基于SPOT5遥感影像和DEM的河流流量估算
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摘要
针对目前遥感手段估算河流流量的方法适用性不强的问题,提出一种基于SPOT5遥感影像和DEM的河流流量估算方法。该方法借助DEM数据获取河段上某一断面位置处的地形剖面,从SPOT5遥感影像中提取水域并得到水面宽度,进而得到过水断面面积和水力半径,然后通过该河段上下游断面的水面高差及河段长度求取水面比降,确定糙率后使用曼宁公式估算该断面的流量。利用该方法对选取的东江干流及秋香江、西枝江上7个断面的流量进行了估算,并用实测流量数据对其精度进行了验证。结果显示绝对误差最小为-2.71 m~3/s,最大为-78.28 m~3/s,相对误差最小为9.02%,最大为37.69%,有6个断面的相对误差皆小于20%,而平均相对误差为15.87%。在这7个断面中,河宽最小达到75.51 m,3个在150 m以内,都被成功提取出来。对比分别使用BJ03式和曼宁公式估算河流流量的结果,发现曼宁公式精度更高。结果表明该估算河流流量的方法可行,并能够估算宽度在100 m以内河流上任意断面的流量,对获取缺乏水文资料地区的河流流量有借鉴意义。
An estimation method of river discharge was presented based on the SPOT5 remote sensing imagine and DEM aiming to solve the poor practicability of remote sensing techniques in estimating the river discharge. DEM data was used to acquire the topographic profile along a certain cross section place in river reach, while the SPOT5 remote sensing imagine was extracted with water area and the width of water surface. Then the cross-section area and hydraulic radius were calculated. Also, the water surface slope was calculated from the water surface elevation difference between the upstream and dowstream and the length pf the whole reach. Manning formula was finally used to estimate the discharge of this cross-section after identifying the roughness coefficient. The discharge of seven cross-sections on the main stream of Dong River, Qiuxiang River, and Xizhi River were estimated based on proposed method, and the result precision was verified by the actual measured discharge. The results showed that the minimum and maximum absolute error were-2.71 m~3/s and-78.28 m~3/s respectively, while the minimum and maximum relative error were 9.02% and 37.69%. The average relative error is 15.87% as six sections were less than 20%. Among the seven sections, the minimum river width was 75.51 m, and the width of three sections were less than 150 m. All of them were successfully extracted using SPOT5 remote sensing image. It was also found that the discharge estimation accuracy of Manning formula was higher than BJ03. Generally, it proved this proposed method could be applied on the estimation of river discharge, and cross-section width within 100 m can be well estimated. This can further provide reference for the estimation of discharge in regions that lacks hydrological data.
An estimation method of river discharge was presented based on the SPOT5 remote sensing imagine and DEM aiming to solve the poor practicability of remote sensing techniques in estimating the river discharge. DEM data was used to acquire the topographic profile along a certain cross section place in river reach, while the SPOT5 remote sensing imagine was extracted with water area and the width of water surface. Then the cross-section area and hydraulic radius were calculated. Also, the water surface slope was calculated from the water surface elevation difference between the upstream and dowstream and the length pf the whole reach. Manning formula was finally used to estimate the discharge of this cross-section after identifying the roughness coefficient. The discharge of seven cross-sections on the main stream of Dong River, Qiuxiang River, and Xizhi River were estimated based on proposed method, and the result precision was verified by the actual measured discharge. The results showed that the minimum and maximum absolute error were-2.71 m~3/s and-78.28 m~3/s respectively, while the minimum and maximum relative error were 9.02% and 37.69%. The average relative error is 15.87% as six sections were less than 20%. Among the seven sections, the minimum river width was 75.51 m, and the width of three sections were less than 150 m. All of them were successfully extracted using SPOT5 remote sensing image. It was also found that the discharge estimation accuracy of Manning formula was higher than BJ03. Generally, it proved this proposed method could be applied on the estimation of river discharge, and cross-section width within 100 m can be well estimated. This can further provide reference for the estimation of discharge in regions that lacks hydrological data.
