| 基于多类型无人机数据的红树林遥感分类对比 |
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| 引用本文： | 刘凯,龚辉,曹晶晶,朱远辉.基于多类型无人机数据的红树林遥感分类对比[J].热带地理,2019,39(4):492-501. |
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| 作者姓名： | 刘凯 龚辉 曹晶晶 朱远辉 |
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| 作者单位： | （1. 中山大学 地理科学与规划学院//广东省公共安全与灾害工程技术研究中心//广东省城市化与地理环境空间模拟重点实验室，广州 510275； 2. 广州大学 地理科学学院 公共安全地理信息分析中心，广州 510006） |
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| 基金项目： | 广东省自然科学基金项目（2016A030313261、2016A030313188）；海洋公益性行业科研专项经费项目（201505012） |
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| 摘 要： | 使用固定翼无人机、消费级旋翼无人机和专业级旋翼无人机获取广东珠海淇澳岛红树林保护区多类型无人机遥感影像，使用基于面向对象分类的K-最近邻与随机森林分类器对研究区影像进行红树林树种精细分类和对比分析，并探讨了不同类型无人机平台在红树林资源调查应用中的优缺点。结果表明：1）固定翼无人机、消费级旋翼无人机和专业级旋翼无人机数据使用K-最近邻法的分类精度分别为：73.8%、72.8%和79.7%；使用随机森林法的分类精度分别为：81.1%、84.8%和89.3%。3种平台类型的无人机数据均适用于红树林精细分类研究，对于无人机红树林遥感数据，随机森林的分类方法优于K-最近邻方法。2）以拍摄面积与用时之比估算采集效率，固定翼无人机、消费级旋翼无人机和专业级旋翼无人机分别为0.036、0.013和0.003 km2/min。固定翼无人机的采集效率具有明显优势。3）固定翼无人机适合大范围红树林数据采集，要求较高；消费级旋翼无人机适于获取小范围精细数据，成本低且易学易用；专业级旋翼无人机适合搭载质量稍大的如成像光谱仪、LiDAR等专业传感器获取多源数据。最后给出了无人机在红树林遥感研究中的注意事项和建议。
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| 关 键 词： | 无人机 遥感 红树林 随机森林分类 K-最近邻法 面向对象分类 |
Comparison of Mangrove Remote Sensing Classification Based on Multi-type UAV Data |
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| Liu Kai,Gong Hui,Cao Jingjing and Zhu Yuanhui.Comparison of Mangrove Remote Sensing Classification Based on Multi-type UAV Data[J].Tropical Geography,2019,39(4):492-501. |
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| Authors: | Liu Kai Gong Hui Cao Jingjing and Zhu Yuanhui |
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| Institution: | (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) |
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| 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. |
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| Keywords: | UAV remote sensing mangrove random forest classification K-nearest neighbor method object-oriented classification |
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