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无人机遥感技术在生产建设项目水土保持监测中的应用——以鄂北水资源配置工程为例 被引量:41

Application of the UAV remote sensing technology in soil and water conservation monitoring in construction projects:A case study of water resources allocation for the region of north Hubei
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摘要 无人机遥感技术在水土保持监测工作中的应用尚未形成统一有效的方法与标准,针对这一问题,笔者以鄂北地区水资源配置工程为例,从遥感基础数据获取、基本监测信息提取、监测信息应用3个方面,开展水土保持监测实例分析。首先,针对案例工程的几个典型区,在航拍的基础上,利用Agisoft Photoscan Professional软件处理原始影像,获取各区域的DOM和DEM成果,其水平精度分别可达0.05和0.2 m。然后,基于DOM和DEM提取监测对象的土地利用类型、位置、面积、体积等信息,构建三维虚拟模型。结果表明计算机自动识别更加快速,但存在将监测对象的阴影、苫盖误判为植被的问题。最后,将提取的信息逐一应用于案例工程的水土保持监测工作,直接获取各典型区扰动土地面积、弃渣体积、苫盖与否、苫盖面积等监测结果。无人机监测结果与运用传统方法监测的结果对比表明,2种方法的监测结果均满足相关技术要求,但无人机监测效率是传统人工监测效率的3~5倍。成果可为无人机遥感技术在水土保持监测中的应用提供有效的方法借鉴。 [Background]The Unmanned Aerial Vehicle(UAV) remote sensing is a new technology for soil and water conservation monitoring.It fills the gap between satellite remote sensing and surface observation,which provides a flexible method for users.However,as to combine this new technology with the criterion and specification in soil and water conservation monitoring in construction projects,more practices are needed.[Methods] We conducted a case study for the application of UAV remote sensing technology in soil and water conservation monitoring.This case study was based on a practical construction project called "Water Resources Allocation for the Region of North Hubei"and was divided into three parts:the acquisition of basic remote sensing data,the extraction of monitoring information and the application of information in monitoring work.First,we took the aerial images of some typical regions of the project,then using the software Agisoft Photoscan Professional,we processed the raw data to generate two products,the Digital Elevation Model(DEM) and Digital Orthophoto Map(DOM) for each region.Finally,based on the criterion and specification in soil and water conservation monitoring inconstruction projects,the application of extracted information from the DEM and DOM results aims to apply effective information into practical monitoring work [Results] The result showed that horizontal resolutions for all the DOM were less than 0.05 m and those for all the DEM were less than 0.2 m.The computer automatic classification was much faster than visual interpretation for classifying the land cover type.However,the computer automatic method had some problems of misclassifying the shadow of the water soil and coverings into vegetation.Based on the UAV method,the disturbed production and living areas in construction project for the exit of Jihong Tunnel,the entrance of 16^(th) section,the 1^(th) and 2^(th) disposal area in the 16^(th) section were 16 562.2 m^2,5 153.8 m^2,4 268.6 m^2 and 19 108.5 m^2,respectively.The volume of the 1^(th) and 2^(th) disposal area in the 16^(th) section was 11^(th) ousand m^3 and 172thousand m^3.The 1^(th) and 2^(th) temporary cover area of the 1^(th) disposal area in the 16^(th) section was 166.9 m^2 and 1 229.9 m^2,respectively.Compared to the statistical results of the construction organization,the monitoring values based on the UAV and the traditional methods were mostly controlled in 10% of the difference,meeting the relevant requirements.However,the monitoring efficiency of the UAV WAS three to five times of that by the traditional methods.[Conclusions] These monitoring information are all required by the criterion and specification in soil and water conservation monitoring in construction projects.Therefore,by applying UAV remote sensing technology in soil and water conservation monitoring,we may acquire some essential information quickly and easily.Our study shows the feasibility of this method and also reveals that compared to traditional monitoring methods,the information extracted by this method are more accurate and can also save the monitoring cost and improve the monitoring efficiency.
出处 《中国水土保持科学》 CSCD 北大核心 2017年第2期132-139,共8页 Science of Soil and Water Conservation
基金 水利部"948"计划"无人机载微型高光谱成像仪"(201507)
关键词 无人机遥感技术 水土保持监测方法 生产建设项目 遥感数据 实例分析 UAV remote sensing technology soil and water conservation monitoring construction projects remote sensing data case study
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