| Gully extraction and mapping in Kajoo-Gargaroo watershed- Comparative evaluation of DEM-based and image-based machine learning algorithm |
| Paper ID : 1159-GEOSPATIAL (R6) |
| Authors: |
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Mehdi Bokaei1, Meisam Samadi *2, Ahmad Hadavand3, Amir Payam Moslem4, Majid Soufi5, Abdollah Bameri6, Alireza Sarvarinezhad6 1Geographic Information System (GIS) Department, Ideh Pardazan Tosseah Consulting Engineering Company, Tehran, Iran 2Department of Natural Resources and Watershed Management, Ideh Pardazan Tosseah Consulting Engineering Company, Tehran, Iran 3Photogrammetry Department, Ideh Pardazan Tosseah Consulting Engineering Company, Tehran, Iran. 4Department of Natural Resources and Watershed management, Ideh Pardazan Tosseah Consulting Engineering Company, Tehran, Iran. 5Department of soil conservation and watershed management, Fars research and education center for agriculture and natural resources., and scientific consultant for gully erosion in Ideh Pardazan Tosseah Consulting Engineering Company, Tehran, Iran. 6Natural Resources and Watershed Management Organization of Sistan and Balouchestan Province, Zahedan, Iran |
| 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 were 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. |
| Keywords: |
| Gully erosion, UAV photogrammetry, DTM, orthophoto, machine learning, manual digitizing, and image classification. |
| Status : Paper Accepted (Poster Presentation) |
