Introduction

A suitability study is a preliminary step when assessing whether land or any other area is likely to be practical and successful for sustainable development of an intended venture. In many instances aquaculture has been promoted in regions which are unsuitable in terms of climatic conditions, water and soil quality, and other facilities. A suitable site is a prerequisite for successful aquaculture. Fish have traditionally been cultured in the Sitakunda region without adopting any scientific and systematic inputs. In recent years horizontal expansion of fish farming (Nile tilapia, carp, and catfish) has been increasing in the Sitakunda area without consideration of the suitability of the locations. To ensure sustainable aquaculture development there is, therefore, a great need to allocate aquaculture to suitable locations to resolve competing demands for land, avoid undesirable effects on the environment, and ensure the profitability of the operation (Kapetsky 1987; GESAMP 1997).

Satellite remote sensing data are useful for updating an existing map or generating new thematic maps. During observations many earth features of interest have already been identified, mapped, and studied on the basis of their spectral characteristics (Lillisand and Kiefer 2000). GIS and remote sensing are essential tools for planning of aquaculture development (Burrough 1986). GIS also serve as an analytical and predicting tools for aquaculture development and to test the consequence of various development decisions before their use in the landscape (Aguilar-Manjarrez and Ross 1995). Other uses of GIS include efficient storage, management, and analysis of spatial and non-spatial data (Kapetsky et al. 1987; Burrough and McDonnel 1998). Similar observations were made by Vibulsresth et al. (1993), Venkataratnam et al. (1997), Pe´rez et al. (2003), Giap et al. (2005), and Rajitha et al. (2007). In recent years, significant advances have been made in remote sensing in Bangladesh, especially in shrimp-farming development in the coastal zone (Hossain et al. 2001), detection of changes in Sunderbans mangrove forest (Islam et al. 1997), mapping shrimp farming and agricultural areas (Shahid et al. 1992), mapping suitable areas for saltpan development (Hossain et al. 2003a), mangrove afforestation (Hossain et al. 2003b), and assessing suitable carp-farming areas (Salam et al. 2005).

The main problem in selecting suitable sites for tilapia farming is the lack of baseline information about physicochemical and topographic conditions, and existing land-use patterns. In Sitakunda the land area used for Nile tilapia farming has increased in recent years, because of steadily increasing demand in local and city markets. Expansion of Nile tilapia culture in traditional carp farming areas, grassland, and agriculture fields may, however, change ecological and environmental conditions and affect aquatic biodiversity. Thus, site selection incorporating water quality, soil characteristics, topography, infrastructure and socio-economic factors that affect suitability for the intended purpose is essential for Nile tilapia farming development. Inappropriate land use for tilapia farming without considering these factors can lead to misuse of natural resources and degradation of the environment, breeding poverty, and other social conflicts. The failure of shrimp farms in Ecuador, Taiwan, Thailand, Bangladesh, Vietnam, and elsewhere has been attributed to the deterioration of environmental conditions (Hossain et al. 2004). Farms in some areas are located on unsuitable sites and some are also constructed too densely for the carrying capacity of the environment. Site selection is rarely based on thorough assessment of the natural or men-made environment, however, and limited consideration is often given to technical, economic, logistic, and socio-political factors (Fegan 1994). To alleviate these potential problems, land evaluation can be used to predict land performance on the basis of its attributes by using a variety of analytical models. The structures of these models range from qualitative to quantitative, functional to mechanistic, or specific to general (Rossiter 1996). The objective of this study was to evaluate, using GIS-based models, quantitative land suitability for Nile tilapia farming development in Sitakunda Upazila, Bangladesh.

Study area

The study area, Sitakunda Upazila, is situated in the northwestern part of Chittagong district, between 22°34′ N and 22°43′ N latitude and 91°38′ E and 91°41′ E longitude (Fig. 1); it occupies an area of 18,488 ha and the population is 168,648. Soils are generally loamy, sandy loam, clay loam, and clay. Grey piedmont soils and brown hill soils formed in hill outwash alluvium along the hill ranges. Water bodies, for example ponds, stream corridors, canals, and natural depressions support multitudes of species of plants, fish, prawns, and other organisms suitable for culture. Fisheries are the most important resource in the aquatic ecosystem that is a major source of employment for the poor and also the main dietary source of protein for the rural population. The rural people are engaged in growing seasonal vegetables, paddy cultivation, fishing, and, to a lesser extent, fish culture.

