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Development of an agricultural land evaluation and site assessment (LESA) decision support tool using remote sensing and geographic information system.

Currently, the rate of conversion of agricultural land in the United States (particularly prime farmland) to non-agricultural uses is increasing (Daniels and Bowers, 1997; AFT, 2002; USDA, 2003). Current trends in the United States indicate that two to six acres of farmland are lost every minute to development projects such as subdivisions and shopping centers (Happe-vonArb, 2000; Hoobler et al., 2003). If this trend continues, the development could affect the economies of states like Iowa that are agriculturally based. According to the Iowa Department of Agriculture and Land Stewardship (IDALS) (2004), 89 percent of the land area in the state is in farms, the highest percentage in the country and it ranks second in the nation in total agricultural exports. Iowa produces approximately 17 percent of the nation's corn for grain, and about 14 percent of the nation's soybeans (IDALS, 2002). However, recent trends in the state have indicated that prime agricultural lands are being lost to natural hazards such as soil erosion, and land degradation, in addition to human activities such as urban development (Schoan, 1991; Dung, 2003a; INRCOG, 2003; Robbins, 2000). As the rate of conversion of prime agricultural land has increased, evaluation of these lands will have to be done by exploring different methods or techniques that will maximize the efficiency of the process. Lately, a combination of land evaluation methods such as Land Evaluation and Site Assessment (LESA) and geospatial technologies such as remote sensing, geographical information systems (GIS), and the global positioning system (GPS), have shown to be promising tools for land evaluation (Hoobler et al., 2003; Kalogirou, 2002; National Research Council, 1997; Sugumaran, 2002; Williams, 1985).

LESA is a framework for combining multiple factors into an integrated assessment of the importance of a particular site for continued agricultural use. Such factors as soil quality, agricultural productivity, development pressure, and measures of other public values are combined into a single score that allows for the identification and protection of important agricultural land (DeMers et al., 2003). LESA was developed by the U.S. Soil Conservation Service (SCS) to implement the 1981 Farmland Protection Policy Act (USDA, 1983). The system's primary purpose was to provide local decision makers with an objective and consistent numerically-based system of determining which farmland should be available for development and what should be protected for farming (Daniels, 2003). The basic idea is to identify the land that is the best farmland in two senses: its inherent productive capacity and the possibility that a farm on the site can be economically and politically viable (Smith and Montgomery, 2004). The LESA system is a numerical rating system for scoring sites to help in formulating policy or making land use decisions on farmland or zoning. The system is designed to take into account both soil quality and other factors affecting a site's importance for agriculture. Currently there are over 200 LESA systems being used in 26 states in the United States (DeMers et al., 2003; Hoobler et al., 2003).

The LESA method has several advantages over other methods such as agro-ecological zones (AEZ) (Rossiter, 2003), fertility capability classification (FCC) (Rossiter, 2003), and soil potential ratings (SPR) (Rossiter, 2003) among others, because it is a procedure and framework that can be refined and calibrated locally (Dung, 2003a; Hobbler et al., 2003; INRCOG, 2003; Steiner, 2003). For example, Rossiter (2003) has critisized fertility capability classifications for some of its specific class limits (e.g., why 15 to 35 percent coarse soil fragments for the "prime" modifier, why not 10 to 20 percent etc.). Many of these correspond to the limits in soil taxonomy. Another criticism is that the classes are not precise enough to make specific fertility management recommendations. It was not intended to create detailed management plans, only the conservation part of these plans. These is thus an implicit ranking of major kinds of land uses: very intense cultivation (1), intense cultivation (1-2), moderately intense cultivation (1-3), limited cultivation (1-4), intense grazing (1-5), moderate grazing (1-6), limited grazing (1-7), forestry (1-7), wildlife (1-8). In fact, all qualifying terms are vague and undefined. It is a written record of the best available judgment, not an objective system of land classification, although in most applications there are tables that give limits of land characteristics that can be accepted in each class, e.g., slope must be less than five percent to be in class one or two. It is therefore a very narrowly focused interpretive soil classification. The application of agro-ecological zones is limited by the lack of geospatial data and it is too general. These methods when considered as stand-alone lack spatial contexts even though land uses are spatially related in nature. Thus it is imperative to adopt an alternative approach to land evaluation. In this study the LESA system is considered as very relevant and appropriate. Although LESA is said to be objective and numeric, in fact some subjectivity occur. However, the subjectivity is not hidden but explicit in the formulation and application of the system. It is composed of two major criteria (a) inherent productive quality of the land, and (b) local development pressure vs. existing agricultural economy; the first usually contributing 100 points and the second 200-points to a maximum score of 300 points (the mix can be adjusted for a local LESA system).

