Special Issue "Remote Sensing in Flood Monitoring and Management"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 June 2015).

Special Issue Editor

Guest Editor
Dr. Guy J.-P. Schumann Website E-Mail
School of Geographical Sciences, University of Bristol, BS81SS Bristol, UK, RSS-Hydro Sarl-S, Dudelange Innovation Hub, L-3593 Dudelange, Luxembourg
Interests: flood hydrology; hydraulic modelling; remote sensing; SAR; flood disaster response assistance

Special Issue Information

Dear Colleagues,

It is well known that floods can be mapped and monitored with remotely sensed data acquired by aircraft and satellites. The sensors and data processing techniques that exist to derive information about floods are numerous. Instruments that record flood events may operate in the visible, thermal and microwave range of the electromagnetic spectrum. Due to the limitations posed by adverse weather conditions during flood events, active radar (SAR and altimetry) is invaluable for monitoring floods; however, if a visible image of flooding can be acquired, retrieving useful information from this is often more straightforward. Apart from providing direct information about flooding, remote sensing data can also be integrated with flood models (via model calibration or validation, and data assimilation techniques) or provide floodplain topography data to augment the amount and type of information available for efficient flood management. There have been notable studies on integrating remotely sensed data with flood modeling since the late 1990s and there is now a general consensus among space agencies to strengthen the support that satellite missions can offer. This trend has stimulated more research in this area, and significant progress has been achieved in recent years in fostering our understanding of the ways in which remote sensing can support flood monitoring and management. This research goes considerably further than using a wet/dry flood map for model validation as in early studies of this type. Therefore, this Special Issue aims to collect papers on current efforts to aid advancing flood monitoring and management through remotely sensed data. The following list gives an overview of the topics we are looking for, but is by no means exhaustive:

  • Remote sensing in flood mapping applications
  • Remote sensing and flood risk (e.g. for damage assessment, etc.)
  • The use of remotely sensed flood-related data (e.g. water level, flooded area) in flood model calibration / validation studies
  • Remotely sensed flood-related data and integration with flood models via data assimilation (DA)
  • The use of remote sensing-derived floodplain topography (floodplain DEM) in flood studies

Dr. Guy J-P. Schumann
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • radar altimetry
  • LiDAR
  • synthetic aperture radar imagery
  • visible imagery
  • flooding
  • hydrodynamic (hydraulic) modeling
  • flood risk
  • flood hazard mapping

Published Papers (24 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

Open AccessFeature PaperEditorial
Preface: Remote Sensing in Flood Monitoring and Management
Remote Sens. 2015, 7(12), 17013-17015; https://doi.org/10.3390/rs71215871 - 16 Dec 2015
Cited by 10
Abstract
This Special Issue is a collection of papers studying the use of remote sensing data and methods for flood monitoring and management. The articles contributed span a wide range of topics and present novel processing techniques, review methods and discuss limitations, and also [...] Read more.
This Special Issue is a collection of papers studying the use of remote sensing data and methods for flood monitoring and management. The articles contributed span a wide range of topics and present novel processing techniques, review methods and discuss limitations, and also report on current capabilities and outline emerging needs. This preface provides a brief overview of the content. [...] Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Graphical abstract

Research

Jump to: Editorial

Open AccessArticle
Development and Evaluation of a Multi-Year Fractional Surface Water Data Set Derived from Active/Passive Microwave Remote Sensing Data
Remote Sens. 2015, 7(12), 16688-16732; https://doi.org/10.3390/rs71215843 - 09 Dec 2015
Cited by 37
Abstract
The sensitivity of Earth’s wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities of methane gas are key global change questions. We present a microwave satellite-based approach for mapping fractional surface water (FW) globally at [...] Read more.
The sensitivity of Earth’s wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities of methane gas are key global change questions. We present a microwave satellite-based approach for mapping fractional surface water (FW) globally at 25-km resolution. The approach employs a land cover-supported, atmospherically-corrected dynamic mixture model applied to 20+ years (1992–2013) of combined, daily, passive/active microwave remote sensing data. The resulting product, known as Surface WAter Microwave Product Series (SWAMPS), shows strong microwave sensitivity to sub-grid scale open water and inundated wetlands comprising open plant canopies. SWAMPS’ FW compares favorably (R2 = 91%–94%) with higher-resolution, global-scale maps of open water from MODIS and SRTM-MOD44W. Correspondence of SWAMPS with open water and wetland products from satellite SAR in Alaska and the Amazon deteriorates when exposed wetlands or inundated forests captured by the SAR products were added to the open water fraction reflecting SWAMPS’ inability to detect water underneath the soil surface or beneath closed forest canopies. Except for a brief period of drying during the first 4 years of observation, the inundation extent for the global domain excluding the coast was largely stable. Regionally, inundation in North America is advancing while inundation is on the retreat in Tropical Africa and North Eurasia. SWAMPS provides a consistent and long-term global record of daily FW dynamics, with documented accuracies suitable for hydrologic assessment and global change-related investigations. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Correction of Interferometric and Vegetation Biases in the SRTMGL1 Spaceborne DEM with Hydrological Conditioning towards Improved Hydrodynamics Modeling in the Amazon Basin
Remote Sens. 2015, 7(12), 16108-16130; https://doi.org/10.3390/rs71215822 - 02 Dec 2015
Cited by 10
Abstract
In the Amazon basin, the recently released SRTM Global 1 arc-second (SRTMGL1) remains the best topographic information for hydrological and hydrodynamic modeling purposes. However, its accuracy is hindered by errors, partly due to vegetation, leading to erroneous simulations. Previous efforts to remove the [...] Read more.
