dc.contributor.author | Maingi, John K | |
dc.contributor.author | Luhn, William M | |
dc.date.accessioned | 2013-07-10T12:12:40Z | |
dc.date.available | 2013-07-10T12:12:40Z | |
dc.date.issued | 2005 | |
dc.identifier.citation | GIScience & Remote Sensing Volume 42, Number 3 / July-September 2005 | en |
dc.identifier.issn | 1548-1603 | |
dc.identifier.uri | http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/47085 | |
dc.description.abstract | A decision tree classifier was used to create a three-species conifer map of the Daniel Boone National Forest, Kentucky using Landsat TM images and ancillary data. The resulting map had an overall classification accuracy of approximately 82%. In the second part of the study, Landsat TM and ETM+ images acquired in 1995 and 2002, respectively, were used to evaluate five change-detection techniques for mapping conifer damage caused by southern pine beetle (SPB). PCA and SARVI2 change-detection techniques resulted in the highest classification accuracies. Over 60% of the conifer species were killed as a result of SPB infestation. | en |
dc.language.iso | en | en |
dc.publisher | University of Nairobi | en |
dc.title | Mapping insect-induced pine mortality in the Daniel Boone National Forest, Kentucky using Landsat TM and ETM+ data | en |
dc.type | Article | en |
local.publisher | Department of Geography and Environment | en |