Showing papers in "Earth Science Informatics in 2015"

Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS

Abstract: The main goal of this study was to investigate the analytical hierarchy process (AHP), frequency ratio (FR), and certainty factor (CF) models for groundwater potential mapping using geographical information system (GIS) at Varamin Plain, Tehran province, Iran In the first step, the groundwater conditioning factors such as altitude, slope angle, slope aspect, topographic witness index, rainfall, drainage density, water table level, aquifer thickness, lithology, and distance from rivers were prepared The groundwater yield dataset was prepared using earlier reports, and extensive field surveys In total, 71 groundwater data with high potential yield values of ≥40 m3/h were collected and mapped in GIS Out these, 50 (70 %) cases were randomly selected for models training, and the remaining 21 (30 %) cases were used for the validation purposes Subsequently, groundwater potential maps were produced using AHP, FR, and CF models in ArcGIS 102 Finally, the receiver operating characteristic (ROC) curves for all the groundwater potential models were constructed and the areas under the curves (AUC) were computed From the analysis, it is seen that the FR model (AUC = 7755 %) performs better than AHP (AUC = 7347 %) and CF (AUC = 6508 %) models The results of groundwater potential map can be helpful for future planning in groundwater resource management and land use planning

Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran

Abstract: The purpose of current study is to produce groundwater qanat potential map using frequency ratio (FR) and Shannon's entropy (SE) models in the Moghan watershed, Khorasan Razavi Province, Iran. The qanat is basically a horizontal, interconnected series of underground tunnels that accumulate and deliver groundwater from a mountainous source district, along a water- bearing formation (aquifer), and to a settlement. A qanat locations map was prepared for study area in 2013 based on a topographical map at a 1:50,000-scale and extensive field surveys. 53 qanat locations were detected in the field surveys. 70 % (38 locations) of the qanat locations were used for groundwater potential mapping and 30 % (15 locations) were used for validation. Fourteen effective factors were considered in this investigation such as slope degree, slope aspect, altitude, topographic wetness index (TWI), stream power index (SPI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Using the above conditioning factors, groundwater qanat potential map was generated implementing FR and SE models, and the results were plotted in ArcGIS. The predictive capability of frequency ratio and Shannon's entropy models were determined by the area under the relative operating characteristic curve. The area under the curve (AUC) for frequency ratio model was calculated as 0.8848. Also AUC for Shannon's entropy model was 0.9121, which depicts the excellence of this model in qanat occurrence potential estimation in the study area. So the Shannon's entropy model has higher AUC than the frequency ratio model. The produced groundwater qanat potential maps can assist planners and engineers in groundwater development plans and land use planning.

DrinC: a software for drought analysis based on drought indices

Abstract: Drought is a complex phenomenon which can be characterised mainly by its severity, duration and areal extent. Among these three dimensions, drought severity is the key factor which can be used for drought analysis. Drought indices are typically used to assess drought severity in a meaningful way. DrinC (Drought Indices Calculator) is a software package which was developed for providing a simple, though adaptable interface for the calculation of drought indices. The paper aims at presenting the overall design and the implementation of the software along with the utilisation of various approaches for drought analysis. DrinC can be used for the calculation of two recently developed indices, the Reconnaissance Drought Index (RDI) and the Streamflow Drought Index (SDI), as well as two widely known indices, the Standardised Precipitation Index (SPI) and the Precipitation Deciles (PD). Moreover, the software includes a module for the estimation of potential evapotranspiration (PET) through temperature based methods, useful for the calculation of RDI. The software may be used in a variety of applications, such as drought monitoring, assessment of the spatial distribution of drought, investigation of climatic and drought scenarios, etc. The applications of DrinC in several locations, especially in arid and semi-arid regions, show that it is gaining ground as a useful research and operational tool for drought analysis.

An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

Abstract: Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre- and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre- and post-event images and attributed to landslide classes.

Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates

Abstract: An improved neuro-fuzzy based group method of data handling using the particle swarm optimization (NF-GMDH-PSO) is developed as an adaptive learning network to predict the localized scour downstream of a sluice gate with an apron. . The input characteristic parameters affecting the scour depth are the sediment size and its gradation, apron length, sluice gate opening, and the flow conditions upstream and downstream of the sluice gate. Six non-dimensional parameters were yielded to define a functional relationship between the input and output variables. The training and testing of the NF-GMDH network are performed using published scour data from the literature. The efficiency of the training stages for the NF-GMDH-PSO is investigated. The testing results for the NF-GMDH network are compared with the traditional approaches based on regression method. A sensitivity analysis is carried out to assign the most significant parameter for the scour prediction. The results showed that the NF-GMDH-PSO network produced lower error in scour prediction than all other models.

Assessment of groundwater potential zones in an arid region based on catastrophe theory

Abstract: Evaluation of groundwater potential is a multi-criteria and multi-level comprehensive assessment system that needs judgment of decision makers in making decision. To avoid subjectivity or the preference of decision makers in the assessment, catastrophe theory based evaluation method is proposed in this study which calculates the importance of one criterion over other by its inner mechanism and thus, avoid subjectivity. The proposed method is applied for the assessment of groundwater potential zones in the arid region of lower Balochistan province of Pakistan. The groundwater is considered as a system with five sub-systems namely, geology, soil, drainage density, slope and rainfall. Seventeen sub-system indicators of groundwater potential are selected for modeling groundwater potential zone. The catastrophe theory is applied to derive the relative weights of indicators in predicting groundwater potential. Thematic maps of sub-systems are integrated within a geographical information system and the groundwater potential zones of the integrated layer are calculated by using the weights of indicators. The results are verified by existing number of tube wells operating in the study area. It has been found that the number of tube wells is more in the area where the groundwater potential is high. The study reveals that catastrophe theory is suitable for assessing groundwater potential.

Towards intelligent GIServices

Abstract: Distributed information infrastructures are increasingly used in the geospatial domain. In the infrastructures, data are being collected by distributed sensor services, served by distributed geospatial data services, transformed by processing services and workflows, and consumed by smart clients. Consequently, Geographical Information Systems (GISs) are moving from GISystems to GIServices. Intelligent GIServices are enriched with new capabilities including knowledge representation, semantic reasoning, automatic workflow composition, and quality and traceability. Such Intelligent GIServices facilitate information discovery and integration over the network and automate the assembly of GIServices to provide value-added products. This paper provides an overview of intelligent GIServices. The concept of intelligent GIServices is described, followed by a review of the state-of-the-art technologies and methodologies relevant to intelligent GIServices. Visions on how GIServices can perceive, reason, learn, and act intelligently are highlighted. The results can provide better services for big data processing, semantic interoperability, knowledge discovery, and cross-discipline collaboration in Earth science applications.

Groundwater targeting using remote sensing, geographical information system and analytical hierarchy process method in hard rock aquifer system, Karur district, Tamil Nadu, India

Abstract: Remote sensing (RS) and Geographic Information System (GIS) are potential tools for competent planning and administration of essential groundwater resources. In this study, a typical methodology is proposed to delineate groundwater target zones using integrated RS, GIS and Analytical Hierarchy Process (AHP) method. The developed methodology is confirmed by a case study in Karur district of Tamil Nadu, Southern India. Seven thematic layers, viz., Lithology, Lineament Density, Geomorphology, Slope, Post– Monsoon Water Level, Drainage Density and Landuse/Land cover were considered in this study. Selected seven thematic layers and their features were assigned suitable weights on the Saaty’s scale according to their virtual significance in groundwater incidence. The assigned weights of the thematic layers and their features were then normalized by using AHP. Finally, the selected seven thematic maps were incorporated by weighted linear combination method in a GIS environment to produce a groundwater targeting map. Thus, five groundwater targeting zones were identified and demarcated in the study area, viz., ‘very good’, ‘good’, ‘moderate’, ‘poor’ and ‘very poor’. The groundwater targeting map was finally verified using the well discharge data and the result was found acceptable. The ultimate result depicts the favorable groundwater targeting zones in the study area and can be helpful in better planning and managing of groundwater resources particularly in hard rock terrains.

