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Relevant bibliographies by topics / Spatial database

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Academic literature on the topic 'Spatial database'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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Journal articles on the topic "Spatial database"

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Salzberg, Betty, and David B. Lomet. "Spatial database access methods." ACM SIGMOD Record 20, no. 3 (September 1991): 6–15. http://dx.doi.org/10.1145/126482.126483.

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Devogele, Thomas, Christine Parent, and Stefano Spaccapietra. "On spatial database integration." International Journal of Geographical Information Science 12, no. 4 (June 1998): 335–52. http://dx.doi.org/10.1080/136588198241824.

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Abdulrahman Kareem Zada, Ashna. "Assess the Changing Role of Database Technology within Geographic Information System Over the Past 45 Years." Polytechnic Journal 9, no. 1 (June 30, 2019): 26–31. http://dx.doi.org/10.25156/ptj.v9n1y2019.pp26-31.

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Spatial data stored in databases have been become progressively crucial in the geographic information system (GIS) field within recent years. Spatial data are increasingly being noted for its significance in representing for real-life relationships. The value of spatial databases cannot be underestimated since it has been established to be an efficient and flexible means of handling enormous spatial datasets. This paper shows the different methods, in which spatial data are integrated by major database vendors and database within GIS. The current report has also examined the new technologies and advancements by database and GIS vendors in relational database management systems in the past four decades, which have facilitated spatial data to become integrated into databases.

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Kranjčić, Nikola, Bojan Đurin, Dragana Dogančić, and Lucija Plantak. "Improving Management of Spatial Data through Spatial Database." Environmental Sciences Proceedings 5, no. 1 (December 2, 2020): 5. http://dx.doi.org/10.3390/iecg2020-08865.

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Entering the European Union, the Republic of Croatia took over the INSPIRE directive called the National Spatial Data Infrastructure. A large amount of spatial data can be found through the National Spatial Data Infrastructure Geoportal. Data are available for viewing or downloading via different services, such as a web mapping service or web feature service. Although different spatial data are available, it is hard to access useful information through Geoportal. The aim of this paper is to prepare a spatial database which will gather different spatial data related to environmental engineering and present different queries and the visualization of the results. The main data used are related to protected areas in the Republic of Croatia, which register the environmental pollutants, air quality, exploitation and research fields of mineral resources, waste management, water management, and so forth. Alongside the national spatial data, the Copernicus Land monitoring service EU-DEM, the digital elevation model, is used. The classification of Sentinel-2 MSI data is used to provide land cover. Remotely sensed data are used in queries where aspect, slope, and land cover affect the results. Two predefined SQL queries are discussed. The first query discusses the danger of landslides, and the second query discusses threats from illegal landfills and the effect that they have on the environment. Predefined SQL queries enable users to quickly access needed data, even when the original data is updated. All data, databases, visualization, and results are presented in open access software.

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Di Martino, Ferdinando, and Salvatore Sessa. "Fuzzy Reliability in Spatial Databases." Advances in Fuzzy Systems 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/107358.

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Today it is very difficult to evaluate the quality of spatial databases, mainly for the heterogeneity of input data. We define a fuzzy process for evaluating the reliability of a spatial database: the area of study is partitioned in isoreliable zones, defined as homogeneous zones in terms of data quality and environmental characteristics. We model a spatial database in thematic datasets; each thematic dataset concerns a specific spatial domain and includes a set of layers. We estimate the reliability of each thematic dataset and therefore the overall reliability of the spatial database. We have tested this method on the spatial dataset of the town of Cava de' Tirreni (Italy).

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Kong, Ling Yan, Ying Chun Tao, Jing Liu, and Ai Hua Wu. "Study on Integrated Spatial Database for Composite Urban Disaster Risk Assessment." Applied Mechanics and Materials 738-739 (March 2015): 285–88. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.285.

