Journal of Geo-information Science ›› 2019, Vol. 21 ›› Issue (8): 1295-1306.doi: 10.12082/dqxxkx.2019.180631

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Object-Based Karst Wetland Vegetation Classification Method Using Unmanned Aerial Vehicle images and Random Forest Algorithm

GENG Renfang1,2,FU Bolin1,*(),CAI Jiangtao1,3,CHEN Xiaoyu4,LAN Feiwu1,YU Hangming1,LI Qingxun1   

  1. 1. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
    2. School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;
    3. School of Geographic Information Science, Nanjing University of Information Science & Technology, Nanjing 210044, China;
    4. College of Civil Engineering and Architecture, Guilin University of Technology, Guilin 541004, China
  • Received:2018-12-05 Revised:2019-03-20 Online:2019-08-25 Published:2019-08-25
  • Contact: FU Bolin
  • Supported by:
    National Natural Science Foundation of China, No.41801071(41801071);Guangxi Natural Science Foundation of China, No.2018GXNSFBA281015(2018GXNSFBA281015);Guilin University of Technology research Foundation of China, No.GUTQDJJ2017096(GUTQDJJ2017096);Guangxi Bagui Scholars" Foundation Support"


Wetlands are among the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water, and also provide habitats for many plants and animals. A unique wetland type, karst wetland, is widely distributed in southwest China, as influenced by the special soil and water structure of karst landforms. Currently, domestic and foreign scholars pay much less attention to karst wetland than other wetland types, and lack targeted research on high-precision vegetation identification of karst wetland using remote sensing technology. However, like other wetland types, karst wetland has seriously degraded, and many problems need to be solved urgently. Huixian National Wetland Park, located in Guilin, Guangxi province, is a typical karst wetland. In this paper, part of the core area of the Huixian National Wetland Park was selected as the study area, which is greatly affected by human activities and severely degraded. The aerial photography images from an unmanned aerial vehicle (UAV) were used as the data source, and the object-based random forest algorithm was used to realize the high-precision classification of karst wetland vegetation. In so doing, we explored the applicability of UAV RGB remote sensing image and object-based random forest algorithm in karst wetland vegetation recognition, and provided a technical reference for the research and protection of karst wetland by using UAV remote sensing technology. First, the multiscale iterative segmentation algorithm was used to segment the image layers in eCognition Developer 9.0. Then, the texture features calculated based on the grey level co-occurrence matrix (GLCM) and spectral features of the images, the vegetation indexes, geometric features, and the elevation information (DSM) derived from the UAV remote sensing data were fully considered in the feature selection. Finally, the tuning of random forest algorithm parameters, model construction, and classification were implemented in RStudio. Results showed that the object-based random forest algorithm had a high recognition ability for the Huixian wetland vegetation. The overall accuracy was 86.75% and the Kappa coefficient was 0.83 in the 95% confidence interval. In the identification accuracy of vegetation in a single typical karst wetland, the user accuracy of the vegetation cluster of Bermudagrass-Cogongrass-Ludwigia was above 90%, the producer accuracy was over 80%. And the producer accuracy of the Bamboo-Thorny Wingnic-Sweet Olive was higher than 80%, but its user accuracy was only 70.59%.

Key words: UAV remote sensing, object-based, random forest, multiscale iterative segmentation, feature selection, karst wetland, vegetation classification, Huixian National Wetland Park, located in Guilin, Guangxi province