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COMBINING IMAGE SEGMENTATION AND MULTISPECTRALCLASSIFICATION FOR GENERATING LAND-USE INFORMATION:

A CASE STUDY OF MAROS AREA, SOUTH SULAWESI, INDONESIA

Projo Danoedoro1, Jes Sammut2, Wirastuti Widyatmanti1, and Nur M. Farda1

1 Department of Geographical Information Science and Regional Development, Faculty of Geography,Gadjah Mada University Yogyakarta, Indonesia

2 School of Biological, Earth and Environmental Sciences (BEES), The University of New South Wales,Australia

Keywords: remote sensing, multispectral classification, image segmentation, land-use, VLUIS

This research developed methods for generating land-use information which is relevant to abroader study, i.e. land capability assessment and classification for sustainable development ofbrackish water aquaculture systems in Indonesia. The broader study requires land-useinformation covering coastal areas with various utilisations, e.g. coastal fishponds, rice fields,rural settlement, mangrove-based conservation, and urban uses. In order to meet thatrequirement, the land-use information needs to be delivered in terms of spatial, ecological,and socio-economic dimensions. To do so, a versatile land-use information system (VLUIS)which has been developed for local planning in Indonesia was used as a reference. In theVLUIS, the land-use information is broken down into five layers representing spectral, spatial,temporal, ecological, and socio-economic dimensions. As the study area, a small portions ofLandsat ETM+ image covering Maros, South Sulawesi, Indonesia was chosen. In this study, acombination of multispectral classification and object-based image segmentation was applied.The multispectral classification was carried out to generate spectral-related land-cover types,while the object-based image segmentation was run to derive spatial dimension of land-use. Aterrain unit map obtained from visual interpretation was used to support the integration of thespectral-related land-cover and the spatial dimension maps. A knowledge-based classificationincorporating spectral, spatial and terrain characteristics of the study area was carried out. Bythis method, new spatial information in terms of maps representing ecological and socio-economic dimension of land-use were generated using different rules. This study showed thata single source of imagery could be processed in various ways to derive different types ofspatial information, and all information could then be integrated to generate versatile land-useinformation relevant to particular planning tasks. The multispectral classification was found tobe accurate enough to provide spectral-related land-cover types. It was also found, however,that the object-based image segmentation was still less accurate to classify objects withrespect to their shape, size, and pattern simultaneously, particularly in comparison with thevisual interpretation. Nevertheless, in the near future, improved methods of this approach maybe expected to provide more useful and accurate information.

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1. INTRODUCTION: BACKGROUND AND PROBLEM FORMULATION

Background

Land-use information is a key element to land assessment and spatial planning. Duringthe past two decades, land-use surveys have mostly been carried out using remote sensingtechnology, where the use of digital classification is continuously increasing. In Indonesia andmany other developing countries, remote sensing is used for rapid mapping and assessment ofpoorly accessible areas and rapidly changing land-cover and land-use phenomena. However, itwas found that most spatial data related to land-use in Indonesia could not fully support thelocal planning processes due to the lack of compatibility, relevance and accuracy (Danoedoro,2006). In addition, remotely sensed image digital classification was rarely used in systematicland-cover/land-use mapping for land resources management. This problem was due to cloudcover on one hand, and difficulties to transform spectral-related land-cover information intomore relevant land-use categories on the other hand.

Image processing techniques normally derive land-cover instead of land-useinformation, because most algorithms were developed to classify objects using their spectralresponses (pixel values) as a single criterion. In contrast to land-cover, land-use is a moreabstract concept. Similar land-cover classes can express different land-use categories, and viceversa. In order to extract land-use information from digital imagery, analysts usuallyincorporated other spatial data including soil, landform, and slope maps. Recently, efforts inextracting spatial information instead of classifying individual pixels have been intensivelycarried out with the rise of object-based image segmentation. The image segmentation tries tocluster image features into ‘segments’ based on their similarity in colour, texture, shape, andother spatial consideration.

Apart from the previously mentioned circumstances, problems in extracting land-useinformation from satellite imagery also relate to the use of classification schemes. Manyclassification schemes tend to mix land cover and land-use categories (Danoedoro et al., 2004).In Indonesia, the existing classification schemes were not designed for digital classification.Therefore, when those classification schemes were used as references for digital classificationof land-cover/land-use, accurate results could not be achieved. In response to that problem, theuse of classification scheme that accommodates dimension of land-use and purposivelydesigned for digital classification is recommended.

