Title: Analyzing remote sensing data in R: the landsat package Author
Submitted to: Journal of Statistical Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 11, 2011
Publication Date: July 20, 2011
Citation: Goslee, S.C. 2011. Analyzing remote sensing data in R: the landsat package. Journal of Statistical Software. 43(4):1-25. Interpretive Summary: Over three decades of Landsat satellite images are available for the entire United States. These images are an invaluable resource for retrospective assessment of agricultural lands. Differing atmospheric conditions from image to image, and topographic variation within an image can overwhelm changes in ground surface reflectance due to differences in land cover. Atmospheric and topographic correction algorithms are being actively developed, but few of these methods are available in existing software. The R statistical software provides a means for rapid implementation and assessment of these algorithms. Eight atmospheric correction algorithms and seven topographic correction algorithms have been written in R and included in the landsat package. This work will provide a foundation for the development of new algorithms and speed the assessment of Landsat images for agricultural uses.
Technical Abstract: Research and development on atmospheric and topographic correction methods for multispectral satellite data such as Landsat images has far outpaced the availability of those methods in Geographic Information Systems software. As Landsat and other data become more widely available, demand for these improved correction methods will increase. The R statistical software can help bridge the gap between research and implementation. Sophisticated spatial data routines are already available, and the ease of program development in R makes it possible to quickly implement new correction algorithms and to assess the results. Collecting radiometric, atmospheric, and topographic correction routines into the landsat package will make them readily available for evaluation for particular applications.