In the Amazon basin, the recently released SRTM Global 1 arc-second (SRTMGL1) remains the best topographic information for hydrological and hydrodynamic modeling purposes. However, its accuracy is hindered by errors, partly due to vegetation, leading to erroneous simulations. Previous efforts to remove the vegetation signal either did not account for its spatial variability or relied on a single assumed percentage of penetration of the SRTM signal. Here, we propose a systematic approach over an Amazonian floodplain to remove the vegetation signal, addressing its heterogeneity by combining estimates of vegetation height and a land cover map. We improve this approach by interpolating the first results with drainage network, field and altimetry data to obtain a hydrological conditioned DEM. The averaged interferometric and vegetation biases over the forest zone were found to be −2.0 m and 7.4 m, respectively. Comparing the original and corrected DEM, vertical validation against Ground Control Points shows a RMSE reduction of 64%. Flood extent accuracy, controlled against Landsat and JERS-1 images, stresses improvements in low and high water periods (+24% and +18%, respectively). This study also highlights that a ground truth drainage network, as a unique input during the interpolation, achieves reasonable results in terms of flood extent and hydrological characteristics. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Prompt Proxy Mapping of Flood Damaged Rice Fields Using MODIS-Derived Indices
Remote Sens. 2015, 7(12), 15969-15988; https://doi.org/10.3390/rs71215805 - 30 Nov 2015
Cited by 16
Abstract
Flood mapping, particularly hazard and risk mapping, is an imperative process and a fundamental part of emergency response and risk management. This paper aims to produce a flood risk proxy map of damaged rice fields over the whole of Bangladesh, where monsoon river [...] Read more.
Flood mapping, particularly hazard and risk mapping, is an imperative process and a fundamental part of emergency response and risk management. This paper aims to produce a flood risk proxy map of damaged rice fields over the whole of Bangladesh, where monsoon river floods are dominant and frequent, affecting over 80% of the total population. This proxy risk map was developed to meet the request of the government on a national level. This study represents a rapid, straightforward methodology for estimating rice-crop damage in flood areas of Bangladesh during the large flood from July to September 2007, despite the lack of primary data. We improved a water detection algorithm to achieve a better discrimination capacity to discern flood areas by using a modified land surface water index (MLSWI). Then, rice fields were estimated utilizing a hybrid rice field map from land-cover classification and MODIS-derived indices, such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The results showed that the developed method is capable of providing instant, comprehensive, nationwide mapping of flood risks, such as rice field damage. The detected flood areas and damaged rice fields during the 2007 flood were verified by comparing them with the Advanced Land Observing Satellite (ALOS) AVNIR-2 images (a 10 m spatial resolution) and in situ field survey data with moderate agreement (K = 0.57). Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
On the Use of Global Flood Forecasts and Satellite-Derived Inundation Maps for Flood Monitoring in Data-Sparse Regions
Remote Sens. 2015, 7(11), 15702-15728; https://doi.org/10.3390/rs71115702 - 23 Nov 2015
Cited by 23
Abstract
Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision-making applications needs to be assessed. In this study, [...] Read more.
Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision-making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using 10 major flood events recorded over 2012–2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS). Furthermore, we compared the GFDS flood maps with those from NASA’s two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results reveal that: (1) general agreement was found between the GFDS and MODIS flood detection systems, (2) large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and (3) the quantitative validation of global flood disasters in data-sparse regions is highly challenging. Overall, satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large-scale flood monitoring tools. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Detection and Delineation of Localized Flooding from WorldView-2 Multispectral Data
Remote Sens. 2015, 7(11), 14853-14875; https://doi.org/10.3390/rs71114853 - 06 Nov 2015
Cited by 13
Abstract
Remote sensing technology serves as a powerful tool for analyzing geospatial characteristics of flood inundation events at various scales. However, the performance of remote sensing methods depends heavily on the flood characteristics and landscape settings. Difficulties might be encountered in mapping the extent [...] Read more.
Remote sensing technology serves as a powerful tool for analyzing geospatial characteristics of flood inundation events at various scales. However, the performance of remote sensing methods depends heavily on the flood characteristics and landscape settings. Difficulties might be encountered in mapping the extent of localized flooding with shallow water on riverine floodplain areas, where patches of herbaceous vegetation are interspersed with open water surfaces. To address the difficulties in mapping inundation on areas with complex water and vegetation compositions, a high spatial resolution dataset has to be used to reduce the problem of mixed pixels. The main objective of our study was to investigate the possibilities of using a single date WorldView-2 image of very high spatial resolution and supporting data to analyze spatial patterns of localized flooding on a riverine floodplain. We used a decision tree algorithm with various combinations of input variables including spectral bands of the WorldView-2 image, selected spectral indices dedicated to mapping water surfaces and vegetation, and topographic data. The overall accuracies of the twelve flood extent maps derived with the decision tree method and performed on both pixels and image objects ranged between 77% and 95%. The highest mapping overall accuracy was achieved with a method that utilized all available input data and the object-based image analysis. Our study demonstrates the possibility of using single date WorldView-2 data for analyzing flooding events at high spatial detail despite the absence of spectral bands from the short-waveform region that are frequently used in water related studies. Our study also highlights the importance of topographic data in inundation analyses. The greatest difficulties were met in mapping water surfaces under dense canopy herbaceous vegetation, due to limited water surface exposure and the dominance of vegetation reflectance. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Assimilation of GRACE Terrestrial Water Storage Observations into a Land Surface Model for the Assessment of Regional Flood Potential
Remote Sens. 2015, 7(11), 14663-14679; https://doi.org/10.3390/rs71114663 - 05 Nov 2015
Cited by 24
Abstract
We evaluate performance of the Catchment Land Surface Model (CLSM) under flood conditions after the assimilation of observations of the terrestrial water storage anomaly (TWSA) from NASA’s Gravity Recovery and Climate Experiment (GRACE). Assimilation offers three key benefits for the viability of GRACE [...] Read more.