Problem Formulation

From the aforementioned perspective, a project funded by the ACIAR (AustralianCentre for International Agricultural Research) found a relevant problem. The ACIAR projectis carried out in South Sulawesi, Indonesia, in the context of land capability assessment andclassification for sustainable development of brackish water aquaculture systems in Indonesia.One of the project’s activities is developing mapping methods at various scales, i.e. 1:100,000,1:50,000, and 1:10,000 to support the land capability assessment. The land-use informationrequirements for those scales are different, where the larger scales need more detailedcategorisations. Instead of using more established visual interpretation of the hardcopies of

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satellite imagery, the project also tries to develop more efficient techniques for generating land-use information, which is relevant to its main objectives.

Since the mapping activity at 1:100,000 scale must cover the entire coastal area ofSouth Sulawesi, a relatively rapid mapping method should be developed. The developedmethod should also be capable of being carried out in other parts of Indonesia with differentlandscape characteristics. It was already realised that the standard multispectral classificationprocedure could not achieve the expected result due to its underlying assumptions, whichneglect the spatial and ecological context of the phenomena of interest. Therefore, anintegration of spectral-based and spatio-ecological context approaches need to be elaborated.

2. STUDY OBJECTIVE

The main objective of this study was to develop methods for generating land-useinformation from multispectral satellite imagery by integrating multispectral classification andobject-based image segmentation. The method will be evaluated with respect to its potential tobe used for mapping the entire area of ACIAR Project.

3. PREVIOUS WORKS

Several authors have paid serious attention to developing land-use information as aderivate of land-cover interpretations. The works of Kannegieter (1988), Van Gils et al (1990),and Cihlar and Jansen (2001) are useful examples. Derivation of land-use information usingremotely sensed data could be carried out using several approaches (Van Gils et al., 1990),namely (a) photo-guided approach, by which the image is only used as the guidance duringfield observation and measurement so that there is no interpretation or image analysis carriedout; (b) photo-key approach, by which the land-use units are delineated mainly based on thephotomorphic appearances on the hard copy images; and (c) land(scape)-ecological approach,by which the land-use phenomenon is considered as a unity with the terrain-related features, sothat the delineation of land-use units are carried out by taking into account the terrain or landcharacteristics as a whole.

According to Skidmore (1997), the land(scape)-ecological approach is underlain by aholistic approach, which is more suitable to apply with remote sensing –particularly with theair photo interpretation (API). In contrast to this approach, Skidmore stated that a reductionistapproach is applied using GIS. The main difference between the two is that the derived ‘landunit’ or spatial land-use information unit obtained from API is delineated directly on the image,while the land-use information obtained from the GIS overlay comes up as a consequence ofknowledge-driven map analysis process.

The three approaches described by van Gils et al. (1990) are used with hardcopy imagessuch as aerial photographs and printed satellite images, so that different methods are requiredwhen the source of data is in digital format. Schowengerdt (1997), Jensen (2004), and Mather(2004) have discussed the methods of information extraction using digital remotely sensedimages in detail. Liu et al. (2002) suggested the combination of several image classificationmethods, i.e. maximum likelihood, expert system, and neural network classifiers for improving

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land-cover map accuracy. It should be noted, however, that the methods developed forautomatic information extraction are concentrated on land-cover information.

The most established approach in digital classification of remotely sensed data is multi-spectral analyses, by which the land-cover/land-use features of interest are analysed andclassified based on their statistical signatures in several spectral bands at once. With thisapproach, several methods or algorithms have regularly been used, e.g. maximum likelihood,nearest neighbour, parallelepiped, and minimum distance to mean (Jensen, 2004, Mather,2004).

Object-based classification approach, on the other hand, has been developed to improvethe previously mentioned multi-spectral classification based on the assumption that thosealgorithms work on per-pixel basis. The object-based classification could be done using per-field classification (Aplin et al., 2001) or image segmentation (Baatz et al., 2004). Stuckens etal. (2000) explained that image segmentation could be carried out using regiongrowing/merging, boundary detection, or combination of both, e.g. ECHO (Extraction andClassification of Homogeneous Objects) algorithm (Kettig and Ladgrebe, 1976), HWSO(Hierarchical Stepwise Optimisation) algorithm (Bealieau and Goldberg, 1989) and MORM(Mutually Optimum Region Merging) algorithm (Lobo, 1997). The boundary detectionassumes that two adjacent pixels with great difference belong to different segments, andconsequently an edge or boundary can be drawn between them. The edge pixels can be mergedwith most similar segments. This procedure can be applied using gradient filter (based on localvariance), Sobel gradient, or more sophisticated filters. Meanwhile, the hybrid segmentationmakes use of principles developed in region growing/merging and boundary detection, eventhough it should be seen as a framework rather than as ready-to-use algorithms.

