An evaluation of high-resolution land cover and land use classification accuracy by thematic, spatial, and algorithm parameters
MetadataShow full item record
High resolution land cover and land use classifications have applications in many fields of study such as land use and cover change, carbon storage measurements and environmental impact assessments. The wide range of available imagery at different spatial resolutions, potential thematic classes, and classification methods introduces the problem of understanding how each aspect affects accuracy. This study investigates how these three aspects affect the results of land cover classification. Results show that the maximum likelihood classifier was able to produce the most consistent results with the highest average accuracy (82.9%). Classifiers were able to identify a spatial resolution for each thematic resolution that achieved a distinctly higher overall accuracy. In addition, the effects of different land cover classifications as input to an object-based classification of land use at the parcel scale were evaluated. Results showed that land use classification requires higher resolution imagery to obtain satisfactory results than what is required for land cover classification. Also, the highest accuracy land cover classification did not produce the highest accuracy for land use, where a higher number of thematic classes performs better than fewer thematic classes. The highest accuracy LC classification by MLC with 8 classes occurred at 640 cm and achieved an overall accuracy of 83.3%. The highest accuracy LU classification was produced by the 80 cm LC with 8 classes and achieved an overall accuracy of 88.0%. Aside from the produced land cover and land use classifications, this study produces a lookup table in the form of multiple graphs for future research to reference when selecting imagery and determining thematic classes and classification methods.
Cite this version of the work
Alexander Kirby Smith (2017). An evaluation of high-resolution land cover and land use classification accuracy by thematic, spatial, and algorithm parameters. UWSpace. http://hdl.handle.net/10012/12506