Expert Systems and GIS an Application of Land Suitability Evaluation

Expert systems and GIS: an application of landsuitability evaluation

S. Kalogiroua,*aDepartment of Geography, University of Newcastle, Daysh Building, Newcastle Upon Tyne, NE1 7RU, UK

Accepted 13 September 2001

Abstract

In this paper expert systems and geographical information systems technologies are com-bined to help with an implementation of a land suitability evaluation model. The result is the

LEIGIS software, which allows empirical work within the framework of this paper. Themodel used is based on the FAO land classification for crops, and data which describe anagricultural area in terms of soil mechanics and environment. The land evaluation has twoparts; the physical evaluation and the economic evaluation. For the physical evaluation of the

land, data for 17 land characteristics have been used and a Boolean classification method hasbeen applied. The implementation includes models for general cultivation and five (wheat,barley, maize, seed cotton, sugar beet) specific crops. A new interpolation function is intro-

duced to map values to scores in terms of land characteristics. The economic evaluationincludes income-maximization taking into account market restrictions. The expert system hasbeen designed to help with the evaluation of land and to allow alteration in its rules based on

different performance observed in local areas. The GIS functions help in managing the spatialdata and visualizing the results. The software developed allows the evaluation and presenta-tion of any equivalent spatial dataset and does not require special computer skills. # 2002Elsevier Science Ltd. All rights reserved.

Keywords: Physical and Economic land evaluation; Expert Systems in Agriculture; Custom GIS

1. Introduction

The problem of selecting the correct land for the cultivation of a certain agri-culture product is a long-standing and mainly empirical issue. Although manyresearchers, organizations, institutes and governments have tried to provide a fra-mework for optimal agricultural land use, it is suspected that much agriculturalland is used at below its optimal capability. The increased need for food production

Computers, Environment and Urban Systems

26 (2002) 89–112

www.elsevier.com/locate/compenvurbsys

0198-9715/02/$ - see front matter # 2002 Elsevier Science Ltd. All rights reserved.

PI I : S0198-9715(01 )00031 -X

* Tel.: +44-191-222-6359; fax: +44-191-222-5421.

E-mail address: [email protected] (S. Kalogirou).

and the shortage of resources stimulate a need for sophisticated methods of landevaluation to aid decision makers in their role to both preserve highly suitable landsand satisfy producers’ demand for increased profit.This paper presents a methodology for the physical evaluation of the land based

on the FAO’s (1976) framework. It introduces a rather simplistic economic evalua-tion using the results of a physical evaluation and information on crop yields. Themethodology is applied using a piece of specially written software that incorporatesexpert system and GIS components. The area of interest is agricultural land inCentral Macedonia, Greece. The information concerning yields and prices has beenavailable for 1995 from FAO Statistics for Agriculture (FAO, 2001).The main aim of this paper is to illustrate how important it is for rural planners to

provide the methodology for physical land evaluation along with economic evalua-tion, and to motivate further research on the latter. The initial aim of this research isto provide the geographic society with a stand-alone, powerful and user-friendlyapplication for land use assessment that has customized capabilities and function-alities. However, the focus of the paper is on discussing a method for economic aswell as physical evaluation of the land. It aims also to link the theory with the realworld. Thus, it provides empirical work based on the results of the land evaluationand the real economic conditions of the area of study. This paper mentions potentialresearch questions that have not been investigated due to time constrains and a lackof data for the area of interest.It is important to clarify how the integration of some advanced information tech-

nologies could be used in research and how this research could be applied in the realworld. The system described here could be applied to other geographical or spatialproblems (such as urban planning, human resource management); indeed, anywherethere is a need for a rule-based classification and evaluation of information. Anargument of this paper, concerning the described software, is that expert systems aresuitable for solving classification problems and allowing on-the-fly customisation. TheGIS component was used in managing spatial data and visually presenting the results.In this stage of research, no new survey methods or classification models are sug-

gested. Instead, the FAO classification model for crops (FAO, 1976, 1984, 1985) isadopted, which allows land evaluation based on soil and environmental character-istics into five classes of suitability (three suitable and two not suitable) for certaincrops. To date, evaluation for general cultivation as well as for the cultivation of wheat,barley, maize, seed cotton, and sugar beet have been implemented. Current researchexamines the participation of social and economic characteristics, such as local cultureand labour force, product prices andmarket conditions, for themodel to bemore realisticand useful in rural planning. In this paper a simple approach is used: the allocation ofspecific crop cultivation in each parcel of land is based on the maximum estimatedincome out of the five crops possible (wheat, barley, maize, seed cotton, sugar beet).In the literature it has been suggested that fuzzy classification (Ahamed, Rao, &

Murthy, 2000; Burrough, 1989; Davidson, Theocharopoloulos, & Blocksma, 1994;Hall, Wang, & Subaryonon, 1992) and multi-criteria decision-making (McClean,Cherrill, & Fuller, 1995; Pereira & Duckstein, 1993) give more accurate results forland suitability than Boolean classification. However, some authors (Burrough, 1989)

90 S. Kalogirou /Comput., Environ. and Urban Systems 26 (2002) 89–112

used only three classes for their Boolean classification compared to seven or moreclasses for their Fuzzy classification. Other problems with earlier studies include thescale of maps used, the form of data (nominal, ordinal, ratio), and the land character-istics measurement methods (survey, calculation from satellite images, estimation).The main problem with map scale is that soil maps have been produced for

scales 1:250,000 (soil map of the UK) to 1:1,000,000 (FAO’s soil map of the world).Many of those maps have been produced with coarse soil sampling and soil classi-fication of large areas (hundreds or thousands of hectares) using satellite images.For the latter method large area satellite images with pixels of 1 km have been usedalong with land-use indexes (such as NDVI for vegetation). The creators of thosesoil maps have tried to estimate the underlying soil based on the grounds land-useand vegetation.Fuzzy methods are more suitable for data extracted from maps such as soils where

there is a high possibility of inaccuracies. For example one polygon at a scale of1:250,000 can overlap as many as a few thousand land parcels and tens of thousandsof land properties. One should note that in the area of interest a land property usedin agriculture could be as small as 1000 square feet. During empirical work using themethod presented in this paper, it was found that it is important for a soil survey tobe conducted for more accurate results to be obtained. When this is not possible, alow-cost soil sampling could be conducted instead. Obviously, this method can onlybe applied for small areas (district level) where data might have been already col-lected by local government.The database used here has been produced using existing soil, geology and land-

use maps, along with a soil survey that gave a more accurate image of the area. Forthe analysis, maps in vector format were used. Because of the intersection of maps indifferent scales (a geology map is usually in a different scale than a soil map), thespatial dataset consists of approximately 4000 polygons covering an area of 32,000hectares. Urban areas, water streams and water reservoirs can be clearly identified inthe maps (Figs. 1–4). Polygon sizes vary from less than a hectare to a 1000 ha forsome homogeneous areas. A polygon is equivalent to a parcel of land.This paper argues that the existing methods for producing soil maps and the scale

of the latter are not sufficient for effective land use capability evaluation. The exist-ing soil maps in developed countries are often at a very coarse scale and thus notappropriate for the state of agricultural practice in Greece which is characterized bylow availability of land for agriculture and generally small land properties. Accord-ing to the National Statistics for the number of agricultural holdings by order ofmagnitude of agricultural land in Greece, 12.1% of the holdings are less than 1.25acres in area, 25.4% of the holdings are less than a hectare and only 2.6% are over20 ha (National Statistical Service of Greece, 2001). Thus, methodologies in the lit-erature that use soil maps of scales from 1:250,000 up to 1:1,000,000 that assume acertain level of soil homogeneity are not applicable in this case.Another peculiarity of the Greek landscape is the strong variation of some of the

land characteristics such as gravel type and magnitude, slope and flood danger. Thisis based on a different land development culture among individual farmers. The lowavailability of resources for agriculture and the high cost of land development