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In the research of urban disaster risk assessment, data is the most basic element. Accurate, comprehensive, high quality data is essential to ensure the accuracy of disaster analysis and disaster risk classification. The systematic methods of data acquisition, processing and management and analysis need to be studied. In this paper, based on the analysis of characteristics of urban-disasters composite risk assessment data, the risk assessment database is designed. According to processing phase, the database is divided into four sub-databases: gathering database, general database, study area database and history database. The logic design for all the databases are discussed. The updating mechanism is built for the database and continuance updating is realized.

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Yu, Yong Ling, Tao Guan, and Jin Fa Shi. "Spatial Data Mining Based on Campus GIS." Advanced Materials Research 282-283 (July 2011): 641–45. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.641.

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With the rapid development of spatial database technology, spatial databases have been widely used in many engineering fields and rapidly increase in data capacity. Thus, to mine useful information from large spatial databases turns into a difficult but important task. In this paper, we apply the traditional data mining into spatial database and give a mining model for spatial data based on Campus GIS. Moreover, based on campus GIS, we implement a spatial data mining prototype system that is able to discovery the useful spatial features and patterns in spatial databases. The application in the campus GIS of a university has shown the feasibility and validity of the system.

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Istiyanto, Bambang, Yan El Rizal Unzilatirrizqi D, Mouli De Rizka Dewantoro, and Rahmat Ahmad. "Geographic Information System (GIS-T) database design for transportation safety system (case study: spatial database for road transportation system in Indonesia)." IOP Conference Series: Earth and Environmental Science 1117, no. 1 (December 1, 2022): 012023. http://dx.doi.org/10.1088/1755-1315/1117/1/012023.

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Abstract This study aimed to develop a spatial database design on transportation safety system. Spatial databases is a fundamental factor that is very important in the utilization of GIS for transportation, so it is important that a good preparation of spatial databases. Preparation of spatial database will be effective if tailored to the needs. Factors affecting transportation safety is different from one region to another. Factors that affect transportation safety system in an area can be influenced by the transportation system infrastructure in the area. Database design for transportation safety in this study is expected to be a model of the spatial database according to the highway transportation system in a small town in Indonesia. Characteristics of the transport system in Indonesia was largely dominated by private vehicles and less of public transport. Spatial analysis has the advantage to combine data and analyze various factors based on spatial location. Spatial data that can be used to support the analysis include the basic data, thematic data path, data that can be supporting the highway transportation system. Spatial data and GIS - T can be used as an analysis tool and alternative approach to improve road transport safety.

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Lanter, David P. "A Lineage Meta-Database Approach Toward Spatial Analytic Database Optimization." Cartography and Geographic Information Systems 20, no. 2 (January 1993): 112–21. http://dx.doi.org/10.1559/152304093782610315.

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Boyal, Parthasarathi, and Rituparna Chaki. "New Database Architecture for Smart Query Handler of Spatial Database." Procedia Technology 4 (2012): 766–71. http://dx.doi.org/10.1016/j.protcy.2012.05.125.

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Dissertations / Theses on the topic "Spatial database"

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Shou, Yutao Sindy, and 壽玉濤. "Efficient query processing for spatial and temporal databases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29853655.

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Pfeifle, Martin. "Spatial Database Support for Virtual Engineering." Diss., lmu, 2004. http://nbn-resolving.de/urn:nbn:de:bvb:19-27018.

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Yeung, Tony. "Storing Protein Structure in Spatial Database." Digital Archive @ GSU, 2005. http://digitalarchive.gsu.edu/cs_theses/8.

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In recent years, the field of bioinformatics has exploded in a scale that is unprecedented. The amount of data generated from different genome projects demands a new and efficient way of information storage and retrieval. The analysis and management of the protein structure information has become one of the main focuses. It is well-known that a protein’s functions differ depending on its structure’s position in 3-dimensional space. Due to the fact that protein structures are exceedingly large, complex, and multi-dimensional, there is a need for a data model that can fulfill the requirements of storing protein structures in accordance to its spatial arrangement and topological relationships and, at the same time, provide tools to analyze the information stored. With the emergence of spatial database, first used in the field of Geographical Information Systems, the data model for protein structure could be based on the geographic model, as they share several similar uncanny traits. The geometry of proteins can be modeled using the spatial types provided in a spatial database. In a similar way, special geometry queries used for geographical analysis can also be used to provide information for analysis on the structure of the proteins. This thesis will explore the mechanics of extracting structural information for a protein from a flat file (PDB), storing that information into a spatial data model based on a spatial data model, and making analysis using geometric operators provided by the spatial database. The database used is Oracle 9i. Most features are provided by the Oracle Spatial package. Queries using the ideas aforementioned will be demonstrated.