A tool for hierarchical climate regionalization

Abstract: Climate regionalization is an important but often under-emphasized step in studies of climate variability. While most investigations of regional climate make at least an implicit attempt to focus on a study region or sub-regions that are climatically coherent in some respect, rigorous climate regionalization––in which the study area is divided on the basis of the most relevant climate metrics and at a resolution most appropriate to the data and the scientific question––has the potential to enhance the precision and explanatory power of climate studies in many cases. To facilitate the application of rigorous regionalization for climate studies, we introduce an improved hierarchical clustering method, describe a new open-source R package designed specifically for climate regionalization, and offer concise suggestions for performing appropriate regionalization. This paper describes the regionalization algorithms and presents a demonstration application in which the R package is used to regionalize Africa on the basis of interannual precipitation variability. Both the proposed methodology and the R package can be used for a broad range of applications and over different areas of the globe.

A MATLAB-based program for processing geochemical data using fractal/multifractal modeling

Abstract: In the field of applied geochemistry, it is important to obtain quantitative descriptions of geochemical patterns and identify geochemical anomalies In this paper, we present a MATLAB-based program for processing geochemical data by means of fractal/multifractal modeling The procedure consists of two functional parts First, we quantify the spatial distribution characteristics of geochemical patterns using the multifractal spectrum Second, geochemical anomalies are identified using various fractal/multifractal models These models include the concentration-area fractal model, spectrum-area multifractal model, and multifractal singularity analysis The results can be visualized in the MATLAB platform or saved for further analysis, ie, by geographic information systems software We demonstrate the applicability of this program by processing a geochemical dataset from soil samples taken in Inner Mongolia, China We examine the concentrations of Ag in these soil samples, and show that the results obtained by our program are highly correlated with known Ag deposits in the region of interest

Porphyry Cu potential area selection using the combine AHP - TOPSIS methods: a case study in Siahrud area (NW, Iran)

Abstract: In this article, by using AHP-TOPSIS technique we propose a new method for mineral potential mapping that commonly used to other science. AHP and TOPSIS are practical and useful techniques respectively for determining the relative importance of the criteria and ranking - selection of a number of externally determined alternatives through distance measures. AHP method employed to determine the importance weights of evaluation criteria, then TOPSIS technique use for selection and ranking of study area. We used AHP-TOPSIS and GIS to provid potential maps for porphyry copper mineralization on the basis of criteria derived from geological and geochemical controls, and remote sensing data including alterations and faults in Siahrud area in North West Iran. The results demonstrate the acceptable outcomes for copper porphyry exploration.

Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters

Abstract: Spatiotemporal groundwater quality simulation is very important for management of water resources and environmental activities. The present study integrated a number of hybrid methods such as Adaptive Neuro Fuzzy Inference System (ANFIS) with Genetic Algorithm (GA), and ANFIS with Partial Swarm Optimization (PSO) to simulate three groundwater quality parameters in Kerman plain (including Chloride concentration, Electrical Conductivity (EC), and PH). This research investigated the abilities of hybrid techniques as well, to predict groundwater quality. Considering the complexity of different aquifer materials and difficulty of collecting desirable samples, as it is both time- and cost-consuming, a number of hybrid models have been developed, presuming various combinations of monthly variables of rainfall and groundwater level and three different quality parameters. The results suggested that both models indicated an acceptable efficiency in the spatiotemporal simulation of groundwater quality. The study also revealed that groundwater level fluctuations across the aquifer as well as rainfall contribute as two important factors in predicting groundwater quality.

A geologic timescale ontology and service

Abstract: We have developed an OWL ontology for the geologic timescale, derived from a Unified Modeling Language (UML) model that formalized the practice of the International Commission for Stratigraphy (ICS) (Cox and Richard 2005). The UML model followed the ISO/TC 211 modeling conventions, and was the basis for an XML implementation that was integrated into GeoSciML 3.0. The OWL ontology is derived using rules for generating OWL ontologies from ISO-conformant UML models, as provided in a (draft) standard from ISO/TC 211. The basic ontology is also aligned with SKOS to allow multilingual labels, and to enable delivery through a standard vocabulary interface. All versions of the International Stratigraphic Chart from 2004 to 2014 have been encoded using the ontology. Following ICS practice, the elements of the timescale retain the same identifiers across the multiple versions, though the information describing each geochronologic unit evolves with the versions of the timescale. The timescales are published through multiple web interfaces and APIs.