S. Kalogirou /Comput., Environ. and Urban Systems 26 (2002) 89–112 91

Fig. 1. Land suitability evaluation for general cultivation.

Fig. 2. Land suitability evaluation for wheat cultivation. Map Detail.


Fig. 3. Economic land evaluation of five crops (maximum expected income). Suggested cultivation.

Fig. 4. Economic land evaluation of three grains (maximum expected income). Suggested cultivation.


methods deterred farmers to develop large land parcels. Therefore, it is importantfor the use of this methodology that fine soil surveys take place and data are collectedfor the area of study. This becomes more important under the framework of thecontemporary agriculture culture. The latter strongly focuses on the production oforganic food, thus it is necessary that accurate information about the land is col-lected frequently. Consequently, the cost of such a method is high.However, it is the responsibility of the local government and individual farmers to

adopt and use scientific approaches for the long-term profit maximization and nat-ural resources preservation. In line to this the National Agricultural ResearchFoundation (N.AG.RE.F.) has a research team and funds projects for the conduc-tion of soil surveys, soil mechanics analysis aiming to produce accurate soil maps forall the Hellenic territory. These maps are at a scale of 1:20,000. For the latter,satellite imagery and aerophotography methods have been incorporated along withsoil sampling (National Agricultural Research Foundation, 2001). More informa-tion about the local status of agriculture can be also found in the official website ofthe Hellenic Ministry of Agriculture (Hellenic Ministry of Agriculture, 2001).

2. Background

The expert system used in this work has been implemented using the expert systemshell ‘‘CLIPS’’. Jackson (1999) discusses expert systems in general, several expertsystem shells and programming languages, a few very well known problems and theapplications of expert systems. In the last chapter he introduces programming in‘‘CLIPS’’. Most of the information concerning ‘‘CLIPS’’ used in this work can bealso found on the Internet (see Web references for CLIPS) and in Giarratano (1993)as well as in Giarratano and Riley (1998). ‘‘CLIPS’’ has been developed by a team ofresearchers at the Lyndon B. Johnson Space Centre, NASA and is a freeware pro-duct (Software Technology Branch, 1993a; 1993b; 1993c). Recent versions 6 and6.01 can be found in the official site as well as in links to it (see Web references forCLIPS).A fundamental text discussing land evaluation is the ‘‘A framework for land

evaluation’’ by FAO (1976). It provides standards, definitions and a description ofland qualities that can be formed from land characteristics. It also sets the guidelinesfor physical and economical land evaluation. Guidelines for land evaluation forrainfed and irrigated agriculture were provided by FAO in Soils Bulletin 52 (FAO,1984) and Soils Bulletin 55 (FAO, 1985), respectively. However, FAO allows localvariations and many different implementation methods. In fact, not all researchersfollow this framework, but policy makers should do since this has been so far theonly standard approach to land classification. Other texts include a book on soilsurvey and land evaluation by Dent and Young (1981); a very detailed text usefulfor rural development in topics and subtropics by EUROCONSULT (1989); and theHandbook of soil science (Sumner & Malcolm, 1999).Two main categories of papers are relevant to this research. The first cate-

gory includes papers that discuss the use of a combined GIS and expert systems


technology and the second includes other approaches to implementing a land suit-ability evaluation model. The former provide more evidence supporting the useful-ness of expert systems but the applications discussed concern mainly animal habitatsand forestry. For the latter, expert systems have not been always used. Papers forforestry planning provide a great range in different way of physical land evaluation.Almost all authors do not include economic and social factors in their research.Keechoo and Tim (1996) discuss a hybrid travel demand problem with GIS and

expert systems (ES). By integrating the same technologies, Kirkby (1996) tries toidentify and manage dryland salinization. The classification of urban land coverbased on expert systems and GIS are discussed both by Moller-Jensen (1997) andLuckman, Jessen, and Gibb (1990). In a recent paper, Plant and Vayssieres (2000)discuss the combination of ES and GIS technologies to implement a state transitionmodel of oak woodlands.Other approaches of implementing land suitability evaluation models include a

multiple criteria decision-making methods (Pereira & Duckstein, 1993), and fuzzyclassification methods (Ahamed et al., 2000; Burrough, 1989; Davidson et al., 1994;Hall et al., 1992). Bojorquez-Tapia, Diaz-Mondragon, and Ezcurra (2001) present aGIS-based multivariate application for land suitability assessment with a publicparticipation base. Their work is one of the few including spatial structure (distancefrom roads, coast) and competing land uses (agriculture, aquaculture, fisheries) butthe use of land characteristics is quite poor. Joerin, Theriault, and Musy (2001)introduce a method called ELECTRI-TRI and account sustainable developmentand economic competitiveness. They provide an example of suitability of land forhousing, but it would be interesting to examine whether their approach could beapplied in rural planning since it is more sophisticated than the others. The latter twopapers along with a linear programming and GIS for land use modelling (Chuvieco,1993) focus on land use allocation, but partly discuss land suitability and providefindings that can stimulate further research in the inclusion of social and economicfactors in land evaluation modelling.It is worth mentioning the mixed qualitative/quantitative approach presented by

Van Lanen, Hack-ten Broeke, Bourma, and De Groot (1992), the Cropping SystemsModel PERFECT as a quantitative Tool in Land Evaluation (Thomas, Gardener,Littleboy, & Shields, 1995) and the integration of three land classifications withinthe Decision Support System for land use planning by McClean et al. (1995).Hall et al. (1992) discuss a comparison between the Boolean and fuzzy classifica-

tion methods, which is a good example of the Boolean method itself. They con-cluded that although the Boolean method is capable of aiding decision makers, thefuzzy approach gives more realistic results due to overcoming the two level classifi-cation of the former method. However, they use data which are already classifiedsuch as Cation Exchange Capacity (CEC) and include only 4 classes for the Booleanapproach. They include only 13 land characteristics, while the FAO frameworksuggests more than 50 land characteristics to measure 25 land qualities (FAO, 1985)with more than half of them considered significant. This makes their evidenceweaker, and thus further research is required to support the argument that the fuzzyapproach is more realistic than the Boolean.