引文
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[4] 卢善龙,吴炳方,闫娜娜,等. 河川径流遥感监测研究进展[J].地球科学进展,2010,25(8):820-826.
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[8] VACHTMAN D, LARONNE J B. Remotely sensed estimation of water discharge into the rapidly dwindling Dead Sea[J]. Hydrological Sciences Journal, 2014, 59(8):1593–1605.
[9] BJERKLIE D M, LAWRENCE D S, VOROSMARTY C J, et al. Evaluating the potential for measuring river discharge from space[J]. Journal of Hydrology, 2003, 278:17-38.
[10] LEFAVOUR G, ALSDORF D. Water slope and discharge in the Amazon River estimated using the shuttle radar topography mission digital elevation model[J]. Geophysical Research Letters, 2005, 32(17):395-403.
[11] 贾界峰,赵井卫,陈客贤. 曼宁公式及其误差分析[J]. 山西建筑,2010,36(7):313-314.
[12] JUNG H C, HAMSKI J, DURAND M, et al. Characterization of complex fluvial systems using remote sensing of spatial and temporal water level variations in the Amazon, Congo, and Brahmaputra Rivers[J]. Earth Surface Processes and Landforms, 2010, 35(3):294-304.
[13] WOLDEMICHAEL A T, DEGU A M, HOSSAIN F. Role of Land–Water Classification and Manning's Roughness Parameter in Space-Borne Estimation of Discharge for Braided Rivers:A Case Study of the Brahmaputra River in Bangladesh[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2010, 3(3):395-403.
[14] BIRKINSHAW S J, MOORE P, KILSBY C G, et al. Daily discharge estimation at ungauged river sites using remote sensing[J]. Hydrological Processes, 2014, 28:1043-1054.
[15] SICHANGI A W, WANG L, YANG K, et al. Estimating continental river basin discharges using multiple remote sensing data sets[J]. Remote Sensing of Environment, 2016, 179:36-53.
[16] 张罗号. 明渠水流阻力研究现状分析[J].水利学报,2012,43(10):1154-1162.
[17] MANNING R. On the flow of water in open channels and pipes[J]. Transactions Institute of Civil Engineers of Ireland, 1891, 20:161-209.
[18] 广东省地方史志编纂委员会. 广东省志地理志[M]. 广州:广东人民出版社,1999.
[19] 陈蕾,邓孺孺,彭小娟. TM影像与DEM的地形光照模型配准法研究——以广州市为例[J]. 热带地理,2008,28(5):224-227.
[20] 陈蕾,邓孺孺,柯锐鹏,等. 基于地面耦合的TM影像的大气校正——以珠江口为例[J]. 地理与地理信息科学,2004,20(2):34-37.
[21] MCFEETERS S K. The Use of Normalized Difference Water Index(NDWI) in the Delineation of Open Water Features[J].International Journal of Remote Sensing, 1996, 17(7):1425-1432.
[22]黄汝根,孙祥鹏,牟舵. 基于“高分一号”遥感影像水体提取方法的对比分析[J]. 人民珠江,2017,38(3):66-70.
[23]OTSU N. A Threshold Selection Method from Gray-Level Histograms[J]. IEEE Transactions on Systems Man and Cybernetics, 2007, 9(1):62-66.
[24] 张小琴,包为民,梁文清,等. 河道糙率问题研究进展[J]. 水力发电,2008,34(6):98-100.
[25] 徐慧敏. 关于水利工程中河道糙率的研究[J]. 水利科技与经济,2010(11):1253-1256.
[2] CALMANT S, SEYLER F. Continental surface waters from satellite altimetry[J]. Comptes Rendus Geoscience, 2006, 338(14):1113-1122.
[3] ALSDORF D E, MELACK J M, DUNNE T, et al. Interferometric radar measurements of water level changes on the Amazon flood plain[J]. Nature, 2000, 404(6774):174-177.
[4] 卢善龙,吴炳方,闫娜娜,等. 河川径流遥感监测研究进展[J].地球科学进展,2010,25(8):820-826.