We evaluate performance of the Catchment Land Surface Model (CLSM) under flood conditions after the assimilation of observations of the terrestrial water storage anomaly (TWSA) from NASA’s Gravity Recovery and Climate Experiment (GRACE). Assimilation offers three key benefits for the viability of GRACE observations to operational applications: (1) near-real time analysis; (2) a downscaling of GRACE’s coarse spatial resolution; and (3) state disaggregation of the vertically-integrated TWSA. We select the 2011 flood event in the Missouri river basin as a case study, and find that assimilation generally made the model wetter in the months preceding flood. We compare model outputs with observations from 14 USGS groundwater wells to assess improvements after assimilation. Finally, we examine disaggregated water storage information to improve the mechanistic understanding of event generation. Validation establishes that assimilation improved the model skill substantially, increasing regional groundwater anomaly correlation from 0.58 to 0.86. For the 2011 flood event in the Missouri river basin, results show that groundwater and snow water equivalent were contributors to pre-event flood potential, providing spatially-distributed early warning information. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Flood Hazard Mapping Combining Hydrodynamic Modeling and Multi Annual Remote Sensing data
Remote Sens. 2015, 7(10), 14200-14226; https://doi.org/10.3390/rs71014200 - 27 Oct 2015
Cited by 15
Abstract
This paper explores a method to combine the time and space continuity of a large-scale inundation model with discontinuous satellite microwave observations, for high-resolution flood hazard mapping. The assumption behind this approach is that hydraulic variables computed from continuous spatially-distributed hydrodynamic modeling and [...] Read more.
This paper explores a method to combine the time and space continuity of a large-scale inundation model with discontinuous satellite microwave observations, for high-resolution flood hazard mapping. The assumption behind this approach is that hydraulic variables computed from continuous spatially-distributed hydrodynamic modeling and observed as discrete satellite-derived flood extents are correlated in time, so that probabilities can be transferred from the model series to the observations. A prerequisite is, therefore, the existence of a significant correlation between a modeled variable (i.e., flood extent or volume) and the synchronously-observed flood extent. If this is the case, the availability of model simulations over a long time period allows for a robust estimate of non-exceedance probabilities that can be attributed to corresponding synchronously-available satellite observations. The generated flood hazard map has a spatial resolution equal to that of the satellite images, which is higher than that of currently available large scale inundation models. The method was applied on the Severn River (UK), using the outputs of a global inundation model provided by the European Centre for Medium-range Weather Forecasts and a large collection of ENVISAT ASAR imagery. A comparison between the hazard map obtained with the proposed method and with a more traditional numerical modeling approach supports the hypothesis that combining model results and satellite observations could provide advantages for high-resolution flood hazard mapping, provided that a sufficient number of remote sensing images is available and that a time correlation is present between variables derived from a global model and obtained from satellite observations. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Combining Multispectral Imagery with in situ Topographic Data Reveals Complex Water Level Variation in China’s Largest Freshwater Lake
Remote Sens. 2015, 7(10), 13466-13484; https://doi.org/10.3390/rs71013466 - 15 Oct 2015
Cited by 8
Abstract
Lake level variation is an important hydrological indicator of water balance, biodiversity and climate change in drainage basins. This paper illustrates the use of moderate-resolution imaging spectroadiometer (MODIS) data to characterize complex water level variation in Poyang Lake, the largest freshwater lake in [...] Read more.
Lake level variation is an important hydrological indicator of water balance, biodiversity and climate change in drainage basins. This paper illustrates the use of moderate-resolution imaging spectroadiometer (MODIS) data to characterize complex water level variation in Poyang Lake, the largest freshwater lake in China. MODIS data were used in conjunction with in situ topographic data, otherwise known as the land-water contact method, to investigate the potential of this hybrid water level spatiotemporal variability measurement technique. An error analysis was conducted to assess the derived water level relative to gauge data. Validation results demonstrated that the land-water contact method can satisfactorily capture spatial patterns and seasonal variations in water level fluctuations. The correlation coefficient ranged from 0.684 to 0.835, the root-mean-square-error from 0.79 m–1.09 m, and the mean absolute bias error from 0.65 m to 0.86 m for five main gauge stations surrounding the lake. Additionally, seasonal and interannual variations in the lake’s water level were revealed in the MODIS-based results. These results indicate that the land-water contact method has the potential to be applied in mapping water level changes in Poyang Lake. This study not only provides a foundation for basic hydrological and ecological studies, but is also valuable for the conservation and management of water resources over gauge-sparse regions in Poyang Lake. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Water Quality and River Plume Monitoring in the Great Barrier Reef: An Overview of Methods Based on Ocean Colour Satellite Data
Remote Sens. 2015, 7(10), 12909-12941; https://doi.org/10.3390/rs71012909 - 30 Sep 2015
Cited by 23
Abstract
A strong driver of water quality change in the Great Barrier Reef (GBR) is the pulsed or intermittent nature of terrestrial inputs into the GBR lagoon, including delivery of increased loads of sediments, nutrients, and toxicants via flood river plumes (hereafter river plumes) [...] Read more.
