for advancing understanding of global plankton systems.

Insight into the key aspects of the regeneration process in the NOx storage reduction (NSR) reaction probed using fast transient kinetics coupled with isotopically labelled (NO)-N-15 over Pt and Rh-containing Ba/Al2O3 catalysts

For the first time, the coupling of fast transient kinetic switching and the use of an isotopically labelled reactant (15NO) has allowed detailed analysis of the evolution of all the products and reactants involved in the regeneration of a NOx storage reduction (NSR) material. Using realistic regeneration times (ca. 1 s) for Pt, Rh and Pt/Rh-containing Ba/Al2O3 catalysts we have revealed an unexpected double peak in the evolution of nitrogen. The first peak occurred immediately on switching from lean to rich conditions, while the second peak started at the point at which the gases switched from rich to lean. The first evolution of nitrogen occurs as a result of the fast reaction between H2 and/or CO and NO on reduced Rh and/or Pt sites. The second N2 peak which occurs upon removal of the rich phase can be explained by reaction of stored ammonia with stored NOx, gas phase NOx or O2. The ammonia can be formed either by hydrolysis of isocyanates or by direct reaction of NO and H2.

The study highlights the importance of the relative rates of regeneration and storage in determining the overall performance of the catalysts. The performance of the monometallic 1.1%Rh/Ba/Al2O3 catalyst at 250 and 350 °C was found to be dependent on the rate of NOx storage, since the rate of regeneration was sufficient to remove the NOx stored in the lean phase. In contrast, for the monometallic 1.6%Pt/Ba/Al2O3 catalyst at 250 °C, the rate of regeneration was the determining factor with the result that the amount of NOx stored on the catalyst deteriorated from cycle to cycle until the amount of NOx stored in the lean phase matched the NOx reduced in the rich phase. On the basis of the ratio of exposed metal surface atoms to total Ba content, the monometallic 1.6%Pt/Ba/Al2O3 catalyst outperformed the Rh-containing catalysts at 250 and 350 °C even when CO was used as a reductant.

The use of short time-on-stream in situ spectroscopic transient kinetic isotope techniques to investigate the mechanism of hydrocarbon selective catalytic reduction (HC-SCR) of NOx at low temperatures

The problem of differentiating between active and spectator species that have similar infrared spectra has been addressed by developing short time-on-stream in situ spectroscopic transient isotope experimental techniques (STOS-SSITKA). The techniques have been used to investigate the reaction mechanism for the reduction of nitrogen oxides (NOx) by hydrocarbons under lean-burn (excess oxygen) conditions on a silver catalyst. Although a nitrate-type species tracks the formation of isotopically labeled dinitrogen, the results show that this is misleading because a nitrate-type species has the same response to an isotopic switch even under conditions where no dinitrogen is produced. In the case of cyanide and isocyanate species, the results show that it is possible to differentiate between slowly reacting spectator isocyanate species, probably adsorbed on the oxide support, and reactive isocyanate species, possibly on or close to the active silver phase. The reactive isocyanate species responds to an isotope switch at a rate that matches that of the rate of formation of the main product, dinitrogen. It is concluded that these reactive isocyanates could potentially be involved in the reduction of NOx whereas there is no evidence to support the involvement of nitrate-type species that are observable by infrared spectroscopy.

Integrating geophysical data and upscaling techniques to support regional groundwater flow modelling: A practical example of the Lagan Valley, Northern Ireland

When studying heterogeneous aquifer systems, especially at regional scale, a degree of generalization is anticipated. This can be due to sparse sampling regimes, complex depositional environments or lack of accessibility to measure the subsurface. This can lead to an inaccurate conceptualization which can be detrimental when applied to groundwater flow models. It is important that numerical models are based on observed and accurate geological information and do not rely on the distribution of artificial aquifer properties. This can still be problematic as data will be modelled at a different scale to which it was collected. It is proposed here that integrating geophysics and upscaling techniques can assist in a more realistic and deterministic groundwater flow model. In this study, the sedimentary aquifer of the Lagan Valley in Northern Ireland is chosen due to intruding sub-vertical dolerite dykes. These dykes are of a lower permeability than the sandstone aquifer. The use of airborne magnetics allows the delineation of heterogeneities, confirmed by field analysis. Permeability measured at the field scale is then upscaled to different levels using a correlation with the geophysical data, creating equivalent parameters that can be directly imported into numerical groundwater flow models. These parameters include directional equivalent permeabilities and anisotropy. Several stages of upscaling are modelled in finite element. Initial modelling is providing promising results, especially at the intermediate scale, suggesting an accurate distribution of aquifer properties. This deterministic based methodology is being expanded to include stochastic methods of obtaining heterogeneity location based on airborne geophysical data. This is through the Direct Sample method of Multiple-Point Statistics (MPS). This method uses the magnetics as a training image to computationally determine a probabilistic occurrence of heterogeneity. There is also a need to apply the method to alternate geological contexts where the heterogeneity is of a higher permeability than the host rock.