[5] SMITH L C, ISACKS B L, FORSTER R R, et al. Estimation of discharge from braided glacial rivers using ERS 1 synthetic aperture radar: First results[J]. Water Resources Research, 1995, 31(5):1325-1329.
[6] KOURAEV A V, ZAKHAROVA E A, SAMAIN O, et al. Ob'river discharge from TOPEX/Poseidon satellite altimetry (1992–2002)[J]. Remote Sensing of Environment, 2004, 93(1):238-245.
[7] ZAKHAROVA E A, KOURAEV A V, CAZENAVE A, et al. Amazon River discharge estimated from TOPEX/Poseidon altimetry[J]. Comptes Rendus Geoscience, 2006, 338(3):188-196.
[8] VACHTMAN D, LARONNE J B. Remotely sensed estimation of water discharge into the rapidly dwindling Dead Sea[J]. Hydrological Sciences Journal, 2014, 59(8):1593–1605.
[9] BJERKLIE D M, LAWRENCE D S, VOROSMARTY C J, et al. Evaluating the potential for measuring river discharge from space[J]. Journal of Hydrology, 2003, 278:17-38.
[10] LEFAVOUR G, ALSDORF D. Water slope and discharge in the Amazon River estimated using the shuttle radar topography mission digital elevation model[J]. Geophysical Research Letters, 2005, 32(17):395-403.
[11] 贾界峰,赵井卫,陈客贤. 曼宁公式及其误差分析[J]. 山西建筑,2010,36(7):313-314.
[12] JUNG H C, HAMSKI J, DURAND M, et al. Characterization of complex fluvial systems using remote sensing of spatial and temporal water level variations in the Amazon, Congo, and Brahmaputra Rivers[J]. Earth Surface Processes and Landforms, 2010, 35(3):294-304.
[13] WOLDEMICHAEL A T, DEGU A M, HOSSAIN F. Role of Land–Water Classification and Manning's Roughness Parameter in Space-Borne Estimation of Discharge for Braided Rivers:A Case Study of the Brahmaputra River in Bangladesh[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2010, 3(3):395-403.
[14] BIRKINSHAW S J, MOORE P, KILSBY C G, et al. Daily discharge estimation at ungauged river sites using remote sensing[J]. Hydrological Processes, 2014, 28:1043-1054.
[15] SICHANGI A W, WANG L, YANG K, et al. Estimating continental river basin discharges using multiple remote sensing data sets[J]. Remote Sensing of Environment, 2016, 179:36-53.
[16] 张罗号. 明渠水流阻力研究现状分析[J].水利学报,2012,43(10):1154-1162.
[17] MANNING R. On the flow of water in open channels and pipes[J]. Transactions Institute of Civil Engineers of Ireland, 1891, 20:161-209.
[18] 广东省地方史志编纂委员会. 广东省志地理志[M]. 广州:广东人民出版社,1999.
[19] 陈蕾,邓孺孺,彭小娟. TM影像与DEM的地形光照模型配准法研究——以广州市为例[J]. 热带地理,2008,28(5):224-227.
[20] 陈蕾,邓孺孺,柯锐鹏,等. 基于地面耦合的TM影像的大气校正——以珠江口为例[J]. 地理与地理信息科学,2004,20(2):34-37.
[21] MCFEETERS S K. The Use of Normalized Difference Water Index(NDWI) in the Delineation of Open Water Features[J].International Journal of Remote Sensing, 1996, 17(7):1425-1432.
[22]黄汝根,孙祥鹏,牟舵. 基于“高分一号”遥感影像水体提取方法的对比分析[J]. 人民珠江,2017,38(3):66-70.
[23]OTSU N. A Threshold Selection Method from Gray-Level Histograms[J]. IEEE Transactions on Systems Man and Cybernetics, 2007, 9(1):62-66.
[24] 张小琴,包为民,梁文清,等. 河道糙率问题研究进展[J]. 水力发电,2008,34(6):98-100.
[25] 徐慧敏. 关于水利工程中河道糙率的研究[J]. 水利科技与经济,2010(11):1253-1256.
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