Assessing soil erosion hazard -a raster based GIS approach with spatial principal component analysis (SPCA)

Abstract: Soil erosion is one of the most serious environmental problems affecting the quality of soil, land, and water resources upon which humans depend for their sustenance. A soil erosion hazard map is highly useful for environmental planning, soil conservation and management in soil erosion prone areas. In order to assess the soil erosion hazard, remote sensing (RS) and geographical information system (GIS) technologies were adopted, and a numerical model was developed using spatial principal component analysis (SPCA). Here, an integrated soil erosion hazard index (SEHI) was computed and classified into four levels of soil erosion hazard viz. low, average, high, and very high. In the process, nine factors were selected together with the degree of importance of the factors in hazard of the soil erosion. Integrated RS and GIS techniques and models were applied to generate the necessary factors for the SPCA approach. In addition, erosion hazard and calculated rate of soil erosion were used to find out the risk of soil erosion. Soil erosion risk was identified as actual occurrence and potential risk. Results show that, in general, an average hazardous condition of soil erosion was found in the area. The potential risk was more extensive in terms of involved area compared to the actual occurrence, and both actual occurrence and potential risk were higher at the mid-level elevation of the area. This study highlighted that priority should be given where the actual occurrence is high to very high and the probability of potential risk is average to high for protecting the land at present and in the future as well. Therefore, the application of SPCA combined with RS and GIS provided an effective methodology to solve the complex decisional problem for soil erosion hazard and risk assessment.

Exploring a graph theory based algorithm for automated identification and characterization of large mesoscale convective systems in satellite datasets

Abstract: Mesoscale convective systems are high impact convectively driven weather systems that contribute large amounts to the precipitation daily and monthly totals at various locations globally. As such, an understanding of the lifecycle, characteristics, frequency and seasonality of these convective features is important for several sectors and studies in climate studies, agricultural and hydrological studies, and disaster management. This study explores the applicability of graph theory to creating a fully automated algorithm for identifying mesoscale convective systems and determining their precipitation characteristics from satellite datasets. Our results show that applying graph theory to this problem allows for the identification of features from infrared satellite data and the seamlessly identification in a precipitation rate satellite-based dataset, while innately handling the inherent complexity and non-linearity of mesoscale convective systems.

Fuzzy logic modeling for hydrothermal gold mineralization mapping using geochemical, geological, ASTER imageries and other geo-data, a case study in Central Alborz, Iran

Abstract: The study area is located ~50 km in the north of Tehran capital city, Iran, and is a part of central Alborz Mountain. The intrusive bodies aged post Eocene have intruded in the Eocene volcanic units causing hydrothermal alterations in these units. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images were used to map hydrothermal alteration zones. The propylitic, phyllic and argillic alteration and iron oxide minerals identified using Spectral Angle Mapper (SAM) method. Structural lineaments were extracted from ASTER images by applying automatic lineament extraction processes and visual interpretations. An exploration model was considered based on previous studies, and appropriate evidence maps were generated, weighted and reclassified. Ore Forming Potential (OFP) map was generated by applying Fuzzy SUM operator on alteration and Pb, Cu, Ag, and Au geochemical anomaly maps. Finally, Host rock, geological structures and OFP were combined using Fuzzy Gamma operator (γ ) to produce mineral prospectivity map. Eventually, the conceptual model discussed here, fairly demonstrated the known hydrothermal gold deposits in the study area and could be a source for future detailed explorations.