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Eriksson, Ludvig. "Using IndexedDB with a spatial database." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148355.

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Web technologies are becoming increasingly useful with new features and the gap to native apps is narrowing. Recently, IndexedDB was added to the web standard to provide large scale storage solutions directly in the browser. Is it performant enough to be used with a spatial database? In this study, such a system is developed for Foran Sverige AB and we learn that IndexedDB indeed can be used for this purpose. Even storage demanding geospatial applications can be developed as a multi- platform system with a single codebase, all while broadening the possible audience reach by avoiding an app installation process.

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Prasher, Sham. "Query processing in multiresolution spatial databases /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18682.pdf.

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Ingemarsson, Gabriel. "Database Performance for GIS : A Comparison of Database Schemas for Measurements with Spatial Attributes." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255022.

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Spatial databases are becoming more and more important. One type of spatial database is a database of measurements, which combine positions with alphanumerical attributes. While these databases are important, little research has been done on them. There is therefore need for research on how to store this type of data most effectively.In this thesis a read performance benchmark and an execution plan analysis of three different database schemas using two different datasets of different size are presented. The three schemas investigated are the existing schema of the dataset, a schema where the spatial data have been split into a separate table, and a star schema. Additionally, these benchmarks were performed in two database management systems, Oracle Spatial and PostGIS.The results show that the choice of database schema has a real impact on performance, but none of the designs performs best in all cases. Star schema seems promising and may perform better than the existing schema in some cases, but more research is required to investigate further the specific cases where one schema outperforms the other. Finally, the results also show that PostGIS might perform better than Oracle Spatial for this type of data.
Spatiala databaser blir mer och mer populära. En typ av spatial databas är en databas med mätningar som kombinerar positioner med alfanumeriska attribut. Även om dessa databaser är viktiga, så har det skett ytterst lite forskning om dessa databaser. Därför finns det behov av mer forskning om hur man lagrar denna typ av data mest effektivt.I denna examensrapport presenteras en prestandamätning av läsprestanda och en exekveringsplansanalys för tre olika databasscheman på två olika datamängder av olika storlek. De tre scheman som undersöks är det existerande schemat för datamängden, ett schema där spatialdata har delats upp i en separat tabell, samt ett stjärnschema. Dessa prestandamätningar utfördes dessutom i två databashanteringssystem, Oracle Spatial och PostGIS.Resultaten visar att valet av schema påverkar prestandan, men inget schema presterar bäst i samtliga fall. Stjärnschema verkar lovande och kan i vissa fall prestera bättre än det befintliga schemat, men mer forskning krävs för att ytterligare undersöka de specifika fallen där så sker. Slutligen visar resultaten också att PostGIS kan fungera bättre än Oracle Spatial för denna typ av data.

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Tao, Yufei. "Indexing and query processing of spatio-temporal data /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20TAO.

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Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 208-215). Also available in electronic version. Access restricted to campus users.

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Liu, Qing Computer Science &amp Engineering Faculty of Engineering UNSW. "Summarization of very large spatial dataset." Awarded by:University of New South Wales. School of Computer Science and Engineering, 2006. http://handle.unsw.edu.au/1959.4/25489.

