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Thirty-meter resolution flood map generated from the ATMS and DEM on 1 November 2012, overlapped and compared with the FEMA SSF flood product.
Source publication
In this study, we present an approach to estimate the extent of large-scale coastal floods caused by Hurricane Sandy using passive optical and microwave remote sensing data. The approach estimates the water fraction from coarse-resolution VIIRS and ATMS data through mixed-pixel linear decomposition. Based on the water fraction difference, using the...
Contexts in source publication
Context 1
... applying this inundation model and the DEM derived from the 30-m SRTM observations, water fraction and flood map derived from the ATMS can be enhanced to fine 30-m resolution. Figure 6 shows the flood map at 30-m resolution derived from the SRTM DEM data with the inundation model. ...
Context 2
... the Hurricane Sandy period. The SSF product, i.e. the flood information distributed by FEMA during Hurricane Sandy, included the states of NJ (New Jersey), NY (New York), and CT (Connecticut; Figure 6). For quantitative evaluation, the SSF product was re-sampled from 3-m to the same 30-m resolution and overlapped with our ATMS-derived flood map. ...
Context 3
... flood map derived from the ATMS and DEM data shows even more inundated area than the FEMA SSF product. Further quantitative assessment indicates a good agreement between the ATMS-derived and the FEMA SSF flood map ( Figure 6) with a correlation of 0.95. One of the main sources for the remaining inconsistency or errors may be due to spatial resolution differences of the various datasets. ...
Citations
... For example, ten years ago, urban agglomerations in China were formed to agglomerate economic and population resources, thus drove the development of the region as the growth pole. Now, the development task and priority are regional coordination and integration, which is especially evident in faster-growing urban agglomerations [50,64,65]. Similarly, the manifestation and distribution of urban agglomeration centers may also differ at different times. ...
... NPP/VIIRS data have stronger radiation detection capabilities and more refined numerical characteristics. In addition, NPP/VIIRS data have more complete global coverage and higher time quality, making it more widely used in nighttime light applications compared to other nighttime light data [65]. The NTL data from 2011 to 2023 for the Kunyu urban agglomeration can be obtained by visiting http://www.ngdc.noaa.gov ...
... NPP/VIIRS data have stronger radiation detection capabilities and more refined numerical characteristics. In addition NPP/VIIRS data have more complete global coverage and higher time quality, making it more widely used in nighttime light applications compared to other nighttime light data [65]. The NTL data from 2011 to 2023 for the Kunyu urban agglomeration can be obtained by visiting http://www.ngdc.noaa.gov ...
The polycentric spatial structure is the most common spatial form of urban agglomerations, so exploring the evolution of this structure and analyzing its influencing factors is of great significance for the optimization of the spatial structure of urban agglomerations. However, there are relatively few studies on the topic that fuse multisource big data analysis, especially in the urban agglomeration of Western China. Therefore, this study uses a fusion of nighttime light (NTL) data, point of interest (POI) data and LandScan data to identify the polycentric spatial structure and its evolution in the Kunming–Yuxi (Kunyu) urban agglomeration and analyzes the factors that have dominated its evolution at different periods using geographic detectors. Results show that the fusion of multisource big data are more in line with the actual development process of the Kunyu urban agglomeration and the factors that have dominated the spatial evolution at different periods vary but the government and sectors have gradually become increasingly important. This study provides a feasible path for exploring urban spatial evolution through the fusion analysis of multisource big data in the Kunyu urban agglomeration and provides a reference for the key directions of urban agglomeration planning and development at different periods.
... Nowadays, remote sensing plays an important role in flood monitoring . Previous researches have used remote sensing images for monitoring flood events, like optical satellite images or specifically synthetic aperture radar (SAR) images (Sun et al., 2016). However, given the satellite revisit limitations, remote sensing images may be unavailable because of cloud cover of optical remote sensing images and the distortion effects of SAR data (Balz et al., 2015). ...
