Proceedings Chapter
Reference
Toward the Development of an Integrated Spatial Data Infrastructure in Armenia
ASTSATRYAN, Hrachya, et al.
Abstract
Armenia as a developing country has a very severe need to integrate and collabo- rate with the international Geospatial community in order to build a reliable and efficient geospatial infrastructure. Moreover, the availability of such an infrastructure could foster the availability of geospatial data, could facilitate the development of a data sharing policy at national level, and could help to efficiently provide Armenian data to the local and inter- national communities.
The goal of the article is to introduce the proposed integrated Geo- spatial infrastructure that is composed of a Spatial Data Infrastructure, tools, services, geo- computational facilities and benchmarking results of some services. The infrastructure will be discussed in the context of hydrological modeling in Armenia.
ASTSATRYAN, Hrachya, et al . Toward the Development of an Integrated Spatial Data Infrastructure in Armenia. In: ICT Innovations 2012, Web proceedings . S. Markovski, M.
Gusev, 2012. p. 85-92
Available at:
http://archive-ouverte.unige.ch/unige:25132
Toward to the Development of an Integrated Spatial Data Infrastructure in Armenia
H. Astsatryan1, W. Narsisian1, V. Ghazaryan1, A. Saribekyan1, Sh.
Asmaryan2, V. Muradyan2, Y. Guigoz3,4, G. Giuliani3,4,5 , N. Ray3,4,5
1 Institute for Informatics and Automation Problems of the National Academy of Sciences of the Republic of Armenia, 1, P. Sevak str., Yerevan, 0014, Armenia
[email protected], [email protected], [email protected], [email protected]
2 Center for Ecological-Noosphere Studies of the National Academy of Sciences of the Republic of Armenia, 68, Kh. Abovtan str., Yerevan, 0025, Armenia
[email protected], [email protected]
3 Institute for Environmental Sciences, University of Geneva, 7 route de Drize, CH 1227 Carouge / GE, Switzerland
4United Nations Environment Programe, Global Resource Information Database, 11 chemin des Anémones, CH 1219 Châtelaine/GE, Switzerland
5Forel Institute, University of Geneva, 10 route de Suisse, CP 416, CH-1290 Versoix, Swit- zerland
{nicolas.ray, gregory.giuliani}@unige.ch, [email protected]
Abstract. Armenia as a developing country has a very severe need to integrate and collabo- rate with the international Geospatial community in order to build a reliable and efficient geospatial infrastructure. Moreover, the availability of such an infrastructure could foster the availability of geospatial data, could facilitate the development of a data sharing policy at national level, and could help to efficiently provide Armenian data to the local and inter- national communities. The goal of the article is to introduce the proposed integrated Geo- spatial infrastructure that is composed of a Spatial Data Infrastructure, tools, services, geo- computational facilities and benchmarking results of some services. The infrastructure will be discussed in the context of hydrological modeling in Armenia.
Keywords: SDI, geospatial data, interoperability, distributed computing, Lake Sevan, SWAT, hydrological model.
1 Introduction
Geographic information systems (GIS) provide facilities to handle spatially refer- enced data (commonly known as geospatial data) and information. GIS allow one to integrate and handle datasets from different distributed sources. Even when a GIS is available, additional technology is needed to facilitate data sharing and processing across various users and producers. A Spatial Data Infrastructure (SDI) provides such a framework. In this context the deployment of a SDI at national level can facilitate harmonious production, management and usage of geospatial data.
Armenia as a developing country has an essential need to join regional and in- ternational geospatial communities to collaborate in building a reliable and effi- cient infrastructure. Moreover, the availability of such an infrastructure could fos- ter the availability of geospatial data and facilitate the development of a data sharing policy at national level. This would help to efficiently share Armenian da- ta with the local and international communities.
The issue is essential for Armenia, because the country was one of the most industrialized republics of the Soviet Union. Its large industrial enterprises were integrated into a single industrial conglomerate. Large-scale industrial activities (e.g., mining, chemical and electrical industry, machine construction) lead to se- vere impacts on the environment. Till now the economic policy shifted towards a strong support to industrial development, greatly ignoring ecological interests.
Geographically, Armenia lays the highest position in the South Caucasus and is a place of origination of major water arteries: rivers Kura and Araks. All countries of the region sharing borders with Armenia use Kura-Araks catchments and share emerging environmental problems. Consequently, information regarding the eco- logical state of shared environmental compartment of catchments is necessary for development of short-term and long-term plans for economic and social develop- ment of all countries of the Southern Caucasus region, especially considering planned large projects for construction of oil and gas pipelines, construction of a Europe–Caucuses–Asia transport corridor, and other projects. Environment re- search, awareness and conservation in Armenia are vital for the country’s future and for the greater area, which is a direct neighbor and partner of Europe. Thus one of the responses to threats is to build research capacity, the first stage of which is the deployment of the SDI, which will support decision-making at local and re- gional level.
This paper introduces the technical concepts of an integrated SDI platform for Armenia, which consists of a harmonization approach for tools and services, geo- computational facilities (see fig. 1), and benchmarking results of some services.
