Monitoring of flood channel bank collapse in the southern section of the Jingjiang River based on high-resolution remote sensing images: taking the Dengpanzhou bend as an example

doi:10.13474/j.cnki.11-2246.2023.0276

Bulletin of Surveying and Mapping ›› 2023, Vol. 0 ›› Issue (9): 124-128.doi: 10.13474/j.cnki.11-2246.2023.0276

Monitoring of flood channel bank collapse in the southern section of the Jingjiang River based on high-resolution remote sensing images: taking the Dengpanzhou bend as an example

HE Qiuhua^1,2,3, ZOU Juan^1,2,3, YU Shuchen^1,2,3, YU Deqing^1,2,3, TANG Hui^1,2,3, MEI Jinhua^1,2,3, LUO Jianqiang^1,2,3, ZHAO Dong¹, ZOU Cong¹

1. Hunan Center of Natural Resources Affairs, Changsha 410004, China;
2. Hunan Provincial Key Laboratory of Remote Sensing Monitoring of Ecological Environment in Dongting Lake Area, Changsha 410004, China;
3. Dongting Lake Basin Ecological Protection and Restoration Engineering Technology Innovation Center, Ministry of Natural Resources, Changsha 410004, China

Received:2023-04-24 Published:2023-10-08

Abstract

Abstract: To study the situation of bank collapse in the Yangtze River tributary, high-resolution remote sensing image data and a quantitative description method was used to analyze the Songzi River in the southern part of the Jingjiang River section,Taking Dengpanzhou Bend and Jiangxinzhou as examples, monitoring the bank collapse situation from 2013 to 2022. Monitoring shows that there are varying degrees of bank collapses in both the river channel and the Jiangxinzhou. The width of the southern river channel at the monitoring point increased from 203.11 meters in April 2013 to 287.94 meters in November 2022,with an average annual expansion of 8.5 meters and a monthly expansion of 0.74 meters. During the monitoring period, all five monitoring points experienced varying degrees of bank collapse. The distance from the most severe monitoring point to the riverbank decreased from 122.56 meters in April 2013 to 60.13 meters in November 2022. The results indicate that using high-resolution remote sensing images for monitoring bank collapse is a very effective monitoring method and a beneficial supplement to conventional monitoring; It is recommended to strengthen the monitoring and protection of riverbank collapses in the Yangtze River tributaries while carrying out monitoring and construction of protection projects for the main stream of the Yangtze River.

Key words: bank collapse, high-resolution remote sensing images, the Jingjiang River, Dengpanzhou bend

CLC Number:

P237

HE Qiuhua, ZOU Juan, YU Shuchen, YU Deqing, TANG Hui, MEI Jinhua, LUO Jianqiang, ZHAO Dong, ZOU Cong. Monitoring of flood channel bank collapse in the southern section of the Jingjiang River based on high-resolution remote sensing images: taking the Dengpanzhou bend as an example[J]. Bulletin of Surveying and Mapping, 2023, 0(9): 124-128.

References

[1] 王延贵,匡尚富,黄永健. 洪水期岸滩崩塌有关问题的研究[J]. 中国水利水电科学研究院学报,2003,1(2):90-97.
[2] 王延贵,匡尚富. 河岸崩塌类型与崩塌模式的研究[J]. 泥沙研究,2014(1):13-20.
[3] 杨涵苑,李志威. 黄河源泥炭型弯道悬臂式崩岸模型研究[J]. 水力发电学报,2018,37(11):85-96.
[4] 包承纲,李青云. 关于崩岸研究和预测的若干意见[J]. 水利水电科技进展,2003,23(1):14-16.
[5] 杨涵苑,李志威. 基于流量过程的若尔盖黑河下游崩岸规律研究[J]. 水力发电学报,2019,38(4):33-42.
[6] 许全喜,谈广鸣,张小峰. 长江河道崩岸预测模型的研究与应用[J]. 武汉大学学报(工学版),2004,37(6):9-12.
[7] 余文畴,岳红艳. 长江中下游崩岸机理中的水流泥沙运动条件[J]. 人民长江,2008,39(3):64-66.
[8] 冯传勇,郑亚慧,周儒夫. 长江中下游崩岸监测技术应用研究[J]. 水利水电快报,2018,39(3):47-50.
[9] 邓宇,赖修蔚,郭亮. 长江中下游崩岸监测及分析研究[J]. 人民长江,2018,49(15):13-17.
[10] 龙慧,严光云,段光磊. 荆江河段近岸河床演变规律及崩岸机理[J]. 人民长江,2006,37(12):75-77.
[11] 杨则东,鹿献章. 安徽境内长江岸带崩塌遥感调查[J]. 国土资源遥感,1998,10(3):22-25.
[12] 杨则东,鹿献章,褚进海,等. 长江安庆段河道演变及塌岸分析[J]. 中国地质灾害与防治学报,2005,16(1):61-63.
[13] 李长安,杨则东,鹿献章,等. 长江皖江段岸崩特征、形成机理及治理对策[J]. 第四纪研究,2008,28(4):578-583.
[14] YAO Z,TA W,JIA X,et al. Bank erosion and accretion along the Ningxia-Inner Mongolia reaches of the Yellow River from 1958 to 2008[J]. Geomorphology,2011,127(1-2):99-106.
[15] 李溢汶,夏军强,周美蓉,等. 三峡工程运用后沙市段洲滩形态调整特点分析[J]. 水力发电学报,2018,37(10):76-85.

Monitoring of flood channel bank collapse in the southern section of the Jingjiang River based on high-resolution remote sensing images: taking the Dengpanzhou bend as an example

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