A strong driver of water quality change in the Great Barrier Reef (GBR) is the pulsed or intermittent nature of terrestrial inputs into the GBR lagoon, including delivery of increased loads of sediments, nutrients, and toxicants via flood river plumes (hereafter river plumes) during the wet season. Cumulative pressures from extreme weather with a high frequency of large scale flooding in recent years has been linked to the large scale reported decline in the health of inshore seagrass systems and coral reefs in the central areas of the GBR, with concerns for the recovery potential of these impacted ecosystems. Management authorities currently rely on remotely-sensed (RS) and in situ data for water quality monitoring to guide their assessment of water quality conditions in the GBR. The use of remotely-sensed satellite products provides a quantitative and accessible tool for scientists and managers. These products, coupled with in situ data, and more recently modelled data, are valuable for quantifying the influence of river plumes on seagrass and coral reef habitat in the GBR. This article reviews recent remote sensing techniques developed to monitor river plumes and water quality in the GBR. We also discuss emerging research that integrates hydrodynamic models with remote sensing and in situ data, enabling us to explore impacts of different catchment management strategies on GBR water quality. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China
Remote Sens. 2015, 7(9), 12539-12562; https://doi.org/10.3390/rs70912539 - 23 Sep 2015
Cited by 17
Abstract
Remote sensing is recognized as a valuable tool for flood mapping due to its synoptic view and continuous coverage of the flooding event. This paper proposed a hybrid approach based on multiple endmember spectral analysis (MESMA) and Random Forest classifier to extract inundated [...] Read more.
Remote sensing is recognized as a valuable tool for flood mapping due to its synoptic view and continuous coverage of the flooding event. This paper proposed a hybrid approach based on multiple endmember spectral analysis (MESMA) and Random Forest classifier to extract inundated areas in Yuyao City in China using medium resolution optical imagery. MESMA was adopted to tackle the mixing pixel problem induced by medium resolution data. Specifically, 35 optimal endmembers were selected to construct a total of 3111 models in the MESMA procedure to derive accurate fraction information. A multi-dimensional feature space was constructed including the normalized difference water index (NDWI), topographical parameters of height, slope, and aspect together with the fraction maps. A Random Forest classifier consisting of 200 decision trees was adopted to classify the post-flood image based on the above multi-features. Experimental results indicated that the proposed method can extract the inundated areas precisely with a classification accuracy of 94% and a Kappa index of 0.88. The inclusion of fraction information can help improve the mapping accuracy with an increase of 2.5%. Moreover, the proposed method also outperformed the maximum likelihood classifier and the NDWI thresholding method. This research provided a useful reference for flood mapping using medium resolution optical remote sensing imagery. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Remotely Sensed Nightlights to Map Societal Exposure to Hydrometeorological Hazards
Remote Sens. 2015, 7(9), 12380-12399; https://doi.org/10.3390/rs70912380 - 22 Sep 2015
Cited by 4
Abstract
This study used remotely sensed maps of nightlights to investigate the etiology of increasing disaster losses from hydrometeorological hazards in a data-scarce area. We explored trends in the probability of occurrence of hazardous events (extreme rainfall) and exposure of the local population as [...] Read more.
This study used remotely sensed maps of nightlights to investigate the etiology of increasing disaster losses from hydrometeorological hazards in a data-scarce area. We explored trends in the probability of occurrence of hazardous events (extreme rainfall) and exposure of the local population as components of risk. The temporal variation of the spatial distribution of exposure to hydrometeorological hazards was studied using nightlight satellite imagery as a proxy. Temporal (yearly) and spatial (1 km) resolution make them more useful than official census data. Additionally, satellite nightlights can track informal (unofficial) human settlements. The study focused on the Samala River catchment in Guatemala. The analyses of disasters, using DesInventar Disaster Information Management System data, showed that fatalities caused by hydrometeorological events have increased. Such an increase in disaster losses can be explained by trends in both: (i) catchment conditions that tend to lead to more frequent hydrometeorological extremes (more frequent occurrence of days with wet conditions); and (ii) increasing human exposure to hazardous events (as observed by amount and intensity of nightlights in areas close to rivers). Our study shows the value of remote sensing data and provides a framework to explore the dynamics of disaster risk when ground data are spatially and temporally limited. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Rapid Response to a Typhoon-Induced Flood with an SAR-Derived Map of Inundated Areas: Case Study and Validation
Remote Sens. 2015, 7(9), 11954-11973; https://doi.org/10.3390/rs70911954 - 18 Sep 2015
Cited by 8
Abstract
We report the successful case of a rapid response to a flash flood in I-Lan County of Taiwan with a map of inundated areas derived from COSMO-SkyMed 1 radar satellite imagery within 24 hours. The flood was caused by the intensive precipitation brought [...] Read more.
