An algorithm for rapid flood inundation mapping from optical data using a reflectance differencing technique

2013 ◽  
Vol 7 (3) ◽  
pp. 239-250 ◽  
Author(s):  
G. Amarnath
Keyword(s):  
Optical Data ◽  
Flood Inundation ◽  
Inundation Mapping ◽  
Flood Inundation Mapping

A Comparison of Rapid DTM Based Approaches for on-Demand Flood Inundation Mapping

2018 ◽  
Author(s):  
Heather McGrath ◽  
Jean-Samuel Proulx-Bourque ◽  
Jean-Francois Bourgon ◽  
Miroslav Nastev ◽  
Ahmed Abo El Ezz
Keyword(s):  
Flood Inundation ◽  
On Demand ◽  
Inundation Mapping ◽  
Flood Inundation Mapping

scholarly journals Dam break analysis and flood inundation mapping using HEC-RAS for piping failure

2020 ◽  
Vol 8 (5) ◽  
pp. 1989-1992
Author(s):  
Vinay Khalkho ◽  
Dr. Alex Thomas ◽  
Manmohan Singh ◽  
Shilpi Dadel
Keyword(s):  
Dam Break ◽  
Flood Inundation ◽  
Inundation Mapping ◽  
Flood Inundation Mapping

Intercomparison of Satellite Remote Sensing-Based Flood Inundation Mapping Techniques

2018 ◽  
Vol 54 (4) ◽  
pp. 834-846 ◽  
Author(s):  
Dinuke Munasinghe ◽  
Sagy Cohen ◽  
Yu-Fen Huang ◽  
Yin-Phan Tsang ◽  
Jiaqi Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Flood Inundation ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Mapping Techniques

scholarly journals Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine

PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0237324 ◽  
Author(s):  
Varun Tiwari ◽  
Vinay Kumar ◽  
Mir Abdul Matin ◽  
Amrit Thapa ◽  
Walter Lee Ellenburg ◽  
...  
Keyword(s):  
Detection Method ◽  
Google Earth ◽  
Flood Inundation ◽  
Threshold Detection ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Google Earth Engine

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.

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