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Integration of Remote Sensing data with GIS technology for the acceleration of the activities in national mapping agencies

Shyamali Chithraleka Perera
Center for Remote Sensing
Survey Department
P.O.Box 506
colombo, Sri Lanka
Tel: 94-587988, Fax: 94-584532
K. D. Parakum Shantha
Institute of Surveying & Mapping
Diyatalawa, Sri Lanka
Tel: 94-57-3016, Fax: 94-57-2004

Abstract
As the world changes more rapidly, the demand for upto-date information for resource management, environment monitoring, planning are increasing exponentially. Mapping agencies must respond to the concerns of the public, and do so with increasing efficiency and effectiveness. Integration of Remote Sensing with GIS technology will significantly promoted the ability for addressing these concerns.

Despite a decade of effort to active completely automated integration process, authors identify a conceptual model that permits utilization of computers with Remote Sensing and GIS technology with human contextual analysis in order to support the National Mapping Agencies to acceleration their activities.

Introduction
The well-worn argument that geo-information is a pre-requisite for” development. Most map makers absolve themselves from responsibility for c) “‘ the poor state of mapping with their territory. As the mapping will take several years to complete, it is clear that the National Survey and Mapping organization has to take initiatives to supply upto date geo- information for the users on their various requirements and expectations.

The integration of satellite data into a Geographic Information System (GIS) is one of the great idea that focus on the rapid acceptance of GIS technology in to the geo-information oriented applications in operational environments. Institutionalizing of the GIS and Remote Sensing process into everyday decision making has greater efficiency to overcome the problems identified in mapping at a National Mapping agency.

Hence, authors identified key issues of integration of Remote Sensing with GIS and the proposed structure perhaps most significant, however, is that the integrated approach leads to a new view of supplying geo- information rather than being static documents to completely recreated at periodic intervals.

Integration Of Remote Sensing With GIS
The volume of Remote Sensing is so large, its associated powerful image processing technology is used to manage geo-information with preprocessing analysis, accuracy assessment and information distribution.

GIS are more and more being used for the storage and analysis of geo- referenced data and also it handles the linkages between spatial entities and their discrete attributes. GIS system have become accepted as a standard way of handling geographic data and performing analysis on those data for a number of earth related disciplines-

With the availability of high resolution satellite data and its processing technologies. integration of digital image analyzing systems with advance GIS systems permit compositing data sources as well as promoting a partnership between man and machine. Furthermore. a GIS when combine with upto date remote sensing data could assist in the automated interpretation. change detection and map revision processes. Satellite : data offer repetitive. synoptic and accurate information of the earth’s r surface and as such offer the potential to monitor the dynamic changes F’ with GIS.

However. one should bear in mind the integration will largely depend on the ability to understand and conceptualize the transition between one representation to another.

Technical Impediments to Integration
Geographic phenomena do not occur with a specific data structure. Obviously certain types of objects are well represented in a raster data structure (eg.. elevation. soil type) while others more appropriately represented as vectors (eg. .boundaries. point information) . .’f Consequently. it can be significant strength for a GIS to incorporate advantages 0: both data types. New commercial systems designed expressly around data 1ntegrat10n are also emerged. However, the full potential of integrate GIS with remote sensing will not be realized unless we overcome the dichotomy of data structures for GIS and remote sensing (Ehlers, 1991) .Data accuracy and system communications are other major issues that discussed under problems related in integration.

Data Structures
The major problem is caused by the different in the structures used to acquire and store data. Remote Sensing detectors produce raster digital information directly then the raster processing of these data seems ‘natural” .GIS systems typically used the vector data structure.

In a model of geo-information extraction from raster imagery, at lower level processed raster data can be used to extract and manipulate at “..””,,-, pattern recognition in middle level. At the highest level with the knowledge based information. models the predictive description of the “imaged” object. Hence. at the middle and the highest level the image information can be stored as vectors or ojects than gray values. thus ,f;; fac1l1tat1ng the 1ntegrat10n approach. Add1tionally. the data structures that used for computer vision, quad trees and other tessellation are also possible data structures to manipulate remote sensing data (Samet.,1984).

Presently, the common used approach dealing with this problem is data conversion eventhough the raster to vector conversion leads to a loss of accuracy of information.

Data Accuracy
The classification accuracy, mapping accuracy and spatial resolution are main data accuracy problem which have to considered when integrating Remote Sensing data with GIS. The problem of classification accuracy present a major difficulty in the integration. Researchers has been suggested to improve Remote Sensing image classification accuracy by referencing the information already available in GIS’s.

