Abrasion control on dune colour: Muleshoe Dunes, SW USA

The Muleshoe Dunes, an east-west trending dunefield on the border separating Texas and New Mexico, consist of two distinct components: a white (carbonate rich) component and an overlying pink (quartz rich) component. The pink component exhibits significant spatial variation in redness. The reddest sands, in the western part of the dunefield, decrease in redness towards the east. This gradient is thought to result from abrasion of all iron-rich, red clay coating as the sediments were transported eastward by Late Quaternary aeolian processes. The effects of aeolian abrasion on the spectral signature and surface texture of the sediments were examined using laboratory abrasion experiments. Changes in spectral reflectance of abrasion samples from the laboratory were compared to field samples that were abraded naturally because of sediment transport. The changes resulting from increased time of abrasion are similar to those observed with increased distance downwind in the dunefield. These results suggest that downwind abrasion can explain the pattern of dune colour in the Muleshoe Dunes, although this does not preclude other possible causes. (C) 2008 Elsevier B.V. All rights reserved.

Abrasion control on dune colour: Muleshoe Dunes, SW USA

The Muleshoe Dunes, an east-west trending dunefield on the border separating Texas and New Mexico, consist of two distinct components: a white (carbonate rich) component and an overlying pink (quartz rich) component. The pink component exhibits significant spatial variation in redness. The reddest sands, in the western part of the dunefield, decrease in redness towards the east. This gradient is thought to result from abrasion of all iron-rich, red clay coating as the sediments were transported eastward by Late Quaternary aeolian processes. The effects of aeolian abrasion on the spectral signature and surface texture of the sediments were examined using laboratory abrasion experiments. Changes in spectral reflectance of abrasion samples from the laboratory were compared to field samples that were abraded naturally because of sediment transport. The changes resulting from increased time of abrasion are similar to those observed with increased distance downwind in the dunefield. These results suggest that downwind abrasion can explain the pattern of dune colour in the Muleshoe Dunes, although this does not preclude other possible causes. (C) 2008 Elsevier B.V. All rights reserved.

Optimisation of high temperature puffing of potato cubes using response surface methodology

One cubic centimetre potato cubes were blanched, sulfited, dried initially for between 40 and 80 min in air at 90 degreesC in a cabinet drier, puffed in a high temperature fluidised bed and then dried for up to 180 min in a cabinet drier. The final moisture content was 0.05 dwb. The resulting product was optimised using response surface methodology, in terms of volume and colour (L-*, a(*) and b(*) values) of the dry product, as well as rehydration ratio and texture of the rehydrated product. The operating conditions resulting in the optimised product were found to be blanching for 6 min in water at 100 degreesC, dipping in 400 ppm sodium metabisulfite solution for 10 min, initially drying for 40 min and puffing in air at 200 degreesC for 40 s, followed by final drying to a moisture content of 0.05 dwb. (C) 2003 Elsevier Ltd. All rights reserved.

Surface based hypothesis verification in intensity images using geometric and appearance data

In this paper we discuss current work concerning Appearance-based and CAD-based vision; two opposing vision strategies. CAD-based vision is geometry based, reliant on having complete object centred models. Appearance-based vision builds view dependent models from training images. Existing CAD-based vision systems that work with intensity images have all used one and zero dimensional features, for example lines, arcs, points and corners. We describe a system we have developed for combining these two strategies. Geometric models are extracted from a commercial CAD library of industry standard parts. Surface appearance characteristics are then learnt automatically by observing actual object instances. This information is combined with geometric information and is used in hypothesis evaluation. This augmented description improves the systems robustness to texture, specularities and other artifacts which are hard to model with geometry alone, whilst maintaining the advantages of a geometric description.

Modelling of surface identifying characteristics using Fourier series

Texture and small-scale surface details are widely recognised as playing an important role in the haptic identification of objects. In order to simulate realistic textures in haptic virtual environments, it has become increasingly necessary to identify a robust technique for modelling of surface profiles. This paper describes a method whereby Fourier series spectral analysis is employed in order to describe the measured surface profiles of several characteristic surfaces. The results presented suggest that a bandlimited Fourier series can be used to provide a realistic approximation to surface amplitude profiles.

Spatio-temporal surface soil heat flux estimates from satellite data: results for the AMMA experiment at the Fakara (Niger) supersite

This paper describes a method that employs Earth Observation (EO) data to calculate spatiotemporal estimates of soil heat flux, G, using a physically-based method (the Analytical Method). The method involves a harmonic analysis of land surface temperature (LST) data. It also requires an estimate of near-surface soil thermal inertia; this property depends on soil textural composition and varies as a function of soil moisture content. The EO data needed to drive the model equations, and the ground-based data required to provide verification of the method, were obtained over the Fakara domain within the African Monsoon Multidisciplinary Analysis (AMMA) program. LST estimates (3 km × 3 km, one image 15 min−1) were derived from MSG-SEVIRI data. Soil moisture estimates were obtained from ENVISAT-ASAR data, while estimates of leaf area index, LAI, (to calculate the effect of the canopy on G, largely due to radiation extinction) were obtained from SPOT-HRV images. The variation of these variables over the Fakara domain, and implications for values of G derived from them, were discussed. Results showed that this method provides reliable large-scale spatiotemporal estimates of G. Variations in G could largely be explained by the variability in the model input variables. Furthermore, it was shown that this method is relatively insensitive to model parameters related to the vegetation or soil texture. However, the strong sensitivity of thermal inertia to soil moisture content at low values of relative saturation (<0.2) means that in arid or semi-arid climates accurate estimates of surface soil moisture content are of utmost importance, if reliable estimates of G are to be obtained. This method has the potential to improve large-scale evaporation estimates, to aid land surface model prediction and to advance research that aims to explain failure in energy balance closure of meteorological field studies.

Modeling plant transpiration under limited soil water: comparison of different plant and soil hydraulic parameterizations and preliminary implications for their use in land surface models

Accurate estimates of how soil water stress affects plant transpiration are crucial for reliable land surface model (LSM) predictions. Current LSMs generally use a water stress factor, β, dependent on soil moisture content, θ, that ranges linearly between β = 1 for unstressed vegetation and β = 0 when wilting point is reached. This paper explores the feasibility of replacing the current approach with equations that use soil water potential as their independent variable, or with a set of equations that involve hydraulic and chemical signaling, thereby ensuring feedbacks between the entire soil–root–xylem–leaf system. A comparison with the original linear θ-based water stress parameterization, and with its improved curvi-linear version, was conducted. Assessment of model suitability was focused on their ability to simulate the correct (as derived from experimental data) curve shape of relative transpiration versus fraction of transpirable soil water. We used model sensitivity analyses under progressive soil drying conditions, employing two commonly used approaches to calculate water retention and hydraulic conductivity curves. Furthermore, for each of these hydraulic parameterizations we used two different parameter sets, for 3 soil texture types; a total of 12 soil hydraulic permutations. Results showed that the resulting transpiration reduction functions (TRFs) varied considerably among the models. The fact that soil hydraulic conductivity played a major role in the model that involved hydraulic and chemical signaling led to unrealistic values of β, and hence TRF, for many soil hydraulic parameter sets. However, this model is much better equipped to simulate the behavior of different plant species. Based on these findings, we only recommend implementation of this approach into LSMs if great care with choice of soil hydraulic parameters is taken