Agrobiodiversity of Muda Rice Agroecosystem:A case Study in Largest Granary Area of Malaysia

A survey was carried out at the largest rice cultivation area in Peninsular Malaysia,the Muda rice agroecosystem.The main objective of this study was to document the overall biodiversity associated with this unique agroecosystem by using a combination of sampling techniques in order to record different groups of fauna and flora.The total number of biota recorded and identified from the rice field ecosystem during the study period consisted of 46 species of zooplankton,81 species of aquatic insects,5 species of rodents,7 species of bats,87 species of birds,11 species of fishes and 58 species of weeds.A long-term study should be carried out as more species are expected to be recorded when more of the Muda rice agroecosystem area has been sampled to obtain sufficient information on the Muda rice agrobiodiversity.     

Extensional tectonic origin of gneissosity and related structures of the Feiran–Solaf metamorphic belt, Sinai, Egypt

The Feiran–Solaf metamorphic belt consists of low-P high-T amphibolite facies, partly migmatized gneisses, schists, amphibolites and minor calc-silicate rocks of metasedimentary origin. There are also thick concordant synkinematic sheets of diorite, tonalite and granodiorite orthogneiss and foliated granite and pegmatite dykelets. The gneissosity (or schistosity) is referred to as S₁, and is almost everywhere parallel to lithological layering, S₀. This parallelism is not due to transposition. The gneissosity formed during an extensional tectonic event (termed D1), before folding of S₀. S₁ formed by coaxial pure shear flattening strain (Z normal to S₀, i.e. vertical; with X and Y both extensional and lying in S₁). This strain also produced chocolate tablet boudinage of some layers and S₁-concordant sills and veins. S₁ has a strong stretching lineation L₁ with rodding characteristics. Within-plane plastic anisotropy (lower ductility along Y compared to along X) resulted in L₁-parallel extensional ductile shears and melt filled cracks. Continued shortening of these veins, and back-rotation of foliations on the shears produced intrafolial F₁ folds with hinges parallel to the stretching lineation. F₁ fold asymmetry variations do not support previous models involving macroscopic F₁ folds or syn-gneissosity compressional tectonics. The sedimentary protoliths of the Feiran–Solaf gneisses were probably deposited in a pre-800 Ma actively extending intracratonic rift characterizing an early stage of the break-up of Rodinia.     

Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit,NW Iran

In this contribution, we used discriminant analysis (DA) and support vector machine (SVM) to model subsurface gold mineralization by using a combination of the surface soil geochemical anomalies and earlier bore data for further drilling at the Sari-Gunay gold deposit, NW Iran. Seventy percent of the data were used as the training data and the remaining 30 % were used as the testing data. Sum of the block grades, obtained by kriging, above the cutoff grade (0.5 g/t) was multiplied by the thickness of the blocks and used as productivity index (PI). Then, the PI variable was classified into three classes of background, medium, and high by using fractal method. Four classification functions of SVM and DA methods were calculated by the training soil geochemical data. Also, by using all the geochemical data and classification functions, the general extension of the gold mineralized zones was predicted. The mineral prediction models at the Sari-Gunay hill were used to locate high and moderate potential areas for further infill systematic and reconnaissance drilling, respectively. These models at Agh-Dagh hill and the area between Sari-Gunay and Agh-Dagh hills were used to define the moderate and high potential areas for further reconnaissance drilling. The results showed that the nu-SVM method with 73.8 % accuracy and c-SVM with 72.3 % accuracy worked better than DA methods.     

Composition,radioactivity, and possible applications of kaolin deposits of Sinai,Egypt

This work discusses the composition, radioactivity, and possible utilization of the kaolin resources in Sinai which are hosted in thick sandstone sequences belonging to the Carboniferous (Wadi Khaboba) and Early Cretaceous (Wadi Iseila and Abansakar) ages. The characterization of kaolin was done by microscopic and SEM examination, supported by XRD and ICP-MS analyses. The studied kaolin resources consist of kaolinite, as the main constituent, associated with subsidiary dickite and halloysite, and minor contribution of smectite and illite. The most dominant non-clay mineral is quartz, besides minor gypsum, dolomite, and hematite. Ferrugination dominates, in most cases, at the upper boundaries of the kaolin lenses, suggesting possible supergene activity. The high Al₂O₃/SiO₂ ratio for the Cretaceous kaolin (0.54, in average) specifies its better grade relative to the Carboniferous kaolin (0.43, in average). The kaolin of the middle part of lens C in Wadi Iseila contains Si/Al molecular ratio of about unity, suggesting high-grade kaolin. The Carboniferous kaolin has enriched the radionuclides: U, Th, and Ra (at disequilibrium state due to leaching of eU relative to Ra) and the REE, relative to that of the Cretaceous age. The Carboniferous kaolin is characterized by a higher contribution of HREE (zircon signature), whereas LREE seems to be more influential for the Early Cretaceous kaolin (monazite signature). In spite of the very high CIA index (93 to 99), none of the analyzed kaolin deposits displays Ce abnormality. The high radioactivity of some Carboniferous kaolin can be a serious impediment for its utilization or its exportation. The kaolin of Sinai does not satisfy the international standards for paperwork industries and refractory manufacturing, but beneficiation may overcome this challenge. However, some lenses have high-grade kaolin with a low percentage of oxides of iron, magnesium, calcium, sodium, and potassium and a low radioactivity, hence nominated for the local refractory industry. The high-grade kaolin of Sinai fulfills the standards required for ceramics manufacturing in the global market. Grade 3 kaolin (< 30%, Al₂O₃) can be used in the manufacturing of white Portland cement and red glaze manufacturing on both local and global markets.     