We report the successful case of a rapid response to a flash flood in I-Lan County of Taiwan with a map of inundated areas derived from COSMO-SkyMed 1 radar satellite imagery within 24 hours. The flood was caused by the intensive precipitation brought by Typhoon Soulik in July 2013. Based on the ensemble forecasts of trajectory, an urgent request of spaceborne SAR imagery was made 24 hours before Typhoon Soulik made landfall. Two COSMO-SkyMed images were successfully acquired when the center of Typhoon Soulik had just crossed the northern part of Taiwan. The standard level-1b product (radiometric-corrected, geometric-calibrated and orthorectified image) was generated by using the off-the-shelf SARscape software. Following the same approach used with the Expert Landslide and Shadow Area Delineating System, the regional threshold of each tile image was determined to delineate still water surface and quasi-inundated areas in a fully-automatic manner. The results were overlaid on a digital elevation model, and the same tile was visually compared to an optical image taken by Formosat-2 before this event. With this ancillary information, the inundated areas were accurately and quickly identified. The SAR-derived map of inundated areas was published on a web-based platform powered by Google Earth within 24 hours, with the aim of supporting the decision-making process of disaster prevention and mitigation. A detailed validation was made afterwards by comparing the map with in situ data of the water levels at 17 stations. The results demonstrate the feasibility of rapidly responding to a typhoon-induced flood with a spaceborne SAR-derived map of inundated areas. A standard operating procedure was derived from this work and followed by the Water Hazard Mitigation Center of the Water Resources Agency, Taiwan, in subsequent typhoon seasons, such as Typhoon Trami (August, 2013) and Typhoon Soudelor (August, 2015). Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Evaluation of Airborne Lidar Elevation Surfaces for Propagation of Coastal Inundation: The Importance of Hydrologic Connectivity
Remote Sens. 2015, 7(9), 11695-11711; https://doi.org/10.3390/rs70911695 - 14 Sep 2015
Cited by 3
Abstract
Detailed information about coastal inundation is vital to understanding dynamic and populated areas that are impacted by storm surge and flooding. To understand these natural hazard risks, lidar elevation surfaces are frequently used to model inundation in coastal areas. A single-value surface method [...] Read more.
Detailed information about coastal inundation is vital to understanding dynamic and populated areas that are impacted by storm surge and flooding. To understand these natural hazard risks, lidar elevation surfaces are frequently used to model inundation in coastal areas. A single-value surface method is sometimes used to inundate areas in lidar elevation surfaces that are below a specified elevation value. However, such an approach does not take into consideration hydrologic connectivity between elevation grids cells resulting in inland areas that should be hydrologically connected to the ocean, but are not. Because inland areas that should drain to the ocean are hydrologically disconnected by raised features in a lidar elevation surface, simply raising the water level to propagate coastal inundation will lead to inundation uncertainties. We took advantage of this problem to identify hydrologically disconnected inland areas to point out that they should be considered for coastal inundation, and that a lidar-based hydrologic surface should be developed with hydrologic connectivity prior to inundation analysis. The process of achieving hydrologic connectivity with hydrologic-enforcement is not new, however, the application of hydrologically-enforced lidar elevation surfaces for improved coastal inundation mapping as approached in this research is innovative. In this article, we propagated a high-resolution lidar elevation surface in coastal Staten Island, New York to demonstrate that inland areas lacking hydrologic connectivity to the ocean could potentially be included in inundation delineations. For inland areas that were hydrologically disconnected, we evaluated if drainage to the ocean was evident, and calculated an area exceeding 11 ha (~0.11 km2) that could be considered in inundation delineations. We also assessed land cover for each inland area to determine the type of physical surfaces that would be potentially impacted if the inland areas were considered as part of a coastal inundation. A visual analysis indicated that developed, medium intensity and palustrine forested wetland land cover types would be impacted for those locations. This article demonstrates that hydrologic connectivity is an important factor to consider when inundating a lidar elevation surface. This information is needed for inundation monitoring and management in sensitive coastal regions. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe?
Remote Sens. 2015, 7(9), 11403-11433; https://doi.org/10.3390/rs70911403 - 08 Sep 2015
Cited by 42
Abstract
Data assimilation (DA) of satellite soil moisture (SM) observations represents a great opportunity for improving the ability of rainfall-runoff models in predicting river discharges. Many studies have been carried out so far demonstrating the possibility to reduce model prediction uncertainty by incorporating satellite [...] Read more.