Burrough (1989) concluded that fuzzy set theory offers a way around incon-sistencies occurred using the Boolean approach by allowing users to define flexibleclass membership functions that match practical experience. He recognized that themain sources of bias are the classes and weights chosen for land characteristicsrather than the methodology and encouraged research in addressing those. Howeverhe rejected the Boolean method as inadequate to help in an exploration of data andmodel the real world. Again in that paper less than 10 land characteristics were used,and only three classes used in the Boolean approached whereas the fuzzy approachwas modelled using seven classes.The immediate question is why those researchers have tried to increase the com-

plexity of the methodology by involving fuzzy theory and have not put efforttowards the incorporation of more land characteristics in their empirical work?Fuzzy theory works effectively with numerical data. However, it does not serve itsaim to improve the quality of the classification since many of the variables used in themodel have already had their values classified (such as many of the soil datasets are).It is argued here that research should focus on developing amore realistic model of landevaluation by incorporating more sources of data, such as geographical, climatological,socio-economic and cultural along with environmental and agro-ecological.Expert systems work without problems with both Boolean and Fuzzy approaches.

The Boolean approach, which has been implemented in this paper, has been testedand gave correct results in a few milliseconds per polygon.Rossiter (1995) recognizes the value of an economic land evaluation for rural

planning and resource management. He identified a gap in the literature concerningeconomic evaluation along with the plethora of works in physical evaluation. Thetwo key points of his paper are that the physical attributes of the land affect itseconomic value, and that these effects can be quantified in economic terms by theland evaluator.Rossiter (1990) has also discussed the Automated Land Evaluation System

(ALES). ALES is a computer program that allows evaluators to build their ownknowledge-based systems with which they can compute the physical and economicsuitability of land map units. Using ALES, decision trees can be build. Customiza-tion is fully supported in ALES. However, the implementation of the software doesnot seem to be very user-friendly and it is rather difficult for an non-IT-expert tomake use of it. ALES is one of the few implementations with a knowledge-basebased on FAO’s Framework for Land Evaluation (FAO, 1976), therefore it shouldbe considered as an alternative solution.

3. Methodology

This research is multidimensional. The aim of the work is to provide a clear phy-sical land evaluation methodology; to introduce a rather simple economic landevaluation methodology; and to integrate computer technologies.The physical evaluation consists of a model that assigns a score to every land

parcel based on its value on 17 characteristics (Section 4). To do this, initially scores


are assigned to individual land characteristics. The latter are then combined to formthree major groups of characteristics and then the total score is calculated. Thisprocedure is necessary in order for every characteristic to contribute with differentweight to the final score. This is important because the methodology is based in ahierarchical importance of land qualities and a land quality is described with one ormore than land characteristics.The data provided from a soil survey are often continuous data and therefore it is

necessary to apply a classification scheme that assigns scores to individual landcharacteristics. This scheme is based on linear interpolation functions that mapvalue intervals to score intervals. If the observed value is x and it falls into theinterval [a,b] it needs to get a score y that falls into the interval [c,d]. The formula tocalculate y is:

y ¼d � c

b� a� xþ c�

d � c

b� a� a or y ¼

c� d

a� b� xþ

ad� bc

a� b

The code in CLIPS is:

(deffunction interpolation (?observed ?a ?b ?c ?d)(+(* ? observed (/ (� ?c ?d) (� ?a ?b))) (/(�(* ?a ?d) (* ?b ?c)) (� ?a ?b))))

For land characteristics with their values already classified, such as flood hazard,the middle score of the class was given. For example, a land parcel with its floodhazard equal to F2 will get a score for flood hazard equal to 75, the middle score ofFAO class 2 (65–85). After the physical evaluation finishes and scores have beenassigned to each land parcel, the parcel is classified into one of the FAO classes (N2–S1) based on that score (Table 1). The score intervals for each class are the same forthe individual land characteristics and the total score a land parcel gets.For the economic evaluation, the expected yield is calculated based on the score of

the land parcel for cultivation, and the corresponding maximum yield. Then theexpected income for all possible types of cultivation is calculated and the cultivationthat gives the highest expected income is selected.In order to obtain a better understanding of the state of practice in the agriculture

industry, some fieldwork, mainly interviews of farmers took place. The result wasthe creation of statistics concerning the average producer value for crops in Mace-donia, and some indication of the expenses attached to growing these crops. Thelatter have been included in this paper because it was not possible for their accuracyto be verified from official sources. The local experience concerning producers priceof these products as well as the observed yields is similar with the correspondingaverages recorded by both word statistics (FAO, World Bank) and national statis-tics (Hellenic Ministry of Agriculture, Hellenic Statistics Office). However, the yieldand product quality in Macedonia is not the same as in that of Thessaly, the regionthat contributes the most in the national yield. Therefore the average values areoverestimates of the observed, thus the latter is used here.Based on the individual farmers’ experiences it can be argued that the cost of the

cultivation varies across land properties. Major factors include the rent of the land if


not owned by the farmer, the cost of the tractor’s oil and maintenance for tillage andthe cost of the seed. The most profitable way is for the farmer to use part of theprevious year’s yield as the current year’s seed, own the land and own the machinerynecessary to carry out the cultivation. Another finding of the qualitative researchwas that new seeds have been developed in order to give higher yields based on thelocal climate and soil mechanics. Therefore, it is necessary for the evaluator to beable to review the classification scheme periodically and alter the value intervals ofthe land characteristics that correspond to the appropriate suitability score.The tasks of the land evaluation are achieved through the creation of software

termed Land Evaluation using an Intelligent Geographical Information System. Theacronym of this long term is LEIGIS. The software supports three main functions:the application of the model to a set of data for an individual land parcel; theapplication of the model in a rural area that consists of many land parcels, which are

Table 1

Summary table of land characteristics and score assignment. Classes suggested for General Cultivation

Land characteristics/

classes and scores

Class 0 1 2 3 4

Score 100–98 98–85 85–65 60–40 <40

Factor A

Soil toxitities var1 % Organic >1.5 1.5–1 1–0.6 <0.6 –

var2 % Base Saturation (BS) >60 60–50 50–40 <40 –

var3 Cation Exchange

Capacity (CEC)

