A Knowledge-Based Approach for Land
Evaluation using Rs And Gis Techniques
D. Amarsaikhan, M.Ganzorig
Informatics & RS Centre, Mongolian Academy of Sciences
av.Enkhtaivan-54B, Ulaanbaatar-51, Mongolia
Abstract
Application of a knowledge-based approach in the earth sciences is of great interest nowadays. The main aim of the research was to build a prototype geo expert system (GES) which can be used in decision making on the basis of the knowledge-base gathered from RS and GIS experts within GIS environment. For this purpose, land evaluation for wheat suitability was performed. In the selected area, the basic object consists of the surface elements characterized by attributes some of which are unknown. They were determined by the use of RS and GIS techniques cooperating with the experts from this field.
1. Introduction
Land evaluation is a method applied to the assessment of land suitability for a specific use. Land evaluation is itself knowledge-based and requires an extensive knowledge and different conditions to be fulfilled. This can be done automatically by the use ALES, LECS and GIS systems. At present, the use of knowledge- !'"' based (expert) systems in the geo-related sciences is not anew the phenomenon. Different types of these systems have been and are )., being developed depending upon the structure of knowledge
representation. Knowledge can be represented in the forms of semantic networks, object-attribute-value (O-A-V) triplets, frames, decision trees, production rules, etc. The characteristics of these techniques are discussed by Amarsaikhan (1994) .At present, in the field of geoinformatics the widely used structures of knowledge representation are the frame-based and rule-based types. Our aim in this study is to use a knowledge engineering approach for land suitability analysis. For this purpose, land evaluation for wheat suitability was performed. The crop requirements were conditionally defined by the use of knowledge acquisition techniques.
2. The Study Area
As a test site was selected an area situated in the central part of the Orkhon-Selenge basin, Mongolia. The area is included into the mountain-forest-steppe zone and its landscape is divided into 5 zones within which the agricultural land use is distributed (Ganzorig et al. 1995) .The area has different types of relief and soil and vegetation cover changes according to the relief morphology. The relief and weather conditions in this area are very suitable for agriculture. The selected area is located on the south-facing low slope and has zonal dark chestnut soil. The main cultivated plants are wheat and luzern.
3. Data Collection and Knowledge Acquisition
For the land evaluation the following materials were available:
-
SPOT XS image of May 1988;
- Soil map, scale 1:50, 000;
- Topographic map, 1:50, 000;
- ground truth data.
Knowledge acquisition is the gathering of the facts and the ~ relationships between them from any available sources. In this ~ study we used such knowledge acquisition techniques, like the.
authors' own knowledge about the study area, literature review
interviews with experts in this field, ancillary datasets stored , within a GIS, and RS and GIS processing due to which some new facts were generated.
4. Research Problems and Methods
4.1. In the selected area the basic object (landunit (LU) ) consists of surface elements characterized by attributes:
LU(#, name, depth, ph, stony, rockiness, erosion class, slope, texture, moisture).
So there is a model for land evaluation and we conditionally assumed that some important values (slope and moisture) are unknown. They will be determined using RS and GIS techniques cooperating with experts from this field. To reach the goal in the study, the following basic problems should be considered:
- How to select appropriate algorithms from a subroutine library of
RS/GIS software (ILWIS) ?
- How to choose parameters of the selected algorithms for optimal land evaluation using RS and GIS data?
- How much does the selection 9f procedures and parameters depend on context ?
4.2. In the problem solution, the unknown values will be determined. In the following way:
Slope will be calculated using contour map of the area.
Moisture will be determined by statistical pattern recognition. In
this case, by maximum likelihood decision rule using prior
probability which will be defined from the ancillary data by ' experts in geo science whose knowledge is important for the best C.'~II training sample selection. In the Hypotheses domain 4 hypotheses will be proposed . (Sl,S2,S3,S4 which are highly suitable, marginally suitable, moderately suitable and not suitable, respectively)
