Ellen Van De Vijver

In environmental assessments, the characterization of urban soils relies heavily on invasive investigation, which is often insufficient to capture their full spatial heterogeneity. Non-invasive geophysical techniques enable rapid collection of high-resolution data and provide a cost-effective alternative to investigate soil in a spatially comprehensive way. This paper presents the results of combining multi-receiver electromagnetic induction and stepped-frequency ground penetrating radar to characterize a former garage site contaminated with petroleum hydrocarbons. The sensor combination showed the ability to identify and accurately locate building remains and a high-density soil layer, thus demonstrating the high potential to investigate anthropogenic disturbances of physical nature. In addition, a correspondence was found between an area of lower electrical conductivity and elevated concentrations of petroleum hydrocarbons, suggesting the potential to detect specific chemical disturbances. We conclude that the sensor combination provides valuable information for preliminary assessment of urban soils. Environmental impact The proper management of urban soils is a key issue in our urbanizing world. However, the heterogeneity of these soils poses severe challenges to the conventional soil survey approach that relies on spatially discrete observations from soil borings and groundwater monitoring wells. Non-invasive geophysical techniques provide a cost-effective alternative to investigate soil in a spatially comprehensive way. This study demonstrates the high-resolution application of multi-receiver electromagnetic induction and stepped-frequency ground penetrating radar on a contaminated former garage site. Various geophysical anomalies that can serve as a proxy for different anthropogenic soil disturbances are indicated. These results highlight how these sensing technologies can contribute to urban soil assessment and management.

Knowledge and understanding of the spatio-temporal variability of soil physical and chemical properties at the field or micro-catchment scale are of prime importance for many agricultural and environmental applications that aim at soil, water and carbon conservation. Geophysical methods, such as electromagnetic induction (EMI), are nowadays a key tool to monitor these properties across relevant scales, as a result of their non-destructive nature and their capability to survey repeatedly large areas within a small time window. Geophysical instrument response depends on the electromagnetic properties of the subsoil and for EMI in particular moist soil conditions are generally considered as most suitable for data acquisition. In water-limited environments, such as those under Mediterranean climate, these conditions are not met during large periods of the year, apparently hampering the usefulness of the method in these regions. The aim of this study is to obtain a better understanding o...

In environmental assessments, the characterization of urban soils relies heavily on invasive investigation, which is often insufficient to capture their full spatial heterogeneity. Non-invasive geophysical techniques enable rapid collection of high-resolution data and provide a cost-effective alternative to investigate soil in a spatially comprehensive way. This paper presents the results of combining multi-receiver electromagnetic induction and stepped-frequency ground penetrating radar to characterize a former garage site contaminated with petroleum hydrocarbons. The sensor combination showed the ability to identify and accurately locate building remains and a high-density soil layer, thus demonstrating the high potential to investigate anthropogenic disturbances of physical nature. In addition, a correspondence was found between an area of lower electrical conductivity and elevated concentrations of petroleum hydrocarbons, suggesting the potential to detect specific chemical disturbances. We conclude that the sensor combination provides valuable information for preliminary assessment of urban soils.

ABSTRACT Paddy rice (Oryza sativa L.) fields are repeatedly puddled at water saturation to loosen the top soil and to form a uniformly compacted plow pan required to reduce losses of water and nutrients. In paddy growing conditions however, the non-invasive detection of compaction variation within the plow pan layer is challenging. This paper evaluates a proximal soil sensing based methodology to identify areas compacted by different intensities of puddling to support precise land preparation of paddy rice fields. Therefore, a 1.6-ha alluvial paddy field of silt loam texture (Aeric Haplaquepts) located in the Brahmaputra floodplain of Bangladesh was selected. Three areas within the field were variably puddled and characterized by soil properties and soil apparent electrical conductivity (ECa). The Floating Soil Sensing System (FLoSSY) employing the EM38 electromagnetic induction sensor was used to record the ECa of the soil for each of the puddling intensities. The effects of plow pan compaction on water percolation losses and paddy yield were assessed. The results show that the variably puddled areas had significant differences in soil bulk density which was linked to the soil penetration resistance. Spatial comparison of ECa data sets showed that the ECa values increased systematically under increased puddling. Sufficient soil compaction could be identified when percolation losses were significantly the lowest and paddy yield was significantly the highest. It was concluded that a non-invasive ECa survey using electromagnetic induction based soil sensing allowed the detection of soil heterogeneity linked to soil compaction caused by puddling, which offers an interesting potential for precision puddling and land preparation in paddy rice fields.

