Abstract
Abstract. Impervious surface areas are artificial structures covered by materials such as asphalt, stone, brick, rooftops and concrete. Buildings, parking lots, roads, driveways and sidewalks are shown as impervious surfaces. They increase depending on the population growth. The spatial development of impervious surface expansion is necessary for better understanding of the urbanization status and its effect on environment. There are different impervious surface determining approaches met in literature. In this paper, it is aimed to extract the impervious surface areas of Kayseri city, Turkey by using remote sensing techniques. It is possible to group these techniques under a few main topics as V-I-S (vegetation-impervious surface-soil) model, based on spectral mixture analysis or decision tree algorithms or impervious surface indices. According to these techniques, we proposed a new technique by using RUSBoost algorithm based on decision tree in this study. In this scope, Landsat 8 LDCM image belonging to July, 2013 was used. Determining of impervious surface areas accurately depends on accuracy of image classification methods. Therefore, satellite image was classified separately by using Classification Tree and RUSBoost boosting method which increases accuracy of the classification method based on decision tree. Classification accuracies of these supervised classification methods were compared and it was observed that the best overall accuracy was obtained with RUSBoost method. For this reason, RUSBoost method was preferred to determine impervious surface areas. The overall accuracies were obtained 95 % with Classification Tree and 97 % with RUSBoost boosting method.
Highlights
When population distribution throughout the world is examined, it is indicated that 54% of people are living in urban areas, this condition will increasingly continue and this rating will reach to the level of 66% percent by 2050 according to 2014 reports of United Nations
Classification was performed with respect to 1000 different iterations by using each of the Classification tree and RUSBoost methods on the same data set and it was determined which combination of these methods was the best in delivering the classification result
One of the most significant factors affecting the accuracy of the study performed is the success of the classification method
Summary
When population distribution throughout the world is examined, it is indicated that 54% of people are living in urban areas, this condition will increasingly continue and this rating will reach to the level of 66% percent by 2050 according to 2014 reports of United Nations. The constructions built in line with increasing requirements are signs that the natural environment and climate are adversely affected in contrast to being positive signs of growth and wealth. The increasing number of buildings lead to disruptions in natural habitat by giving rise to decrease in stream levels, decrease in water quality, changes in land surface temperatures, increase in the frequency of storm and flood disasters (Weng, 2012). It is of great importance that environmental changes are examined and the interaction between man and environment is identified
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