Fig. 1
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Geographical location of the study area in Sitakunda Upazila, Bangladesh

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The area, like other parts of the country, is much affected by seasonal monsoon winds. Mean annual rainfall in the study area is 3207 mm and mean annual temperature is 26.24°C. The monsoon or rainy season (June–October) is characterized by high rainfall, humidity, and cloud cover. Sediment load and water levels of the area also increase during this period. The salinity along the coast is reduced because of the effect of onrushing fresh water from the nearby upstream hilly regions. Occasional thunderstorms, cyclones, and storm surges occur during the monsoon season. The winter season (November–February), with a northeast monsoon wind, is characterized by dry, cool, and sunny weather with occasional rain. The lowest temperatures (8–10°C) occur in December–January, when days are dry and sunny but heavy dewfall at night and morning greatly disturbs navigational activity. The summer or pre-monsoon (March–May) with a southwest monsoon wind is characterized by southerly winds, high temperature, and evapotranspiration rates with occasional heavy thunderstorms (locally called Kalbaishakhi) and hail. Sunshine hours are minimum during the rainy and winter seasons and maximum in summer. Annual mean sunshine is approximately 5 h per day and the mean annual wind speed recorded for the years 1991–2001 in Sitakunda was 1.8 knots (Khan et al. 2003).

Materials and methods

Data

An ASTER (advanced spaceborne thermal emission and reflection radiometer) image acquired on 29 November 2003 was used for this study. Topographic maps, soil maps, and land-use maps of 1:10000, 1:50000, and 1:50000 scale, published by the Survey of Bangladesh in 1999, SRDI in 1990, and LGED in 1998, respectively, were used for geometric correction. Geometric correction was performed with a fourth-order polynomial (Research Systems 2000a) and the monitored error (0.47) was less than one pixel (15 m) for 35 ground control points (GCP). Road junctions, crossing points of roads and railways, historical hard infrastructure, and prominent features were selected as most of the GCPs in the image. The image processor for the analysis was ENVI (the environment for visualizing image; Research Systems, USA). Ground data were obtained by field investigation of approximately 55 plots, some of which were permanent. Other ancillary data, for example water quality, soil quality, slope, elevation, land use, and infrastructure and socio-economic data are also important for the analysis.

Classification procedure

ISODATA unsupervised classification (use of information from the image itself to identify spectral clusters, which are interpreted as classes) was performed by considering minimum and maximum classes of 5–10, 10–15, and 15–20, among which the 10–15 classes were found to be useful. Supervised classification was performed on the basis of regions of interest (ROIs), in which the ground truth, or so-called training areas (collected during field investigation), were regions of terrain with known properties or characteristics (Research Systems 2000b). Maximum likelihood classification strategy was applied and found to be most useful for discriminating the category of interest.

Water and soil quality conditions, topography, road networks, markets, sources of fish fry, electricity, and population density were weighted and scored in terms of significance for tilapia farming. After image processing, reference points were chosen for ground verification. All the reference points were surveyed for the ground truthing of Sitakunda Upazila and compared on the preliminary map with real position. ArcView (Environmental Systems Research Institute, USA) and MS Office (Microsoft, USA) software were used to digitize and analyze all the classified and other necessary maps.

Weight and score

Within the framework of a GIS system (ArcView), a procedure was set up to attribute suitability classes to individual land units and to present the results of the classification on maps. The range of land characteristics was divided into three classes—most suitable, moderately suitable, and not suitable (FAO 1993)—on the basis of requirements for tilapia farm development. This study focused on basic factors, for example, eighteen base layers (thematic maps) were developed for tilapia farming, namely water source and quality (temperature, salinity, pH, dissolved oxygen, transparency), soil quality (pH, salinity, organic matter, texture), topography (slope, elevation, land-use type), infrastructure and socio-economic factors (road network, markets, sources of fish fry, electricity, population density). Weight was given according to the effectiveness of the criteria. Each class was also given a score according to suitability, i.e. most suitable scored 3, moderately suitable scored 2, and not suitable scored 1 (FAO 1976; Hossain et al. 2003a, b) (Table 1).