>18 18–12 12–6 <6 –

var4 % Carbonate (CaCO3) 0.3–10 10–30 30–50 50–80 >80

var5 % Sulfate (CaSO4) 0–2 2–4 4–10 10–15 >15

var6 Reaction (pH) 6–7.5 5.5–6 4.5–5.5 4.0–4.5 <4

8.5–7.5 9.0–8.5 9.0–9.5 >9.5

Rooting

conditions

var7 Depth >90 90–60 60–40 40–20 <20

var8a % Fine Gravel Volume 0–15 15–40 40–75 >75 –

% Coarse Gravel

Volume

0–3 3–15 15–40 40–75 >75

var9b % Stones Volume 0–3 3 3–15 15–40 40–75

var10 % Slope 0–3 (A) 3–12 (B) 12–18 (C) 18–36 (D) >36 (E)

var11 Erosion Hazard E0 E1 E2 E3 E4

var12 Soil Mechanics SiCL, SCf,

SiL, L,

C–50f

Si, SCL,

C–60f

SL, Cm,

SiCm*

LS,

SC+60m

S,

C+60m

Factor B

Excess of salts var13 Salinity (EC) 4–0 8–4 10–8 14–10 >14

var14 Sodicidy (ESP) 8–0 12–8 20–12 30–20 >30

Factor C

var15 Water level >120 60–120 40–60 20–40 <20

var16 Flood Hazard F0 F1 F2 F3 F4

var17 Drainage A B C D or E F or G

a Gravel type {Fine, coarse, Stones}.b % Gravel in soil (0,100).


represented as polygons on a map; and the presentation of the results on a map. Theexpert system was implemented separately for land suitability evaluation for generaland specific cultivation (six files).The programming environment for the software is the graphic language MS Visual

Basic (VB). VB is well documented and supported by both its vendor (MicrosoftCorporation) and many web sites of individuals and groups of developers. Techni-cally speaking, the code written in VB manages the input to and the output fromthe expert system component and the input to the map controls (GIS component).The expert system has the form of a text file for every crop which can easily be altered.At run time, the expert system shell ‘‘CLIPS’’, in the form of aMSWindows DynamicLink Library (dll), allocates memory for the fact base, loads and compiles the indivi-dual expert systems (text files), and waits for data to satisfy the conditions of rule(s)and start running the classification model. The idea is that an expert system is a setof rules with conditions and a set of facts that satisfy these conditions. The factsactivate rules that run and produce other facts and so on and so forth, until there arenot enough facts within the fact base to satisfy any rule and the process ends. Duringthat process, classification values are being produced and error messages are beingreported. Those results then are saved in the corresponding databases.GIS functionality was implemented using the MapObjects ActiveX Controls

developed by ESRI (ESRI Press, 1996). MapObjects technology is based on thearchitecture used in ESRI ArcView. MapObjects support the visualization of theresults (GIS component). The reason they have been selected in the place of othersoftware (e.g. ArcView) is for the user to save time and money, as well as for LEIGISto be a standalone tool. The GIS component allows vector maps (ESRI shape fileformat) to be plotted, data for every polygon to be explored and results to bepresented. The system reads the land attributes from the loaded map and after theclassification it stores the results in the map’s database. However it is possible tostore those results in an external database along with the id of each polygon to allowa link between the spatial and classification data using that common id. The user candefine a colour for each class of suitability, and the system then can create a choro-pleth map, ready for printing.Concerning the classification model itself; it is executed by the rules of the expert

system. Firstly, it checks the validity of the values entered for a field (e.g. pH isalways between 0 and 14); secondly it maps continuous values to distinct classes (1–5); and finally it calculates the suitability for a crop by combining the single vari-ables linearly. When data for a land characteristic are not nominal, an interpolationmethod is used to assign a score of 1–100 rather than a class (S1–N1) to that char-acteristic. For nominal or already classified values the middle score of a class wasassigned to allow numerical computing (e.g. score 99 for class S1).

4. Data, analysis and results

This research started during author’s undergraduate dissertation, which wasconducted with a joint supervision of the Department of Informatics and the


Department of Agriculture, University of Thessaloniki, Greece. The first part of thisresearch concentrated in the software development and not in the land evaluationmodelling. It focused in selecting and integrating computer technologies to addressclassification models. Data and some experience were provided from colleagues inAgriculture Department in that stage. This paper reviews existing work in this fieldand introduces findings of further research.The database used here was produced for another project and was provided under

the condition that it will be used for academic purposes only. Data have been usedunder the condition that no identification of individual properties and land perfor-mance could be possible from published information. Thus, results are presentedusing choropleth maps only. However, new data will be available as soon as theGreek Cadastre is completed which will provide boundary data for individual fields(land properties) with up to date information for non-agriculture land, forest andland use in digital form (vector GIS). Contemporary methods in soil surveys, landdevelopment and EU agricultural policy, have introduced new ways of land suit-ability evaluation and land use in general.For the physical evaluation 17 land characteristics were combined. Those char-

acteristics form three Factors that then are combined to result in a class of landcapability.The three Factors include the following land characteristics:

Factor A: Soil Mechanics and Toxicities, Slope, Erosion hazard, Rooting Condi-tions.Factor B: Excess of Salts.Factor C: Water level, Flood hazard and Drainage.

The classes of land suitability (capability) suggested by FAO are five:

Class S1: Highly Suitable.Class S2: Moderately Suitable.Class S3: Marginally Suitable.Class N1: Currently not Suitable.Class N2: Permanently not Suitable.

However, the equal interval classification (five intervals of 20% in the range 0–100) was not used in this work. Instead, for the highly suitable class, the scoreinterval 98–100 was adopted which makes this score almost impossible for any land.The detailed classes of suitability and scoring for land qualities for general cultiva-tion are given in Table 1. For an explanation of those see Sys (1985), Silleos (1990)and FAO (1984, 1985). It is beyond of the aim of this paper to analyse in detail thereasons why this classification scheme was used. However, evidence for the impor-tance of this scheme can be seen in Table 2, in which the expected land performanceand profit for each class is given. Note that for a land to be highly suitable, mini-mum input (irrigation, fertilizers, land development) is required, thus the profit ismaximized.The Boolean classification was implemented in a way that for a value that is

already classified (e.g. flood hazard=F2) the average score of the class was given


(e.g. 75) whereas for continues values an interpolation function used to assign ascore (e.g. for water level 44 cm the score is 69). Scores are available for each poly-gon along with the final classification (S1–N2). An indication of the weights used tocalculate the final score can been obtained from the calculation formulas follow:

Final Score ¼ Factor Að Þ� Factor Bð Þ

� Factor Cð Þ=1000:

Factor A ¼ soil tox þ var7þ var8þ var9þ var10þ var11þ var12ð Þ=6

soil-tox ¼ var1þ var2þ var3þ var4þ var5þ var6ð Þ=6

Factor B ¼ var13þ var14ð Þ=2

Factor C ¼ var15þ var16þ var17ð Þ=3

It is very common for the attributes to have some degree of measurement error.Further research is required to address this issue. If extreme values or no valuespresented, the model returns a zero score prompting the user to examine the data inthe corresponding polygon. The analysis of the given dataset resulted in an accuratesuitability map for general cultivation, and different suitability maps for individualcrops. It was interesting that every polygon was assigned a class for general culti-vation, but this was not the case for the five specific crops. The land requirements forbarley, wheat, maize, seed cotton and sugar beets resulted in a zero score for manypolygons. That was because the single characteristics’ classes are formed with dif-ferent value intervals compared with the general cultivation. The detailed classes ofsuitability and scoring for land qualities for specific cultivation are given in Appen-dix A at the end of the paper. The land some of the big polygons represent is notnecessarily agricultural, however their score can motivate land development.Some of the results are presented in the map series section; Figs. 1 and 2. In those

maps, urban areas represent any residential, commercial or industrial area. Waterrepresents rivers, streams, water reservoirs, moist soils and seasonally dry streams.The ‘‘No data’’ class represents lands that had zero score after the evaluation. Thatzero score was obtained because of wrong data, missing or out of classes values. The