Fig. 1. Structure of Suggested SDI in Armenia
2 Integrated Spatial Data Infrastructure in Armenia
2.1 Geospatial data
There is an obvious acknowledgment of the effective use of geospatial data and information, and standardization is essential in this context. Standardization al- lows data from one source to be easily used with those from another source to cre- ate richer and more useful data. The infrastructure that best permits standardiza- tion is the SDI that can be defined as "the relevant base collection of technologies, policies and institutional arrangements that facilitate the availability of and access to spatial data" (Nebert, 2001). The SDI can be complemented with other man- agement tools in order to better handle and process the data. For this purpose the OpenGeo Suite [1] community edition used in conjunction with GeoNetwork al- lows building a SDI based on free and open source software. These web server applications permit to store data in a PostgreSQL/PostGIS database, to publish da- ta and metadata using interoperability OGC and ISO standards (e.g., Web Map Service, Web Feature Service, Web Coverage Service), to document and catalog data and services in a metadata management system (e.g. GeoNetwork), to dis- seminate data in various formats, and finally to build webGIS applications.
OpenGeo Suite incorporates wide database and raster format support and is de- signed for strong interoperability. Moreover, it publishes data from any major spa-
2.2 Tools and Services
The integrated SDI developed for Armenia is composed of several tools that facili- tate spatial analysis. We discuss below each of those tools.
GRASS GIS [2-3] is a free and open source GIS software used for geospatial data management and analysis, image processing, graphics/maps production, spa- tial modeling, and visualization. GRASS is currently used in academic and com- mercial settings around the world, as well as by many governmental agencies and environmental consulting companies. GRASS is written in a fully modular way.
The latest stable release provides more than 400 modules for data management and analysis. GRASS can be run fully automated on distributed computing infra- structures, such as on high performance computing clusters (HPC). However, it is not an easy job to port GRASS modules directly to HPC environment, because we should have good balancing of both data and task distribution, and effective solu- tion to distribute data and tasks among single or multiple clusters environments.
Series of experiments to predict and investigate the scalability of GRASS modules on the available computational platform have been carried out.
For example, the module of the Normalized Difference Vegetation Index (NDVI) [4] has been implemented (see fig. 2) for the region including Lake Sevan [5]. Lake Sevan is the unique large water body of Armenia and has a crucial meaning not only in the water balance of the whole South Caucasus, but also in the northern regions of neighbor countries. It is the main strategic supply source of drinking water for Armenia and neighboring countries.
Fig. 2. GRASS NDVI output for Lake Sevan
The benchmarking of NDVI (see fig. 3) has been carried using four computa- tional nodes (each 8 cores) of the Armenian National Grid Initiative (ArmNGI) [6].
Fig. 3. NDVI benchmarking using four nodes
The experiments show good scalability of the NDVI module and justify the use of distributed computational resources for geoprocessing. For example in this particular case, the running time is about six times less in case of serial using.
Some services to stakeholders via PyWPS [7] and WPS GRASS BRIDGE [8] will be later provided, which will use HPC resources in case of complex requests, such as in case of high-resolution satellite image processing.
SWAT (Soil and Water Assessment tool) is another tool [9] that is available in the infrastructure. SWAT is a river basin scale model developed to quantify the impact of land management practices in large, complex watersheds. The im- portance of the implementation of this tool in Armenia is crucial because there is no exact or accurate estimation of any land use impact on any watershed in Arme- nia. Additionally, we have to mention the importance of getting the HRU (Hydro- logical Response Units) properties of each sub-basin in the watershed. HRUs are the smaller spatial units describing watershed properties (soil type, landuse, etc).
A calibrated SWAT model can then be used to obtain outputs on various im- portant aspects of the watershed such as: water quality, water quantity, sediment concentration and more. Series of experiments have been carried out using the model based on ArcGIS (version 9.3.1) [10] with ArcSWAT [11]. As an initial implementation, corresponding HRU for Argichi river basin (part of Lake Sevan catchment basin) have been delineated and obtained (see fig. 4). The HRU anal- yses full report includes land use, soils and slope distribution, and final HRU dis- tribution.
Fig. 4. Delineation of HRU for Argichi river basin (red - mask border, green – sub-basin border, violet - HRU border)
Finally, as services it is planned to implement the standards promoted by the Open Geospatial Consortium [12]. Some experiments have been carried out using WPS (Web Processing Service), which provides rules for standardizing how in- puts and outputs (requests and responses) for geospatial processing services (such as polygon overlay) are encoded in the web service. The WPS standard also de- fines how a client can request the execution of a process, and how the output from the process is handled.
2.3 Computational and Storage Resources
The heterogeneous computational (more than 500 cores) and storage resources of- fered by ArmNGI and located in the leading research (National Academy of Sci- ences, Yerevan Physics Institute) and educational (Yerevan State University, State Engineering University) organizations of Armenia, was used for the distributed re- sources for SDI. For instance, spatial satellite image processing requires a large amount of computation time due to its complex and large processing criteria. In the future the integrated SDI could benefit from other VOs outside the ArmNGI, such as the common geocomputation infrastructure of the ENVIROGRIDS [13]
community.
3 Conclusion
The suggested infrastructure is compatible with OGC international standards, and can help sharing Armenian data to International initiatives such as GEOSS
(Global Earth Observation System of Systems) and INSPIRE (Infrastructure for Spatial Information in the European Community). The infrastructure is scalable over a distributed infrastructure. The various tools to complement the SDI are now integrated for strengthening research capacities and promoting innovative ways of approaching challenges related to the ever increasing flow of environmental data in Armenia. These new capacities will facilitate data exchange, sharing and updating, and will improve the accessibility to Armenian environmental data. Due to new facilities, Armenian researchers will be empowered with new highly- competitive technical skills by enhancing their national, regional and international networking.
Acknowledgements
This work was supported by the Swiss National Science Foundation (grant n°
137325) through the project SCOPES ARPEGEO (“Deploying ARmenian distrib- uted Processing capacities for Environmental GEOspatial data” [14]).
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