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Nowadays there are a large number of applications, such as digital library information retrieval, business data analysis, CAD/CAM, multimedia applications with images and sound, real-time process control and scientific computation, with data sets about gigabytes, terabytes or even petabytes. Because data distributions are too large to be stored accurately, maintaining compact and accurate summarized information about underlying data is of crucial important. The summarizing problem for Level 1 (disjoint and non-disjoint) topological relationship has been well studied for the past few years. However the spatial database users are often interested in a much richer set of spatial relations such as contains. Little work has been done on summarization for Level 2 topological relationship which includes contains, contained, overlap, equal and disjoint relations. We study the problem of effective summatization to represent the underlying data distribution to answer window queries for Level 2 topological relationship. Cell-density based approach has been demonstrated as an effective way to this problem. But the challenges are the accuracy of the results and the storage space required which should be linearly proportional to the number of cells to be practical. In this thesis, we present several novel techniques to effectively construct cell density based spatial histograms. Based on the framework proposed, exact results could be obtained in constant time for aligned window queries. To minimize the storage space of the framework, an approximate algorithm with the approximate ratio 19/12 is presented, while the problem is shown NP-hard generally. Because the framework requires only a storage space linearly proportional to the number of cells, it is practical for many popular real datasets. To conform to a limited storage space, effective histogram construction and query algorithms are proposed which can provide approximate results but with high accuracy. The problem for non-aligned window queries is also investigated and techniques of un-even partitioned space are developed to support non-aligned window queries. Finally, we extend our techniques to 3D space. Our extensive experiments against both synthetic and real world datasets demonstrate the efficiency of the algorithms developed in this thesis.

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Brabec, František. "Optimizing client-server communication for remote spatial database access." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/3087.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Valdivia, Martinez Angélica. "Implementing of G.I.S. spatial operations in a database system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0007/MQ44308.pdf.

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Books on the topic "Spatial database"

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Yeung, Albert K. W., and G. Brent Hall, eds. Spatial Database Systems. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/1-4020-5392-4.

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Institute, Environmental Systems Research, ed. Spatial Database Engine (SDE). Redlands, California: Environmental Systems Research Institute, 1996.

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Ploix, R. A. H. Database of qualitative spatial information. Manchester: UMIST, 1997.

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Schneider, Markus, ed. Spatial Data Types for Database Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0028319.

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Urbano, Ferdinando, and Francesca Cagnacci, eds. Spatial Database for GPS Wildlife Tracking Data. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03743-1.

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Harold, Moellering, and International Cartographic Association, eds. Spatial database transfer standards: Current international status. London: Published on behalf of the International Cartographic Association by Elsevier Applied Science, 1991.

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Mamoulis, Nikos. Spatial data management. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2012.

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1943-, Scholl Michel O., and Voisard Agnes, eds. Spatial databases: With applications to GIS. San Francisco: Morgan Kaufmann Publishers, 2002.

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Rigaux, Philippe. Spatial databases: With application to GIS. San Francisco: Morgan Kaufmann Publishers, 2002.

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Symposium SSD (1st 1989 Santa Barbara, Calif.). Design and implementation of large spatial databases. Berlin: Springer-Verlag, 1990.

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Book chapters on the topic "Spatial database"

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Bertino, Elisa, Beng Chin Ooi, Ron Sacks-Davis, Kian-Lee Tan, Justin Zobel, Boris Shidlovsky, and Barbara Catania. "Spatial Databases." In Indexing Techniques for Advanced Database Systems, 39–75. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6227-6_2.

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Kalnis, Panos, and Gabriel Ghinita. "Spatial Anonymity." In Encyclopedia of Database Systems, 1–7. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_352-2.

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Mamoulis, Nikos. "Spatial Join." In Encyclopedia of Database Systems, 1–9. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4899-7993-3_356-2.

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Vaisman, Alejandro A., and Esteban Zimányi. "Spatial Datawarehousing." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_80810-1.

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Kalnis, Panos, and Gabriel Ghinita. "Spatial Anonymity." In Encyclopedia of Database Systems, 2685–90. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_352.

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Mamoulis, Nikos. "Spatial Join." In Encyclopedia of Database Systems, 2707–14. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_356.

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Kalnis, Panos, and Gabriel Ghinita. "Spatial Anonymity." In Encyclopedia of Database Systems, 3564–70. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_352.

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Mamoulis, Nikos. "Spatial Join." In Encyclopedia of Database Systems, 3598–606. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_356.

