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Modeling Better Decisions
Continued

The Benefit of Using Raster Data

Land scoring for many LESA implementations is based on property parcel boundaries and calculated either by hand or by using an electronic spreadsheet. Either method takes time and does not necessarily value the landscape as a whole but instead values individual portions of that landscape.

In some cases, GIS-based farmland preservation rating frameworks have been implemented using property parcels and ancillary data. When using a LESA approach for land preservation prioritization and only some parcels are scored, high-scoring parcels may not be the most desirable areas for preservation because better candidates may not have been considered. In addition, digital information on land parcels may not be freely and/or readily available, and in most areas of the country, this information is likely to change over time.

Therefore, communities that depend on parcel data may be limited when implementing a GIS-based LESA. Conceptualizing the landscape as a continuous value surface lends itself to use of the raster data model as well as identifying relative landscape value more completely. A similar approach was used more than two decades ago by T. H. Lee Williams (1985) but was limited because of data and computing power.

Using ModelBuilder

In this ELESA implementation, ModelBuilder was used to automate the rating as well as factor weighting as described in the LESA guidebook by Pease and Coughlin. The model can be thought of in terms of three primary components: LE factors, Site Assesment (SA) factors, and overall factor weighting. The analysis cell size was primarily based on the National Land Cover Dataset (NLCD 2001) and topographic data used in the model. The model described in this article used 12 factors that are individually rated within the model.

The relationship between individually related factors is considered using the weighted overlay procedure of Spatial Analyst. To explore model functionality, specific weightings were determined from the authors' expertise. However, actual factor rating and weighting can be accomplished through procedures outlined in the LESA guidebook. ModelBuilder benefits the process by allowing different rating and weighting combinations to be explored during public meetings depending on land-use problems. Because rating is done on a cell-by-cell basis, it is potentially desirable to use Spatial Analyst's Focal operations to smooth the output using a moving window approach on acreage size that is meaningful to the land-use discussion (as suggested by C. D. Tomlin's cartographic modeling methodological concepts). This smoothing allows for rating visualization of landscape areas rather than looking at only cells in isolation.

click to enlarge
The final model output identifies the most and least suitable areas. Areas that best fit the model's equally weighted assumptions received 10 points and are shown in blue. Areas that were the least suitable as evaluated by the model received 0 points and are shown in red. Graduated colors denote areas with values between 1 and 9.

Individual LESA factor GRIDs were classified using a scale of 1–10, with 10 being the most suitable in every case. These GRIDs were combined using the Weighted Overlay tool in Spatial Analyst.

Using the Weighted Overlay tool, knowledge supplied by members of the public or by experts can be intertwined with GIS data quickly during public meetings. When using the Weighted Overlay tool, each GRID is assigned a percentage value denoting its relative importance such that when all importance percentages assigned are added together, they equal 100. For example, a public meeting process may reveal that the soil importance GRID is critical and is given a percentage value of 50, while aspect data is deemed less important and receives a percentage value of 5, with the other factor GRIDs receiving the remaining 45 percent. This weighting is crucial to tailoring LESA to local needs and interests and is a hallmark of the LESA framework.

Conclusion

The resulting GRID for the study area provides insights that typical individual parcel LESA scoring systems do not consider. Raster-based ELESA allows the landscape to be thought of as a continuous surface of LESA process rating. This rating visualization can help identify suitable acreage for people interested in agricultural land for a variety of reasons. The factor rating and weighting can be tailored to meet the desires of an individual or an entire community.

The model has the ability to include land protected by land trusts or other entities under agreements such as conservation easements and purchase of development rights programs. The model can also visualize how ELESA ratings change relative to agricultural uses when urbanization proposals are anticipated. The Lexington-Fayette County Purchase of Development Rights Program (www.lfucg.com/pdr/) is an award-winning program that protects almost 21,000 acres. (However, data from that program was not used in this article due to data licensing and privacy issues.)

Enhancing LESA: Ideas for Improving the Use and Capabilities of the Land Evaluation and Site Assessment System, a workshop held in 2003 in Nebraska City, Nebraska, generated recommendations that LESA should be enhanced with GIS capabilities and strengthened with applications for land-use planning and growth management.