Combining multi-scale geophysical techniques for robust hydro-structural characterisation in catchments underlain by hard rock in post-glacial regions

Accurate conceptual models of groundwater systems are essential for correct interpretation of monitoring data in catchment studies. In surface-water dominated hard rock regions, modern ground and surface water monitoring programmes often have very high resolution chemical, meteorological and hydrological observations but lack an equivalent emphasis on the subsurface environment, the properties of which exert a strong control on flow pathways and interactions with surface waters. The reasons for this disparity are the complexity of the system and the difficulty in accurately characterising the subsurface, except locally at outcrops or in boreholes. This is particularly the case in maritime north-western Europe, where a legacy of glacial activity, combined with large areas underlain by heterogeneous igneous and metamorphic bedrock, make the structure and weathering of bedrock difficult to map or model. Traditional approaches which seek to extrapolate information from borehole to field-scale are of limited application in these environments due to the high degree of spatial heterogeneity. Here we apply an integrative and multi-scale approach, optimising and combining standard geophysical techniques to generate a three-dimensional geological conceptual model of the subsurface in a catchment in NE Ireland. Available airborne LiDAR, electromagnetic and magnetic data sets were analysed for the region. At field-scale surface geophysical methods, including electrical resistivity tomography, seismic refraction, ground penetrating radar and magnetic surveys, were used and combined with field mapping of outcrops and borehole testing. The study demonstrates how combined interpretation of multiple methods at a range of scales produces robust three-dimensional conceptual models and a stronger basis for interpreting groundwater and surface water monitoring data.

Spatial and Temporal Changes in Soil Organic Carbon Assessment using an Integrated Geostatistical and Compositional Data Analysis Approach

The Irish and UK governments, along with other countries, have made a commitment to limit the concentrations of greenhouse gases in the atmosphere by reducing emissions from the burning of fossil fuels. This can be achieved (in part) through increasing the sequestration of CO2 from the atmosphere including monitoring the amount stored in vegetation and soils. A large proportion of soil carbon is held within peat due to the relatively high carbon density of peat and organic-rich soils. This is particularly important for a country such as Ireland, where some 16% of the land surface is covered by peat. For Northern Ireland, it has been estimated that the total amount of carbon stored in vegetation is 4.4Mt compared to 386Mt stored within peat and soils. As a result it has become increasingly important to measure and monitor changes in stores of carbon in soils. The conservation and restoration of peat covered areas, although ongoing for many years, has become increasingly important. This is summed up in current EU policy outlined by the European Commission (2012) which seeks to assess the relative contributions of the different inputs and outputs of organic carbon and organic matter to and from soil. Results are presented from the EU-funded Tellus Border Soil Carbon Project (2011 to 2013) which aimed to improve current estimates of carbon in soil and peat across Northern Ireland and the bordering counties of the Republic of Ireland.
Historical reports and previous surveys provide baseline data. To monitor change in peat depth and soil organic carbon, these historical data are integrated with more recently acquired airborne geophysical (radiometric) data and ground-based geochemical data generated by two surveys, the Tellus Project (2004-2007: covering Northern Ireland) and the EU-funded Tellus Border project (2011-2013) covering the six bordering counties of the Republic of Ireland, Donegal, Sligo, Leitrim, Cavan, Monaghan and Louth. The concept being applied is that saturated organic-rich soil and peat attenuate gamma-radiation from underlying soils and rocks. This research uses the degree of spatial correlation (coregionalization) between peat depth, soil organic carbon (SOC) and the attenuation of the radiometric signal to update a limited sampling regime of ground-based measurements with remotely acquired data. To comply with the compositional nature of the SOC data (perturbations of loss on ignition [LOI] data), a compositional data analysis approach is investigated. Contemporaneous ground-based measurements allow corroboration for the updated mapped outputs. This provides a methodology that can be used to improve estimates of soil carbon with minimal impact to sensitive habitats (like peat bogs), but with maximum output of data and knowledge.