Image segmentation can also be done using mean shift algorithm. According to Farda(2008), the mean shift procedure-based image segmentation is a straightforward extension ofthe discontinuity preserving smoothing algorithm. Each pixel is associated with a significantmode of the joint domain density located in its neighborhood, after nearby modes were prunedas in the generic feature space analysis technique. Let xi and zi, i = 1, …, n, be the d-dimensional input and filtered image pixels in the joint spatial-range domain and Li the label ofthe ith pixel in the segmented image (Comaniciu and Meer, 2002). The general procedure forrunning the mean shift segmentation can be described as follows: (1) Run the mean shiftprocedure filtering for the image and store all the information about d-dimensionalconvergence point in zi, i.e, zi = yi,c. (2) Delineate in the joint domain the clusters {Cp}p=1…m bygrouping together all zi which are closer than hs in the spatial domain and hr in the rangedomain, i.e., concatenate the basins of attraction of the corresponding convergence points. (3)For each i = 1, …, n, assign Li = {p | zi Cp}. It should be noted that there is an option toeliminate spatial regions containing less then M pixels.

4. STUDY AREA

In order to test the method, a small portion of the Landsat ETM+ imagery covering29,18 x 29.18 km2 was chosen. This small area covers the middle and northern parts of Maros

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Regency, South Sulawesi province. In this sub-scene, sea water, coastal aquaculture, and ricefields look prominent. In the hinterland, hilly and mountainous terrain with natural vegetationinterleave with valleys occupied for annual crops planting. The extensive coastal fish andshrimp ponds in South Sulawesi are now facing problem due to their relatively low productivityand less sustainable. The existence of acid sulphate soil contributes to this problem. Since theextensive brackish water aquaculture plays an important role in low- and moderate-incomefarmers’ economy, a sustainable development framework is needed. A land-use map depictingthe spatial distribution of various land-use functions can be utilised as a starting point tounderstand the configuration of all land-use categories and their inter-relationship. The use ofMaros study area did not directly relate to the acid sulphate soil problem. Instead, it was useddemonstrate the advantages and limitations of using various image processing techniques togenerate land-use information.

Figure 1. The Study area shown on the Landsat ETM+

5. MATERIALS AND METHODS

5.1. Materials

A small portion of Landsat Enhanced Thematic Mapper Plus (ETM+) scene, path 116row 063 recorded on 21 August 2002 was used in this study. Six reflective bands ranging fromblue up to far infrared were used, and geometric correction in terms of third ordertransformation was applied. In addition to this image dataset, a topographic map sheet at scaleof 1:50,000 and a geological map at scale of 1:250,000 were also utilised. Image processingsoftwares, i.e. ENVI 4.3 and ILWIS 3.4 Open were used for geometric correction, multispectralclassification, on-screen digitisation and raster-based GIS processing. An object-based image

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7.5 km0

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segmentation developed by Farda (2008), based on the open source programs calledOpenJump, was also involved for classifying the image into segments.

5.2. Methods

In this study, a versatile land-use information system (VLUIS) which has beendeveloped for local planning in Indonesia (Danoedoro, 2006) was used as a reference. In theVLUIS, the land-use information is broken down into five layers representing spectral, spatial,temporal, ecological, and socio-economic dimensions. Each dimension is presented in terms ofthematic map. According to the VLUIS, the spectral-related cover dimension can be generatedusing multispectral approaches, e.g. maximum likelihood algorithm, while the spatialdimension can be extracted using visual interpretation or object oriented image segmentation.The temporal, ecological, and socio-economic dimensions can be derived by integrating thespectral-related cover and spatial dimensions with the spatial ancillary data, e.g. soils,landforms, and slope steepness in a GIS environment. This study only concentrated on threedimensions, i.e. spectral-related cover, ecological, and socio-economic function, since theywere considered more relevant to the broader research. Table 1 shows the categories undereach land-use dimension used in this study.