Several of the workshop's recommendations were realized through the use of ModelBuilder as described in this article. GIS has enhanced the power of LESA for landscape analysis and participatory planning/decision making. The regional/landscape scale analysis performed in this ELESA model allows for broad scale and flexible analysis. The Weighted Overlay tool, used in conjunction with a relatively short reassessment time, is acceptable for participatory planning/decision-making applications used during a meeting or in the field. ELESA has the potential for areawide planning applications, and this model could be adapted for watershed-based applications and modified for use with ArcGIS Server.

For additional information, contact Brian D. Lee, Ph.D., assistant professor (blee@uky.edu), or Collin D. Linebach, undergraduate student (Collin.Linebach@uky.edu), Department of Landscape Architecture, College of Agriculture, University of Kentucky.

Acknowledgments

The authors thank Karen Goodlet, Billy Van Pelt, Lori Garkovich, and C. Dana Tomlin for helpful advice with this project. General guidance for this particular model came from the LESA guidebook and the Lexington-Fayette County Purchase of Development Rights Program.

About the Authors

Brian D. Lee is an assistant professor of landscape architecture at the University of Kentucky. He received his doctorate and bachelor's degree in landscape architecture from Pennsylvania State University and both master's degrees at the University of Pennsylvania. He teaches land-use planning and GIS courses. He coauthored "Why Not Walk to School Today?" which appeared in the 2006 October–December issue of ArcUser.

Collin D. Linebach is a fifth-year landscape architecture undergraduate student at the University of Kentucky. He is involved in research on watershed characterization, urban sprawl assessment, and conservation planning.

References

DeMers, M. N. (1994). "Requirements Analysis for GIS LESA Modeling." In Steiner, F. R., J. R. Pease, and R. E. Coughlin (Eds.), A Decade with LESA: The Evolution of Land Evaluation and Site Assessment (pp. 242–259). Ankeny, Iowa: Soil and Water Conservation Society.

Land Information Bulletin (2000). Farmland Protection and GIS: GIS Interface Helps Pennsylvania Counties Prioritize Farmland for Preservation. National Consortium for Rural Geospatial Innovations, Chesapeake, Pennsylvania State University, University Park, Pennsylvania. Retrieved June 1, 2008, from www.lic.wisc.edu/pubs/Penn1.pdf.

Pease, J. R., and R. E. Coughlin (1996). Land Evaluation and Site Assessment: A Guidebook for Rating Agricultural Lands (2nd ed.). U.S. Department of Agriculture, Natural Resources Conservation Service. Retrieved June 1, 2008, from www.nrcs.usda.gov/programs/lesa/LESA%20Guidebook.pdf.

Soil and Water Conservation Society (2003). Enhancing LESA: Ideas for Improving the Use and Capabilities of the Land Evaluation and Site Assessment System Report. Ankeny, Iowa: Soil and Water Conservation Society. Retrieved June 1, 2008, from www.swcs.org/documents/LESA_Report_112904155120.pdf.

Steiner, F. R. (1994). "Introduction." In Steiner, F. R., J. R. Pease, and R. E. Coughlin (Eds.), A Decade with LESA: The Evolution of Land Evaluation and Site Assessment (pp. 13–19). Ankeny, Iowa: Soil and Water Conservation Society.

Tomlin, C. D. (1990). Geographic Information Systems and Cartographic Modeling. Englewood Cliffs, New Jersey: Prentice-Hall, Inc.

Tulloch, D. L., J. Hasse, J. Myers, P. Parks, and R. Lathrop (2003). "The Challenge of Automating Public Farmland Preservation Techniques." Landscape and Urban Planning, 63(1): pp. 33–48.

Williams, T. H. Lee (1985). "Implementing LESA on a Geographic Information System: A Case Study." Photogrammetric Engineering and Remote Sensing, 51(12): pp. 1923–1932.

Wright, L. E.; W. Zitzmann, K. Young, and R. Googins (1983). "LESA—Agricultural Land Evaluation and Site Assessment." Journal of Soil and Water Conservation, 38(2): pp. 82–86.

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