Table 2

Expected land performance and profitability

Class FAO class Score Expected performance:

percentage of the perfect

performance

Expected profit:

percentage of profit

in perfect conditions

0 S1 98–100 >90 >75

1 S2 85–98 60–90 25–75

2 S3 65–85 35–60 <25

3 N1 40–65 <35 –

4 N2 <40 – –


reason those lands were not assigned the class N1 was because a more detailedinvestigation should be made for them.Fig. 1 shows the class assigned to each polygon in terms of its suitability for gen-

eral cultivation. The histogram of the map illustrated that there are no fields withsuitability of classes S1 and S2. This suggested that the land requires developmentand fertilizers. However, the evaluation for specific crops showed that some fieldswhere moderately suitable (S2) and more fields where suitable marginally suitable(S3) than unsuitable comparing with the suitability for general cultivation. Thesuitability scoring for maize cultivation showed that the area of interest was unsui-table for this cultivation. Thus, for an average yield to be produced, high input willbe required, which increases the cost for the producer. However, for seed cotton andsugar beets cultivation the land is capable after an average input to produce theaverage yield. Most of the land parcels were classified in N1, S3 and S2 classes interm of their suitability for the latter types of cultivation. A detail of the evaluationmap for wheat if given in Fig. 2. Most of the land parcels were either marginallysuitable or currently not suitable for cultivating barley or wheat. However, the cul-tivation of such grains does not require much input, therefore is considered as moreprofitable than the other. A certain amount of fertilizers could significantly increasethe land capability and allow more profitable cultivation.Going one step further, the maximum income per polygon using average produc-

tion per hectare and producer price was estimated. To accomplish that, informationabout the market of agricultural products (producer and retail prices) in Greece in1995 published by FAO was extracted from the on-line Statistics for Agriculture(FAO, 2001) and international market prices were used to confirm the former (TheWorld Bank, 2001). The analysis of those statistics resulted in the calculation of theaverage yield per cultivation per hectare. Furthermore, the expected income inEuros per hectare using the average producer price of the product in the same year(Table 3) was calculated. However, prices vary based on the quality of the product,whereas the yield is correlated with the land capability. The performance was esti-mated in correlation with the land evaluation suitability score using the informationprovided in Table 2. Table 4 shows equations for calculating the expected perfor-mance as a percentage of the perfect performance (interpolation).

Table 3

Average price per kilogram, yield per hectare (in kg), and expected income per hectare. Empirical values

for yield reflect the experience in Greece

Greece 1995 Producer price

(Lc/kg)

Average yield

(kg/ha)

Empirical yield

(kg/ha)

Average income

(Euros/ha)

Barley 45.470 2632.8 1000–5000 351

Wheat 63.290 2634.1 1000–5000 489

Seed Cotton 280.510 3157.1 500–4000 2599

Sugar Beets 17.734 63,290.3 60,000–90,000 3294

Maize (Corn) 44.640 10,076.2 10,000–15,000 1320

Source: FAO—Price for Maize estimated using grain prices in 1995 by The World Bank. Lc, Local cur-

rency (Drachma); kg, kilogram; ha, hectare; Euros, 1 Euro=340.75 Drachmas (fixed currency rate).


Assuming that an average yield should be the result of cultivating a land with anaverage capability, that land should have a suitability score equal to 75 (middlevalue of the middle FAO class). According to information provided in Table 4(equation for S3) the expected performance (EP) is the 47.5% of the perfect perfor-mance. [EP=1.25*Score�46.25)(Score=75) EP=47.5]. Thus, the perfect per-formance should be the 1/0.475=210.5% of the expected performance which is theaverage in this case. Comparing this to the empirical findings for maximum yields(some extremes and not the average of the maximum reported) based the farmers’reports it has been decided to adjust this factor to 201.5%. After multiplying aver-age performance given in Table 3 with 2.015, values for the perfect performance aregiven in Table 5. An immediate observation is that the expected maximum perfor-mance is probably overestimated compared with the empirical range of expectedyield. However, the linear multiplication of the values for the five crops does notchange their ranking order based on the expected income. Since the score for mostof the lands is N1 or S2, those results can be accepted.The objective of this step was to provide a methodology for the modelling of the

maximization of the expected income in order to suggest a land use breakdown forthe area. To do this it was necessary to calculate the expected performance (% ofmaximum) based on the score of the land for each of the five possible cultivations;multiply that with the appropriate maximum expected yield (to obtain the expectedyield); multiply the result of the latter with the average producer price of the crop (toobtain the expected income); and finally select the maximum value out of the fivecalculated. An example of the methodology described above is provided in Table 6.

Table 4

Equations to calculate expected land performance from scores

Class FAO Class Score Expected performance (EP): percentage of the perfect performance

0 S1 98–100 EP=0.2 * Score+80 90–100

1 S2 85–98 EP=2.3 * Score �135.5 60–90

2 S3 65–85 EP=1.25 * Score � 46.25 35–60

3 N1 40–65 EP=1.4 * Score �56 0–35

4 N2 <40 0 0

Table 5

Average price per kilogram, yield per hectare (in kg), and expected income per hectare. Empirical values

for yield reflect the experience in Greece

Greece 1995 Expected maximum

yield (kg/ha)

Producer price

(Euros/t)

Maximum income

(Euros/ha)

Barley 5305.1 133.441 707.92

Wheat 5307.7 185.737 985.84

Seed Cotton 6361.6 280.510 1784.49

Sugar Beets 127,530.0 52.044 6637.17

Corn 20,303.5 131.005 2659.86


A random polygon with scores for the five different crops was selected and theresults of the three phases of calculation (Expected performance; Expected Yield;Expected Income) are shown in the corresponding columns in Table 6. The sug-gested cultivation was that of Sugar Beets. It seems that this was the dominant cul-tivation since the production per hectare is very high even though the price of theproduct is the lowest. This was observed in the whole area of interest (Fig. 3) andmotivated further investigation.The economic evaluation should always take into account local condition of the

market and possible production restrictions. A study of the local news and reportsby the Hellenic Ministry of Agriculture (2001), and newspapers made clear that theproduction of sugar beets is limited (European Agreements) and most of it has longterm contracts with the National Sugar Industry. That makes it impossible for thearea to be used only for sugar beets cultivation. Thus the suggested method needs tobe enhanced with more economical attributes in order to help in decision making forrural planning. In fact the production is limited under the European AgricultureAgreement for all products. However, for crops such as grains, there is more flexi-bility since the product can be used as livestock food and partly not be registered asa profitable investment. Because of that, a second evaluation took place, this timeonly including the cultivation of barley, wheat and maize. The results were accep-table and are presented in Fig. 4.Another issue that has to be examined is the affect of the contiguity of the fields.

