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).