Increase in urban flood hazards has become a major threat to cities, causing considerable losses of life and in the economy. To improve pre-disaster strategies and to mitigate potential losses, it is important to make urban flood susceptibility assessments and to carry out spatiotemporal analyses. In this study, we used standard deviation ellipse (SDE) to analyze the spatial pattern of urban floods and find the area of interest (AOI) based upon related social media data that were collected in Chengdu city, China. We used the social media data as the response variable and selected 10 urban flood-influencing factors as independent variables. We estimated the susceptibility model using the Naïve Bayes (NB) method. The results show that the urban flood events are concentrated in the northeast-central part of Chengdu city, especially around the city center. Results of the susceptibility model were checked by the Receiver Operating Characteristic (ROC) curve, showing that the area under the curve (AUC) was equal to 0.8299. This validation result confirmed that the susceptibility model can predict urban flood with a satisfactory accuracy. The urban flood susceptibility map in the city center area provides a realistic reference for flood monitoring and early warning.
... Flooding itself is the most frequent and devastating natural hazards all around the world [1][2][3][4][5][6][7][8][9][10]. Satellite-based flood maps are proven to be very helpful in the accurate 2 of 15 analysis and evaluation of flooding events [6][7][8][9][10][11][12][13][14][15]. ...
... Flooding itself is the most frequent and devastating natural hazards all around the world [1][2][3][4][5][6][7][8][9][10]. Satellite-based flood maps are proven to be very helpful in the accurate 2 of 15 analysis and evaluation of flooding events [6][7][8][9][10][11][12][13][14][15]. Near real-time flood products, as represented by the floodwater fraction, have been derived from the VIIRS (Visible Infrared Imaging Radiometer Suite) imagery [8][9][10][11][12][13][14][15]. ...
... Satellite-based flood maps are proven to be very helpful in the accurate 2 of 15 analysis and evaluation of flooding events [6][7][8][9][10][11][12][13][14][15]. Near real-time flood products, as represented by the floodwater fraction, have been derived from the VIIRS (Visible Infrared Imaging Radiometer Suite) imagery [8][9][10][11][12][13][14][15]. The VIIRS imagery [16] has a wide swath coverage of 3000 km and a relatively constant moderate spatial resolution of 375 m across the scan. ...
Floods are often associated with hurricanes making landfall. When tropical cyclones/hurricanes make landfall, they are usually accompanied by heavy rainfall and storm surges that inundate coastal areas. The worst natural disaster in the United States, in terms of loss of life and property damage, was caused by hurricane storm surges and their associated coastal flooding. To monitor coastal flooding in the areas affected by hurricanes, we used data from sensors aboard the operational Polar-orbiting and Geostationary Operational Environmental Satellites. This study aims to apply a downscaling model to recent severe coastal flooding events caused by hurricanes. To demonstrate how high-resolution 3D flood mapping can be made from moderate-resolution operational satellite observations, the downscaling model was applied to the catastrophic coastal flooding in Florida due to Hurricane Ian and in New Orleans due to Hurricanes Ida and Laura. The floodwater fraction data derived from the SNPP/NOAA-20 VIIRS (Visible Infrared Imaging Radiometer Suite) observations at the original 375 m resolution were input into the downscaling model to obtain 3D flooding information at 30 m resolution, including flooding extent, water surface level and water depth. Compared to a 2D flood extent map at the VIIRS’ original 375 m resolution, the downscaled 30 m floodwater depth maps, even when shown as 2D images, can provide more details about floodwater distribution, while 3D visualizations can demonstrate floodwater depth more clearly in relative to the terrain and provide a more direct perception of the inundation situations caused by hurricanes. The use of 3D visualization can help users clearly see floodwaters occurring over various types of terrain conditions, thus identifying a hazardous flood from non-hazardous flood types. Furthermore, 3D maps displaying floodwater depth may provide additional information for rescue efforts and damage assessments. The downscaling model can help enhance the capabilities of moderate-to-coarse resolution sensors, such as those used in operational weather satellites, flood detection and monitoring.
... Rainfall-related social media content has also been found to be a good a proxy for rainfall observations, which is particularly useful in areas of the world where there are limited meteorological observations or other remote sensing resources (de Vasconcelos et al., 2016;Andrade et al., 2017;Feng & Sester, 2017). Additionally, Sun et al. (2016) use flood-related images from Flickr to explore its use as a complementary data source for areas with limited remote sensing data. ...
