scholarly journals High-Resolution Inundation Mapping for Heterogeneous Land Covers with Synthetic Aperture Radar and Terrain Data

2019 ◽  
Author(s):  
Fernando Aristizabal ◽  
Jasmeet Judge ◽  
Alejandro Monsivais-Huertero
Keyword(s):  
Machine Learning ◽  
Synthetic Aperture Radar ◽  
Predictive Value ◽  
Synthetic Aperture ◽  
Flood Inundation ◽  
Dense Vegetation ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Land Covers ◽  
Aperture Radar

Floods are one of the most wide-spread, frequent, and devastating natural disasters that continue to increase in frequency and intensity. Remote sensing, specifically synthetic aperture radar (SAR), has been widely used to detect surface water inundation to provide retrospective and near-real time (NRT) information due to its high-spatial resolution, self-illumination, and low atmospheric attenuation. However, the efficacy of flood inundation mapping with SAR is susceptible to reflections and scattering from a variety of factors including dense vegetation and urban areas. In this study, the topographic dataset height above nearest drainage (HAND) was investigated as a potential supplement to Sentinel-1A C-Band SAR along with supervised machine learning to improve the detection of inundation in heterogeneous areas. Three machine learning classifiers were trained on two sets of features SAR only (VV & VH) and VV, VH & HAND to map inundated areas. Three study sites along the Neuse River in North Carolina, USA during the record flood of Hurricane Matthew in October 2016 were selected. The binary classification analysis (inundated as positive vs. non-inundated as negative) revealed significant improvements when incorporating HAND in several metrics including classification accuracy (ACC) (+37.1%), true positive rate (TPR) (+51.2%), and negative predictive value (NPV) (+23.7%), A marginal improvement of +1.4% was seen for positive predictive value (PPV), but true negative rate (TNR) fell -15.1%. By incorporating HAND, a significant number of areas with high SAR backscatter but low HAND values were detected as inundated which increased true positives. This in turn also increased the false positives detected but to a lesser extent as evident in the metrics. This study demonstrates that HAND could be considered a valuable feature to enhance SAR flood inundation mapping especially in areas with heterogeneous land covers with dense vegetation that interfere with SAR.

scholarly journals High-Resolution Inundation Mapping for Heterogeneous Land Covers with Synthetic Aperture Radar and Terrain Data

2020 ◽  
Vol 12 (6) ◽  
pp. 900 ◽  
Author(s):  
Fernando Aristizabal ◽  
Jasmeet Judge ◽  
Alejandro Monsivais-Huertero
Keyword(s):  
Machine Learning ◽  
Synthetic Aperture Radar ◽  
Predictive Value ◽  
Synthetic Aperture ◽  
Flood Inundation ◽  
Dense Vegetation ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Land Covers ◽  
Aperture Radar

Floods are one of the most wide-spread, frequent, and devastating natural disasters that continue to increase in frequency and intensity. Remote sensing, specifically synthetic aperture radar (SAR), has been widely used to detect surface water inundation to provide retrospective and near-real time (NRT) information due to its high-spatial resolution, self-illumination, and low atmospheric attenuation. However, the efficacy of flood inundation mapping with SAR is susceptible to reflections and scattering from a variety of factors including dense vegetation and urban areas. In this study, the topographic dataset Height Above Nearest Drainage (HAND) was investigated as a potential supplement to Sentinel-1A C-Band SAR along with supervised machine learning to improve the detection of inundation in heterogeneous areas. Three machine learning classifiers were trained on two sets of features dual-polarized SAR only and dual-polarized SAR along with HAND to map inundated areas. Three study sites along the Neuse River in North Carolina, USA during the record flood of Hurricane Matthew in October 2016 were selected. The binary classification analysis (inundated as positive vs. non-inundated as negative) revealed significant improvements when incorporating HAND in several metrics including classification accuracy (ACC) (+36.0%), critical success index (CSI) (+39.95%), true positive rate (TPR) (+42.02%), and negative predictive value (NPV) (+17.26%). A marginal change of +0.15% was seen for positive predictive value (PPV), but true negative rate (TNR) fell −14.4%. By incorporating HAND, a significant number of areas with high SAR backscatter but low HAND values were detected as inundated which increased true positives. This in turn also increased the false positives detected but to a lesser extent as evident in the metrics. This study demonstrates that HAND could be considered a valuable feature to enhance SAR flood inundation mapping especially in areas with heterogeneous land covers with dense vegetation that interfere with SAR.