A data processing system must assess levels of data accuracy and associate the level with the data it provides. Based on the assessment a user can understand how reliable the data are and determine how being to use them.

Different methods are used in Remote Sensing and GIS’s for data accuracy assessment. The method are incompatible with each other. Remote Sensing data analyzing mainly uses the error matrix method which provide global accuracy information while GIS operators use error model which provide more local accuracy information. But up to now no effective approach has been reported which facilitate the flow of accuracy information between Remote Sensing image analysis system and GIS’s (Fangin Wang., 1991).

System Communication
In the communication between Remote Sensing system and GIS, spatial and non spatial data must be transferred in an integration fashion. Facilitating the communication have been mainly made on query/reasoning languages and communication procedures. This method developed usually include the steps of language conversion, query optimization and data translation. Even so mismatch is unavoidable and the communication is still expensive (Fangin Wang, 1991).

Issue of New Integrated GIS Design
In the technological and institutional side, potential and problems have been mentioned. The proposed structure (figure 1.0) is to accelerate the activities in a national mapping agencies and this integrated approach permits exploitation of multiple data sources and provides accelerated mapping capabilities.


Figure 1.0: Integrated GIS System
It is possible to input vector data as well as raster format data to above proposed integrated data base model.

Existing analogue form maps can be convert to digital form by manual digitizing or scanning. The digital photogrammetric and digital form field survey data can connected to the databases directly. In this case user can get digital form map as well as analogue form maps. The advantage of use of Remote Sensing data is updating all topographic maps and thematic maps within a short time period. Hence, always possible to supply correct information to user. Further more, this data base can provide not only map information but also statistics and reports.

Since the capability thus exist to implementation the proposed structure, Survey and Mapping organizations should examine the major obstacles which have been encountered to date. The impact of automationon the need of software system architecture, hardware system .architecture and education and training to be recognized.

Software System Architecture
Software system architecture means this “Organizational Context” within .which all the software modules, programs etc. perform certain tasks and communicate with each other and data stores. It reflects the processes r or group of processes defined in the logical architecture.

The software should be selected which has capability for data capture by manual digitizing or scanning, Remote Sensing data on tape and digital form field survey data. Attribute data should be able to enter from alpha-numeric terminals.

Database management system interface should be capable of providing search/query facility to access graphical display from the database to , satisfy a certain search criteria to display certain map elements and also in the opposite direction to access the database from graphical display to textural attribute information about certain map elements and generate a report about them. Also it should have possibility for editing and utility programs to improve the quality of graphical data. Specially there should be image processing system to process and class1fy Remote Sens1ng data.

Software system should be able to give out put in paper plot either as verification plot to check the data quality or precision final plots from graphical out put and attribute data in report form. Also it should be possible to out put digital map sheet on tape as back-up tapes.

Hardware System Architecture
The hardware system should be chosen to match the system capability requirements to perform the task imposed by the logical model and the performance requirements necessary for the software system capabilities.

Education and Training
To implement the database model have to give training to persons with necessary and innovative skills to manage the transition from existing analogue to digital technologies and to decide how conventional and new processes can best be integrated to optimally serve user needs.

Conclusion
Integration of Remote Sensing and GIS technologies will significantly promoted the ability to handle geo-information. It is possible to obtain 1 high benefits in producing and updating maps with proposed system. It has facilities to do repetitive task without complaining, sort things fast, draw and store points, lines or area fastly and retrieving of geo- information rapidly. Thus the new system leads to geo-information which, are accurate and reliable in rather short time for decision making.

But some times it takes the same time to do a job the first time whether using a computer or by hand. The difference is on the second or third time around, repeating the process with only a few changes to make anew map.

References

  • Fangju Wang, 00 Integrating GIS’s and Remote Sensing Image Analysis Systems by Unifying Knowledge Representation Schemes”, IEEE Transactions on Geo Science and Remote Sensing, 1991.
  • Manfred Ehlers, “Remote Sensing and Geo Information Systems : Towards Integrated Spatial Information Processing”, IEEE Transactions on Geo Science and Remote Sensing, 1990.
  • Manfred Ehlers, Geoffry Edwards, Yvan Bedard,”Integration of Remote Sensing with Geographic Information System: A necessary evolution”, International Journal of Photogrammetry and Remote Sensing, 1990.
  • John Leatheradle,”New Strategies for Funding Mapping and Land Information System”, Conference of Common Wealth Surveyors, 1991.
  • Samet H.,”The Quad tree and Related Hierarchical Data Structures”, ACM Comput. Surveys, vol.16, 1984.