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Vaisman, Alejandro A., and Esteban Zimányi. "Spatial Datawarehousing." In Encyclopedia of Database Systems, 3587–92. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_80810.

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Manolopoulos, Yannis, Yannis Theodoridis, and Vassilis J. Tsotras. "Spatial Access Methods." In Advanced Database Indexing, 117–39. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4419-8590-3_6.

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Conference papers on the topic "Spatial database"

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Wu, Mengquan, Wuihong Cui, Xiaodong Yang, and Yongqi Huang. "Research of spatial database based on spatial database engine." In Geoinformatics 2007, edited by Jingming Chen and Yingxia Pu. SPIE, 2007. http://dx.doi.org/10.1117/12.760458.

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Zhu, Hongmei, and Yu Luo. "Spatial-temporal database model based on geodatabase." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838541.

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Yan, Xun, Rongguo Chen, Changxiu Cheng, and Xiaobo Peng. "Spatial query processing engine in spatially enabled database." In 2010 18th International Conference on Geoinformatics. IEEE, 2010. http://dx.doi.org/10.1109/geoinformatics.2010.5567750.

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Zhang, Hongxia, and Falah H. Abed. "Development of a GIS database for Iraqi marshlands ecosystem studies." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837948.

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Jeang-Kuo Chen. "Concurrency control of spatial join on spatial database." In Fourth Annual ACIS International Conference on Computer and Information Science (ICIS'05). IEEE, 2005. http://dx.doi.org/10.1109/icis.2005.39.

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Hasan, Ahmad, Ashraf Qadir, Ian Nordeng, and Jeremiah Neubert. "Construction Inspection through Spatial Database." In 34th International Symposium on Automation and Robotics in Construction. Tribun EU, s.r.o., Brno, 2017. http://dx.doi.org/10.22260/isarc2017/0118.

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Yan Zhou, Qing Zhu, and Yeting Zhang. "Spatial data declustering method considering spatial locality for parallel spatial database." In 2010 Second IITA International Conference on Geoscience and Remote Sensing (IITA-GRS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iita-grs.2010.5603280.

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Wang, Hong, Shanwu Su, Yuxiang Li, and Ronghua Yu. "Measuring the amount of information of national topographic database based on entropy." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838012.

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Tang, Jing, and Tao Liu. "An improved trajectory query on moving objects in a spatio-temporal database." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838061.

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Bhima, K., T. Aruna Sri, K. D. Ramaiah, and A. Jagan. "Exerting spatial join and KNN queries on spatial database." In 2012 International Conference on Recent Advances in Computing and Software Systems (RACSS). IEEE, 2012. http://dx.doi.org/10.1109/racss.2012.6212678.

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Reports on the topic "Spatial database"

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Gudivada, V. N., Vijay V. Raghavan, and Dwayne Carr. A Spatial Similarity Measure for Image Database Applications. Fort Belvoir, VA: Defense Technical Information Center, April 1992. http://dx.doi.org/10.21236/ada253570.

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Cobb, Maria. Object-Oriented Distributed Database Design and Spatial Data Modeling. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada407824.

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Rishe, Naphtali, David Barton, and Mario Sanchez. Storage and Visualization of Spatial Data in a High-Performance Semantic Database System. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada308598.

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Oliver, David V., Roy V. Ladner, Frank P. McCreedy, and Ruth A. Wilson. Efficient Storage of Large Volume Spatial and Temporal Point-Data in an Object-Oriented Database. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada406876.

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Trevor, Budge J., and Nichols E. William. Compilation of Site Names and Spatial Extents for Sites in the Hanford Site Disposition Database. Office of Scientific and Technical Information (OSTI), June 2020. http://dx.doi.org/10.2172/1635015.

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Ringo, Chris, Alan A. Ager, Michelle A. Day, and Sarah Crim. A spatial database for restoration management capability on national forests in the Pacific Northwest USA. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2016. http://dx.doi.org/10.2737/pnw-gtr-919.

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Zhang, Renduo, and David Russo. Scale-dependency and spatial variability of soil hydraulic properties. United States Department of Agriculture, November 2004. http://dx.doi.org/10.32747/2004.7587220.bard.

