低空遥感无人机影像反演河道流量
Measuring streamflow with low-altitude UAV imagery
查看参考文献55篇
|
文摘
|
河道流量在维持水圈系统稳定性、估算国家水能资源可开发量等方面具有重要作用。卫星遥感受其分辨率限制很难准确反演中小河流流量,近地面遥感流量计算方法及传统水文测流方法技术复杂、设备昂贵、测算效率低,限制了其在无资料区、灾害突发事件非接触式应急监测等方面的广泛应用。为此,在充分吸收国内外遥感反演河道流量方法优点的基础上,基于低空遥感无人机(UAV)影像,提出了一种适用于各类尺度河流的高效、非接触、简易快速反演河道流量的方法。该方法提供了有、无地面实测大断面两类情况下流量反演途径,通过无人机影像生成点云和表面高程(DSM),基于点云和DSM获取水面宽、糙率、水面比降以及水上大断面信息,采用水力学方法计算河道流量。并根据地面336组野外站点实测数据验证了方法的精度,进一步分析了无地面实测大断面情况下的流量计算误差。结果表明,反演流量在高值区略高于实测流量,可以满足灾害应急监测流量精度需求(R~2= 0.997,RMSE = 4.55 m~3/s);无地面实测大断面资料而进行概化时,流量计算误差随水位升高、河宽增大而减小,最大累积误差为最大过水流量的8.28%,误差主要来自于水位低、河宽小、流量小的过水断面底部。考虑到研究区大断面多样性受限,而人类活动影响下的河底断面复杂多样,未来尚需进一步研究提高近河底处大断面概化精度,以提高无地面实测大断面情况下的流量反演精度。本文利用无人机遥感影像反演河道流量的思路可为灾害应急监测提供快速流量监测的新途径,也可为无资料地区遥感水文测站的建立提供重要参考依据。 |
|
其他语种文摘
|
Stream flows are of great importance in maintaining a stable hydrosphere and assessing available water resources of a nation. However, previous satellite- methods are difficult to retrieve stream flows for middle- or small- scale rivers due to the satellite course spatial resolution whereas near- ground measuring methods have too complex procedure, requirement of expensive apparatus, or low-efficiency. These shortcomings hindered them to be used widely in non- gauged areas and situations needing non- contact measurement, e.g., accidental pollution events. This paper presented a novel, non-contact, fast method to calculate streamflow using UAV images which can be easily applied to rivers with different scales of width. Using this method, stream flows can be calculated with or without ground- measured cross-section data. With UAV images it produced point-cloud and DSM (digital surface model) which were then used to calculate values of river-width, roughness, longitudinal water-surface slope and cross- section above water surface. With all these values, the hydraulic method was finally adopted to calculate stream flows. Results show that the method has a satisfactory performance with modelled streamflow values slightly higher than observed ones at high-flow periods (R~2 = 0.997, RMSE = 4.55 m~3/s) with ground- observed cross- section data. When the cross-section data were absent, the cross-section under water can be generalized with the UAV measured above-water cross-section data. Errors in estimating stream flows induced by crosssection generalization decreased with increment of water-level and water-width. The maximum accumulated errors accounted for 8.28% of the bankfull streamflow. The errors were resulted from the generalization of river bottom with un- regular cross- sections. All the results and methodologies could be of great help in streamflow measurement in accidental pollution events and in ungauged areas across the globe. |
|
来源
|
地理学报
,2019,74(7):1392-1408 【核心库】
|
|
DOI
|
10.11821/dlxb201907009
|
|
关键词
|
河道流量
;
无人机
;
水力学
;
济南
|
|
地址
|
1.
北京师范大学水科学研究院, 城市水循环与海绵城市技术北京市重点实验室, 北京, 100875
2.
北京师范大学地理科学学部, 遥感科学国家重点实验室, 北京, 100875
3.
山东农业大学水利土木工程学院, 泰安, 271018
4.
济南市水文局, 济南, 250013
5.
东营市水文局, 东营, 257000
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
0375-5444 |
|
学科
|
地球物理学;自动化技术、计算机技术 |
|
基金
|
国家重点研发计划
|
|
文献收藏号
|
CSCD:6540734
|
参考文献 共
55
共3页
|
|
1.
