Research Article Open Access

Use of Satellite Data to Study the Impact of Land-Cover/Land-Use Change in Madison County Alabama

Tomas Ayala-Silva1, Garry Gordon1 and Robert Heath1
  • 1 USDA, ARS, National Germplasm Repository Subtropical Horticulture Research Station, United States


The monitoring of land/use land cover changes along the northern part of Madison County Alabama are essential for the developers, planners, policy makers and management of government, public and private organizations. Remote sensing was used to analyze and study land-use/land-cover use changes impact on the environment of Madison County Alabama. This study area was selected because it is one of the fastest growing areas in the state of Alabama. The study used data sets obtained from several sources. Remote sensing images, land-use/land-cover use maps, global positioning data. The remote sensing images were LANDSAT Thematic Mapper (TM) images acquired during April 1987 and May 1997. The data was processed and analyzed using MAP-X/RS and ERDAS. Six classes or categories of land-use/land-cover were analyzed to determine changes and the relationship to suburban sprawl. Each method used was assessed and checked in field. Six land use/land cover classes are produced. The overall accuracy for the 1987 image is (78.92%) and for the 1997 image is (85.44%) Analysis of the images for 1987 and 1997 showed a (26 and 15%) increase in the urbanization and industrial development respectively and a decrease in all other classes. The most significant decrease (25%) was in the pastures class, however, less significant changes were observed for the water resources and forest. The results from this study could be beneficial to state/county planners, researchers and policy makers.

American Journal of Applied Sciences
Volume 6 No. 4, 2009, 656-660


Submitted On: 6 March 2008 Published On: 30 April 2009

How to Cite: Ayala-Silva, T., Gordon, G. & Heath, R. (2009). Use of Satellite Data to Study the Impact of Land-Cover/Land-Use Change in Madison County Alabama . American Journal of Applied Sciences, 6(4), 656-660.

  • 36 Citations



  • Remote sensing
  • geospatial
  • classification