The LESA model not only requires data such as up-to-date land use map, soil parameters, yield, and other non-agricultural data including roads, zoning, parcel, environmental, sewer, and township data, but also the need of systems to analyze these datasets. Geospatial technologies such as remote sensing and GIS possess the necessary tools for land evaluation. An important source of information for land cover is remotely sensed data, especially satellite imagery mainly because of its advantages such as repetitiveness, real time data acquisition, cost-effectiveness, synoptic view etc. over traditional methods that include aerial photos and ground surveys. Several authors reported the advantages of satellite-based land cover map development for agricultural land (Alho, 2003; Gao, 1996; King, 1994; Lillesand and Kiefer, 2000). In addition, GIS assists in land evaluation by reducing the cost of information acquisition and variable rate of input application, as well as time saving decisions for better management and planning (Lyon, 2004; Robert, 1997). GIS is used for generating spatial data layers, developing decision rules, and assessing land evaluation (Lowe, 2004). This is because land evaluation is conceptualized as a multiple criteria decision-making problem (Hopkins, 1987; Jansen et al., 2004), and also the quality of a site for a specific use does not only lie on the values of the environmental variables at the site, but also on its vicinity (Eastman, 1990). In creating a GIS database, GPS has been found to be valuable in that it is used for the collection of the field data as well as for locating and updating of data.

In addition, LESA is adaptable for a GIS implementation since so many of the factors are spatial: e.g., adjacency to farm or nonfarm properties, fragmentation of the landscape, and distance to agricultural or urban infrastructure. Some of these are too difficult for routine determination without a GIS. Also, local planning committee needs a tool to interactively change the importance of different criteria and see how it impacts the outcome. The LESA scores of an entire area can be summed to give an index of farm friendliness, and this score can be evaluated for different development scenarios. In order to accurately develop scenarios requires up-to-date land cover information, which was provided for by remote sensing data. Thus, the integration of GIS and remote sensing data with LESA will ensure the effective implementation of the system.

Recently, few researchers have demonstrated the importance of integrating GIS with LESA for land evaluation and Land suitability modeling (Day et al., 2000; DeMers et al., 2003; Hoobler et al., 2003). For example Day et al. (2000) combined LESA with GIS to develop a farmland evaluation system to protect farmlands in Pennsylvania. The system, which uses U.S. Department of Agriculture (USDA) soil data to determine agricultural productivity for a parcel, also considers non-agricultural factors such as distance to water and sewer, current zoning of surrounding land uses, and distance to population centers in the evaluation. The prototype, which was tested in Lancaster County, is in use in at least five counties, and provides the user options to vary the importance of each variable in the assessment models. In an attempt to enhance the capabilities of LESA, DeMers et al. (2003) considered the integration of LESA with GIS as one way of developing a generic LESA model for agricultural land suitability assessment. According to them it helps people visualize the environmental implications of the future trends; can be applied at different scales, and permits rapid calculation of LESA scores. Similarly, Hoobler et al. (2003) integrated LESA and GIS to assess their use for land use planning in East Park County, Wyoming. They used land capability classification factors, prime farmland data, and irrigated sugar beet yield to calculate land evaluation scores; while distance from city limits, major roads, and sewer lines constituted the site assessment factors. Results show the development of maps of suitable agricultural lands in the study area, and conclude that the combination of LESA and GIS aid decisions on land management. The present study was designed to develop a decision support tool for agricultural land evaluation using geospatial technologies and LESA model for Black Hawk County, Iowa to protect agricultural lands, and also for zoning and tax purposes in the county.