魏桢. 再生水补水河道中流速对浮游藻类生长影响的模拟实验.
环境工程学报,2017,11(12):6540-6546
|
CSCD被引
7
次
|
|
|
|
|
2.
Ruiz J. Turbulence increases the average settling velocity of phytoplankton cells.
Proceedings of the National Academy of Sciences of the United States of America,2004,101(51):17720-17724
|
CSCD被引
6
次
|
|
|
|
|
3.
Escartin J. Flow structure and dispersion within algal mats.
Estuarine, Coastal and Shelf Science,1995,40(4):451-472
|
CSCD被引
10
次
|
|
|
|
|
4.
邓斅学. 河道流量测量与计算方法研究.
中国农村水利水电,2015(6):18
|
CSCD被引
1
次
|
|
|
|
|
5.
王平.
基于视觉分析的淤积河道流量测量方法的研究,2006
|
CSCD被引
1
次
|
|
|
|
|
6.
张继群. 基于TOPEX/Poseidon卫星数据的江河流量测算.
地理学报,2004,59(1):95-100
|
CSCD被引
2
次
|
|
|
|
|
7.
江辉.
基于多源遥感的鄱阳湖水质参数反演与分析,2011
|
CSCD被引
5
次
|
|
|
|
|
8.
李自立.
地波雷达表面流探测与深层流反演算法研究,2010
|
CSCD被引
2
次
|
|
|
|
|
9.
李蔚.
基于立体视觉与LSPIV的河流水动力过程近距遥感测量系统,2016
|
CSCD被引
2
次
|
|
|
|
|
10.
Hirpa F A. Upstream satellite remote sensing for river discharge forecasting: Application to major rivers in South Asia.
Remote Sensing of Environment,2013,131:140-151
|
CSCD被引
1
次
|
|
|
|
|
11.
Costa J E. Use of radars to monitor stream discharge by noncontact methods.
Water Resources Research,2006,42(7):27-42
|
CSCD被引
1
次
|
|
|
|
|
12.
Costa J E. Measuring stream discharge by non-contact methods: A Proof-of-Concept Experiment.
Geophysical Research Letters,2000,27(4):553-556
|
CSCD被引
5
次
|
|
|
|
|
13.
LeFavour G. Water slope and discharge in the Amazon River estimated using the shuttle radar topography mission digital elevation model.
Geophysical Research Letters,2005,32(17):404-405
|
CSCD被引
7
次
|
|
|
|
|
14.
Jung H C. Characterization of complex fluvial systems using remote sensing of spatial and temporal water level variations in the Amazon, Congo, and Brahmaputra rivers.
Earth Surface Processes and Landforms,2010,35(3):294-304
|
CSCD被引
4
次
|
|
|
|
|
15.
Smith L C. Estimation of discharge from braided glacial rivers using ERS 1 synthetic aperture radar: First results.
Water Resources Research,1995,31(5):1325-1329
|
CSCD被引
8
次
|
|
|
|
|
16.
Smith L C. Estimation of discharge from three braided rivers using synthetic aperture radar satellite imagery: Potential application to ungaged basins.
Water Resources Research,1996,32(7):2021-2034
|
CSCD被引
11
次
|
|
|
|
|
17.
宋平. 陆地水体参数的卫星遥感反演研究进展.
地球科学进展,2011,26(7):731-740
|
CSCD被引
16
次
|
|
|
|
|
18.
Xu K. Estimating river discharge from very high-resolution satellite data: A case study in the Yangtze River, China.
Hydrological Processes,2004,18(10):1927-1939
|
CSCD被引
1
次
|
|
|
|
|
19.
Leopold L B. The hydraulic geometry of stream channels and some physiographic implications.
US Government Printing Office,1953,252:22-53
|
CSCD被引
1
次
|
|
|
|
|
20.
Pavelsky T M. Using width-based rating curves from spatially discontinuous satellite imagery to monitor river discharge.
Hydrological Processes,2014,28(6):3035-3040
|
CSCD被引
7
次
|
|
|
|
|
了解气象卫星更多信息，请访问