ALBERT

Description: The introduction of web based mapping facilities that use satellite imagery, offers local people the possibility to map their environment. However, maps need to be accurate, which is the reason why map making is assigned to professionals. In this paper, we investigated the classification accuracy of road infrastructure from high resolution satellite imagery of an urban area in western Kenya achieved by surveyors and non-surveyors alike, with and without local knowledge. Those with local knowledge classified roads with over 92% accuracy on average, irrespective of surveying background. Professional surveyors and laymen without local knowledge achieved lower accuracies of 67.7% and 42.9% respectively. We argue that local knowledge is also likely to improve the classification accuracy of many other attributes featured in topographic maps and thus conclude that there is reason to consider engaging local expertise in the production and updating of topographic maps.

Description: The monitoring of soil salinity levels is necessary for the prevention and mitigation of land degradation in arid environments. To assess the potential of remote sensing in estimating and mapping soil salinity in the El-Tina Plain, Sinai, Egypt, two predictive models were constructed based on the measured soil electrical conductivity (ECe) and laboratory soil reflectance spectra resampled to Landsat sensor’s resolution. The models used were partial least squares regression (PLSR) and multivariate adaptive regression splines (MARS). The results indicated that a good prediction of the soil salinity can be made based on the MARS model (R2 = 0.73, RMSE = 6.53, and ratio of performance to deviation (RPD) = 1.96), which performed better than the PLSR model (R2 = 0.70, RMSE = 6.95, and RPD = 1.82). The models were subsequently applied on a pixel-by-pixel basis to the reflectance values derived from two Landsat images (2006 and 2012) to generate quantitative maps of the soil salinity. The resulting maps were validated successfully for 37 and 26 sampling points for 2006 and 2012, respectively, with R2 = 0.72 and 0.74 for 2006 and 2012, respectively, for the MARS model, and R2 = 0.71 and 0.73 for 2006 and 2012, respectively, for the PLSR model. The results indicated that MARS is a more suitable technique than PLSR for the estimation and mapping of soil salinity, especially in areas with high levels of salinity. The method developed in this paper can be used for other satellite data, like those provided by Landsat 8, and can be applied in other arid and semi-arid environments.

Description: This paper presents the results of an extensive study of two thermal energy storage (TES) systems. The goal of the research is to make solar energy cost-competitive with other forms of electricity. A small-scale TES system was first built. The inner to outer layers were made of firebrick (FB), autoclaved aerated concrete (AAC) and reinforced concrete brick (CB). The experiments were conducted at temperatures of up to 1000 °C for sustained periods of time. AAC was found to be prone to cracking at temperatures exceeding 900 °C; as a result, AAC was eliminated from the second TES system. The second, larger-scale TES system was subsequently built of multiple layers of readily available materials, namely, insulating firebrick (IFB), perlite concrete (PC), expansion joint (EJ), and CB. All of the surfaces were instrumented with thermocouples to estimate the heat loss from the system. The temperature was maintained at approximately 800 °C to approximate steady state conditions closely. The steady state heat loss was determined to be approximately 4.4% for a day. The results indicate that high-temperature TES systems can be constructed of readily available materials while meeting the heat loss requirements for a falling particle receiver system, thereby contributing to reducing the overall cost of concentrating solar power systems.

Description: A conceptual flash flood early warning system for developing countries is described. The system uses rainfall intensity data from terrestrial microwave communication links and the geostationary Meteosat Second Generation satellite, i.e., two systems that are already in place and operational. Flash flood early warnings are based on a combination of the Flash Flood Guidance method and a hydrological model. The system will be maintained and operated through a public-private partnership, which includes a mobile telephone operator, a national meteorological service and an emergency relief service. The mobile telephone operator acts as both the supplier of raw input data and the disseminator of early warnings. The early warning system could significantly reduce the number of fatalities due to flash floods, improve the efficiency of disaster risk reduction efforts and play an important role in strengthening the resilience to climate change of developing countries in Africa. This paper describes the system that is currently being developed for Kenya.

Description: This paper describes the setting and results of a real-time experiment of irrigation scheduling by a time series of optical satellite images under real conditions, which was carried out on durum wheat in the Haouz plain (Marrakech, Morocco), during the 2012/13 agricultural season. For the purpose of this experiment, the irrigation of a reference plot was driven by the farmer according to, mainly empirical, irrigation scheduling while test plot irrigations were being managed following the FAO-56 method, driven by remote sensing. Images were issued from the SPOT4 (Take5) data set, which aimed at delivering image time series at a decametric resolution with less than five-day satellite overpass similar to the time series ESA Sentinel-2 satellites will produce in the coming years. With a Root Mean Square Error (RMSE) of 0.91mm per day, the comparison between daily actual evapotranspiration measured by eddy-covariance and the simulated one is satisfactory, but even better at a five-day integration (0.59mm per day). Finally, despite a chaotic beginning of the experiment—the experimental plot had not been irrigated to get rid of a slaking crust, which prevented good emergence—our plot caught up and yielded almost the same grain crop with 14% less irrigation water. This experiment opens up interesting opportunities for operational scheduling of flooding irrigation sectors that dominate in the semi-arid Mediterranean area.