Exploring Real‐Time Geoprocessing in Cloud Computing: Navigation Services Case Study

Today, many real‐time geospatial applications (e.g. navigation and location‐based services) involve data‐ and/or compute‐intensive geoprocessing tasks where performance is of great importance. Cloud computing, a promising platform with a large pool of storage and computing resources, could be a practical solution for hosting vast amounts of data and for real‐time processing. In this article, we explored the feasibility of using Google App Engine (GAE), the cloud computing technology by Google, for a module in navigation services, called Integrated GNSS (iGNSS) QoS prediction. The objective of this module is to predict quality of iGNSS positioning solutions for prospective routes in advance. iGNSS QoS prediction involves the real‐time computation of large Triangulated Irregular Networks (TINs) generated from LiDAR data. We experimented with the Google App Engine (GAE) and stored a large TIN for two geoprocessing operations (proximity and bounding box) required for iGNSS QoS prediction. The experimental results revealed that while cloud computing can potentially be used for development and deployment of data‐ and/or compute‐intensive geospatial applications, current cloud platforms require improvements and special tools for handling real‐time geoprocessing, such as iGNSS QoS prediction, efficiently. The article also provides a set of general guidelines for future development of real‐time geoprocessing in clouds.     

Surficial and Deep Earth Material Prediction from Geochemical Compositions

Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study, the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus Project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.

     

An ecological economics framework for assessing environmental flows: the case of inter-basin water transfers in Lesotho

This paper used the Lesotho Highlands Water Project (LHWP) that transfers water from the Orange River Basin in Lesotho to the Vaal River Basin in South Africa as a case study to show how environmental sustainability aspects can be integrated into economic development planning. Using the Ecological Social Accounting Matrix (ESAM) for Lesotho that integrates ecological implications of the LHWP with economic benefits of the project, the paper analysed the impact of lost ecological services downstream the LHWP dams in Lesotho on the well-being of households directly affected by the project (riparians) and the general economy of the country. The results revealed that despite significant economic benefits, the project has unintended impacts on ecological resources and services with resultant deleterious well-being implications for riparians. The results from the ESAM analysis indicated that not only the income of riparians is likely to suffer, but also that of other households and social groups, as well as the general economy of Lesotho. While results of the ESAM analysis did not indicate large income impacts on the economy at large, they were significant for riparians. The importance of integrating ecological consequences into impact assessment of IBWT before such transfers can be implemented to ensure sustainable development and considering economy-wide impacts associated with IBWT was proven necessary for a holistic impact assessment of IBWT.     

Validation of numerical ground water models used to guide decision making

Many sites of ground water contamination rely heavily on complex numerical models of flow and transport to develop closure plans. This complexity has created a need for tools and approaches that can build confidence in model predictions and provide evidence that these predictions are sufficient for decision making. Confidence building is a long-term, iterative process and the author believes that this process should be termed model validation. Model validation is a process, not an end result. That is, the process of model validation cannot ensure acceptable prediction or quality of the model. Rather, it provides an important safeguard against faulty models or inadequately developed and tested models. If model results become the basis for decision making, then the validation process provides evidence that the model is valid for making decisions (not necessarily a true representation of reality). Validation, verification, and confirmation are concepts associated with ground water numerical models that not only do not represent established and generally accepted practices, but there is not even widespread agreement on the meaning of the terms as applied to models. This paper presents a review of model validation studies that pertain to ground water flow and transport modeling. Definitions, literature debates, previously proposed validation strategies, and conferences and symposia that focused on subsurface model validation are reviewed and discussed. The review is general and focuses on site-specific, predictive ground water models used for making decisions regarding remediation activities and site closure. The aim is to provide a reasonable starting point for hydrogeologists facing model validation for ground water systems, thus saving a significant amount of time, effort, and cost. This review is also aimed at reviving the issue of model validation in the hydrogeologic community and stimulating the thinking of researchers and practitioners to develop practical and efficient tools for evaluating and refining ground water predictive models.     

Developing a long-term monitoring network under uncertain flowpaths

A long-term monitoring well network is developed using complementary and simple approaches in conjunction with a stochastic ground water flow and transport model. The development is illustrated for a case study of a U.S. nuclear testing site (Shoal) that is undergoing environmental restoration. The network design builds on three different, yet complementary, tools for locating the monitoring wells with a main objective of detection monitoring. The first tool is applied to select potential siting horizons where monitoring wells could be located. The second tool is used to place monitoring wells in locations with high success probability. The success here is defined as the detection of migrating stochastic plumes before a certain mass percentage reaches a compliance boundary. The third tool is used to analyze detection efficiency of multiple combinations of three wells. Seventy-six different three-well networks are selected from 20 candidate locations and are evaluated for detection efficiency. From the 76 networks analyzed, 28 attain detection efficiency close to or above 70%. The results of the different analyses provide multiple alternatives for the locations of the three wells, which will become part of the long-term monitoring network at Shoal. A number of combinations are equally good, and the final choice will depend on practical considerations and future agreements between model sponsor and regulators.