The frequency and severity of extreme weather events such as flooding,
hurricanes/storms and heatwaves are increasing as a result of climate change. There
is a need for information to better understand when, where and how these events are
impacting people. However, there are currently limited sources of impact information
beyond traditional meteorological observations.
Social sensing, which is the use of unsolicited social media data to better understand
real world events, is one method that may provide such information. Social sensing
has successfully been used to detect earthquakes, floods, hurricanes, wildfires,
heatwaves and other weather hazards. Here social sensing methods are adapted to
explore potential for collecting impact information for meteorologists and decision
makers concerned with extreme weather events.
After a review of the literature, three experimental studies are presented. Social
sensing is shown to be effective for detection of impacts of named storms in the UK
and Ireland. Topics of discussion and sentiment are explored in the period before,
during and after a storm event. Social sensing is also shown able to detect high-impact
rainfall events worldwide, validating results against a manually curated database.
Additional events which were not known to this database were found by social sensing.
Finally, social sensing was applied to heatwaves in three European cities. Building on
previous work on heatwaves in the UK, USA and Australia, the methods were
extended to include impact phrases alongside hazard-related phrases, in three
different languages (English, Dutch and Greek).
Overall, social sensing is found to be a good source of impact information for
organisations that need to better understand the impacts of extreme weather. The
research described in this project has been commercialised for operational use by
meteorological agencies in the UK, including the Met Office, Environment Agency and
Natural Resources Wales.
... Schnebele and Cervone (2013) also used the same technique to create flood hazard maps by fusing social sensed data (photos, videos and news), remote sensing data, DEM, meteorological data and river gauge data. Another study by Sun et al. (2016) found that the fusion of social sensing and remote sensing were in line with each other as 95% of Flicker images were relative to the spatial distribution of the satellite-derived flood extent. Interestingly, Jongman et al. (2015) conducted three different analyses where one of them focused on event understanding to enhance response efforts. ...
Flood events cause substantial damage to infrastructure and disrupt livelihoods. Timely monitoring of flood extent helps authorities identify severe impacts and plan relief operations. Remote sensing through satellite imagery is an effective method to identify flooded areas. However, critical contextual information about the severity of structural damage or urgent needs of affected population cannot be obtained from remote sensing alone. On the other hand, social sensing through microblogging sites can potentially provide useful information directly from eyewitnesses and affected people. Therefore, this paper explores the integration of remote sensing and social sensing data to derive informed flood extent maps. For this purpose, we employ state-of-the-art deep learning methods to process heterogeneous data obtained from four case-study areas, including two urban regions from Somalia and India and two coastal regions from Italy and The Bahamas. On the remote sensing side, we observe that deep learning models perform generally better than Otsu in flood water prediction. For example, for highly urban areas from Somalia and India, U-Net achieves better F1-scores (0.471 and 0.310, respectively) than Otsu (0.297 and 0.251, respectively). Similarly, for coastal areas, FCN yields a better F1-score for Italy (0.128) than Otsu (0.083) while FCN and Otsu perform on par for The Bahamas (0.102 and 0.105, respectively). Then, on the social sensing side, we add two data layers representing relevant tweet text and images posted from the case-study regions to highlight different ways these heterogeneous data sources complement each other. Our extensive analyses reveal several valuable insights. In particular, we identify three types of signals: (i) confirmatory signals from both sources, which puts greater confidence that a specific region is flooded, (ii) complementary signals that provide different contextual information including needs and requests, disaster impact or damage reports and situational information, and (iii) novel signals when both data sources do not overlap and provide unique information.
... Most of the photos in Flickr are associated with textual data including title, description, and tags (which indicate what is present in the photo), and most of the photos are geo-located. As the majority of photos in Flickr include geolocation, they are being used for various analyses such as environmental and natural disaster monitoring (Sun et al., 2016), location-based behavioural analyses (Kisilevich et al., 2010), location prediction based on images (Weyand et al., 2016) to name a few. The use of social media data for biodiversity monitoring is not well supported by the experts in this field; however, previous studies suggest that Flickr images can be used as a complementary source to citizen science platforms of collecting biodiversity observations (ElQadi et al., 2017). ...
