scholarly journals Testing Urban Flood Mapping Approaches from Satellite and In-Situ Data Collected during 2017 and 2019 Events in Eastern Canada

2020 ◽  
Vol 12 (19) ◽  
pp. 3141
Author(s):  
Ian Olthof ◽  
Nicolas Svacina
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Urban Areas ◽  
Cloud Cover ◽  
Data Availability ◽  
Flood Mapping ◽  
Eastern Canada ◽  
Urban Flood ◽  
Water Height

The increasing frequency of flooding worldwide has driven research to improve near real-time flood mapping from remote-sensing data. Improved automation and processing speed to map both open water and vegetated area flooding have resulted from these research efforts. Despite these achievements, flood mapping in urban areas where a significant number of overall impacts are felt remains a challenge. Near real-time data availability, shadowing caused by manmade infrastructure, spatial resolution, and cloud cover inhibiting optical transmission, are all factors that complicate detailed urban flood mapping needed to inform response efforts. This paper uses numerous data sources collected during two major flood events that impacted the same region of Eastern Canada in 2017 and 2019 to test different urban flood mapping approaches presented as case studies in three separate urban boroughs. Cloud-free high-resolution 3 m PlanetLab optical data acquired near peak-flood in 2019 were used to generate a maximum flood extent product for that year. Approaches using new Lidar Digital Elevation Models (DEM)s and water height estimated from nineteen RADARSAT-2 flood maps, point-based flood perimeter observations from citizen geographic information, and simulated traffic camera or other urban sensor network data were tested and verified using independent data. Coherent change detection (CCD) using multi-temporal Interferometric Wide (IW) Sentinel-1 data was also tested. Results indicate that while clear-sky high-resolution optical imagery represents the current gold standard, its availability is not guaranteed due to timely coverage and cloud cover. Water height estimated from 8 to 12.5 m resolution RADARSAT-2 flood perimeters were not sufficiently accurate to flood adjacent urban areas using a Lidar DEM in near real-time, but all nineteen scenes combined captured boroughs that flooded at least once in both flood years. CCD identified flooded boroughs and roughly captured their flood extents, but lacked timeliness and sufficient detail to inform street-level decision-making in near real-time. Point-based flood perimeter observation, whether from in-situ sensors or high-resolution optical satellites combined with Lidar DEMs, can generate accurate full flood extents under certain conditions. Observed point-based flood perimeters on manmade features with low topographic variation produced the most accurate flood extents due to reliable water height estimation from these points.

scholarly journals Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

2019 ◽  
Vol 11 (21) ◽  
pp. 2492 ◽  
Author(s):  
Bo Peng ◽  
Zonglin Meng ◽  
Qunying Huang ◽  
Caixia Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Real Time ◽  
Spectral Reflectance ◽  
Urban Areas ◽  
Flood Mapping ◽  
Urban Flooding ◽  
Urban Flood ◽  
Before And After ◽  
Flood Maps

Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes).

Assessing the potential for real-time urban flood forecasting based on a worldwide survey on data availability

2013 ◽  
Vol 11 (7) ◽  
pp. 573-583 ◽  
Author(s):  
Jeanne-Rose René ◽  
Slobodan Djordjević ◽  
David Butler ◽  
Henrik Madsen ◽  
Ole Mark
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Data Availability ◽  
Urban Flood

MARINE-EO project: Monitoring and forecast of eutrophication around fish farms, a Maliakos Gulf case (Eastern Mediterranean).

2021 ◽  
Author(s):  
Amandine Declerck ◽  
Matthias Delpey ◽  
Thibaut Voirand ◽  
Ioanna Varkitzi
Keyword(s):  
Water Quality ◽  
High Resolution ◽  
Real Time ◽  
Chlorophyll A ◽  
Satellite Observation ◽  
Risk Level ◽  
Fish Farms ◽  
Ocean Modelling ◽  
Monitoring And Forecasting