Methods and Materials

Study area. Black Hawk County is located in the northeastern part of Iowa (Figure 1). The county is generally a prairie landscape comprised of gently rolling terrain dominated by agriculture, and interspersed by densely wooded areas (INRCOG, 2003). The County's climate is described as having cold, snowy winters with humid hot summers, which is conducive for growing crops. Black Hawk County, Iowa was selected as a case study for the development of the agricultural land evaluation decision support tool because a substantial amount (about 44 percent) of its 368,640 acres (576 [mi.sup.2]) is devoted to agriculture. Annual precipitation ranges from 31.5 inches to 32.5 inches, and approximately 71 percent of annual precipitation falls between the months of April and September (NOAA, 2003). The county's soil-resources, which are ideal for producing agricultural crops, are considered "prime" by county soil standards, which generally have land evaluation score of 50 and above. In terms of land use, the county has agricultural, residential, commercial, and industrial. The county has 17 townships and a number of small cities and towns with a population of about 128,012 people (as of 2000). This accounts for about 4.37 percent of the state of Iowa for the same period.

Black Hawk County initially developed a corn suitability rating for determining land suitability for corn production. Lately, the county started using a LESA model, which was implemented using Microsoft Excel[TM] (INRCOG, 2003). This system has two parts (Figure 2). Part one deals with land evaluation, where the soil database, land capability class and yield are used to determine average site value of a piece of property using the weighted factor scoring method, while part two deals with site assessment, made up of non-soil factors. For the land evaluation component, three criteria, which include land capability class, corn suitability rating, and land-area in agriculture, were selected (Figure 2). Ten criteria were selected for the site assessment component of the LESA. These are non-agricultural land use factors that are used for determining the value of a piece of land for agricultural use (Figure 2). The site assessment criteria were determined by reviewing, among other things, the comprehensive land use plan, zoning ordinance, and maps of the county (INRCOG, 2003).

Data development. Data for this research were obtained from various sources (Table 1). These data, which were of different types, were obtained at various projections and forms. The satellite image used for producing a land cover map, and the color infrared image which was used as reference data for training the computer were obtained from Iowa Department of Natural Resources (IDNR); the soil map and several other data layers including roads, parcel, and zoning were obtained from the Black Hawk County's Management Information System and Planning and Zoning Department. The sewer line and fire/rescue services layers were not available and so the authors created them using ArcMap editor, and Xtools, an extension in ArcGIS, which permits the customization of shape files. In addition, current land cover map is not available in the county; hence satellite-based Landsat ETM was used.

Remote sensing data analysis. Landsat[TM] satellite images were used to develop a land cover map of Black Hawk County (Table 1). The hybrid image classification method was used for the remote sensing data processing, mainly in order to benefit from the advantages of both supervised and unsupervised classifiers. This method helps to improve the accuracy of purely supervised or unsupervised procedures (Lillesand and Kiefer, 2000). The hybrid method was adopted in the processing stage using the iterative self organizing data analysis technique (ISODATA) for unsupervised classification, and the maximum likelihood for supervised classification with ERDAS imagine software. The accuracy of the final map was assessed using the Kappa statistics (Lillesand and Kiefer, 2000) and Error matrix (confusion matrix).

Decision support tool development. The advent of ArcGIS, and the extensibility that ArcGIS offers through the use of Visual Basic for applications, Visual Basic, and other Component Object Model compliant development languages, enables the creation of customized application functions with robust behavior (ESRI, 2003). This enables the users to customize their software for specific applications using Visual Basic for applications and ArcObjects (Razavi, 2002). In this study, the initial interface prototyping and development employed Visual Basic for Applications within ArcGIS. The choice of Visual Basic for Applications programming is because the Visual Basic for Applications editor comes as part of ArcGIS software, and selection of ArcGIS software is mainly because of the availability of this software in the county. This will enable the county to directly utilize the prototype because they have the familiarity and expertise with the software, and will not involve additional cost of purchase of the software and training. The agricultural land evaluation interface tool created has two components namely land evaluation and site assessment. The agricultural land evaluation support tool was developed with the help of the county's planning and zoning department staff that is directly involved with the determination of zoning rights and taxing in the county.