Evaluation of urban growth effects on surface runoff using SCS-CN method and Green-Ampt infiltration model

Abstract: Evaluation of land cover/use change and its impact on hydrological design aspects plays a vital role in assessing the capacity of existing storm water conveyance systems and its capability towards the anticipated peak flow. This paper attempts to illustrate an application of Soil Conservation Service (SCS-CN) method for runoff estimation using continuous time series rainfall. And also, the present work proposes a Land use factor based on the Curve Number to correct the infiltration rate according to the prevailing land cover/use. Indian remote sensing (IRS) satellite data for the years 1989, 1992, 1995, 1998, 2001, 2004, 2007, and 2010 are used to evaluate the growth of Tiruchirapalli city, India city and to assess its impact on surface runoff. The Geographical information system (GIS) is used to prepare the different layers belonging to various land covers identified from remotely sensed data. The sub-watersheds are created using Digital Elevation Model (DEM) prepared by ARC GIS software. The single storm event and 50-year return period rainfall data are used as a hydrological input to the model to evaluate the increase in runoff over the years due to change in the land cover of the study area. The study reveals that the impact of land cover/use change is more significant for longer duration storm than short duration storm at the same magnitude. Further, it is noticed from the study that the runoff depth and runoff co-efficient is large for short duration rainfall than long duration rainfall at the same magnitude.

Application of remote sensing and GIS for evaluation of the recent morphological characteristics of the lower Brahmaputra-Jamuna River, Bangladesh

Abstract: This paper deals with the morphological changes of the lower Brahmaputra-Jamuna River (BJR) in Bangladesh Within few decades, the planform of the river has been changed abruptly by the combined effect of natural process and human interventions Morphological features observing in this study were river planform, channel width, bankline migration and channel bed elevation Eighteen sets of remote sensing data series from 1973 to 2011were analyzed using ERDAS/Imagine and GIS to document the variation of geomorphic elements of the lower BJR GIS analysis of remote sensing data showed that the changes of channel planform were quite significant over the past 40 years, occurring two major phases of channel development The changing patterns in the first phase (ie, between 1973 and 1992) were quite irregular However, the second phase ranging between 1992 and 2011 was unidirectional (mostly eastward) In general, the studied river reach was widened and the average rate of migration was 225 m y–1 that was three–folds the values of the first phase The height and slope of sand bars were gradually increasing, showing the highest value around the Jamuna Multipurpose Bridge (JMB) section The planform characteristics of BJR at the downstream of JMB showed that the river reach was gradually widening and shifting eastwards However, the reach at the upstream of the JMB showed westward migration The recent trends of channel changes suggest that the river planform has lost it’s naturally condition and it may, therefore, be predicted that increasing nature of channel width likely to continue in the immediate future

Application of Google earth to investigate the change of flood inundation area due to flood detention dam

Abstract: This paper is intended to investigate the effect of Narmab storage dam on downstream inundated area for three reservoir operation scenarios. ArcView GIS was coupled with HEC-RAS to produce a flood map for flood discharge of different return periods. Google-Earth software was applied to depict the extent of floodplain in actual landscape. The inflow hydrographs for 4 return periods (50-, 100-, 500- and 1,000-year) were routed through Puls method in order to obtain the outflow hydrographs passing through dam spillway. Results show that, through the operation of reservoir, the downstream inundated area can be reduced up to 75.1 % as a function of reservoir available storage. Furthermore, calculations showed that the reduction rate of inundated area for 50-year floods is largely more than 1,000-year floods, implies the high ability of Narmab dam on flood control especially for floods with shorter return period.

Harmonic coefficients of the Earth’s Spectral Crustal Model 180 – ESCM180

Abstract: We compile the harmonic coefficients, which describe the Earth’s crustal density structure with a spectral resolution complete to degree/order 180. These coefficients can be used in gravimetric studies of the Earth’s lithosphere structure, isostasy, crustal loading, sedimentary basins and related topics. The crustal structure of the Earth’s Spectral Crustal Model 180 (ESCM180) is separated into 9 individual layers of the topography, bathymetry, polar ice sheets, sediments (3-layers) and consolidated crust (3-layers). The harmonic coefficients describe uniformly the geometry and density (or density contrast) distribution within each individual crustal component. The topographic and bathymetric coefficients are generated from the topographic/bathymetric model ETOPO1 and the global geoid model GOCO03s. A uniform density model is adopted for the topography. The ocean density distribution is approximated by the depth-dependent seawater density model. The ETOPO1 topographic and the DTM2006.0 ice thickness data are used to generate the ice coefficients, while assuming a uniform density of the glacial ice. The geometry and density distribution within sediments is described by the 3 stratigraphic layers of a laterally varying density model, and the same structure is used to describe the density distribution within the consolidated crust down to the Moho interface. The sediment and consolidated crust coefficients are generated from the global crustal model CRUST1.0. The density contrasts of the ocean, ice, sediments and remaining crustal structures are taken relative to the reference crustal density.

