Remke L. Van Dam

A Revised Chronology for Mississippi River Subdeltas

Radiocarbon measurements by accelerator mass spectrometry relating to three of the four late Holocene Mississippi River subdeltas yielded consistent results and were found to differ by up to 2000 carbon-14 years from previously inferred ages. These geological dataare in agreement with archaeological carbon-14 data and stratigraphic ages based on ceramic seriation and were used to develop a revised chronologic framework, which has implications for prehistoric human settlement patterns, coastal evolution and wetland loss, and sequence-stratigraphic interpretations.

Iron oxides as a cause of GPR reflections

Iron oxides frequently occur as secondary precipitates in both modern and ancient sediments and may form bands or irregular patterns. We show from time-domain reflectometry (TDR) field studies that goethite iron-oxide precipitates significantly lower the electromagnetic wave velocity of sediments. Measured variations in magnetic permeability do not explain this decrease. The TDR measurements and a dielectric mixing model also show that neither electrical conductivity nor relative permittivity of the solid material are altered significantly by the iron-oxide material. From drying during all of the measurements, the amount of iron oxides appears to correlate with the volumetric water content, which is the result of differences in water retention capacity between goethite and quartz. These variations in water content control relative permittivity and explain the observed variation in electromagnetic wave velocity. Using 2-D synthetic radar sections, we show that the pattern of iron-oxide precipitation may have a profound influence on the GPR reflection configuration and can cause major difficulties in interpretation.

Subsurface imaging of vegetation, climate, and root-zone moisture interactions

Changes in global climate and land use affect important prolesses from evapotranspiration and groundwater recharge to carbon storage and biochemical cycling. Near surface soil moisture is pivotal to understand the consequences of these changes. However, the dynamic interactions between vegetation and soil moisture remain largely unresolved because it is difficult to monitor and quantify subsurface hydrologic fluxes at relevant scales. Here we use electrical resistivity to monitor the influence of climate and vegetation on root-zone moisture, bridging the gap between remotely-sensed and in-situ point measurements. Our research quantifies large seasonal differences in root-zone moisture dynamics for a forest-grassland ecotone. We found large differences in effective rooting depth and moisture distributions for the two vegetation types. Our results highlight the likely impacts of land transformations on groun ter recharge, streamflow, and land-atmosphere exchanges.

Methods for prediction of soil dielectric properties: a review

Electromagnetic sensors such as ground penetrating radar and electromagnetic induction sensors are among the most widely used methods for the detection of buried land mines and unexploded ordnance. However, the performance of these sensors depends on the dielectric properties of the soil, which in turn are related to soil properties such as texture, bulk density, and water content. To predict the performance of electromagnetic sensors it is common to estimate the soil dielectric properties using models. However, the wide variety of available models, each with its own characteristics, makes it difficult to select the appropriate one for each occasion. In this paper we present an overview of the available methods, ranging from phenomenological Cole-Cole and Debye models to volume-based dielectric mixing models, and (semi-) empirical pedotransfer functions.

Influence of Organic Matter in Soils on Radar-Wave Reflection: Sedimentological Implications

ABSTRACT Soils are excellent reflectors of ground-penetrating radar (GPR) signals because of the ability of organic matter to hold water. In this paper, GPR profiles of an eolian sedimentary succession are combined with textural, dielectric, and moisture-retention characteristics to illustrate the influence of soil moisture on radar-wave reflection. Organic matter in this succession varies strongly, from ;lt 0.15% for clean sand to 7% for the most prominent soil, whereas grain-size distributions are comparable. Moisture-retention curves show a complex relationship between suction potential (pF) and volumetric water content (). As a result of their uniform pore-size distribution, clean sand and weakly developed soils with ;lt 1% organic matter experience a sudden loss of water between pF 1.5 and pF 1.8, going directly from saturated to almost dry conditions. In contrast, the most prominent soil shows a more gradual decrease in with increasing suction potential. It follows that the dielectric contrast between clean sand and this soil increases sharply above pF 1.5, reaches a maximum value at field-capacity conditions, and then decreases slowly. Synthetic GPR images for different suction potentials show that field-capacity conditions, when reflection coefficients are high, are favorable for tracing one single soil. Dry sediments are preferable when imaging widely spaced soils, whereas saturated sediments are best when imaging closely spaced soils.

