Karst landform classification considering surface flow characteristics derived from digital elevation models
Haoyu Cao
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Search for more papers by this authorCorresponding Author
Liyang Xiong
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Correspondence
Liyang Xiong, School of Geographic Science, Nanjing Normal University, No.1 Wenyuan Road, Qixia District, Nanjing 210023, China.
Email: [email protected]
Search for more papers by this authorJunfei Ma
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Search for more papers by this authorHongen Wang
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Search for more papers by this authorSijin Li
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Search for more papers by this authorFengyize Yu
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Search for more papers by this authorPeng Wang
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Search for more papers by this authorHaoyu Cao
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Search for more papers by this authorCorresponding Author
Liyang Xiong
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Correspondence
Liyang Xiong, School of Geographic Science, Nanjing Normal University, No.1 Wenyuan Road, Qixia District, Nanjing 210023, China.
Email: [email protected]
Search for more papers by this authorJunfei Ma
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Search for more papers by this authorHongen Wang
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Search for more papers by this authorSijin Li
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Search for more papers by this authorFengyize Yu
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Search for more papers by this authorPeng Wang
School of Geography, Nanjing Normal University, Nanjing, China
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, China
Search for more papers by this authorAbstract
Karst landforms are one of the most typical geographical units with a specific physical process on the earth's surface. The classification of karst landforms is an important aspect for understanding their landform processes and mechanisms. However, influenced by various interior and external forces, karst landforms have an extremely complex surface morphology, increasing the difficulty of their automatic classification. In this study, we considered hydrological features as an important factor in characterizing karst landforms and proposed a method that considers surface flow for karst landform classification. In this method, terrain was reversed for hydrological analysis to achieve the landform units. Then, the watershed boundary of the reversed terrain is extracted by hydrological analysis. The boundary of the karst landform unit is determined by erasing the plain area from the watershed boundary. Thereafter, the graph theory segmentation method is employed to merge the landform units belonging to the same karst landform entity. The proposed approach is validated and applied in two sample karst areas, Fenglin and Fengcong, located in Guilin, China, using digital elevation model data with 30 m spatial resolution. In addition, a comparative analysis is conducted to evaluate the accuracy of the proposed method. The results demonstrated that the typical karst landform units of Fenglin and Fengcong can be effectively classified. The overall classification accuracy is 94.44%. The proposed method produced more reasonable and accurate boundaries compared with the contour tree and terrain feature point methods. Furthermore, the classification results indicate various landform development stages of the karst landform process in the study area. The proposed method considering surface flow characteristics can be further extended to other landform types with highly complex landforms.
Open Research
DATA AVAILABILITY STATEMENT
Data will be made avaliable on request.
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