Zones with homogeneous cultivation are more likely to give higher profit thanheterogeneous cultivation. Evidence for this argument is both the cost of cultivationand the environmental effect of contiguity. For example, the cultivation of maizerequires more water and takes place in different time periods than that of barley andthat of wheat. Dry fields next to a field of maize may reduce the yield of the latter, ormay generate the need for more irrigation to keep the soil’s moistness at the requiredlevel. Homogeneous areas allow the use of more cost-effective machinery andrequire less transportation. Similar studies have been conducted in order to designhomogeneous zones using socio-economic data (Openshaw & Alvanides, 1999).However, the concept of designing zones with specific properties for the need ofthe analysis can be taken further as demonstrated by Alvanides, Openshaw, andMacGill (2001). For example, it is possible to group many fields with specific soil

Table 6

Example of a land evaluation in terms of maximum expected income

Product Score FAO class Expected performance

(% of maximum)

Expected yield

(kg/ha)

Expected income

(Euros/ha)

Barley 56 N1 22.4 1188.3 158.6

Wheat 52 N1 16.8 891.7 165.6

Seed Cotton 64 N1 33.6 2137.5 599.6

Sugar Beets 59 N1 26.6 33,923.0 1765.5

Corn 63 N1 32.2 6537.7 856.5

Max (158.6, 165.6, 599.6, 1,765.5, 856.5)=1765.5 thus, suggested cultivation is ‘‘Sugar Beets’’


and size characteristics into larger crop zones in order to minimise the cultivationcost and help in maximising the land’s profitability. Further research is required toaddress this issue since it is beyond the scope of this work.

5. Conclusions

The research has resulted in stand-alone software called LEIGIS for supportingrural planners with a first view of the land suitability for cultivation of certaincrops. This work examined soil and environmental characteristics to produce aphysical evaluation of land capabilities and used those results to provide an eco-nomical evaluation of the land for different types of agriculture. The latter wasbased in the selection of the crop that generated maximum income.Scientists have been heavily criticized for the use of expert systems. This work

supports the argument that the use of expert systems is a faster and more flexiblesolution in addressing classification problems compared with procedural sequentialalgorithms and the corresponding software. Another advantage of expert systems isthat they produce classifications based on given knowledge rather than by makingassumptions or by using heuristic algorithms.A review of methods of physical evaluation concludes that the fuzzy approach

compared with the Boolean increases the complexity of the modelling withoutmaking the results more efficient. It is necessary to test the accuracy of soil data usedby conducting soil surveys or sampling. The estimation of land capability based inremotely sensed images is more likely to give inaccurate results. Finally, the scale ofthe area should be the level of individual fields. The technology today can handlelarge datasets but specific soil data such as those used in this research are not avail-able for that scale and for all areas of a country.The physical and economical evaluation led to the conclusion that it is necessary to

conduct both to aid rural planning. That was because both phases are necessaryto minimize the risk of decisions. The physical evaluation suggested here used aclassification method that set very specific requirements for land to be classified ashighly suitable. However, those parcels of land which are highly suitable requireminimum input during the cultivation process. The economic evaluation was ratherbasic, but is still important. It tried to provide an indication of the role economicfactors may play in altering land-use planning in order to maximize profitability.The latter is the first aim of the producers who do not necessarily care about theprotection of the land.This work showed that further research should be conducted to address issues

such as the contiguity of the lands, the enhancement of the physical evaluation byinvolving more land characteristics (such as climate) and the development of acomplete economic evaluation. The latter should include socio-economic variablessuch as available agriculture labour force, and spatial variables such as accessibility,distance from urban areas (travel cost of workers) and industrial areas (transporta-tion cost of the products). It should look at the maximization of profit for the pro-ducer, along with the preservation of lands at risk of permanent unsuitability.


Acknowledgements

To all my colleagues in the who helped me with their comments during this work.To my father who helped with fieldwork collecting information about the state ofthe market in agriculture in Greece as well as the empirical yield of crops presentedin Table 3 within Section 4. To Professor Peter Fisher for his encouragement, valu-able comments and the update with some previous publications relative to thispaper. To Professor Stewart Fotheringham with his help with the language of thepaper. The dataset used in this work is the copyright of the Laboratory of RemoteSensing and Geographical Information Systems, Department of Agriculture, Uni-versity of Thessaloniki, Greece.

Appendix A. Summary tables of land characteristics and score assignment for

barley, sugar beets, seed cotton, maize and wheat.

Table A1 Summary table of land characteristics and score assignment. Classessuggested for Barley Cultivation


classes and scores

Class 0 1 2 3 4

Score 100–98 98–85 85–65 60–40 <40

Factor A

Soil

toxicities

var1 % Organic >0.5 0.4–0.5 <0.4 – –

var2 % Base Saturation

(BS)

>80 80–50 50–35 <35 –


Capacity

(CEC)

>16 16–8 <8 – –

var4 % Carbonate (CaCO3) 3–20 0–3 or 30–40 40–60 >60

20–30

var5 % Sulfate (CaSO4) 0–3 3–6 6–12 12–20 >20

var6 Reaction (pH) 5.3–6.7 5.1–5.3 4.9–5.1 4.7–4.9 <4.7

Rooting

conditions

var7 Depth >90 90–50 50–20 20–10 <10


% Coarse Gravel

Volume

0–3 3–15 15–40 40–75 >75

var9b % Stones Volume 0–3 3 3–15 15–40 >40

var10 % Slope 0–3 (A) or

3–12 (B)

12–18 (C) 18–36 (D) >36 (E) –

var11 Erosion Hazard E0, E1 E2 E3 E4 –

var12 Soil Mechanics SiL, L,

SCL, CL,

SiCL, Si

SC-60,

C-60,

SiC, C

SL, C+60,

SiC, SC+60

LS, Cm+60,

SCm+60

S

Factor B var13 Salinity (EC) 0–4 4–8 8–12 12–16 >16

var14 Sodicidy (ESP) 0–15 15–25 25–35 35–45 >45



classes and scores

Class 0 1 2 3 4

Score 100–98 98–85 85–65 60–40 <40

Factor C Var15 Water level >90 50–90 20–50 10–20 <10

Var16 Flood Hazard F0, F1 F2 F3 F4 –

Var17 Drainage A or B C D E F or G

Table A2 Summary table of land characteristics and score assignment. Classessuggested for Sugar Beets Cultivation


classes and scores

Class 0 1 2 3 4

Score 100–98 98–85 85–65 60–40 <40

Factor A

Soil toxicities var1 % Organic >2.5 1.2–2.5 0.6–1.2 <0.4 –



Capacity (CEC)