Results and Discussion

Figure 3 shows the up-to-date land cover map produced using the satellite remote sensing data for Black Hawk County. The results of the area occupied by each land cover type compared to 1982 and 1992 land cover maps show a steady increase in built-up areas and a steady decrease in agricultural lands. About 145,000 acres were under agriculture in 1986; this area dropped to about 138,000 in 1992. By the year 2000, agricultural land experienced a steady decline to about 120,000 acres. In contrast, there was a steady increase in built-up areas within the same period. For instance whereas built-up areas accounted for about 38,000 acres in 1986, this figure rose to about 42,000 acres, and a further 1,000 acres in 1992 and 2000. These results seem to confirm the results of previous studies that agricultural lands are being converted to non-agricultural uses at an alarming rate in the study area. Figure 4 is the agricultural LESA decision support tool developed for the evaluation of agricultural land in the Black Hawk County. There are several tools such as clip layer, update area, calculate land evaluation score, delete layer, and clear selection/full extent that were added in the decision support tool (Figure 4). The following example explains step by step how this decision support tool can help planners to make effective decisions.

Application example. A user comes with a request for changing the zoning of an existing piece of property. In order for the county to make a decision as to whether the request is granted or rejected, it resorts to the LESA system, which is an objective way of arriving at a decision. This is because LESA enables the county to analyze which areas are suitable for development from existing soils, ownership boundaries (parcels), urban/rural areas, corporate limits, and other layers the county considers while doing site analysis. From these inputs, the agricultural decision support tool gives each parcel or a portion of it a score. This score can be used to determine whether permission for rezoning should be granted or not. The following example walks through the decision process involved in the agricultural decision support tool as presented in Figure 5. The first section deals with land evaluation, followed by site assessment, and finally integrated LESA score.

Land evaluation (LE). The first step is selection of a parcel or portion of parcel for the evaluation (Figure 5). After selecting a target parcel based on the user request, it is clipped to the soil map whose database is used for the determination of the land evaluation score. The update area tool helps to update the area of the clipped parcel. The Calculate LE function calculates the land evaluation score based on the soil parameters within the clipped layer and the area covered by each soil type within the selected area. In this example, a land evaluation score of 81.56 was obtained (Figure 6). After a feature has been selected, clipped and the land evaluation score calculated, the output is a map and its attributes added to the table of contents in ArcMap, and the attributes are saved in the temporary directory. The delete function deletes both the attributes file and the map so that new parcels can be selected. Finally, before the user selects a new parcel, the previous selection has to be cleared from the screen, and in the event of either a zoom in or out, the full extent function returns the layer to its original position. The determination of the land evaluation score using this tool is time saving and accurate compared to the traditional method used in the study area.

Site assessment. As mentioned earlier, site assessment is the non-agricultural component of a LESA system and more complicated and controversial than the land evaluation component. This is because of its subjectivity where a local committee considers and weights factors that favor agriculture such as size of farm, proportion of class one and two farmland on farm, proximity to support services (such as feed and fertilizer dealers), and those that favor development, such as extent of non-agricultural development within a certain distance, current zoning, and proximity to municipal services. There are several site assessment factors that are used and grouped into categories such as: (a) development potential factors; (b) farm related factors; and (c) other factors that lack development pressure (Figure 2). In order to calculate the land evaluation score, we developed a simple interface, which is shown in Figure 6. Users can enter their own weight and evaluate the land evaluation score for their input. The individual scores are automatically calculated based on the weight factors of the criteria. In the Figure 6 example, the user got a parcel score of 138.

Integrated land evaluation and site assessment. The final individual scores are calculated when all the points from land evaluation and site assessment have been determined, and integrated into the LESA interface (Figure 6). In the example, the combined land evaluation and site assessment score was 219.56 as presented in Figure 6. The results of the final LESA scores are used to aid in the decision making process of the county's rezoning and land property tax based on the values from the table (Table 2). The model indicates that there is a greater preference for protecting agricultural lands where LESA scores are greater than 240. In the model example the final LESA score of 219.56 means this area is moderately good for agriculture (Table 2) and could be protected from urban development or granted permission for rezoning. The land evaluation score of 81 in Figure 6 shows that this parcel is suitable for crop production. It is clear that when an emphasis is placed on site assessment factors, areas close to built-up areas are projected as low priority for agricultural protection, which suggests urbanization should be concentrated in these areas instead of being distributed throughout the rest of the study area.

