| 面向中国洪涝灾害应急监测的无人机空港布局 |
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| 引用本文： | 鹿明,廖小罕,岳焕印,黄诗峰,徐晨晨,卢海英,柏艺琴.面向中国洪涝灾害应急监测的无人机空港布局[J].地球信息科学,2019,21(6):854-864. |
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| 作者姓名： | 鹿明 廖小罕 岳焕印 黄诗峰 徐晨晨 卢海英 柏艺琴 |
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| 作者单位： | 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101;天津中科无人机应用研究院,天津301800;中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京,100101;中国水利水电科学研究院遥感技术应用中心,北京,100038;中国电子技术标准化研究院物联网研究中心,北京,100007;中国民航科学技术研究院,北京,100028 |
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| 基金项目： | 国家自然科学基金项目（41771388）;国家重点研发计划项目（2017YFB0503005）;博士后基金项目（2018M640170） |
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| 摘 要： | 当前洪涝灾害对社会的经济发展和人民生命财产安全构成严重威胁。无人机机动、灵活,安全性高,可迅捷甚至实时获取灾区影像,对灾情的快速评估和应急救援决策意义重大。遥感无人机在洪涝灾害救援中能够发挥的重要作用已得到广泛共识,但是由于灾害的突发性,缺乏就近部署的资源制约了无人机遥感观测和救援作用的发挥。针对突发灾害,在区域和全国范围内建立起一定的无人机遥感应急体系成为国家面向未来正在考虑的选项。基于此,本研究提出了基于中国科学院的野外台站构建全国无人机遥感观测网的设想。本研究以中国防范洪涝灾害等级分布数据、行政区划数据、中国科学院野外台站分布数据和当前无人机性能数据库为数据源;以行政区划离散并提取的中心点作为需求点,台站作为设施点,不同洪涝等级区域内需要无人机进行应急观测的重要程度作为权重,利用最大覆盖选址模型进行空港选址布局;利用成本-效益曲线确定台站的最佳数量,最终从268个台站中选取出81个作为支撑全国洪涝灾害无人机遥感观测网络的无人机空港。无人机空港布局结果在理论上能够实现对中国绝大数突发洪涝灾害在2 h内初步完成洪涝观测,这对于构建中国空天地一体化的洪涝灾害监测体系具有重要意义。同时,本研究中的方法和成果对于进一步构建行业和综合性的全国无人机遥感观测网也具有一定借鉴和参照意义。
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| 关 键 词： | 无人机 空港布局 遥感观测网 洪涝灾害 应急救援 中国 |
| 收稿时间： | 2018-04-10 |
Determining the Distribution of Unmanned Aerial Vehicles Airports for the Emergency Monitoring of Floods in China |
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| Ming LU,Xiaohan LIAO,Huanyin YUE,Shifeng HUANG,Chenchen XU,Haiying LU,Yiqin BAI.Determining the Distribution of Unmanned Aerial Vehicles Airports for the Emergency Monitoring of Floods in China[J].Geo-information Science,2019,21(6):854-864. |
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| Authors: | Ming LU Xiaohan LIAO Huanyin YUE Shifeng HUANG Chenchen XU Haiying LU Yiqin BAI |
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| Abstract: | Frequent flood hazards affect large areas and cause great losses, which have posed a serious threat to economic development and people's lives and properties. Unmanned Aerial Vehicles (UAVs) have proven to be useful in monitoring disaster status and providing decision-making support for emergency rescue, because they are able to arrive at floods area timely, obtain flood images and videos quickly, low-risk to operate and flexible to carry out different sensors. The important roles of UAV in emergency rescue have been widely recognized. However, the lack of available UAV resources nearby at the sudden onset of floods seriously limits the capability of UAVs' rapid response to flood disasters. For addressing this challenge, a multi-UAVs remote sensing observation network is highly required, and now has been planned in China to enhance our ability of emergency response. Key problems include where and how to allocate UAVs resources such that the UAVs can reach destination timely when floods occur. To help close this gap, we proposed to build a number of UAV airports in China to establish a remote sensing observation network of UAVs. Field stations of Chinese Academy of Science (CAS) were considered as the potential locations of UAV airports because of their extensive geographical distribution and good cooperation with the UAV application and regulation research center of CAS. With the available flood risk prevention data, administrative division data, CAS field stations data and the UAV database as data sources, we created a fishnet of 0.5° multiply 0.5° to discretize administrative divisions and regarded the central points of these grids as potential demand points, and then calculated the importance of UAV to those demand points based on their flood risk prevention level. Based on this analysis, a Maximum Covering Location Problem (MCLP) model was adopted to determine the optimum stations for UAV airports and a cost-effectiveness curve was used to determine the optimum number of UAV airports. In the end, 81 field stations were selected from 268 field stations, thereby ensuring that UAV airports would be allocated near flood-prone areas and most floods in China could be monitored with UAVs within two hours, which is critical for saving lives and reducing losses. The construction of UAV airport networks will surely contribute to an integrated disaster emergency observation system combining satellite, airplane, UAV and ground observations in China. Additionally, the methods and results in this study can serve as a basis for building a more comprehensive national UAV remote sensing observation network. |
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| Keywords: | UAV airport distribution remote sensing observation network flood disaster emergency rescue China |
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