Table 1. Versatile Land-use Categories (adapted from Danoedoro, 2006) used in this study

Code Socio-economic dimension of Land-useclasses

Code Ecological dimension of land-use classes

F110 Reservoir E110 Deep sea water environment

F122 Brackish water aquaculture E122 Littoral aquatic environment with non-coralseafloor

F150 Multipurpose water use E142 Man-made lake and pondF150a Sea E221 Non-vegetated tidal mudflatF151 Ponds, for other uses E222 Mangrove formationF211 Conservation forest E230 Riparian environmentF211a Conservation coastal forest E311 Lowland evergreen vegetationF320 Tree crop plantation E314 Lowland herbaceous vegetation and open

fieldsF340 Rice fields E320 Alluvial land environmentF350 Dry land cultivation E411 Lower montane rainforestF361 Forest garden and homestead garden E412 Lower montane shrub and herbaceous

vegetationF362 Mix garden E413 Lower montane open fieldsF412 Commercial areas E431 Forest and woodland on non-montane steeper

landsF413 Industrial areas E432 Shrubs and herbaceous vegetation on non-

montane steeper landsF413a Industrial and mining area E433 Grassland and open fields on non-montane

steeper landsF414 Mix of urban settlement and commercial

areasE512 Urban infrastructure with limited paved surface

F421 Rural settlement E520 Urban settlementE540 Lowland rural settlementE550 Montane rural settlementE1423 Man-made brackish water ponds

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In order to generate the land-cover dimension, a standard multispectral classificationusing maximum likelihood algorithm was carried out. Parallel to this processing, a terrain unitclassification was done using visual interpretation/on-screen digitisation. In this visualinterpretation process, more detailed categorisation relevant to the spatial distribution of theland-use phenomena was applied with the aid of the geological map at a coarser scale. Anobject-based image segmentation using mean-shift algorithm was run to derive the ‘spatialpattern units’ of the land-cover types.

Figure 2. Flowchart of the research method

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As shown in the Figure 2, the tentative land-cover classes were then regrouped intofinal land-cover categories, while the digitised terrain units were converted into raster map.The segmentation result in terms of unsupervised clusters related to spatial pattern of land-cover was then converted into vector data model (polygons). In this segmentation process, aset of predefine scales representing the minimum size of ‘segment’ or spatial unit is selected,ranging from 500, 1000, 2500, to 5000. By comparing the polygons’ tentative IDs and theimage and field observation, it was possible to transform them into new IDs with respect to thespatial pattern found in the field. Accuracy assessment applied to the results obtained frommultispectral classification and image segmentation. The best image segmentation result wasselected as a basis for further processing.

Figure 3. Method for generating socio-economic dimension of land-use

Figure 4. Method for generating ecological dimension of land-use

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The land-use information, which consists of socio-economic and ecological dimensions,was generated using two approaches. The first approach used the spectral-related land-covermap in combination with the terrain unit map, while the second approach integrated thespectral-related land-cover map and the segmentation result. Every derived result was thentested with respect to their accuracy level, both using overall accuracy and Kappa (K). Asimple overlay method in terms of two-dimensional table was applied, although the criteria foreach overlay process are different, as shown in Figure 3 and 4.

6. RESULTS AND DISCUSSION

The multispectral classification delivered a tentative map with 38 spectral classes.After that, they were merged into 20 land-cover classes with respect to the VLUIS spectraldimension categorisation. A selective majority filter at a 3 x 3 window size, which keep theshapes of particular linear features like roads and rivers, was applied to derive a final land-cover map. Assessment of those resultant maps showed that the overall accuracy increasesfrom 82.34% (Kappa= 0.8191, original classified map with 38 classes), to 91.23% (Kappa=0.8987, merged to 20 classes), and finally to 92.31% (0.9201, merged to 20 classes andmajority filtered). The final land-cover map (Figure 5) was then used as a basis for furtherprocessing, i.e. to be integrated with landform map and segmentation result.

Figure 5. Land-cover map generated using multispectral classification, class merging, andselective majority filtering

A visual interpretation of the study area gave a terrain unit map with 17 classes relevantto the land-use variation found in the field. A geomorphological approach was used to interpret

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the image hardcopy. Most of terrain units were defined with respect to the parent materials(lithology), relief expression, rates of dissection or predominant process, sites or predominantland-cover/land-use. Since the direct result was obtained in vector format, a vector-to-rasterconversion was run. The resultant map is shown in Figure 6.