Facilitating open exchange of data and information

Abstract: By broad consensus, Open Data presents great value. However, beyond that simple statement, there are a number of complex, and sometimes contentious, issues that the science community must address. In this review, we examine the current state of the core issues of Open Data with the unique perspective and use cases of the ocean science community: interoperability; discovery and access; quality and fitness for purpose; and sustainability. The topics of Governance and Data Publication are also examined in detail. Each of the areas covered are, by themselves, complex and the approaches to the issues under consideration are often at odds with each other. Any comprehensive policy on Open Data will require compromises that are best resolved by broad community input. In the final section of the review, we provide recommendations that serve as a starting point for these discussions.

Fractal and multifractal characteristic of spatial pattern of urban impervious surfaces

Abstract: Urban impervious surface (UIS) has been widely utilized to quantify urban expansion and assess environmental impacts of urbanization. In order to understand the complexity of spatio-temporal change of UIS spatial pattern, we investigated the fractal and multifractal characteristics of UIS spatial pattern in the downtown area of Shanghai, China during 1997–2010. Results suggested that UIS spatial pattern is a typical fractal structure with self-similarity during the study period. The fractal dimension reveals the spatio-temporal complexity of UIS pattern. With the threshold changing from small to large, the spatial complexity of UIS pattern is decreased. The increasing dimension values over time showed the UIS pattern becomes more complex and the spatial distribution becomes more clustered form 1997 to 2010. The multifractal approach transforms irregular UIS fraction data into a compact form and amplifies small differences between different data series. We also specially selected the W-E profile and the N-S profile to check the multifractality of UIS pattern. The results showed that the multifractality was detected in 1997 and 2002 on the W-E profile and only in 1997 on the N-S profile. The UIS pattern is more irregular on the W-E profile than that on the N-S profile according to the probability distribution, and the high fraction pixels are dominant on the two selected profiles by the positive ratio between the regions that the probability measure distributed most concentrated and most rarefied.

Band selection using sparse nonnegative matrix factorization with the thresholded Earth’s mover distance for hyperspectral imagery classification

Abstract: A sparse nonnegative matrix factorization method with the thresholded ground distance (SNMF-TEMD) is proposed to solve the band selection problem in hyperspectral imagery (HSI) classification. The SNMF-TEMD assumes that band vectors are sampled from a union of low-dimensional subspaces and approximates a HSI data matrix with the product of a basis matrix constructed from subspaces and a sparse coefficient matrix. The SNMF-TEMD utilizes the TEMD metric to better measures approximation errors during the optimization of HSI data factorization. The TEMD metric makes up the theoretical drawbacks in the Euclidean distance (ED) and Kullback–Leibler divergence (KLD) metrics when measuring the approximation errors in HSI datasets. The SNMF-TEMD is solved by the combination of min-cost-flow algorithm and multiplicative update rules. The band cluster assignments are found according to positions of largest entries in columns of the coefficient matrix and the desired band subset constitutes with the bands closest to their cluster centers. Three groups of experiments on two HSI datasets are performed to explore the performance of SNMF-TEMD. Four popular band selection methods are used to make comparisons: affinity propagation (AP), maximum-variance principal component analysis (MVPCA), SNMF with ED metric (SNMF-ED) and SNMF with KLD metric (SNMF-KLD). Experimental results show that SNMF-TEMD outperforms all four methods in classification accuracy and its computational speed is slower than SNMF-ED and SNMF-KLD. SNMF-TEMD is a better choice for band selection among all five methods because of its overwhelming advantage in classification and the popular speed remedy scheme from parallel computing and high-performance computers.

