Faculty Publications

Development Of An Agricultural Land Evaluation And Site Assessment (Lesa) Decision Support Tool Using Remote Sensing And Geographic Information System

Document Type

Article

Keywords

ArcGIS, GIS, LESA, Prime agricultural land, Remote sensing, Spatial decision support systems

Journal/Book/Conference Title

Journal of Soil and Water Conservation

Volume

60

Issue

5

First Page

228

Last Page

235

Abstract

The general trend in North America, which is agriculturally based, have indicated that prime agricultural lands are being constantly lost to natural hazards such as soil erosion, land degradation, and especially through human activities such as urban development. In order to control and manage these prime agricultural lands, there is a need for improved agricultural land evaluation methods. Lately, a combination of geospatial technologies (GST) such as remote sensing, geographical information systems (GIS), and the global positioning system (GPS), and land evaluation methods such as Land Evaluation and Site Assessment (LESA) have shown to be promising tools for land evaluation. Even though numerous studies have demonstrated the importance of integrating land evaluation models with GIS, few studies have combined the popular LESA model with GIS to provide a decision support tool for mangers. The goal of the study is to develop a decision support tool for agricultural land evaluation using geospatial technologies and a LESA model in Black Hawk County, Iowa. Currently, the county is using an Excel-based land evaluation and site assessment system, which lacks a spatial context even though land evaluation factors are spatial in nature. In order to develop a decision support tool, LESA criteria, and relevant data for these criteria were collected from different sources. Then, these data were analyzed using ERDAS IMAGINE and ArcGIS software. Finally, a decision support tool was developed by customizing ArcGIS software using Visual Basic Application. The results of the study found that soil productivity, development potentials, and farmland are the major factors in the LESA model in this county. 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.

Department

Department of Geography

Original Publication Date

12-1-2005

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