In the final LESA a maximum of 300 points can be scored for any one parcel. There were however differences in so many respects especially in the choice of criteria to be used and in assigning weight factors due largely to the flexibility of the system and its applicability at the local level. The LESA system shows that it can be flexible in response to growth and readily adaptable to create a balance between human needs and agriculture because they can be continually updated to adjust to local parameters. More so in the case of Black Hawk County, which is supported by a digital database and a geographic information system. This technology will make the LESA analysis different from the county's current plan because after model development, adjustments can be made easily and results can be rapidly produced in map form. LESA can also be used as a stand-alone tool or as a component of a land use plan. For example, LESA could be formulated to work as a component of the existing county plan in order to technically support local decisions and to identify community land preservation concerns while taking advantage of available databases, map layers and GIS capabilities. It was generally agreed by the county staff that a GIS can be the deciding factor in many cases, and that it provides the sound evidence needed to make valuable land decisions and know where to place resources (personal communication with Aric Schoeder Waterloo, Iowa, 2003). The tool developed in this project is easy to use, saves time and money for the planners in the county.

Summary and Conclusion

The main goal of this research was to develop a decision support tool for agricultural land evaluation in Black Hawk County, Iowa using geospatial technologies. This was implemented using the LESA method within a GIS environment to support agricultural land evaluation. This tool uses data from different sources including up-to-date land cover map from the satellite remote sensing data. The results of the implementation using Visual Basic for Applications have shown that the integration of GIS has proved to be very valuable in providing a decision support tool for land evaluation. The decision support tool developed in this study is easy to use, accurate, combines spatial and non-spatial data, and saves time and money for the planners in the county over traditional methods. However, there were a few limitations to this study. First, the LESA-GIS model developed for this study lacks the economic component of land evaluation. LESA scores in this study should not be used for interpretation or to make any judgments on existing or proposed land uses in Black Hawk County. This is because certain data were either not available or were not in a format that was readily usable. As such the reliability of some of the data cannot be guaranteed. For instance, the sewer and fire/rescue services layers were not available for security reasons and these layers were created in ArcGIS using available tools. Some of these factors will be rectified in the future research.

Acknowledgements

The authors wish to acknowledge Barb Berquam, the Black Hawk County's GIS coordinator, Kim Veeder of the county's MIS unit, and Aric Schoeder with the county's planning and zoning department for providing most of the data for the study and also valuable feedback. Our special thanks also go to Brian Shoan of INRCOG for helping in various ways in the LESA model. The authors would like to also thank three anonymous reviewers for their helpful comments. The authors are grateful to the STORM project at the University of Northern Iowa for providing financial support for this study.

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Elisha J. Dung is a Ph.D. student and Ramanathan Sugumaran is an associate professor in the Department of Geography at the University of Northern Iowa in Cedar Falls, Iowa.
Table 1. Data used in the study.

Data                     Data source    Description

Satellite image          Iowa DNR/      Raster dataset: No up-to-date
path/row                 STORM project  land cover map available.
25 and 26/31                            Therefore remote sensing land
                                        cover data created.
CIR image                Iowa DNR       Raster dataset, 1 m resolution
                                        2002 image used as reference
                                        data for accuracy assessment.
Soil map                 BHC            Contains digital soil map of the
                                        county with attributes-polygon.
Parcel                   BHC            Contains property boundaries.
Zoning                   BHC            Contains information on adjacent
                                        zoning.
Roads                    BHC            Major and minor roads.
Agricultural statistics  NASS           Crop yield and acreage,
                                        statistical data used to create
                                        graphs.
Fire/rescue service      Dung, 2003a    Fire and police stations point,
                                        created using ArcGIS.
Sewer                    Dung, 2003a    Sewer lines in corporate areas,
                                        created using ArcGIS.

DNR = Department of Natural Resources
BHC = Black Hawk County
NASS = National Agricultural Statistics Service
CIR = color infrared
STORM = Science Center for Teaching, Outreach and Research on
Meteorology

Table 2. Land evaluation site assessment classification range (INRCOG,
2003).

Land value category          LESA score range

Low agricultural value         0-196
Moderate agricultural value  197-241
High agricultural value      242-300
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Author:Dung, E.J.; Sugumaran, R.
Publication:Journal of Soil and Water Conservation
Geographic Code:100NA
Date:Sep 1, 2005
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