Data assimilation (DA) of satellite soil moisture (SM) observations represents a great opportunity for improving the ability of rainfall-runoff models in predicting river discharges. Many studies have been carried out so far demonstrating the possibility to reduce model prediction uncertainty by incorporating satellite SM observations. However, large discrepancies can be perceived between these studies with the result that successful DA is not only related to the quality of the satellite observations but can be significantly controlled by many methodological and morphoclimatic factors. In this article, through an experimental study carried out on the Tiber River basin in Central Italy, we explore how the catchment area, soil type, climatology, rescaling technique, observation and model error selection may affect the results of the assimilation and can be the causes of the apparent discrepancies obtained in the literature. The results show that: (i) DA of SM generally improves discharge predictions (with a mean efficiency of about 30%); (ii) unlike catchment area, the soil type and the catchment specific characteristics might have a remarkable influence on the results; (iii) simple rescaling techniques may perform equally well to more complex ones; (iv) an accurate quantification of the model error is paramount for a correct choice of the observation error and, (v) SM temporal variability has a stronger influence than the season itself. On this basis, we advise that DA of SM may be not a simple task and one should carefully test the optimality of the assimilation experiment prior to drawing any general conclusions. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images
Remote Sens. 2015, 7(8), 10347-10363; https://doi.org/10.3390/rs70810347 - 12 Aug 2015
Cited by 27
Abstract
Change detection based on satellite images acquired from an area at different dates is of widespread interest, according to the increasing number of flood-related disasters. The images help to generate products that support emergency response and flood management at a global scale. In [...] Read more.
Change detection based on satellite images acquired from an area at different dates is of widespread interest, according to the increasing number of flood-related disasters. The images help to generate products that support emergency response and flood management at a global scale. In this paper, a novel unsupervised change detection approach based on image fusion is introduced. The approach aims to extract the reliable flood extent from very high-resolution (VHR) bi-temporal images. The method takes an advantage of the spectral distortion that occurs during image fusion process to detect the change areas by flood. To this end, a change candidate image is extracted from the fused image generated with bi-temporal images by considering a local spectral distortion. This can be done by employing a universal image quality index (UIQI), which is a measure for local evaluation of spectral distortion. The decision threshold for the determination of changed pixels is set by applying a probability mixture model to the change candidate image based on expectation maximization (EM) algorithm. We used bi-temporal KOMPSAT-2 satellite images to detect the flooded area in the city of N′djamena in Chad. The performance of the proposed method was visually and quantitatively compared with existing change detection methods. The results showed that the proposed method achieved an overall accuracy (OA = 75.04) close to that of the support vector machine (SVM)-based supervised change detection method. Moreover, the proposed method showed a better performance in differentiating the flooded area and the permanent water body compared to the existing change detection methods. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
A Sharable and Efficient Metadata Model for Heterogeneous Earth Observation Data Retrieval in Multi-Scale Flood Mapping
Remote Sens. 2015, 7(8), 9610-9631; https://doi.org/10.3390/rs70809610 - 29 Jul 2015
Cited by 4
Abstract
Remote sensing plays an important role in flood mapping and is helping advance flood monitoring and management. Multi-scale flood mapping is necessary for dividing floods into several stages for comprehensive management. However, existing data systems are typically heterogeneous owing to the use of [...] Read more.
Remote sensing plays an important role in flood mapping and is helping advance flood monitoring and management. Multi-scale flood mapping is necessary for dividing floods into several stages for comprehensive management. However, existing data systems are typically heterogeneous owing to the use of different access protocols and archiving metadata models. In this paper, we proposed a sharable and efficient metadata model (APEOPM) for constructing an Earth observation (EO) data system to retrieve remote sensing data for flood mapping. The proposed model contains two sub-models, an access protocol model and an enhanced encoding model. The access protocol model helps unify heterogeneous access protocols and can achieve intelligent access via a semantic enhancement method. The enhanced encoding model helps unify a heterogeneous archiving metadata model. Wuhan city, one of the most important cities in the Yangtze River Economic Belt in China, is selected as a study area for testing the retrieval of heterogeneous EO data and flood mapping. The past torrential rain period from 25 March 2015 to 10 April 2015 is chosen as the temporal range in this study. To aid in comprehensive management, mapping is conducted at different spatial and temporal scales. In addition, the efficiency of data retrieval is analyzed, and validation between the flood maps and actual precipitation was conducted. The results show that the flood map coincided with the actual precipitation. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Sensitivity of a Floodplain Hydrodynamic Model to Satellite-Based DEM Scale and Accuracy: Case Study—The Atchafalaya Basin
Remote Sens. 2015, 7(6), 7938-7958; https://doi.org/10.3390/rs70607938 - 17 Jun 2015
Cited by 6
Abstract
The hydrodynamics of low-lying riverine floodplains and wetlands play a critical role in hydrology and ecosystem processes. Because small topographic features affect floodplain storage and flow velocity, a hydrodynamic model setup of these regions imposes more stringent requirements on the input Digital Elevation [...] Read more.
The hydrodynamics of low-lying riverine floodplains and wetlands play a critical role in hydrology and ecosystem processes. Because small topographic features affect floodplain storage and flow velocity, a hydrodynamic model setup of these regions imposes more stringent requirements on the input Digital Elevation Model (DEM) compared to upland regions with comparatively high slopes. This current study provides a systematic approach to evaluate the required relative vertical accuracy and spatial resolution of current and future satellite-based altimeters within the context of DEM requirements for 2-D floodplain hydrodynamic models. A case study is presented for the Atchafalaya Basin with a model domain of 1190 km2. The approach analyzes the sensitivity of modeled floodplain water elevation and velocity to typical satellite-based DEM grid-box scale and vertical error, using a previously calibrated version of the physically-based flood inundation model (LISFLOOD-ACC). Results indicate a trade-off relationship between DEM relative vertical error and grid-box size. Higher resolution models are the most sensitive to vertical accuracy, but the impact diminishes at coarser resolutions because of spatial averaging. The results provide guidance to engineers and scientists when defining the observation scales of future altimetry missions such as the Surface Water and Ocean Topography (SWOT) mission from the perspective of numerical modeling requirements for large floodplains of O[103] km2 and greater. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany
Remote Sens. 2015, 7(6), 7732-7752; https://doi.org/10.3390/rs70607732 - 11 Jun 2015
Cited by 26
Abstract
In this study, an analysis of multi-temporal and multi-frequency Synthetic Aperture Radar data is performed to investigate the backscatter behavior of various semantic classes in the context of flood mapping in central Europe. The focus is mainly on partially submerged vegetation such as [...] Read more.