>24 24–16 16–8 <8 –

var4 % Carbonate (CaCO3) 3–20 0–3 or 30–40 40–60 >60

20–30

var5 % Sulfate (CaSO4) 0–2 2–4 4–10 10–15 >15

var6 Reaction (pH) 5.8–7.5 5.2–5.8 4.8–5.2 4.2–4.8 <4.2

Rooting

conditions

var7 Depth >90 90–50 50–30 30–15 <15


% Coarse Gravel

Volume

0–3 3–15 15–40 40–75 >75



3–12 (B)

12–18 (C) 18–36 (D) >36 (E) –


var12 Soil Mechanics L, SCL,

SiC, CL,

SiCL, SC

Si, SL,

SiL, C–60

C+60,

SC+60

LS, Cm+60,

SCm+60

S

Factor B

var13 Salinity (EC) 0–8 8–10 10–14 14–18 >18

var14 Sodicidy (ESP) 0–15 15–25 25–35 35–45 >45

(Table continued on next page)

a Gravel type {Fine, Coarse, Stones}.b % Gravel in soil (0,100).



classes and scores

Class 0 1 2 3 4

Score 100–98 98–85 85–65 60–40 <40

Factor C

var15 Water level >120 90–120 60–90 30–60 <30

var16 Flood Hazard F0, F1 F2 F3 F4 –

var17 Drainage A or B C D or E F or G

Table A3 Summary table of land characteristics and score assignment. Classessuggested for Seed Cotton Cultivation


classes and scores

Class 0 1 2 3 4

Score 100–98 98–85 85–65 60–40 <40

Factor A




Capacity (CEC)

>25 25–16 16–10 10–6 <6

var4 % Carbonate (CaCO3) 1–10 0–1 or 10–20 20–30 30–40 >40

var5 % Sulfate (CaSO4) 0–2 2–4 4–10 10–15 >15

var6 Reaction (pH) 5.0–7.5 4.5–5.0 4.0–4.5 3.5–4.0 <3.5

Rooting

conditions

var7 Depth >90 90–50 50–20 20–10 <10


var9b % Coarse Gravel

Volume

0–3 3–15 15–40 40–75 >75

% Stones Volume 0–3 3 3–15 15–40 >40

var10 % Slope 0–3 (A)

or 3–12 (B)

12–18 (C) 18–36 (D) >36 (E) –


var12 Soil Mechanics SiL, L,

SCL, CL,

SiCL

SC, CL,

SiCf, Si

Cm+60,

SiCm, LS

Factor B

var13 Salinity (EC) 0–8 8–10 10–12 12–16 >16

var14 Sodicidy (ESP) 0–15 15–20 20–30 30–40 >40

Factor C

var15 Water level >90 50–90 20–50 10–20 <10


var17 Drainage A or B C D or E F G


a Gravel type {Fine, Coarse, Stones}.b % Gravel in soil (0,100]).


Table A4 Summary table of land characteristics and score assignment. Classessuggested for Maize Cultivation


classes and scores

Class 0 1 2 3 4

Score 100–98 98–85 85–65 60–40 <40

Factor A

Soil toxicities var1 % Organic >1.5 0.8–1.5 <0.8 – –

var2 % Base Saturation (BS) >50 50–35 <35 – –


Capacity (CEC)

>24 24–16 16–8 <8 –

var4 % Carbonate (CaCO3) 0–6 6–15 15–25 25–40 >40

var5 % Sulfate (CaSO4) 0–2 2–4 4–10 10–15 >15

var6 Reaction (pH) 5.4–6.8 5.2–5.4 5.0–5.2 <5.0 –

Rooting

conditions

var7 Depth >90 90–60 60–40 40–20 <20


% Coarse Gravel

Volume

0–3 3–15 15–40 40–75 >75


var10 % Slope 0–3 (A)

or 3–12 (B)

12–18 (C) 18–36 (D) >36 (E) –


var12 Soil Mechanics SiC,

SCL, CL,

SiCL, SCf, Si

SL, L,

C-60, SiL

Cm+60,

SCm+60, LS

– –

Factor B var13 Salinity (EC) 0–2 2–4 4–6 6–8 >8

var14 Sodicidy (ESP) 0–8 8–15 15–20 20–25 >25

Factor C var15 Water level >90 60–90 40–60 20–40 <20


var17 Drainage A or B or C D E F G

Table A5 Summary table of land characteristics and score assignment. Classes sug-gested for Wheat Cultivation


classes and scores

Class 0 1 2 3 4

Score 100–98 98–85 85–65 60–40 <40

Factor A




Capacity (CEC)

>16 8–16 <8 – –


(Table continued on next page)



classes and scores

Class 0 1 2 3 4

Score 100–98 98–85 85–65 60–40 <40

var4 % Carbonate (CaCO3) 3–20 0–3 or

20–30

30–50 50–70 >70

var5 % Sulfate (CaSO4) 0–3 3–6 6–12 12–20 >20

var6 Reaction (pH) 5.4–7.0 5.2–5.4 5.0–5.2 4.8–5.0 <4.8

Rooting

conditions

var7 Depth >90 90–50 50–30 30–20 <20


% Coarse Gravel

Volume

0–3 3–15 15–40 40–75 >75



3–12 (B)

12–18 (C) 18–36 (D) >36 (E) –


var12 Soil Mechanics C, SiC,

SC–60

SiCL, CL,

SCL

L, SiL SL, C,

SCm+60

S, LS

Factor B

var13 Salinity (EC) 0–4 4–8 8–12 12–16 >16

var14 Sodicidy (ESP) 0–15 15–25 25–35 35–45 >45

Factor C

var15 Water level >90 60–90 40–60 20–40 <20


var17 Drainage A or B C D E or F G

References

Ahamed, T. R. N., Rao, K. J., & Murthy, J. S. R. (2000). GIS-based fuzzy membership model for crop-

land suitability analysis. Agricultural Systems, 63, 75–95.

Alvanides, S., Openshaw, S., & Macgill, J. (2001). Zone design as a spatial analysis tool. In N. J. Tate, &

P. M. Atkinson (Eds.),Modelling Scale in Geographical Information Service. New York: JohnWiley & Sons.

Bojorquez-Tapia, L. A., Diaz-Mondragon, S., & Ezcurra, E. (2001). GIS-based approach for participa-

tory decision making and land suitability assessment. International Journal of Geographical Information

Science, 15, 129–151.

Burrough, P. A. (1989). Fuzzy mathematical methods for soil survey and land evaluation. Journal of Soil

Science, 40, 477–492.

Chuvieco, E. (1993). Integration of linear programming and GIS for land-use modelling. International

Journal of Geographical Information Systems, 7, 71–83.

Davidson, D. A., Theocharopoulos, S. P., & Blocksma, R. J. (1994). A land evaluation project in Greece

using GIS and based on Boolean and fuzzy set methodologies. International Journal of Geographical

Information Systems, 8, 369–384.

Dent, D., & Young, A. (1981). Soil survey and land evaluation. London: George Allen & Unwin.

ESRI Press (1996). Building applications with MapObjects. Redlands, Calif.: Environmental Systems

Research Institute Press.