Description: The current paper introduces new prior distributions on the univariate normal model, with the aim of applying them to the classification of univariate normal populations. These new prior distributions are entirely based on the Riemannian geometry of the univariate normal model, so that they can be thought of as “Riemannian priors”. Precisely, if {pθ ; θ ∈ Θ} is any parametrization of the univariate normal model, the paper considers prior distributions G( θ - , γ) with hyperparameters θ - ∈ Θ and γ > 0, whose density with respect to Riemannian volume is proportional to exp(−d2(θ, θ - )/2γ2), where d2(θ, θ - ) is the square of Rao’s Riemannian distance. The distributions G( θ - , γ) are termed Gaussian distributions on the univariate normal model. The motivation for considering a distribution G( θ - , γ) is that this distribution gives a geometric representation of a class or cluster of univariate normal populations. Indeed, G( θ - , γ) has a unique mode θ - (precisely, θ - is the unique Riemannian center of mass of G( θ - , γ), as shown in the paper), and its dispersion away from θ - is given by γ.  Therefore, one thinks of members of the class represented by G( θ - , γ) as being centered around θ - and  lying within a typical  distance determined by γ. The paper defines rigorously the Gaussian distributions G( θ - , γ) and describes an algorithm for computing maximum likelihood estimates of their hyperparameters. Based on this algorithm and on the Laplace approximation, it describes how the distributions G( θ - , γ) can be used as prior distributions for Bayesian classification of large univariate normal populations. In a concrete application to texture image classification, it is shown that  this  leads  to  an  improvement  in  performance  over  the  use  of  conjugate  priors.

Description: The spatial distribution of crops and farming systems in Africa is determined by the duration of the period during which crop and livestock water requirements are met. The length of growing period (LGP) is normally assessed from weather station data—scarce in large parts of Africa—or coarse-resolution rainfall estimates derived from weather satellites. In this study, we analyzed LGP and its variability based on the 1981–2011 GIMMS NDVI3g dataset. We applied a variable threshold method in combination with a searching algorithm to determine start- and end-of-season. We obtained reliable LGP estimates for arid, semi-arid and sub-humid climates that are consistent in space and time. This approach effectively mapped bimodality for clearly separated wet seasons in the Horn of Africa. Due to cloud contamination, the identified bimodality along the Guinea coast was judged to be less certain. High LGP variability is dominant in arid and semi-arid areas, and is indicative of crop failure risk. Significant negative trends in LGP were found for the northern part of the Sahel, for parts of Tanzania and northern Mozambique, and for the short rains of eastern Kenya. Positive trends occurred across western Africa, in southern Africa, and in eastern Kenya for the long rains. Our LGP analysis provides useful information for the mapping of farming systems, and to study the effects of climate variability and other drivers of change on vegetation and crop suitability.

Description: Use and retention of river water in African highlands deprive communities in arid lowlands of their benefits. This paper reviews information on water use in the Ewaso Ng’iro catchment, Kenya, to evaluate the effects of upstream abstraction on the Lorian Swamp, a wetland used by pastoralists downstream. We first assess the abstractions and demands for water upstream and the river water supplies at the upper and the lower end of the Lorian Swamp. Further analysis of 12 years of monthly SPOT-VEGETATION satellite imagery reveals higher NDVI (Normalized Differential Vegetation Index) values in the swamp than nearby rainfed areas, with the difference in NDVI between the two positively related to river water discharged into the swamp. The paper next reviews the benefits derived from water entering the swamp and the vulnerability to abstractions for three categories of water: (i) the surface water used for drinking and sanitation; (ii) the surface water that supports forage production; and (iii) the water that recharges the Merti Aquifer. Our results suggest that benefits from surface water for domestic use and forage production are vulnerable to abstractions upstream whereas the benefits from the aquifer, with significant fossil water, are likely to be affected in the long run, but not the short term.

Description: Two-phase (water and air) flow in the forced-air mechanically-stirred Dorr-Oliver machine has been investigated using computational fluid dynamics (CFD). A 6 m3 model is considered. The flow is modeled by the Euler-Euler approach, and transport equations are solved using software ANSYS-CFX5. Unsteady simulations are conducted in a 180-degree sector with periodic boundary conditions. Air is injected into the rotor at the rate of 2.63 m3/min, and a uniform bubble diameter is specified. The effects of bubble diameter on velocity field and air volume fraction are determined by conducting simulations for three diameters of 0.5, 1.0, and 2.0 mm. Air volume fraction contours, velocity profiles, and turbulent kinetic energy profiles in different parts of the machine are presented and discussed. Results have been compared to experimental data, and good agreement is obtained for the mean velocity and turbulent kinetic energy profiles in the rotor-stator gap and in the jet region outside stator blades.