The image segmentation was carried out using mean shift algorithm developed by Farda(2008), which is also integrated into OpenJump and Object-based Classification (OOIC)PlugIn. By this algorithm, the minimum region was set into 500, 1000, 2500, and 5000respectively. Once the parameters were set up, the segmentation process can be run to deliversegmented images at various numbers of mapping unit or ‘segments’. Figure 7 depicts thesegmentation process. The resultant segmented images were then converted into polygons inorder to undertake an easier vector-based editing process, e.g. relabelling and generalisation. Itwas found that the generated segments tend to have relatively loose relationship with thespectral characteristics of the land-cover features found on the image. Since the method wasbased on object-based segmentation algorithm, the resultant units look closer to spatial patternof the study area. However, it was also found that –in some cases-- a single feature of spatialpattern could be split into two or three segments, or several smaller spatial units may developinto a single segment. An accuracy assessment procedure was applied to the resultantsegmented images. The segmented image with minimum region 500 was chosen based on itshighest overall accuracy (76.38%, Kappa= 0.7477). As shown in Figure 8, the object-basedsegmentation process tends to deliver units containing groups of cover type. The relabellingwas undertaken with respect to this nature so that most of new mapping units represent mixesof land-use, while some others show the predominant land-use (Figure 9).

Figure 6. Terrain unit map generated using visual interpretation.

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By integrating the land-cover and terrain unit maps a land-use map of socio-economicdimension was derived (Figure 10). The map shows various units with respect to theirfunction, so that water body of a river is labelled as ‘multipurpose water use, while themangrove forest in the coastal areas and mix forest in the mountainous terrain are labelled as‘conservation forest’. The overall accuracy of the land-use map is 91.76% (Kappa=0.9131),which is slightly lower in comparison with the final land-cover map’s accuracy. Since theland-use map was generated using both land-cover and terrain unit maps, the terrain unit map’sinaccuracy and the overlay process (which is basically controlled by simultaneous Booleanlogic) may contribute to the lower accuracy.

Figure 7. Segmentation process using mean shift algorithm

Figure 8. Segmented units representing mixes of land-cover

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Figure 9. Segmentation result of the study area using mean shift algorithm

Figure 10. Land-use map of socio-economic dimension, generated using combination of land-cover and terrain unit maps

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Another land-use map of socio-economic dimension was derived using the detailedland-cover map (obtained from the multispectral classification) and the segmented imagerepresenting mix of and predominant land-use categories. A similar overlay process using two-dimensional table was used, although the rules to control the overlay process were different. Inthis process, particular land-cover types might match different segmentation units. Forexample, a class of ‘typical built up area with metal roof’ might match a segmentation unitrepresenting predominantly brackish water aquaculture, and the final label is rural settlementbecause the farmers in the study area are used to utilise metal roof for their houses.

Accuracy assessment of the obtained land-use map showed that the overall accuracy is89.65%, while the Kappa is 0.8872. This means that the combination of multispectralclassification and image segmentation could not generate more accurate land-use information.Figure 11 illustrate this result. It is understandable that, generally speaking, the predominantland-use in the study area is agriculture which relies heavily on the land characteristics. Unlikethe terrain unit map, the segmentation unit map does not contain attributes related to the landcharacteristics so that the overlay process could not be controlled using terrain unit - landcharacteristics – land-use relationship consideration.

Figure 11. Land-use map of socio-economic dimension, generated using combination of land-cover and segmentation unit maps

The ecological dimension of land-use information was also generated using twomethods. The first one involved land-cover and terrain unit maps, while the second involved

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land-cover and segmentation units. However, there is a significant difference between thesocio-economic and ecological dimensions modelled in this study. The former could easily begenerated using either terrain unit or segmentation unit maps, while the later was founddifficult when no ecological information involved. In the integration of land-cover and theterrain unit maps, the ecological context of land-use was taken into account by understandingthe terrain characteristics observed in the field and deducted from the image. Therefore, anycombination of land-cover and terrain unit could be derived into particular ecosystem unit withrespect to the categorisation under VLUIS ecological dimension. For example, a high densitywoody vegetation cover found in the mountainous terrain in the study area could be interpretedas a lower montane rainforest class; while the medium density woody vegetation found in thetidal flat could be labelled as mangrove formation class. Figure 12 explains this result.