In this study, an analysis of multi-temporal and multi-frequency Synthetic Aperture Radar data is performed to investigate the backscatter behavior of various semantic classes in the context of flood mapping in central Europe. The focus is mainly on partially submerged vegetation such as forests and agricultural fields. The test area is located at River Saale, Saxony-Anhalt, Germany, which is covered by a time series of 39 TerraSAR-X data acquired within the time interval December 2009 to June 2013. The data set is supplemented by ALOS PALSAR L-band and RADARSAT-2 C-band data. The time series covers two inundations in January 2011 and June 2013 which allows evaluating backscatter variations between flood periods and normal water level conditions using different radar wavelengths. According to the results, there is potential in detecting flooding beneath vegetation in all microwave wavelengths, even in X-band for sparse vegetation or leaf-off forests. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Monitoring Spatial and Temporal Dynamics of Flood Regimes and Their Relation to Wetland Landscape Patterns in Dongting Lake from MODIS Time-Series Imagery
Remote Sens. 2015, 7(6), 7494-7520; https://doi.org/10.3390/rs70607494 - 05 Jun 2015
Cited by 19
Abstract
Dongting Lake, the second largest freshwater lake in China, is well known for its rapid seasonal fluctuations in inundation extents in the middle reach of the Yangtze River, and it is also the lake most affected by the Three Gorges Project. Significant inter-annual [...] Read more.
Dongting Lake, the second largest freshwater lake in China, is well known for its rapid seasonal fluctuations in inundation extents in the middle reach of the Yangtze River, and it is also the lake most affected by the Three Gorges Project. Significant inter-annual and seasonal variations in flood inundations were observed from Moderate Resolution Imaging Spectroradiometer (MODIS) time-series imagery between 2000 and 2012 in the Dongting Lake. Results demonstrated that temporal changes in inundation extents derived from MODIS data were accordant with variations in annual and monthly precipitation and runoff data. Spatial and temporal dynamics of some related parameters of flood regime were analyzed as well, which included flood inundation probability, duration and start/end date of the annual largest flood. Large areas with high flood inundation probability were identified in 2000 and 2002, but relatively small regions with great flood inundation probability occurred in 2001, 2006, and 2011. Long flood durations were observed in 2000, 2002, 2008, 2010, and 2012, whereas short flood durations occurred in 2001, 2006, and 2011. Correlation analysis techniques were applied to explore spatial-temporal relationships between parameters associated with flood regime and wetland landscape patterns from 2000 to 2012. In addition, this paper presented comprehensive discussions on development of related parameters of flood regime and their influences on wetland landscape pattern after impoundment of the Three Gorges Reservoir, changes in wetland landscape patterns after the flood period, and the role of flooding in wetland evolution and vegetation succession. These results can provide scientific guidance and baseline data for wetland management and long-term monitoring of wetland ecological environment in the Dongting Lake. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
FLCNDEMF: An Event Metamodel for Flood Process Information Management under the Sensor Web Environment
Remote Sens. 2015, 7(6), 7231-7256; https://doi.org/10.3390/rs70607231 - 03 Jun 2015
Cited by 13
Abstract
Significant economic losses, large affected populations, and serious environmental damage caused by recurrent natural disaster events (NDE) worldwide indicate insufficiency in emergency preparedness and response. The barrier of full life cycle data preparation and information support is one of the main reasons. This [...] Read more.
Significant economic losses, large affected populations, and serious environmental damage caused by recurrent natural disaster events (NDE) worldwide indicate insufficiency in emergency preparedness and response. The barrier of full life cycle data preparation and information support is one of the main reasons. This paper adopts the method of integrated environmental modeling, incorporates information from existing event protocols, languages, and models, analyzes observation demands from different event stages, and forms the abstract full life cycle natural disaster event metamodel (FLCNDEM) based on meta-object facility. Then task library and knowledge base for floods are built to instantiate FLCNDEM, forming the FLCNDEM for floods (FLCNDEMF). FLCNDEMF is formalized according to Event Pattern Markup Language, and a prototype system, Natural Disaster Event Manager, is developed to assist in the template-based modeling and management. The flood in Liangzi (LZ) Lake of Hubei, China on 16 July 2010 is adopted to illustrate how to apply FLCNDEM in real scenarios. FLCNDEM-based modeling is realized, and the candidate remote sensing (RS) dataset for different observing missions are provided for LZ Lake flood. Taking the mission of flood area extraction as an example, the appropriate RS data are selected via the model of simplified general perturbation version 4, and the flood area in different phases are calculated and displayed on the map. The phase-based modeling and visualization intuitively display the spatial-temporal distribution and the evolution process of the LZ Lake flood, and it is of great significance for flood responding. In addition, through the extension mechanism, FLCNDEM can also be applied in other environmental applications, providing important support for full life cycle information sharing and rapid responding. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Hydrodynamic and Inundation Modeling of China’s Largest Freshwater Lake Aided by Remote Sensing Data
Remote Sens. 2015, 7(4), 4858-4879; https://doi.org/10.3390/rs70404858 - 20 Apr 2015
Cited by 13
Abstract
China’s largest freshwater lake, Poyang Lake, is characterized by rapid changes in its inundation area and hydrodynamics, so in this study, a hydrodynamic model of Poyang Lake was established to simulate these long-term changes. Inundation information was extracted from Moderate Resolution Imaging Spectroradiometer [...] Read more.