Intelligent polar cyberinfrastructure: enabling semantic search in geospatial metadata catalogue to support polar data discovery

Abstract: Polar regions have garnered substantial research attention in recent years because they are key drivers of the Earth’s climate, a source of rich mineral resources, and the home of a variety of marine life. Nevertheless, global warming over the past century is pushing the polar systems towards a tipping point: the systems are at high-risk from melting snow and sea ice covers, permafrost thawing, and acidification of the Arctic oceans. To increase understanding of the polar environment, the National Science Foundation established a Polar Cyberinfrastructure (CI) program, aimed at utilizing advanced software architecture to support polar data analysis and decision-making. At the center of this Polar CI research are data resources and data discovery components that facilitate the search and retrieval of polar data. This paper reports our development of a semantic search tool that supports the intelligent discovery of polar datasets. This tool is built on latent semantic analysis techniques, which improves search performance by identifying hidden semantic associations between terminologies used in the various datasets’ metadata. The software tool is implemented using an object-oriented design pattern and has been successfully integrated into a popular open source metadata catalog as a new semantic search support. A semantic matrix is maintained persistently within the catalogue to store the semantic associations. A dynamic update mechanism was also developed to allow automated update of semantics once more metadata are loaded into or removed from the catalog. We explored the effects of rank reduction to the effectiveness of this semantic search module and demonstrated its better performance than the traditional search techniques.

Building bridges: experiences and lessons learned from the implementation of INSPIRE and e-reporting of air quality data in Europe

Abstract: The collection, exchange and use of air quality data require diverse monitoring, processing and dissemination systems to work seamlessly together. These systems should supply data in a format usable for other applications such as planning, assessing population exposure and environmental impact assessment. As air quality does not change abruptly near national borders, international cooperation within this domain is highly desirable. This manuscript reports lessons learned during the implementation phase of one the world’s largest data harmonisation effort of environmental information infrastructures, INSPIRE, a backbone of a European wide spatial data reporting system which involves an unprecedented number of actors and volumes of data. It is important within the context of the Digital Earth concept and the establishment of a global spatial data infrastructure (SDI) through the Global Earth Observation System of Systems (GEOSS), as the quality of ambient air is among the most pressing contemporary environmental problems. We summarise our findings from the perspective of national public authorities, obliged by law to transmit standardised spatio-temporal data to streamline reporting and facilitate the use of information, while keeping public expenditure under control. To develop implementation strategies for these type of e-reporting data flows we established a cross-border case study, looking at the process of harmonisation and exchange of data in Belgium and the Netherlands based on interoperable standards. Our results cover the legal, semantic, technological and organisational aspects of reporting. They are relevant to a cross-thematic audience having to undergo similar processes of reporting, such as climate change, environmental noise, marine, biodiversity, water management, etc.

SEM+: tool for discovering concept mapping in Earth science related domain

Abstract: The amount of Earth Science related domain concepts and vocabularies encoded in popular Semantic Web languages such as OWL and SKOS grows rapidly as more and more domain scientists realize the power of Semantic Web Technologies. The interlinking between these concepts will enable the possibility of performing data integration and identity recognition, which is crucial in developing applications that use data from multiple sources. In this paper, we discuss a new tool for performing concept mapping called SEM+. In SEM+, we designed the Information Entropy based Weighted Similarity Model to compute semantic similarity between entity data and suggest possible linking. We also adopted a blocking approach to group possible matching entities into one block and therefore reduce the computation space. We performed evaluations on SEM+ using the Integrated Ocean Observatory System ontology and the Marine Metadata Interoperability ontology and discussed the results and new findings.