EUROCONSULT. (1989). Agricultural Compendium for rural development in the tropics and subtropics.

Amsterdam: Elsevier.

Food and Agriculture Organization of the United Nations (FAO) (1976). A framework for land evalua-

tion. Soils bulletin, No.. 32. Rome: FAO.

Food and Agriculture Organization of the United Nations (FAO) (1984). Guidelines for land evaluation for

rainfed agriculture. Soils bulletin, No. 52. Rome: FAO.

Food and Agriculture Organization of the United Nations (FAO) (1985). Guidelines: land evaluation for

irrigated agriculture. Soils Bulletin, No. 55. Rome: FAO.

Food and Agriculture Organization of the United Nations (FAO) (2001), FAOSTAT agriculture Data.

Available: http://apps.fao.org/page/collections?subset=agriculture, last accessed 10 April 2001.

Giarratano, C. J. (1993). CLIPS: C language integrated production system, CLIPS user’s guide-version 6.0.

Athens, Ga.: COSMIC, University of Georgia.

Giarratano, C. J., & Riley, G. (1998). Expert systems: principles and programming (3rd ed.). Boston: PWS

Publishing Company.

Hall, G. B., Wang, F., & Subaryono (1992). Comparison of Boolean and fuzzy classification methods in

land suitability analysis by using Geographical Information Systems. Environment and Planning A, 24,

497–516.

Hellenic Ministry of Agriculture (2001). Available: http://www.minagric.gr, last accessed 30 July 2001.

Jackson, P. (1999). Introduction to expert systems. New York: Addison-Wesley.

Joerin, F., Theriault, M., & Musy, A. (2001). Using GIS and outranking multicriteria analysis for land-

use suitability assessment. International Journal of Geographical Information Science, 15, 151–174.

Keechoo, C., & Tim, T. J. (1996). A hybrid travel demand model with GIS and expert systems. Compu-

ters, Environment and Urban Systems, 20, 247–259.

Kirkby, S. D. (1996). Integrating a GIS with an expert system to identify and manage dryland saliniza-

tion. Applied Geography, 16, 289–303.

Luckman, P. G., Jessen, M. R., & Gibb, R. G. (1990). Use of expert systems and GIS in land evaluation.

New Zealand Geographer, 46, 15–20.

McClean, C. J., Cherrill, A. J., & Fuller, R. M. (1995). The integration of three land classifications within

a decision support system for land use planning. In P. Fisher (Ed.), Innovations in GIS 2 (pp. 137–149).

London: Taylor & Francis.

Moller-Jensen, L. (1997). Classification of urban land cover based on expert systems, object models and

texture. Computers, Environment and Urban Systems, 21, 291–302.

National Agricultural Research Foundation (2001). Available: http://www.ethiage.gr, last accessed 30

July 2001.

National Statistical Service of Greece (2001). Available: http://www.statistics.gr/en/data/tables/

table71.htm, last accessed 30 July 2001.

Openshaw, S., & Alvanides, S. (1999). Applying geocomputation to the analysis of spatial distributions. In

P. A. Longley, M. F. Goodchild, D. J. Maguire, & D.W. Rhind (Eds.),Geographical information systems

(Vol. 1), principles and technical issues (2nd ed.) (pp. 267–282). New York: John Wiley & Sons, Inc.

Pereira, J. M. C., & Duckstein, L. (1993). A multiple criteria decision-making approach to GIS-based

land suitability evaluation. International Journal of Geographical Information Systems, 7, 407–424.

Plant, R. E., & Vayssieres, M. P. (2000). Combining expert system and GIS technology to implement a

state transition model of oak woodlands. Computers and Electronics in Agriculture, 27, 71–93.

Rossiter, R. G. (1990). ALES: a framework for land evaluation using a microcomputer. Soil Use and

Management, 6, 7–20.

Rossiter, R. G. (1995). Economic land evaluation: why and how. Soil Use and Management, 11, 132–140.

Software Technology Branch, Lyndon B. Johnson Space Center (1993a). Clips reference manual: basic

programming guide (Vol. I). CLIPS Version 6.0. Houston, Tex.: The Artificial Intelligence Section.

Software Technology Branch, Lyndon B. Johnson Space Center (1993b). CLIPS reference manual:

advanced programming guide (Vol. II). CLIPS Version 6.0. Houston, Tex.: The Artificial Intelligence

Section.

Software Technology Branch, Lyndon B. Johnson Space Center (1993c). CLIPS reference manual: CLIPS

interfaces guide (Vol. III). CLIPS Version 6.0. Houston, Tex.: The Artificial Intelligence Section.


Sumner, M. E., & Malcolm, E. (1999). Handbook of soil science. Boca Raton: CRC Press.

Silleos, N. (1990).Mapping and evaluation of agricultural soils and lands. Thessaloniki: Giachoudi & Gia-

pouli.

Sys, C. (1985). Land evaluation. Ghent, Belgium: State University of Ghent.

The World Bank (2001). Pink sheet—January 1998: commodity price data. Available: http://www.world-

bank.org/html/ieccp/pkjan98.html, last accessed 10 April 2001. Readers should contact The World

Bank Pink Sheets Editor at http://www.worldbank.org/prospects/pinksheets/

Thomas, E. C., Gardner, E. A., Littleboy, M., & Shields, P. (1995). The cropping systems model PER-

FECT as a quantitative tool in land evaluation: an example for wheat cropping in the Maranoa area of

Queensland. Australian Journal of Soil Research, 33, 535–554.

Van Lanen, H. A. J., Hack-ten Broeke, M. J. D., Bouma, J., & De Groot, W. J. M. (1992). A mixed

qualitative/quantitative physical land evaluation methodology. Geoderma, 55, 37–54.

Web references for CLIPS

Athena Information Services (2000). Available: http://ourworld.compuserve.com/homepages/olsont/

HOMEPAGE.htm, last accessed 31 July 2001.

FuzzyCLIPS (2001). Available: http://ai.iit.nrc.ca/IR_public/fuzzy/fuzzyClips/fuzzyCLIPSIndex.html,

last accessed 31 July 2001.

Intelligent Software Professionals (2001). Available: http://www.isphouston.com, last accessed 31 July

2001.

Mark Tomlinson—CLIPS Dynamic Link Library / ActiveX (2001). Available: http://ourworld.compu-

serve.com/homepages/marktoml/clipstuf.htm, last accessed 31 July 2001.

NASA CLIPS Rule Based Language (2001). Available: http://www.siliconvalleyone.com/clips.htm, last

accessed 31 July 2001.

CLIPS—A Tool for Building Expert Systems (1999). Available: http://www.ghg.net/clips/CLIPS.html,

last accessed 31 July 2001.

CLIPS Download files (2000). Available: http://www.ghgcorp.com/clips/download, last accessed 31 July

2001.


Expert Systems and GIS an Application of Land Suitability Evaluation

Documents

Transcript of Expert Systems and GIS an Application of Land Suitability Evaluation