Social media data are becoming potential sources of passive VGI (Volunteered Geographic Information) and citizen science, in particular with regard to location-based environmental monitoring. Flickr, as one of the largest photo-sharing platforms, has been used in various environmental analyses from natural disaster prediction to wildlife monitoring. In this article, we have used bird photos from Flickr to illustrate the spatial distribution of bird species in Switzerland, and most importantly to see the correlation between the location of bird species and land cover types. A chi-square test of independence has been applied to illustrate the association between bird species and land cover classes and results illustrated a statistically significant association between the two variables. Furthermore, species distributions in Flickr were compared to eBird data, and the results demonstrated that Flickr can be a possible complementary source to citizen science data.
... For example, DMSP global nightlights data are used for the hazards to weight the indices based on economic activity. However, for natural hazards occurring from 2012 onwards, the monthly VIIRS nightlight data can be used instead, see cases such as GDP in China (Li et al. 2013;Shi et al. 2014) and Africa (Chen and Nordhaus 2015) and for storms and floods in the United States (Cao et al. 2013;Sun et al. 2015). The VIIRS data are publicly available at a monthly rather than annual frequency and have a resolution that is roughly twice as high as the DMSP images, potentially providing better localized activity estimates. ...
This article demonstrates the construction of earthquake and volcano damage indices using publicly available remote sensing sources and data on the physical characteristics of events. For earthquakes we use peak ground motion maps in conjunction with building type fragility curves to construct a local damage indicator. For volcanoes we employ volcanic ash data as a proxy for local damages. Both indices are then spatially aggregated by taking local economic exposure into account by assessing nightlight intensity derived from satellite images. We demonstrate the use of these indices with a case study of Indonesia, a country frequently exposed to earthquakes and volcanic eruptions. The results show that the indices capture the areas with the highest damage, and we provide overviews of the modeled aggregated damage for all provinces and districts in Indonesia for the time period 2004 to 2014. The indices were constructed using a combination of software programs—ArcGIS/Python, Matlab, and Stata. We also outline what potential freeware alternatives exist. Finally, for each index we highlight the assumptions and limitations that a potential practitioner needs to be aware of.
... All disasters have used the DMSP global nightlights data to weight the indices based on economic activity. Recently, the Visible Infrared Imaging Radiometer Suite (VIIRS) nightlight data provided an alternative for assessing economic activity or events, as showcased for the GDP in China (Li et al. 2013;Shi et al. 2014) and Africa (Chen and Nordhaus 2015) and for storms and floods in the US (Cao et al. 2013;Sun et al. 2016). If one is interested in events after 2012, the VIIRS data provide a higher spatial resolution and track changes by month instead of by year. ...
By using freely available remote sensing data, typically used for natural hazard modeling, and combining these with nightlight data as a proxy for economic activity, this paper constructs novel damage indices at the district level for Indonesia, showing how one can quickly find a relative economic impact up to a global scale for different disaster events, such as floods and the 2004 Christmas tsunami. Ex ante, prior to the incidence of a disaster, district-level damage indices could be used to determine the size of the annual fiscal transfers from the central government to the subnational governments. Ex post, or after the incidence of a natural hazard, damage indices are useful for quickly assessing and estimating the damages caused and are especially useful for central and local governments, emergency services, and aid workers so that they can respond efficiently and deploy resources where they are most needed.
... Unfortunately, limited information is available about the extent and consequences of urban flooding which commonly goes unnoticed after the "disturbance" (Galloway et al., 2018). Researchers have used social media data or fused social media data with traditional data (mainly the remote sensing imagery) to estimate the flood extent and the affected areas during a flooding disaster (Eilander et al., 2016;Jongman et al., 2015;Schnebele & Cervone, 2013;Schnebele et al., 2014;Sun et al., 2016). Their results indicate that social media data can help identify affected areas faster than traditional monitoring methods (Zou et al., 2018). ...
... While social media data have the advantages of being real-time and high-volume, they have also long been criticized for the lack of richness in quality if utilized as a standalone data source (Kwan, 2016). The fusion of social media data with traditional data (such as land use data, remote-sensing images, and census data) are expected to provide additional information for meaningful disaster research (Eilander et al., 2016;Jongman et al., 2015;Schnebele et al., 2014;Schnebele & Cervone, 2013;Sun et al., 2016). Fig. 2 demonstrates the ways that this study incorporates social media data with other data in studying public responses towards urban flooding. ...