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Water resources assessment and protection requires quantitative descriptions of field-scale water flow and contaminant transport through the subsurface, which, in turn, require reliable information about soil hydraulic properties. However, much is still unknown concerning hydraulic properties and flow behavior in heterogeneous soils. Especially, relationships of hydraulic properties changing with measured scales are poorly understood. Soil hydraulic properties are usually measured at a small scale and used for quantifying flow and transport in large scales, which causes misleading results. Therefore, determination of scale-dependent and spatial variability of soil hydraulic properties provides the essential information for quantifying water flow and chemical transport through the subsurface, which are the key processes for detection of potential agricultural/industrial contaminants, reduction of agricultural chemical movement, improvement of soil and water quality, and increase of agricultural productivity. The original research objectives of this project were: 1. to measure soil hydraulic properties at different locations and different scales at large fields; 2. to develop scale-dependent relationships of soil hydraulic properties; and 3. to determine spatial variability and heterogeneity of soil hydraulic properties as a function of measurement scales. The US investigators conducted field and lab experiments to measure soil hydraulic properties at different locations and different scales. Based on the field and lab experiments, a well-structured database of soil physical and hydraulic properties was developed. The database was used to study scale-dependency, spatial variability, and heterogeneity of soil hydraulic properties. An improved method was developed for calculating hydraulic properties based on infiltration data from the disc infiltrometer. Compared with the other methods, the proposed method provided more accurate and stable estimations of the hydraulic conductivity and macroscopic capillary length, using infiltration data collected atshort experiment periods. We also developed scale-dependent relationships of soil hydraulic properties using the fractal and geostatistical characterization. The research effort of the Israeli research team concentrates on tasks along the second objective. The main accomplishment of this effort is that we succeed to derive first-order, upscaled (block effective) conductivity tensor, K'ᵢⱼ, and time-dependent dispersion tensor, D'ᵢⱼ, i,j=1,2,3, for steady-state flow in three-dimensional, partially saturated, heterogeneous formations, for length-scales comparable with those of the formation heterogeneity. Numerical simulations designed to test the applicability of the upscaling methodology to more general situations involving complex, transient flow regimes originating from periodic rain/irrigation events and water uptake by plant roots suggested that even in this complicated case, the upscaling methodology essentially compensated for the loss of sub-grid-scale variations of the velocity field caused by coarse discretization of the flow domain. These results have significant implications with respect to the development of field-scale solute transport models capable of simulating complex real-world scenarios in the subsurface, and, in turn, are essential for the assessment of the threat posed by contamination from agricultural and/or industrial sources.

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Mayne, Casey, David May, and David Biedenharn. Empirical analysis of effects of dike systems on channel morphology and flowlines. Engineer Research and Development Center (U.S.), March 2021. http://dx.doi.org/10.21079/11681/39799.

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A phased study of the dike fields within the Vicksburg and Memphis Districts of the US Army Corps of Engineers was conducted to document the channel morphology trends since dike construction on the Lower Mississippi River (LMR). This included the development of the hydrographic survey database and methodology utilized to identify changes in channel geometry in response to dike construction. A subsequent report will provide further refinements to the approach and results of the comprehensive assessment. Recent Mississippi River Geomorphology and Potamology program efforts have employed the database developed by Mr. Steve Cobb to assess the geomorphic changes in 21 dike systems along the LMR. Previous studies using this database have indicated that the dike fields have not caused a loss of channel capacity. Furthermore, these efforts suggested that the trends in the dike fields are closely related to the long-term geomorphic trends along the LMR. Previous efforts using the Cobb database provided considerable insight into the dike effects on the LMR, but they were limited spatially and temporally. In this study, a database and protocols were developed to allow for a more robust assessment of dike field impacts and to extend the spatial and temporal extents of the analysis.

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Gandhi, Vijay, James Kang, and Shashi Shekhar. Spatial Databases. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada473104.

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Rishe, Naphtali, and Maria Martinez. Workshop on Semantic Spatial Databases. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada387627.

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