Table 1 Suitability of water and soil quality, topography, and infrastructure and socio-economic factors for Nile tilapia farming in Sitakunda Upazila, Bangladesh
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The analytical hierarchy process (AHP) is a proven, effective means of dealing with complex decision making and can assist with identifying and weighting selection criteria, analyzing the data collected for the criteria, and expediting the decision-making process. By making pair-wise comparisons at each level of the hierarchy, participants can develop relative weights, called priorities, to differentiate the importance of the criteria. The scale recommended by Saaty (1994) is from 1 to 9, with 1 meaning no difference in importance of one criterion in relation to another and 9 meaning one criterion is much more important than another, with different degrees of importance in between. The essence of AHP calculations is solution of an eigenvalue problem involving the reciprocal matrix of comparisons. Saaty (1980) suggests that if the CI/RI is smaller than 0.10, the degree of consistency is satisfactory; if, however, the CI/RI is larger than 0.10, there are inconsistencies and the AHP method may not yield meaningful results. Liberatore et al. (1992) suggested the structured approach of AHP could better capture the subjective judgments. Millet (1998) found that the process could enhance shared understanding of decisions, save time, and facilitate consensus. Liberatore and Nydick (1997) noted that AHP forced individuals to think through the strength of relationships leading to a process that was less biased and less political, and had more consistency than in the past. In the current study the weight for each factor was determined by pair-wise comparisons in the context of a decision-making process known as the analytical hierarchy process (Saaty 1977, 1990), which has also been recommended by Pereira and Duckstein (1993), Malczewski (1999). The suitability rating for each level of a factor was determined from the survey results and professional judgment of experts (Table 2). An example of spreadsheet calculations for consistency ratio of topographic criteria for tilapia farming is presented in Table 3.

Table 2 Pair-wise comparison matrix for assessing the relative importance of different factors for Nile tilapia culture potential in Sitakunda, Bangladesh (numbers show the rating of the row factor relative to the column factor)
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Table 3 Example of spreadsheet calculations for consistency ratio of topographic criteria for Nile tilapia farming in Sitakunda Upazila, Bangladesh
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Identification of variables and database generation

The model structure for selecting the best sites for tilapia farming in Sitakunda was built on the basis of hierarchical structures. Hierarchical structures break down all criteria into smaller groups (or submodels). To breakdown a hierarchy into clusters, first it was decided which elements to group together in each cluster. This was done according to the similarity of the elements with regard to the function they perform or the property they share (Saaty 1988). Figure 2 depicts suitability analysis for tilapia farming site selection in Sitakunda as a hierarchical structure. The top or first level in the hierarchy is the ultimate goal of the multicriteria decision-making analysis process. The intermediate or second hierarchy level lists the relevant evaluation criteria that were compared pair-wise to assess their relative weights. Each of these clusters was regarded as a submodel. The lowest level in the hierarchy contains the evaluation objects. All these criteria are identified as affecting the goal of the study and may represent primary data or be the result of secondary data (Table 4).

Fig. 2
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Schematic diagram of modeling procedure for Nile tilapia farming area selection in Sitakunda Upazila, Bangladesh

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Table 4 Criteria used for Nile tilapia culture potential in Sitakunda Upazila, Bangladesh
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All data included in the database needed manipulation and reclassification to create the thematic layers, and registration of each layer to a common coordinate system. Given the variety of scales on which all criteria were measured, multicriteria decision analysis requires that the values contained in the different layers be transformed into comparable units. A scoring system of 1–3 was chosen, 3 being the most suitable and 1 not suitable for developing tilapia culture in Sitakunda. At this stage the decision-maker’s preferences with regard to the evaluation criteria were incorporated into the decision model. Eventually the criteria layers and their weights were integrated to provide an overall assessment of the alternatives. This step is known as multicriteria evaluation and was accomplished by use of appropriate decision rules, which are formal mathematical expressions that combine the weights and scores of each of the layers used.

Verification

The model was verified by comparing predicted suitable sites with existing farm locations. Stratified random sampling was performed to identify 28 sites for subsequent visit and assessment. The purpose of verification was to discover whether the existing fish farm locations matched the suitability sites or not. An important assumption was that local people had a preference for more suitable locations based on their indigenous knowledge.