... Obviously, the spatio-temporal variations of public responses towards urban flooding in this study indicate that the current rainstorm report system, adopted in most cities in China, which only publishes the range of cumulative rainfall in the whole city is far from enough. Previously, user-generated text and photos containing water depth information in Twitter and Flickr have been collected to generate urban flooding maps (Eilander et al., 2016;Jongman et al., 2015;Sun et al., 2016). This study further confirms that social media data can provide valuable spatial and timely information on public responses towards urban flooding for use by local governments in emergency management. ...
Applying a novel approach based on the fusion of social media data, land use data and other information, this paper examines the spatio-temporal patterns of public responses towards urban flooding in Nanjing city during July 1–21, 2016. Spatially, “pockets” of high public concern towards urban flooding were found in areas with low altitude, high percentage of water bodies, and rapid urban construction in recent years. Temporally, public responses tend to peak during the rainstorm period, rather than prior to that. Within a day, behavioural and emotional changes in relation to the rainstorm were most discernable during the morning peak hours. Variations in the response among people of different backgrounds, and the impact of urban flooding on different domains of people's daily life have been revealed. Based on the knowledge gained from this study, policy measures are proposed to increase urban flooding resilience, covering both physical infrastructure and human elements.
... Floods are the most devastating, frequent, and widespread natural disaster, affecting about, on average, 80 million people per year around the world, and causing more death and property damage sensing instruments, including passive microwave (MW) instruments [31][32][33][34] and active airborne synthetic aperture radar (SAR) imagery [35], can penetrate clouds and provide flood detection under cloudy conditions. However, passive MW sensors usually have very coarse spatial resolutions (10-25 km) [31][32][33][34]. ...
... Floods are the most devastating, frequent, and widespread natural disaster, affecting about, on average, 80 million people per year around the world, and causing more death and property damage sensing instruments, including passive microwave (MW) instruments [31][32][33][34] and active airborne synthetic aperture radar (SAR) imagery [35], can penetrate clouds and provide flood detection under cloudy conditions. However, passive MW sensors usually have very coarse spatial resolutions (10-25 km) [31][32][33][34]. Even though high-resolution SAR data (10-30 m) can provide very valuable surface information under almost all sky conditions, it usually has limited spatial coverage and a long revisit time (6-12 days) [35]. ...
Since 2 June 2020, unusual heavy and continuous rainfall from the Asian summer monsoon rainy season caused widespread catastrophic floods in many Asian countries, including primarily the two most populated countries, China and India. To detect and monitor the floods and estimate the potentially affected population, data from sensors aboard the operational polar-orbiting satellites Suomi National Polar-Orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration (NOAA)-20 were used. The Visible Infrared Imaging Radiometer Suite (VIIRS) with a spatial resolution of 375 m available twice per day aboard these two satellites can observe floodwaters over large spatial regions. The flood maps derived from the VIIRS imagery provide a big picture over the entire flooding regions, and demonstrate that, in July, in China, floods mainly occurred across the Yangtze River, Hui River and their tributaries. The VIIRS 5-day composite flood maps, along with a population density dataset, were combined to estimate the population potentially exposed (PPE) to flooding. We report here on the procedure to combine such data using the Zonal Statistic Function from the ArcGIS Spatial Analyst toolbox. Based on the flood extend for July 2020 along with the population density dataset, the Jiangxi and Anhui provinces were the most affected regions with more than 10 million people in Jingdezhen and Shangrao in Jiangxi province, and Fuyang and Luan in Anhui province, and it is estimated that about 55 million people in China might have been affected by the floodwaters. In addition to China, several other countries, including India, Bangladesh, and Myanmar, were also severely impacted. In India, the worst inundated states include Utter Pradesh, Bihar, Assam, and West Bengal, and it is estimated that about 40 million people might have been affected by severe floods, mainly in the northern states of Bihar, Assam, and West Bengal. The most affected country was Bangladesh, where one third of the country was underwater, and the estimated population potentially exposed to floods is about 30 million in Bangladesh.