Strength of landmine signatures under different soil conditions: implications for sensor fusion

Most sensors for the detection of buried landmines are influenced by the properties of the soil that surrounds the mine. The temporal and spatial variability in soil properties accounts for a significant part of the detection uncertainty that is associated with most sensors. In particular, most sensor types (e.g. ground-penetrating radar, thermal infrared cameras, and chemical sniffers) are affected by the water content of the soil. However, each sensor type reacts in its own way to variations in soil water content and other soil properties. The resulting variation in sensor performance has serious implications for sensor fusion operations. We show how knowledge of soil physics can contribute to a better understanding of sensor performance and can lead to improved data fusion.

Worldwide distribution of soil dielectric and thermal properties

Ground penetrating radar and thermal sensors hold much promise for the detection of non-metallic land mines. In previous work we have shown that the performance of ground penetrating radar strongly depends on field soil conditions such as texture, water content, and soil-water salinity since these soil parameters determine the dielectric soil properties. From soil physics and field measurements we know that the performance of thermal sensors also strongly depends on soil texture and water content. There is it critical that field soil conditions are taken into account when radar and thermal sensors are employed. The objectives of this contribution are (i) to make an inventory of readily available soil data bases world wide and (ii) to investigate how the information contained in these data bases can be used for derivation of soil dielectric and thermal properties relevant for operation of land mine sensors.

Radar reflections from sedimentary structures in the vadose zone

Abstract Ground penetrating radar (GPR) is a suitable technique for imaging sedimentary structures in the vadose zone because small texture-related capillary-pressure variations lead to changes in water content and electromagnetic properties. To study exactly how GPR reflections are generated by sedimentary structures, GPR profiles of an aeolian sedimentary succession are combined with measurements of textural, electromagnetic and water-retention characteristics from a trench. Time domain reflectometry indicates that small variations in texture in the high-angle dune sediment are associated with changes in water content. Synthetic modelling shows that these changes cause clear GPR reflections. In an experimental approach to estimate the radar response of structures below the wave resolution, i.e. features smaller than λ/4, variations in grain-size distribution and porosity in a thin section were used to reconstruct water-retention curves and impedance models of the thinly layered sediment. Synthetic radar records calculated from the impedance models show that reflections from the studied subcentimetre-scale structures are composites of interfering signals. Although these low-amplitude interfering signals will commonly be overprinted by more prominent reflections, they may cause reflection patterns that change with frequency and do not represent primary bedding.

Spatial variability of magnetic soil properties

The presence of magnetic iron oxides in the soil can seriously hamper the performance of electromagnetic sensors for the detection of buried land mines and unexploded ordnance (UXO). Previous work has shown that spatial variability in soil water content and texture affects the performance of ground penetrating radar and thermal sensors for land mine detection. In this paper we aim to study the spatial variability of iron oxides in tropical soils and the possible effect on electromagnetic induction sensors for buried low-metal land mine and UXO detection. We selected field sites in Panama, Hawaii, and Ghana. Along several horizontal transects in Panama and Hawaii we took closely spaced magnetic susceptibility readings using Bartington MS2D and MS2F sensors. In addition to the field measurements, we took soil samples from the selected sites for laboratory measurements of dual frequency magnetic susceptibility and textural characteristics of the material. The magnetic susceptibility values show a significant spatial variation in susceptibility and are comparable to values reported to hamper the operation of metal detectors in parts of Africa and Asia. The absolute values of susceptibility do not correlate with both frequency dependence and total iron content, which is an indication of the presence of different types of iron oxides in the studied material.

Conceptual model for prediction of magnetic properties in tropical soils

In recent years it has become apparent that the performance of detection sensors for land mines and UXO may be seriously hampered by the magnetic behavior of soils. In tropical soils it is common to find large concentrations of iron oxide minerals, which are the predominant cause for soil magnetism. However, a wide range of factors such as parent material, environmental conditions, soil age, and drainage conditions control soil development. In order to predict whether magnetic-type iron oxide minerals are present it is important to understand the controlling factors of soil development. In this paper we present a conceptual model for predicting magnetic soil characteristics as a function of geological and environmental information. Our model is based on field observations and laboratory measurements of soils from Hawaii, Ghana, and Panama. The conceptual model will lead to the development of pedotransfer functions that quantitatively predict the occurrence and nature of magnetism in soils.