Results

Existing land use areas (Fig. 3) and suitability ranking for Nile tilapia culture are presented in Table 5. Taking into consideration existing land-use pattern, topographic conditions, and financial involvement in converting the existing land, the ranking was performed on the basis of the professional judgment of the researchers and the knowledge of indigenous people. The land-use classification shows that the dominant land-use activities are hill forest (5,758.11 ha), grassland (3,694.76 ha), seasonal vegetables (3,059.29 ha), paddy cultivation (2,801.18 ha) and aquaculture (1,540.96 ha), which account for 31, 20, 17, 15, and 8%, respectively, of total land area. Hill forest, mangrove forest, and town and built-up areas, comprising approximately 7,250 ha (40% of the total land area) are regarded as constrained areas for tilapia farming (Table 5). The results for 18 criteria are presented separately in four submodels, namely water quality, soil quality, topography, and infrastructure and socio-economic factors, enabing comprehensive analysis. The potential area in each submodel is summarized in Table 6 and Fig. 4.

Fig. 3
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Existing land-use patterns in Sitakunda Upazila, Bangladesh

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Table 5 Land-use pattern and its suitability ranking for Nile tilapia farming
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Table 6 Areas (ha) and different suitability (%) of land for Nile tilapia farming in the study area (total land area for suitability analysis is 11,061 ha, excluding 7,250 ha constraint area)
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Fig. 4
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Suitability maps for different land and water use requirements for Nile tilapia farming in Sitakunda, Bangladesh

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The most suitable areas for tilapia farming are recognized as those with water of good quality which is well-oxygenated and of favorable temperature, salinity, and pH. Such areas cover existing agriculture, aquaculture, and grassland. Approximately 100% of land area is most suitable for tilapia farming in terms of temperature, dissolved oxygen, salinity, and pH (Table 6). Moderately suitable areas (22% of land) with regard to water quality are located in areas of paddy and seasonal vegetable cultivation. All hilly streams of the nearby Chandranath reserve forest join together and form two waterfalls, Sahasradhara and Suptadhara. Numerous streams (locally called chhara), and canals (locally called khal) flow in the east-west direction and criss-cross the study area. The road and embankment behind ditches and homestead ponds also retain rainwater that can be used for aquaculture and to enhance agriculture and aquaculture activity in the nearby areas through irrigation. The coastal embankment with afforested greenbelt protects the study area from saline water.

The soil affects the initial cost of pond construction, because soil permeability affects the capacity of the pond to hold water. Permeable soils are least suitable for ponds, inasmuch as water loss from seepage increases the demand for water, and pumping costs. The area of most suitable soil quality for tilapia farming is 95% of the total land area (Table 6). Only 4% of the area is not suitable for tilapia farming, and this covers that currently used for seasonal vegetable cultivation. Approximately 83–98% of land area is most suitable in terms of clay content, organic matter, pH, and soil salinity.

The most suitable areas with regard to topography are found to be 53% of the land area along the coastal embankment with low slope (0–5%) and elevation of less than 5 m with appropriate land-use types and with access to water, a source of fish fry, and markets. These areas cover the existing grassland and aquaculture area and also include 20% of the moderately suitable area. The unsuitable zone of 27% of land area is located mainly in the seasonal vegetable and paddy cultivation areas with higher slope and elevation.

Infrastructure and socio-economic factors limit areas most suitable (74%) for tilapia farming (Table 6). This is mainly because electricity is not available in the area. Electricity is available in the study area within 200–500 m of the Asian Highway in an almost north–south direction; everywhere else is still in the dark, which means 52% is not suitable for tilapia culture. Population density is also important, because high human population, increasing industrialization, and agricultural intensification have resulted in significant pollution—excreta and sewage from the human population, waste and effluents from industry, and runoff from agricultural fields affect tilapia farming. Although the Asian Highway and all-weather motorways serve as the main transportation network, dirt roads are also significant to the local community for developing a network among the forward and backward-linked industries of tilapia farming. Availability of sources of fish fry and easily accessible well-established marketing facilities favor tilapia farming development in the study area. Approximately three-fourths of the total area (74%) is classified as suitable for tilapia farming in terms of infrastructure and socio-economic factors.

The overall evaluation shows that 70% of the total land area (7,744 ha) located in grassland, aquaculture, and paddy fields of the study area is most suitable for tilapia farming (Table 6, Fig. 5). The moderately suitable 22% land area (2,479 ha) is located in the paddy and seasonal vegetable cultivation areas. The remaining 8% of the land (838 ha) is classified as not suitable and is located mainly in the elevated seasonal vegetable areas.

