Evaluating Land-Use Classification Methodology Using Landsat Imagery Alexa Junker (‘16), ES212: Introduction to GIS and Remote Sensing Introduction Geographic Information Systems technology is a powerful tool for visualizing spatial attributes of datasets. The technology can also be used to identify different types of land use from satellite images. This study attempts to recreate existing ground-truth datasets of land-use classifications for Brevard County in east-central Florida from Landsat 5 satellite images. This is accomplished using two different classification tools provided by the GIS program ArcMap, assessing to what extent each classification matches the original ground-truth dataset. Year Ground-Truth vs. Unsupervised Percent Match Ground-Truth vs. Trained 1995 50.35% 67.56% 2000 52.77% 53.70% 2004 54.96% 64.42% 2009 56.48% 66.20% a b Table 1. Results of binary comparisons of unsupervised and trained classifications to ground-truth dataset for all four years Results & Discussion As can be seen in table 1, the trained classification method was more accurate for all years. This difference in accuracy ranged from 17% in 1995 to slightly less than 1% in 2000. The 2004 and 2009 images both had a roughly 10% difference in accuracy between the two classification methods. The difference is relatively consistent throughout the study period, with the exception of the year 2000. This variation can be attributed to a number of factors. First of all, the ground-truth datasets were manually interpreted from aerial photography. Photo-interpreting ground features involves many judgment calls made on the basis on grainy areal images and, sometimes, limited knowledge of the study region. Secondly, some (less than 10%) cloud cover was present in the 2004 and 2009 images, and certainly distorted the trained as well as the unsupervised classifications. Since “clouds” was not one of the available classes, the software had to decide to which of the six classes they should be attributed. Thirdly, not all four Landsat images were taken during the same month of the year (2000 and 2009 were taken in November, 1995 in December, and 2004 in March). Although east-central Florida experiences only limited seasonality, some vegetation sheds its leaves in December through February. This factor probably affected the reflectance values of the three vegetation-based classes (non-forested land, forest, and wetland) and may have caused differing responses from the classification tools. In general, it seems that trained classification, as long as it is based on a repeatable database of training polygons, is a good option when a quick classification of a Landsat image is needed. However, with a 67% match to ground-truth datasets at best, no important findings or decisions should be based on this type of classification. Unsupervised classification, on the other hand, should always be the last choice since it can achieve only a slightly more than 50% match to the ground-truth datasets. Methods The first step in performing this study was obtaining the data. The ground-truth land-use datasets for Brevard County, Florida, were downloaded from the St. Johns River Water Management District website, and the Landsat satellite images of the area for the years 1995, 2000, 2004, and 2009 were downloaded from the USGS’ website http://glovis.usgs.gov/. For each Landsat scene, six of the seven bands (excluding band 6 – thermal infrared) were loaded into an ArcMap document where all bands were converted from digital numbers to at-the-sensor reflectance values. The six reflectance bands were then combined into a composite image for each of the four years. Using the outline of Brevard County (extracted from the ground-truth land-use datasets), the Landsat composites were clipped to the shape of the county for subsequent classification. Next, land-use classes in the ground-truth datasets were condensed from over a hundred classes to 6 classes: urban, agriculture, non-forested land, forest, water, and wetland. These classes were assigned the values 1 through 6, and a new vector layer with only these six classes was created, to be subsequently converted into a raster layer. In the next step, the “unsupervised” feature of the classification tool was used to classify the Landsat images into six classes, which were matched (by sight) with the six classes described above, after which they were assigned the same values (1-6) and the same color scheme as the land-use classes of the ground-truth datasets. The classification tool was used again, but this time it was trained to recognize the six classes. This training was accomplished by defining six polygons within the corresponding area for each class. To analyze the accuracy of the two classification tools, binary comparisons (match/no match) were used to compare the value of each pixel in the ground-truth dataset to the unsupervised classification and the trained classification. An example of this workflow is shown for the year 1995. c d e f Figure a shows a Landsat 5 satellite image of Brevard County, FL,in 1995, figure b ground-truth land-use dataset, figures c and d show unsupervised and trained classification, and figures e and f show binary analysis of match of ground-truth to unsupervised and trained classification, respectively. Acknowledgements St. Johns River Water Management District http://www.sjrwmd.com/gisdevelopment/docs/themes.html United States Geological Survey Landsat Program http://glovis.usgs.gov/