Tropical Geography ›› 2019, Vol. 39 ›› Issue (4): 492-501.doi: 10.13284/j.cnki.rddl.003150

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Comparison of Mangrove Remote Sensing Classification Based on Multi-type UAV Data

Liu Kai1, Gong Hui1, Cao Jingjing1 and Zhu Yuanhui2   

  1. (1. School of Geography and Planning, Sun Yat-sen University//Provincial Engineering Research Center for Public Security and Disaster// Guangdong Key Laboratory for Urbanization and Geo Simulation, Guangzhou 510275, China; 2. Center of Geo Informatics for Public Security, College of Geographical Science, Guangzhou University, Guangzhou 510006, China)
  • Online:2019-07-10 Published:2019-07-10

Abstract:

Mangroves have important ecological functions. Where damage to mangroves is severe, remote sensing can be used to monitor the situation and provide information to support mangrove protection and resource management. Remote sensing using Unmanned Aerial Vehicles (UAVs) is flexible, low cost, and has higher spatial and temporal resolution than does satellite data. It has been successfully applied to a variety of research questions, including habitat classification. To popularize the application of UAVs in mangrove remote sensing, and summarize the data acquisition problems faced in this approach, it is necessary to compare and analyze the classification results of mangrove data from multi-type UAV surveys. In this study, fixed-wing UAVs, consumer rotorcraft UAVs, and professional rotorcraft UAVs are used to acquire images of Qi’ao Island Mangrove Reserve in Zhuhai, Guangdong Province. Using the object-oriented classification method, K-nearest neighbor, and random forest classifier, we classified mangrove species in the study area and compared the results from different UAVs. The classification accuracy of fixed-wing UAVs, consumer rotorcraft UAVs, and professional rotorcraft UAVs using the K-nearest neighbor method was 73.8%, 72.8%, and 79.7%, respectively, and that of the random forest method was 81.1%, 84.8%, and 89.3%, respectively. All three UAV types provided data that was suitable for mangrove classification. Random forest classification results were better than those of K-nearest neighbor classification; so, for UAV mangrove remote sensing data, priority should be given to the random forest method. Data acquisition efficiency was estimated by calculating the ratio of imaging area to acquisition time. Fixed-wing UAV, consumer rotorcraft UAV, and professional rotorcraft UAV collected data at 0.036, 0.013, and 0.003 km2/min, respectively. The fixed-wing UAV collected data much more rapidly than did the other UAVs, making it suitable for large-scale mangrove data acquisition. The professional rotorcraft UAV had the lowest data acquisition efficiency, but is low cost and easy to learn and use, making it suitable for the acquisition of small-scale data. Different UAVs had different sensors; the classification accuracy of the fixed-wing and consumer rotorcraft UAV data were similar, while that of the professional rotorcraft UAV was higher. The professional rotorcraft UAV was the most suitable for acquiring multi-source data as it has slightly heavier sensors, which include an imaging spectrometer and LiDAR. In the application of UAVs to mangrove remote sensing, attention should be paid to flight safety and data acquisition quality, and a reasonable data acquisition plan should be made depending on the research content and area under study. In this paper, we suggest approaches for the effective application of UAVs to mangrove remote sensing research.

Key words: UAV, remote sensing, mangrove, random forest classification, K-nearest neighbor method, object-oriented classification