Near-real-time non-obstructed flood inundation mapping using synthetic aperture radar

2019 ◽  
Vol 221 ◽  
pp. 302-315 ◽  
Author(s):  
Xinyi Shen ◽  
Emmanouil N. Anagnostou ◽  
George H. Allen ◽  
G. Robert Brakenridge ◽  
Albert J. Kettner
Keyword(s):  
Synthetic Aperture Radar ◽  
Real Time ◽  
Synthetic Aperture ◽  
Flood Inundation ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Aperture Radar

scholarly journals Inundation Extent Mapping by Synthetic Aperture Radar: A Review

2019 ◽  
Vol 11 (7) ◽  
pp. 879 ◽  
Author(s):  
Xinyi Shen ◽  
Dacheng Wang ◽  
Kebiao Mao ◽  
Emmanouil Anagnostou ◽  
Yang Hong
Keyword(s):  
Synthetic Aperture Radar ◽  
Optical Sensors ◽  
Risk Mitigation ◽  
Numerical Models ◽  
Synthetic Aperture ◽  
Spatiotemporal Resolution ◽  
Urban Flood ◽  
Inundation Mapping ◽  
Sar Data ◽  
Aperture Radar

Recent flood events have demonstrated a demand for satellite-based inundation mapping in near real-time (NRT). Simulating and forecasting flood extent is essential for risk mitigation. While numerical models are designed to provide such information, they usually lack reference at fine spatiotemporal resolution. Remote sensing techniques are expected to fill this void. Unlike optical sensors, synthetic aperture radar (SAR) provides valid measurements through cloud cover with high resolution and increasing sampling frequency from multiple missions. This study reviews theories and algorithms of flood inundation mapping using SAR data, together with a discussion of their strengths and limitations, focusing on the level of automation, robustness, and accuracy. We find that the automation and robustness of non-obstructed inundation mapping have been achieved in this era of big earth observation (EO) data with acceptable accuracy. They are not yet satisfactory, however, for the detection of beneath-vegetation flood mapping using L-band or multi-polarized (dual or fully) SAR data or for urban flood detection using fine-resolution SAR and ancillary building and topographic data.

scholarly journals Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

2021 ◽  
Author(s):  
Habtamu Tamiru ◽  
Meseret Wagari
Keyword(s):  
Machine Learning ◽  
River Basin ◽  
Water Body ◽  
Hydrological Model ◽  
Daily Rainfall ◽  
Landsat 8 ◽  
Flood Inundation ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Daily Discharge

AbstractThis study presents an integrated machine-learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. ANN and HEC-RAS models were integrated as a predictive hydrological and hydraulic model to generate runoff and the extent of flood, respectively. Daily rainfall and temperature data of 7-years (1999–2005), daily discharge (1999–2005) and 30 m × 30 m gridded Topographical Wetness Index (TWI) were used to train a predictive ANN hydrological model in RStudio. The predictive performance of the developed ANN hydrological model was evaluated in RStudio using Nash–Sutcliffe Efficiency (NSE) values of 0.86 and 0.88 during the training period (1999–2005) and testing period (2006–2008), respectively, with the corresponding observed daily discharge. The validated ANN predictive hydrological model was linked with HEC-RAS to generate the flood extent along the river course. The HEC-RAS model result was calibrated and validated using the water body delineated using Normal Difference Water Index (NDWI) from LANDSAT 8 imagery based on historical flood events of 2005 and 2008. It was found that about 96% of an agreement was made between the flood-prone areas generated in HEC-RAS and the water body delineated using NDWI. Therefore, it is logical to conclude that the integration of a machine-learning approach with the HEC-RAS model has improved the spatiotemporal uncertainties in traditional flood forecasting methods. This integrated model is powerful tool for flood inundation mapping to warn residents of this basin.

scholarly journals Machine learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

2021 ◽  
Author(s):  
Habtamu Tamiru
Keyword(s):  
Machine Learning ◽  
River Basin ◽  
Daily Rainfall ◽  
Simulation Models ◽  
Spatial And Temporal Variability ◽  
Flood Inundation ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Inundation Mapping ◽  
Flood Inundation Mapping

This paper presents the integrated machine learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. A predictive rainfall-runoff and spatially distributed river simulation models were developed using Artificial Neural Networks (ANNs) and HEC-RAS respectively. Daily rainfall and temperature data of 7-yrs and Topographical Wetness Index (TWI) with a spatial resolution of 50 x 50m were used to train the ANN in R studio. The integration of the spatial and temporal variability in this paper improved the accuracy of the predictive models integrated with ANN and HEC-RAS. The predictive ANN model was tested with the observed daily discharge of the same temporal resolution and the rainfall-runoff result obtained from the tested ANN model was used as input for the HEC-RAS. The flood event of 2005 was used to verify the accuracy of flood generated in the HEC-RAS model by implementing the Normal Difference Water Index (NDWI). The comparison was made between the flood inundation map generated by HEC-RAS and flood events of different periods based on coverage percentage areas and a good agreement was reached with 96 % overlapped areas. The performance of ANN and HEC-RAS models were evaluated with 0.86 and 0.88 values at the training and testing period respectively. Finally, it was concluded that the integration of a machine learning approach with the HEC-RAS model in developing a flood inundation mapping is an appropriate tool to warn residents in this river basin.

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