<p>Keywords: eutrophication; high resolution ocean modeling ; Chla satellite data ; biogeochemistry</p><p>Maliakos Gulf corresponds to mesotrophic waters that can reach eutrophic conditions and are occasionally subject to Harmful Algal Blooms (HAB) (Varkitzi et al. 2018). At the same time, it is an important fish farming and aquaculture production area. A large issue is thus related to the monitoring and forecasting of the risk of occurrence of algae blooms in the Gulf. For this purpose, the present study couples predictions from a high-resolution numerical ocean model with satellite observation to improve the monitoring and anticipation of threats for the local fish farms induced by occasional eutrophication.</p><p>This solution is developed in the frame of the MARINE-EO project (https://marine-eo.eu/). It combines satellite observation with high-resolution ocean modelling to provide detailed information as a support to fish farms management and operations. It is implemented in an operational platform, which provides continuous information in real time as well as short term predictions. The deployed solution uses CMEMS physical products as an input data and offers to refine this solution in order to provide a local information on site using a downscaling strategy. High resolution satellite products and ocean modelling allow to include the impact of local coastal processes on currents and water quality parameters to provide a proper monitoring and forecasting solution at the scale of a specific fish farm.</p><p>To model specific eutrophication processes, a NPZD (Nutrients-Phytoplankton-Zooplankton-Detritus) biogeochemical model is used. Included in the MOHID Water modelling system, the water quality module (Mateus, 2006) considering 18 properties: nutrients and organic matter (nitrogen, phosphorus and silica biogeochemical cycles), oxygen and organisms (phytoplankton and zooplankton) was deployed in the western Aegean Sea. The simulated chlorophyll a concentrations are used to compute a risk level for the eutrophication occurrence. To complete this indicator, another risk level was based on the eutrophication variation following Primpas et al. (2010) formulation. In addition to model forecasts, ocean color observations from the Sentinel-2 MSI and Landsat-8 OLI sensors are used to provide high resolution chlorophyll a concentrations maps in case of bloom events. The processing chain uses the sixth version of the Quasi-Analytical Algorithm initially developed by Lee et al. (2002) and an empirical relation based on a database built using the HydroLight software to compute chlorophyll a concentration.</p><p>Two past eutrophication events monitored in situ (Varkitzi et al. 2018) were studied to assess the accuracy of the developed tool. Although few in situ data were available on environmental input (as rivers flow and nutrient concentrations), it was possible using statistics to reproduce qualitatively these blooms. Finally, an operational demonstration was conducted during 2 months of the 2020 autumn season, to showcase real time monitoring and predictive perspectives.</p>

scholarly journals Integrate Weather Radar and Monitoring Devices for Urban Flooding Surveillance

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 825 ◽  
Author(s):  
Shih-Yen Hsu ◽  
Tai-Been Chen ◽  
Wei-Chang Du ◽  
Jyh-Horng Wu ◽  
Shih-Chieh Chen
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Weather Radar ◽  
Water Levels ◽  
Mitigation Measures ◽  
Extreme Weather Events ◽  
Television System ◽  
Urban Flood ◽  
Flood Monitoring ◽  
Image Capturing

With the increase of extreme weather events, the frequency and severity of urban flood events in the world are increasing drastically. Therefore, this study develops ARMT (automatic combined ground weather radar and CCTV (Closed Circuit Television System) images for real-time flood monitoring), which integrates real-time ground radar echo images and automatically estimates a rainfall hotspot according to the cloud intensity. Furthermore, ARMT combines CCTV image capturing, analysis, and Fourier processing, identification, water level estimation, and data transmission to provide real-time warning information. Furthermore, the hydrograph data can serve as references for relevant disaster prevention, and response personnel may take advantage of them and make judgements based on them. The ARMT was tested through historical data input, which showed its reliability to be between 83% to 92%. In addition, when applied to real-time monitoring and analysis (e.g., typhoon), it had a reliability of 79% to 93%. With the technology providing information about both images and quantified water levels in flood monitoring, decision makers can quickly better understand the on-site situation so as to make an evacuation decision before the flood disaster occurs as well as discuss appropriate mitigation measures after the disaster to reduce the adverse effects that flooding poses on urban areas.

scholarly journals A new global real-time Lagrangian diagnostic system for stratosphere-troposphere exchange: evaluation during a balloon sonde campaign in eastern Canada

2012 ◽  
Vol 12 (5) ◽  
pp. 2661-2679 ◽  
Author(s):  
M. S. Bourqui ◽  
A. Yamamoto ◽  
D. Tarasick ◽  
M. D. Moran ◽  
L.-P. Beaudoin ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Diagnostic System ◽  
Weather Forecast ◽  
Identification Algorithm ◽  
Eastern Canada ◽  
Data Set ◽  
Predictive Skill ◽  
Stratospheric Intrusions ◽  
Archived Data

