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    ACRS 1995

    Poster Session 2
    Computer Assisted Monitoring of Vegetation Using Multl-resolution Satellite and Geospatial Data

    4 Object-oriented data model for multi-resolution I multi-temporal remote sensing and

    GIS data
    The object-oriented approach, a relatively new method in computing, is an attempt to improve modeling of the real world. Whereas previous modeling approaches were more record oriented, essentially too close to the computers, this new paradigm is a framework for generating models closer to real world features. The ideal would seem to be to provide an isomorphy, that is a direct correspondence, between real world entities and their computer representation. (LAURINI and THOMPSON, 1992)

    In this section, an object-oriented data model for multi-resolution and multi-temporal remote sensing and GIS data sets is introduced. This data model can be used to solve the problem that is critical to relational spatial information system in handling complex data sets.

    This data model is illustrated by classes and methods that can be used to integrate multiple data sets.

    Proposed object-oriented data model for multi- resolution/multi-temporal remote sensing and GIS data is descripted by the following definition of classes and methods (Lertlum and Murai, 1995) :

    Classes
    1. Point Class: to represent coordinate (x, y)for referencing purpose.
    2. Line Class: to represent pair of coordinate (x, y) that I can be used to define line object.
    3. Polygon Class: to represent set of line object that can ~ be used to define area by user.
    4. Pixel Class: to represent smallest high resolution "data object such as data from Landsat TM.
    5. Subarea Class: to represent smallest standard resolution data object (NOAA A VHRR LAC 1.1 km).
    6. Area Class: to represent smallest global data object : such as NOAA AVHRR GAC or global data set such as , Moisture Index, ARID index.
    7. Subregion Class: to represent country as a data object which include data in both raster and vector formats.
    8. Region Class: to represent multi-temporal region as a data object which also inherits properties from Subregion class.
    Methods
    The following are general type of methods for classes defined above:
    1. Value reclassification from real numbers, that is, interval or ratio scales, to ordered categories.
    2. Value substitutions, such as averages, particular values like a maximum,
    3. Value creation through combinations of values for different attributes via arithmetic operations, perhaps using advance statistical techniques.
    4. Value creation through Boolean logic Combinations via trigonometry or interpolation.
    5. Value assignment, substituting values from one variable for another.
    6. Those which involve a spatial property as well as non- spatial attributes.
    7. Integration method between different resolution pixel (classes) of the same coverage.
    In addition, at high level of abstraction (Subclasss: subregion, region), additional selected types of operations are:
    1. Classification: is a type of categorization of data object using spectral, spatial and temporal information.
    2. Change detection: is the extraction of change between multi-date data object.
    3. Extraction of indices: is the computation of a newly defined index, for example, the vegetation index from satellite data.
    4. Identification of specific features is the identification, for example, of disaster, lineament, archeological and other features.
    5 Conclusion: Object-oriented implement of forest inventory at regional level
    In order to monitor forest area in such a wide area as Southeast Asia Peninsula, high resolution dataset cannot easily be used for such a task because of the high volume of the data. Low resolution dataset such as data from NOAA A VHRR is a solution to such a task. A forest classification technique by using band 1 , 2, 3, and 4 of NOAA AVHRR including thermal bands is proposed in this study for more precise tropical forest classification. In addition, the same methodology is applied to high resolution dataset (Landsat TM), the result of the classification with high resolution dataset can differentiate classes of forest.

    Then we illustrates the use of objected-oriented data model to handle the integration problem of multi- resolution, multi-temporal data sets by defining an objected-oriented data model that can handle multi- resolution, multi-temporal remote sensing and GIS data sets. This data model consists of classes and methods that can be used to integrate multiple data sets. The next step on this research study is to implement the spatial information system from the methodology and data model proposed in this paper.


    Figure 3 Object-Oriented Framework for Multi-resolution / Multi- Temporal Satellite Remote Sensing and GIS Data Sets

    References
    • LAURINI, R., and THOMPSON, D.; Fundamentals of Spatial Information Systems, Academic Press, 1992, pp 644-646
    • LERTLUM, S., and MURAl, S.; Forest Mapping with NOAA A VHRR Data: Case Study: Effect of Thermal and for Refining Forest Mapping, Proceedings of the 15th Asian Conference on Remote Sensing, Nov 17- 23,1994, Bangalore, India, pp E51-E56
    • LERTLUM, S., and MURAl, S.; Object-Oriented Data Model for Multi-Resolution / Multi- Temporal Remote Sensing and GIS Data Sets, Proceedings of the 2nd AM/FM Asia, Aug 17 -23, 1995, Bangkok, Thailand, ppE51-E56
    • LlLLESAND, M., T, and KIEFER, W., R.; Remote Sensing and Image Interpretation, John Wiley and Sons, Republic of Singapore, 1987., pp 15-17, 566-567.
    • MILNE, P., MIL TON, S., and SMITH, J., L. (1993); Geographical Object-oriented Databases-a case study, Int. J. Geographical Information Systems,- 1993, Vol.7, No.1., pp39-55.
    • MURAl, S., (editor), Applications of Remote Sensing in Asia and Oceania-Environment Change Monitoring, Asian Association on Remote Sensing, 1991
    • MURAl, S., (editor), Remote Sensing Note, Japa:1 Association on Remote Sensing, 1993
    • MURAl, S., Special Course on Advanced Re.'T1ote Sensing/GIS Technologies: Data Fusion of Multi-sensor Data, AIT, Bangkok, Thailand, 1994.
    • PEUQUET, D., J. and DUAN, N.An Event-based Spatiotemporal Data Model (EST DM) for Temporal analysis of Geographical Data, I Int. J. Geographical Information Systems. L 1994, Vol.9, No.1 pp7-24.
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