Monitoring the summer flooding in the Poyang Lake area of China in 2020 based on Sentinel-1 data and multiple convolutional neural networks

Dong, Zhen; Wang, Guojie; Amankwah, Solomon Obiri Yeboah; Wei, Xikun; Hu, Yifan; Feng, Ao

doi:10.1016/j.jag.2021.102400

Cited by 60 publications

(37 citation statements)

References 44 publications

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“…As such, this issue might be a key cause of computational errors when compared to the present results. In the case of Deep Convolutional Neural Networks (DCNNs) application, the statistical results of Dong et al's [68] investigation for monitoring summer floods (using Sentinel-1 satellite imagery) are comparable to the RF model in the present study. Although DCNNs are precise methodologies for flood monitoring, the general structure of RF is simpler and is a less time-consuming model.…”

Section: Comparison Of Results With Previous Studiessupporting

confidence: 68%

Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Farhadi

Najafzadeh

2021

Water

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Detecting effective parameters in flood occurrence is one of the most important issues that has drawn more attention in recent years. Remote Sensing (RS) and Geographical Information System (GIS) are two efficient ways to spatially predict Flood Risk Mapping (FRM). In this study, a web-based platform called the Google Earth Engine (GEE) (Google Company, Mountain View, CA, USA) was used to obtain flood risk indices for the Galikesh River basin, Northern Iran. With the aid of Landsat 8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), 11 risk indices (Elevation (El), Slope (Sl), Slope Aspect (SA), Land Use (LU), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Topographic Wetness Index (TWI), River Distance (RD), Waterway and River Density (WRD), Soil Texture (ST]), and Maximum One-Day Precipitation (M1DP)) were provided. In the next step, all of these indices were imported into ArcMap 10.8 (Esri, West Redlands, CA, USA) software for index normalization and to better visualize the graphical output. Afterward, an intelligent learning machine (Random Forest (RF)), which is a robust data mining technique, was used to compute the importance degree of each index and to obtain the flood hazard map. According to the results, the indices of WRD, RD, M1DP, and El accounted for about 68.27 percent of the total flood risk. Among these indices, the WRD index containing about 23.8 percent of the total risk has the greatest impact on floods. According to FRM mapping, about 21 and 18 percent of the total areas stood at the higher and highest risk areas, respectively.

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Section: Comparison Of Results With Previous Studiessupporting

confidence: 68%

Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Farhadi

Najafzadeh

2021

Water

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“…Benefit from the combined utilization of SAR and optical imagery acquired by Sentinel-1/2, a simple and effective method was proposed and used to evaluate the recovery conditions of the severe flooding events in the Yangtze and Huai River basins in July 2020. During the flood period, SAR signals can accurately penetrate through clouds and delineate inundated croplands [33,34]. While in the flood recession period, SAR-optical data fusion provides ample information on plant growth.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, machine learning (ML) techniques (i.e., the Random Forest Classifier, the Support Vector Machine) have been applied in flood monitoring [11,32] with the benefit of combining multi-source features. Also, deep learning (DL, i.e., Convolutional Neural Network) is increasingly developed in this field to achieve rapid and more accurate flood mapping without data annotation [33,34]. However, the computation cost and efficiency remain concerns for large-scale flood detection.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Post-Flood Recovery of Croplands Using the Integrated Sentinel-1/2 Imagery in the Yangtze-Huai River Basin

Zhang

et al. 2022

Remote Sensing

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The increasingly frequent flooding imposes tremendous and long-lasting damages to lives and properties in impoverished rural areas. Rapid, accurate, and large-scale flood mapping is urgently needed for flood management, and to date has been successfully implemented benefiting from the advancement in remote sensing and cloud computing technology. Yet, the effects of agricultural emergency response to floods have been limitedly evaluated by satellite-based remote sensing, resulting in biased post-flood loss assessments. Addressing this challenge, this study presents a method for monitoring post-flood agricultural recovery using Sentinel-1/2 imagery, tested in three flood-affected main grain production areas, in the middle and lower Yangtze and Huai River, China. Our results indicated that 33–72% of the affected croplands were replanted and avoided total crop failures in summer 2020. Elevation, flood duration, crop rotation scheme, and flooding emergency management affect the post-flood recovery performance. The findings also demonstrate rapid intervention measures adjusted to local conditions could reduce the agricultural failure cost from flood disasters to a great extent. This study provides a new alternative for comprehensive disaster loss assessment in flood-prone agricultural regions, which will be insightful for worldwide flood control and management.

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“…This method not only increases the receptive field of the model, but reduces computational consumption. And then Dong, Z. et al [112] employed five existing CNN models, including HRNet, DenseNet, SegNet, ResNet and DeepLab v3+, for detecting flood in the Poyang Lake. The results show that, compared with traditional algorithms, DL methods have a better suppression effect on speckle noise.…”

Section: Water-body Segmentation Based On Existing Network Modelmentioning

confidence: 99%

Water-Body Segmentation for SAR Images: Past, Current, and Future

Guo

Huang

et al. 2022

Remote Sensing

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Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 years in the field of water body extraction in SAR images, and makes some proposals that the community working with SAR image waterbody extraction should consider. Firstly, this review focuses on the main ideas and characteristics of traditional water body extraction on SAR images, mainly focusing on traditional Machine Learning (ML) methods. Secondly, how Deep Learning (DL) methods are applied and optimized in the task of water-body segmentation for SAR images is summarized from the two levels of pixel and image. We also pay more attention to the most popular networks, such as U-Net and its modified models, and novel networks, such as the Cascaded Fully-Convolutional Network (CFCN) and River-Net. In the end, an in-depth discussion is presented, along with conclusions and future trends, on the limitations and challenges of DL for water-body segmentation.

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Monitoring the summer flooding in the Poyang Lake area of China in 2020 based on Sentinel-1 data and multiple convolutional neural networks

Cited by 60 publications

References 44 publications

Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Monitoring Post-Flood Recovery of Croplands Using the Integrated Sentinel-1/2 Imagery in the Yangtze-Huai River Basin

Water-Body Segmentation for SAR Images: Past, Current, and Future

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