China’s largest freshwater lake, Poyang Lake, is characterized by rapid changes in its inundation area and hydrodynamics, so in this study, a hydrodynamic model of Poyang Lake was established to simulate these long-term changes. Inundation information was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and used to calibrate the wetting and drying parameter by assessing the accuracy of the simulated inundation area and its boundary. The bottom friction parameter was calibrated using current velocity measurements from Acoustic Doppler Current Profilers (ADCP). The results show the model is capable of predicting the inundation area dynamic through cross-validation with remotely sensed inundation data, and can reproduce the seasonal dynamics of the water level, and water discharge through a comparison with hydrological data. Based on the model results, the characteristics of the current velocities of the lake in the wet season and the dry season of the lake were explored, and the potential effect of the current dynamic on water quality patterns was discussed. The model is a promising basic tool for prediction and management of the water resource and water quality of Poyang Lake. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
Toward Estimating Wetland Water Level Changes Based on Hydrological Sensitivity Analysis of PALSAR Backscattering Coefficients over Different Vegetation Fields
Remote Sens. 2015, 7(3), 3153-3183; https://doi.org/10.3390/rs70303153 - 19 Mar 2015
Cited by 16
Abstract
Synthetic Aperture Radar (SAR) has been successfully used to map wetland’s inundation extents and types of vegetation based on the fact that the SAR backscatter signal from the wetland is mainly controlled by the wetland vegetation type and water level changes. This study [...] Read more.
Synthetic Aperture Radar (SAR) has been successfully used to map wetland’s inundation extents and types of vegetation based on the fact that the SAR backscatter signal from the wetland is mainly controlled by the wetland vegetation type and water level changes. This study describes the relation between L-band PALSAR and seasonal water level changes obtained from Envisat altimetry over the island of Île Mbamou in the Congo Basin where two distinctly different vegetation types are found. We found positive correlations between and water level changes over the forested southern Île Mbamou whereas both positive and negative correlations were observed over the non-forested northern Île Mbamou depending on the amount of water level increase. Based on the analysis of sensitivity, we found that denser vegetation canopy leads to less sensitive variation with respect to the water level changes regardless of forested or non-forested canopy. Furthermore, we attempted to estimate water level changes which were then compared with the Envisat altimetry and InSAR results. Our results demonstrated a potential to generate two-dimensional maps of water level changes over the wetlands, and thus may have substantial synergy with the planned Surface Water and Ocean Topography (SWOT) mission. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Open AccessArticle
The Strengths and Limitations in Using the Daily MODIS Open Water Likelihood Algorithm for Identifying Flood Events
Remote Sens. 2014, 6(12), 11791-11809; https://doi.org/10.3390/rs61211791 - 27 Nov 2014
Cited by 37
Abstract
Daily, or more frequent, maps of surface water have important applications in environmental and water resource management. In particular, surface water maps derived from remote sensing imagery play a useful role in the derivation of spatial inundation patterns over time. MODIS data provide [...] Read more.
Daily, or more frequent, maps of surface water have important applications in environmental and water resource management. In particular, surface water maps derived from remote sensing imagery play a useful role in the derivation of spatial inundation patterns over time. MODIS data provide the most realistic means to achieve this since they are daily, although they are often limited by cloud cover during flooding events, and their spatial resolutions (250–1000 m pixel) are not always suited to small river catchments. This paper tests the suitability of the MODIS sensor for identifying flood events through comparison with streamflow and rainfall measurements at a number of sites during the wet season in Northern Australia. This is done using the MODIS Open Water Likelihood (OWL) algorithm which estimates the water fraction within a pixel. On a temporal scale, cloud cover often inhibits the use of MODIS imagery at the start and lead-up to the peak of a flood event, but there are usually more cloud-free data to monitor the flood’s recession. Particularly for smaller flood events, the MODIS view angle, especially when the view angle is towards the sun, has a strong influence on total estimated flood extent. Our results showed that removing pixels containing less than 6% water can eliminate most commission errors when mapping surface water. The exception to this rule was for some spectrally dark pixels occurring along the edge of the MODIS swath where the relative azimuth angle (i.e., angle between the MODIS’ and sun’s azimuth angle) was low. Using only MODIS OWL pixels with a low view angle, or a range distance of less than 1000 km, also improves the results and minimizes multi-temporal errors in flood identification and extent. Given these limitations, MODIS OWL surface water maps are sensitive to the dynamics of water movement when compared to streamflow data and does appear to be a suitable product for the identification and mapping of inundation extent at large regional/basin scales. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
Show Figures

Figure 1

Back to TopTop