GULLY EXTRACTION AND MAPPING IN KAJOO-GARGAROO WATERSHED – COMPARATIVE EVALUATION OF DEM-BASED AND IMAGE-BASED MACHINE LEARNING ALGORITHM

Abstract

Monitoring and mapping eroded lands by gully erosion is an essential step to control gully networks. Advances in remote sensing and aerial photography have enabled users to capture data with variant temporal and spatial resolution that is needed in different fields. In addition, introducing different types of unmanned aerial vehicles (UAV) enabled to carry imaging payload. The orthophoto and digital elevation model (DTM) produced from aerial images taken by Aeria-X camera mounted on Sensefly eBee-X drone was employed to identify and map eroded areas by gully in Kajoo-Gargaroo watershed in Chabahar, south-eastern part of Iran. Digitizing gully boarders manually is a tiring and time-consuming process for the operators. Maximum likelihood algorithm as one of the machine learning algorithm was also used to classify orthophoto in order to extract gully borders in the study area. In this study a new algorithm based on analysing geometric features and clustering of the DTM was used to map gullies automatically. The results of the proposed method and machine learning algorithm were compared with the manually digitized gully map. Quantitative evaluation demonstrates that our proposed method reaches better overall accuracy compared to machine learning algorithm with the increase of 7.2 percent in overall accuracy.