Abstract. A new global real-time Lagrangian diagnostic system for stratosphere-troposphere exchange (STE) developed for Environment Canada (EC) has been delivering daily archived data since July 2010. The STE calculations are performed following the Lagrangian approach proposed in Bourqui (2006) using medium-range, high-resolution operational global weather forecasts. Following every weather forecast, trajectories are started from a dense three-dimensional grid covering the globe, and are calculated forward in time for six days of the forecast. All trajectories crossing either the dynamical tropopause (±2 PVU) or the 380 K isentrope and having a residence time greater than 12 h are archived, and also used to calculate several diagnostics. This system provides daily global STE forecasts that can be used to guide field campaigns, among other applications. The archived data set offers unique high-resolution information on transport across the tropopause for both extra-tropical hemispheres and the tropics. This will be useful for improving our understanding of STE globally, and as a reference for the evaluation of lower-resolution models. This new data set is evaluated here against measurements taken during a balloon sonde campaign with daily launches from three stations in eastern Canada (Montreal, Egbert, and Walsingham) for the period 12 July to 4 August 2010. The campaign found an unexpectedly high number of observed stratospheric intrusions: 79% (38%) of the profiles appear to show the presence of stratospheric air below than 500 hPa (700 hPa). An objective identification algorithm developed for this study is used to identify layers in the balloon-sonde profiles affected by stratospheric air and to evaluate the Lagrangian STE forecasts. We find that the predictive skill for the overall intrusion depth is very good for intrusions penetrating down to 300 and 500 hPa, while it becomes negligible for intrusions penetrating below 700 hPa. Nevertheless, the statistical representation of these deep intrusions is reasonable, with an average bias of 24%. Evaluation of the skill at representing the detailed structures of the stratospheric intrusions shows good predictive skill down to 500 hPa, reduced predictive skill between 500 and 700 hPa, and none below. A significant low statistical bias of about 30% is found in the layer between 500 to 700 hPa. However, analysis of missed events at one site, Montreal, shows that 70% of them coincide with candidate clusters of trajectories that pass through Montreal, but that are too dispersed to be detected in the close neighbourhood of the station. Within the limits of this study, this allows us to expect a negligible bias throughout the troposphere in the spatially averaged STE frequency derived from this data set, for example in climatological maps of STE mass fluxes. This first evaluation is limited to eastern Canada in one summer month with a high frequency of stratospheric intrusions, and further work is needed to evaluate this STE data set in other months and locations.

scholarly journals SUITABLE DEM ASSESSMENT STUDY FOR DATA SCARCE CITIES TOWARDS URBAN EXTREME AND NUISANCE FLOOD MAPPING

2021 ◽  
Vol V-4-2021 ◽  
pp. 161-168
Author(s):  
A. Akkimi ◽  
S. Dutta
Keyword(s):  
High Resolution ◽  
Open Source ◽  
Developed Countries ◽  
Flood Mapping ◽  
Topographic Wetness Index ◽  
Flood Modeling ◽  
Alos Palsar ◽  
Urban Flood ◽  
Medium Resolution ◽  
Vertical Accuracy

Abstract. The high-resolution accurate topography data should be used for extreme and nuisance flood inundation modeling and mapping in cities, but not available for many cities, including most developed countries. It is necessary to study and identify an alternate open-source topographic model that satisfies high-resolution topography datasets’ conditions. We analyzed the open-source DEMs visually, elevation histogram statistics, streams and watershed identification, contour statistics, Topographic Wetness Index, and vertical accuracy of other medium-resolution DEMs compared with high-resolution LiDAR data over New York City to determine alternative open-source Digital Elevation Model in the context of urban flood modeling. In high urban sprawl areas, in the context of flood mapping, our findings have shown that the medium resolution DEMs predicted similar to high-resolution DEMs with the same linear errors around RMSE 25–35ft and LE90 30–40ft. Overall, the ALOS AW3D performed better than other open-source DEMs. Even though SRTM predicted well, it inducted smoothness in DEM where more buildings were located. It noted that ALOS PALSAR DEM is not suitable for any urban studies. ASTER DEM has also shown good agreement with LiDAR and observed elevations, but it induced by noise while processing. Finally, it can be suggested that the ALOS AW3D can be used as an alternative source for urban flood modeling which represented footprints of buildings even though it performed average in vertical accuracy.

scholarly journals Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion

2019 ◽  
Vol 11 (19) ◽  
pp. 2231 ◽  
Author(s):  
Yu Li ◽  
Sandro Martinis ◽  
Marc Wieland ◽  
Stefan Schlaffer ◽  
Ryo Natsuaki
Keyword(s):  
Bayesian Network ◽  
Rural Areas ◽  
Urban Areas ◽  
Kappa Coefficient ◽  
Urban Environments ◽  
Aerial Photographs ◽  
Flood Event ◽  
Flood Mapping ◽  
Urban Flood ◽  
Other Information

Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks.