The previously mentioned practicability in generating the ecological information couldnot be found using combination of land-cover and the segmentation unit maps. As shown inthe Figure 3, the land-cover classes of the study area do not exclusively express their ecologicalaspects. For example, a class of ‘Woody-broadleaf vegetation, medium density/typicalmangrove’ also exist in the mountainous terrain, although it only covers less than 70 pixels.When this class was found on the segmentation unit of ‘forest and dry land cultivation’, whichexists across the hilly and mountainous areas, it was difficult to decide the new ecologicallabel.

Figure 12. Ecological dimension of land-use generated using combination of land-cover andterrain unit maps.

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Based on the derived ecological dimension maps, an overall accuracy of 92.53%(Kappa=0.9132) was obtained using the combination of land-cover and terrain unit maps; andoverall accuracy of 67.77% (Kappa=0.6565) was achieved using combination of land-coverand segmentation unit maps. To say it in another way, the segmentation process using meanshift algorithm could not provide spatial data containing accurate information on ecologicalcontext. Similar to the previous reason used for explaining the resultant socio-economicdimension map, terrain units are more expressive to represent the ecological relationshipbetween land-cover, land characteristics, and ecological aspects of land-use.

This study also shows, however, that various themes related to landscape could begenerated from the same image, and then they can be modelled in various ways to generateland-use information relevant to particular planning task. Object-based image segmentationcan reveal similarities in spatial pattern, so that mapping units related to the spatial pattern ofland-cover and land-use features can be generated. The image segmentation also show anadvantage over the visual interpretation, particularly in generating editable mapping units invector model with similar spatial and spectral characteristics without applying digitisationprocess. However, by using overlay model the segmentation units do not contain enoughinformation to be processed further with the spectral-related land-cover information, so thatmore accurate socio-economic and ecological dimensions of land-use information could not beachieved. The integration of multispectral classification and object-based image segmentationmay be improved if different approaches in modelling are used.

7. CONCLUSIONS AND RECOMMENDATION

Image classification can be used to derive land-use information. Various methods ofimage processing can generate different types of data. Land-use information ismultidimensional in character, so that different processing methods can be run to generatedifferent dimensions. In the next steps, the different dimensions can also be analysed inintegrated way to generate other dimensions. This study showed that multispectralclassification could generate very basic dimension of land-use information, i.e. spectral-relatedland-cover types. On the other hand, the object-based image segmentation could provideinformation related to spatial dimension of the land-use, although the spatial configuration ofdifferent features looks more prominent compared with the shape and pattern of individualfeatures.

Integration of multispectral classification and object-based image segmentation couldnot derive maps at the same accuracies, in comparison with other approaches. Models forgenerating socio-economic dimension of land-use could be built using either combination ofspectral-related land-cover and terrain unit maps or combination of spectral-related land-coverand segmentation unit maps. Both models could generate relatively the same accuracies. Theecological dimension of land-use could effectively be extracted using the combination ofspectral-related land-cover and terrain unit maps, since the terrain units also represents a set ofland characteristic attributes relevant to the ecological differentiation in the study area.However, the combination of spectral-related land-cover and segmentation unit maps could notdeliver an accurate map of land-use’s ecological dimension because the segments do not reflectthe ecological aspects of the land-use features.

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Based on the obtained results, further study incorporating spatial pattern analyse of thesegmented image should be carried out in integration with the analysis of spectral and texturalinformation from the digital image. Other studies dealing with higher-spatial resolutionimagery like Ikonos and Quickbird are also recommended in order to check the effectiveness ofthe models used in this study.

8. ACKNOWLEDGEMENTS

The authors wish to thank the Australian Centre for International Agricultural Research(ACIAR) for funding this study, in the frame of joint research between The University of NewSouth Wales (UNSW), Gadjah Mada University (GMU), and Research Institute for CoastalAquaculture (RICA) – Indonesian Ministry of Marine and Fishery. The contribution of Dr.Rachmansyah, Dr. Akhmad Mustafa, Mr. Tarunamulia Mustafah, Mr. Hasnawi and other stafffrom RICA in Maros is also acknowledged. Mr. Pramaditya Wicaksono and Mr. Sigit HeruMurti from the Faculty of Geography, Gadjah Mada University also helped the authors withdata preparation and valuable comments.

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