ABSTRACT Multiple-point statistics (MPS) is a collection of geostatistical simulation algorithms that uses a multiple-point training image (TI) as structural model instead of a two-point variogram. MPS allows to simulate more complex random fields, like phenomena characterized by spatial connectivity. A very recent development is multivariate MPS in which an ensemble of variables can be simulated simultaneously using a multivariate TI. We investigated if multivariate MPS can be used for the processing of proximal soil sensor data, i.e. interpolating the sensor data and predicting the target variable. We measured a field with fossil ice-wedge polygons in the subsoil with an electromagnetic induction sensor and used these measurements to predict the location of wedge material in the subsoil. We built a bivariate TI with a categorical image of a random polygonal network as primary variable and a continuous image of the corresponding sensor values as secondary variable. Then, we performed a bivariate reconstruction with the recently developed Direct Sampling software. The resulting E-types provided an interpolated sensor data map and a probability map predicting the location of wedge material in the subsoil. This procedure was compared to the more traditional approach of interpolating the sensor data with ordinary kriging and performing a fuzzy k-means classification. Comparing the resulting maps with an aerial photograph revealing the location of the ice-wedges through polygonal crop marks, showed that MPS reconstructed the polygonal patterns much better. The local accuracy of the MPS maps was proven by an independent quantitative validation based on nine extra measurement lines and 94 bore hole samples. As a first application in soil science, our case study showed that multivariate MPS can be used for the processing of proximal soil sensor data. The flexibility of the technique opens perspectives for other new applications and therefore multivariate MPS can become a valuable part of the pedometrical toolbox.

ABSTRACT At present, spatially very detailed data sets can be obtained about soil, landscape and crop variability. However, there is a need to select independent key properties to identify management classes needed for precise land management. In a previous study performed in the European loess belt, topsoil pH, apparent electrical conductivity (ECa) and elevation were identified as key properties. In this study we enlarged the number of soil properties by including gamma ray measurements and employed a similar methodology to a field in the sand belt of northern Europe. Based on a principal component analysis we identified the same three variables as key properties. This was surprising given the big differences in landscape topology and pedogenesis between the loess and sand areas. These three key variables were used to delineate management classes using a fuzzy k-means with extragrade classification procedure. This classification was evaluated by mapping the wheat grain yield in the year 2006. A multiple regression model could be constructed that predicted yield from ECa and elevation well (Radj2 = 0.88). To analyse the influence of ECa on crop yield in depth a boundary line analysis was conducted. The boundary line could be modelled with an excellent Radj2 of 0.98. It was concluded that ECa, elevation and pH are generic key variables for the delineation of management classes of the aeolian landscapes of north–west Europe. Given its integral nature and strong relationship with crop performances, the authors plea to upgrade ECa from a “secondary” (proxy) source of information to a “primary” variable which can be used directly as a basis for detailed soil mapping of the bulk soil.

ABSTRACT The commonly used variogram function is incapable of modelling complex spatial patterns associated with repetitive, connected or curvilinear features, because it is a two‐point statistic. Because this was strongly limiting to petroleum‐ and hydrogeologists, they developed multiple‐point geostatistics (MPG), an approach that replaces the variogram by a training image (TI). However, soil scientists also face complex spatial patterns and MPG might be of use to them as well. Therefore, this paper aims to introduce MPG to soil science and demonstrate its potential with a case study of polygonal subsoil patterns caused by Weichselian periglacial frost cracks in Belgium. A high‐resolution proximal soil sensing survey provided a reference image from which a continuous (655 sensor data) and a categorical (100 point observations) dataset were extracted. As a continuous TI, we used the geophysical data of another part of the field, and as categorical TI we used a classified photograph of an ice‐wedge network in Alaska. The resulting MPG maps reconstructed the polygonal patterns very well and corresponded closely to the reference image. Consequently, we identify MPG as a promising technique to map complex soil patterns and suggest that it should be added to the pedometrician's toolbox.

ABSTRACT To date, approaches in environmental risk assessment (ERA) are based on taxonomy-based descriptions of ecosystems. Due to the widespread occurrence of numerous types of chemicals in the environment and ensuing environmental risks, there is a need to get insight in the relationship between the response of the ecosystem to pollution and the characteristics (traits) of the organisms. The main hypothesis of the present research is that the trait composition of macroinvertebrate communities changes in a consistent manner along general environmental disturbance gradients. In this study, the relationship between maximal body length of macroinvertebrates and environmental variables (e.g. Cu concentration) reflecting river sediment quality in Flanders (Belgium) was analysed. It was found that the abundance at almost all body lengths decreased with a decreasing quality of the river sediment, which could be associated with a decrease in abundance of macroinvertebrate taxa. It was also observed that the number of different body lengths decreased with increasing pollution, which can be linked to a decrease in diversity of the macroinvertebrate community. At low levels of general environmental pollution especially small taxa (< 20 mm) experienced negative effects, but with increasing pollution also the abundance of larger taxa (> 60 mm) decreased. The trend observed for general environmental pollution was further analysed for specific types of metal contamination. Basic and zero-inflated Poisson models showed that with increasing copper pollution, the abundance of larger taxa quickly decreased and only relatively small taxa remained abundant. However, the observed trend was not generally applicable for all contaminants. The results of this research indicate that by using only individual metal concentrations it is not possible to explain the shifts in size distribution of macroinvertebrates. Including other environmental characteristics and other traits could enhance the understanding of how the macroinvertebrate community composition responds to environmental disturbances.

... TIMOTHY SAEY1*, PHILIPPE DE SMEDT1, EEF MEERSCHMAN1, MOHAMMAD MONIRUL ISLAM1, FUN MEEUWS1, ELLEN VAN DE VIJVER1, ALEXANDER LEHOUCK2 ... is situated behind a belt of aeolian sand dunes (Baeteman, 1999, 2008; Baeteman and Declercq, 2002). ...