Sensitivity analysis of a GIS-based model: A case study of a large semi-arid catchment

Abstract: Water resource and hydrologic modeling studies are intrinsically related to spatial processes of hydrologic cycle. Due to generally sparse data, and high rainfall variability, the accurate prediction of water availability in complex semi-arid catchment depends to a great extent on how well spatial input data describe realistically the relevant characteristics. The Geographic Information System (GIS) provides the framework within which spatially distributed data are collected and used to prepare model input files. Despite significant recent developments in distributed hydrologic modeling, the over-parameterization is usually a critical issue that can complicate calibration process. Sensitivity analysis methods reducing the number of parameters to be adjusted during calibration are important for simplifying the use of these models. The objective of this paper is to perform a sensitivity analysis for flow in a semi-arid catchment (1,491 km2), located in northwestern of Tunisia, using the Soil and Water Assessment Tool (SWAT) model. The simulation results revealed that among eight selected parameters, curve number (CN2), soil evaporation compensation factor (ESCO), soil available water capacity (SOL_AWC) and threshold depth of water in the shallow aquifer required for return flow (GWQMN) were found to be the most sensitive parameters. Calibration of hydrology, facilitated by the sensitivity analysis, was performed for the period 2001 through 2003. Results of calibration showed that the model accurately predict runoff and performed well with a monthly Nash Sutcliffe efficiency (NSE) of 0,78, a coefficient of determination (R2) of 0,85 and a percent of bias (PBIAS) equal to −13,22 %.

Organization of a geophysical information space by using an event-bush-based collaborative tool

Abstract: Development of knowledge engineering makes it possible to bring an information space relating to an entire domain of knowledge within the field of geoscience into a strict form, which is both computer-tractable and convenient for collaborative research work. Nevertheless, there are issues that seriously hamper this process – the problem of defining key terms, which is often not shared by the colleagueship, and interrelation of concepts developed by different schools within the colleagueship focused on different aspects of this domain. Another issue is the export of results to a wider community unfamiliar with the specificity of local studies. All these issues can be successfully addressed by a novel technique of knowledge engineering, the event bush, brought into the COLLA environment for geoscientific collaborative studies. This paper demonstrates how the said issues can be resolved by the example of one of the most important information domains in the field of seismology, the site effects. Text, graphics, tabular data and a physical model coming from different sources and different contexts are united in one context keeping all the specificity of original understanding and allowing the researchers keep on following their own context and terminology.

Predicting streamflows to a multipurpose reservoir using artificial neural networks and regression techniques

Abstract: Population increase and climate change are stretching not only the world’s but also Pakistan’s water resources. This has directly been responsible for the recurring patterns of floods and droughts in the country which emphasizes the importance of the fact that efficient practices need to be adopted for water resource sustainability. This study investigates the use of upland catchment information, comprising of hydrometeorological datasets for inflow prediction to the Tarbela reservoir (a multipurpose reservoir located on River Indus) using Artificial Neural Networks (ANN) and Regression Techniques (Standard and Step Wise). Input Combination and data length selection for all the selected techniques were performed with the aid of Gamma test (GT). This study has made a significant contribution for future water resource management within the Indus Basin as Tarbela is the main source of irrigation, water supply and hydropower generation in Pakistan along with flood control.

Assessment of relationship between snow cover characteristics (SGI and SCI) and snow cover indices (NDSI and S3)

Abstract: Snow cover characteristics play a vital role in hydrological and climatological analyses. Snow characteristics have been retrieved using different techniques but no study has been conducted hitherto to determine its relationship with snow cover indices. In the present study the relationship of snow cover characteristics i.e., snow grain size index (SGI) and snow contamination index (SCI) with the snow cover indices viz. normalized difference snow index (NDSI) and S3 index is investigated using LANDSAT 8 OLI data in parts of Chenab Basin, western Himalayas. This task has been accomplished through comparative assessment of the relationship of snow cover characteristics with NDSI and S3: first, over two distinct illumination conditions i.e., sunlit snow cover and snow cover under shadow and second, for two different time periods i.e., November 2013 and February 2014 respectively. The results reveal the following observations. First, in the sunlit snow cover, there occurs positive correlation of both NDSI and S3 with SGI whereas they are negatively correlated with SCI, but in the snow cover under shadow, both NDSI and S3 exhibit negative correlation with SGI and SCI each. Second, S3 shows higher correlation with SGI and SCI than NDSI in the respective illumination conditions and time periods. Third, SGI and SCI portray highly positive correlation between them in the shadow side and a smaller negative correlation in the sunlit side. The results provide improved understanding regarding the relationship of the snow cover characteristics with the snow cover indices.