UAV-LiDAR observations increase the precision of urban flood modelling in Accra by detecting critical micro-topographic features

2021 ◽  
Author(s):  
Katerina Trepekli ◽  
Thomas Friborg ◽  
Thomas Balstrøm ◽  
Bjarne Fog ◽  
Albert Allotey ◽  
...  
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Flood Control ◽  
Substantial Effect ◽  
Control Measures ◽  
Damage Potential ◽  
Urban Flood ◽  
Proactive Management ◽  
Precise Identification ◽  
Flood Prone Areas

<p>Rapidly expanding cities are exposed to higher damage potential from floods, necessitating effective proactive management using technological developments in remote sensing observations and hydrological modelling.  In this study we tested whether high resolution topographic data derived by Light and Detection Ranging (LiDAR) and Unmanned Aerial Vehicle (UAV) systems can facilitate rapid and precise identification of high-risk urban areas, at the local scale. Three flood prone areas located within the Greater Accra Metropolitan Area in Ghana were surveyed by a UAV-LiDAR system. In order to simulate a realistic flow of precipitation runoff on terrains, Digital Terrain Models (DTM) including buildings and urban features that may have a substantial effect on water flow pathways (DTMb) were generated from the UAV-LiDAR datasets. The resulting DTMbs, which had a spatial resolution of 0.3 m supplemented a satellite-based DTM of 10 m resolution covering the full catchment area of Accra, and applied to a hydrologic screening model (Arc-Malstrøm) to compare the flood simulations. The precision of the location, extent and capacity of landscape sinks were substantially improved when the DTMbs were utilized for mapping the flood propagation. The semi-low resolution DTM projected unrealistically shallower sinks, with larger extents but smaller capacities that consequently led to an overestimation of the runoff volume by 15% for a sloping site, and up to 65 % for 1st order sinks in flat terrains. The observed differences were attributed to the potential of high resolution DTMbs to detect urban manmade features like archways, boundary walls and bridges which were found to be critical in predictions of runoff’s courses, but could not be captured by the coarser DTM. Discrepancies in the derived water volumes using the satellite-based DTM vs. the UAV-LiDAR DTMbs were also traced to dynamic alterations in the geometry of streams and rivers, due to construction activities occurring in the interval between the aerial campaign and the date of acquisition of the commercially available DTM. Precise identification of urban flood prone areas can be enhanced using UAV-LiDAR systems, facilitating the design of comprehensive early flood-control measures, especially in urban settlements exposed to the adverse effects of perennial flooding. This research is funded by a grant awarded by the Danish Ministry of Foreign Affairs (Danida).</p>

NOAA/AVHRR Satellite Data-Based Indices for Monitoring Agricultural Droughts

2005 ◽  
Author(s):  
Felix N. Kogan
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Thermal Conditions ◽  
Early Years ◽  
Data Availability ◽  
Sources Of Information ◽  
In Situ Data ◽  
Marginal Areas ◽  
The World

Operational polar-orbiting environmental satellites launched in the early 1960s were designed for daily weather monitoring around the world. In the early years, they were mostly applied for cloud monitoring and for advancing skills in satellite data applications. The new era was opened with the series of TIROS-N launched in 1978, which has continued until present. These satellites have such instruments as the advanced very high resolution radiometer (AVHRR) and the TIROS operational vertical sounder (TOVS), which included a microwave sounding unit (MSU), a stratospheric sounding unit (SSU), and high-resolution infrared radiation sounder/2 (HIRS/2). These instruments helped weather forecasters improve their skills. AVHRR instruments were also useful for observing and monitoring earth surface. Specific advances were achieved in understanding vegetation distribution. Since the late 1980s, experience gained in interpreting vegetation conditions from satellite images has helped develop new applications for detecting phenomenon such as drought and its impacts on agriculture. The objective of this chapter is to introduce AVHRR indices that have been useful for detecting most unusual droughts in the world during 1990–2000, a decade identified by the United Nations as the International Decade for Natural Disasters Reduction. Radiances measured by the AVHRR instrument onboard National Oceanic Atmospheric Administration (NOAA) polar-orbiting satellites can be used to monitor drought conditions because of their sensitivity to changes in leaf chlorophyll, moisture content, and thermal conditions (Gates, 1970; Myers, 1970). Over the last 20 years, these radiances were converted into indices that were used as proxies for estimating various vegetation conditions (Kogan, 1997, 2001, 2002). The indices became indispensable sources of information in the absence of in situ data, whose measurements and delivery are affected by telecommunication problems, difficult access to environmentally marginal areas, economic disturbances, and political or military conflicts. In addition, indices have advantage over in situ data in terms of better spatial and temporal coverage and faster data availability. The AVHRR-based indices used for monitoring vegetation can be divided into two groups: two-channel indices, and three-channel indices.

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