Fig. 5
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Overall potential land-suitability map for Nile tilapia farming in Sitakunda, Bangladesh

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Discussion

The total land area in this study is 18,311 ha, of which 7,250 ha (hill forest, mangrove forest, and town and built-up areas) were identified as constraint and the remaining 11,061 ha as classifiable area for Nile tilapia culture development. Different criteria were grouped in four submodels (water quality, soil quality, topography, and infrastructure and socio-economic factors), which were combined to generate a final output showing the most suitable areas for tilapia culture development. Land evaluation using GIS techniques led to estimates that 7,744 ha (70%) and 2,479 ha (22%) of the land were most suitable and moderately suitable, respectively, for tilapia farming. Results predicted as most favorable from this GIS evaluation are validated by the existing location of tilapia and other fish farms in the study area. The compactness of vast flood-free derelict common land with suitable water and soil quality and infrastructure facilities can be an appropriate option for commercial-scale tilapia farming to diversify the economic activities of different stakeholder groups. Polyculture of tilapia with carp can also be adopted, as is commonly practiced in other Asian countries. The most suitable areas for tilapia culture are those in which most of the variables coincide with each other and there is high potential for tilapia production. Farmers in these areas can easily obtain support services and sell their product in good time to earn more profit than in other areas. In contrast, moderately suitable areas can enable moderate tilapia production, although costs may be higher than in the most suitable areas because of the time required to obtain support services, transport requirements, and sell the product.

This study suggests that the land should be divided into different zones on the basis of suitability for tilapia farming, i.e. most suitable, moderately suitable, and not suitable zones. The zoning approach can provide important information enabling potential developers/investors to identify suitable zones that meet requirements ensuring maximum benefit for a long period (Hossain and Lin 2001). Zoning of land and water for tilapia farm development can help in the control of environmental deterioration at the farm level and in the avoidance of adverse social and environmental interactions. Local people prefer more suitable locations on the basis of their indigenous knowledge, which justifies the well known expression “farmers are the real scientists”; thus the present land-suitability evaluation identified the most favorable areas that were also validated by the current location of fish farms in the study area. Grassland and wet meadows are currently used solely for buffalo and cow grazing, although this derelict land can be converted for commercial-scale tilapia farming with added economic benefits and no environmental loss. A fragmented culture system, without any management practice, has occupied only 1,540 ha of the land in the study area, however. The most notable approach consists of stocking fish in water bodies followed by recapture after a period of several months or even a year. These results are very encouraging in terms of tilapia culture development and suggest that the grassland–agriculture areas could be used for Nile tilapia farming to diversify economic activity. After completion of the harvest the pond bottom effluent can be lifted up on to the dike and dried in the sun to produce manure for cultivation of dike-based vegetables. The decision to convert agriculture and wet-meadow grassland to fish ponds is often related to food security and social aspects. To maintain a balance between social and economic aspects, integrated farming systems, for example rice–fish culture (Gregory and Guttman 2002) or garden–pond–livestock and garden–pond–livestock–forest (Luu et al. 2002) may be examined. Other important factors, for example detailed environmental, climatic, economic, and political issues should be considered in the decision-making process. These factors are not considered, because of little variation within the study area and difficulties in spatial surface modeling from a small number of collected samples. These factors may be useful for land evaluation in larger areas.

Members of the local community reported there is a strong bond and social harmony among the religious groups (Muslims 75% and Hindus 25%). Most members of the community extend a helping hand to neighbors during crises or problems. The local community invites their neighbors to religious and social festivals. Women are quite conservative and feel shy about meeting outsiders, for example this research team. No social conflict, poaching, or robbery have been reported in the study area. Incorporating key socio-economic and environmental factors into land evaluation for Nile tilapia farming will enable planners and policy makers to develop better land use policies and make decisions based on objective characteristics and requirements or the overall potential of the land. This study shows that GIS databases of different format and from different sources can be used effectively to establish spatial models in land evaluation for tilapia farming. It is expected that land-evaluation modeling will be useful for identifying areas suitable for tilapia farming in terms of efficient income generation, effective conservation, and sustainable land management. The results of this study could be improved by use of site-specific data for tilapia culture. An important advantage of GIS is the ability to update current ratings easily when new or better information becomes available (Rajitha et al. 2007) and the ability to obtain output from the improved model “on-the-fly”. A new map can be generated in a relatively short time with less effort and lower cost than for additions to files on paper, manual planimetry, and conventional map updating (Kapetsky 1994; William 1994).