Ganga floods of 2010 in Uttar Pradesh, north India: a perspective analysis using satellite remote sensing data

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C.M. Bhatt

2016-03-01

Full Text Available The present study focuses on the unprecedented flood situation captured through multi-temporal satellite images, witnessed along the Ganga River in Uttar Pradesh during September 2010. At three gauge stations (Kannauj, Ankinghat and Kanpur, river water level exceeded the previous high-flood level attained by river more than a decade ago. The present communication with the aid of pre- and post-flood satellite images, coupled with hydrological (river water level and meteorological (rainfall data, explains about the unprecedented flood situation. In the latter part of the study, a novel and cost-effective method for building a library of flood inundation extents based on historical satellite data analysis and tagging the inundation layer with observed water level is demonstrated. During flood season, based on the forecasted water level, the library can be accessed to fetch the spatial inundation layer corresponding to the forecasted stage and anticipate in advance, likely spatial inundation pattern and submergence of villages and hence in alerting the habitation at risk. This method can be helpful in anticipating the areas to be affected in situations where satellite images cannot be effectively utilized due to cloud cover and also for providing information about the areas being partially covered in satellite data.

A Multi-Scale Flood Monitoring System Based on Fully Automatic MODIS and TerraSAR-X Processing Chains

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Enrico Stein

2013-10-01

Full Text Available A two-component fully automated flood monitoring system is described and evaluated. This is a result of combining two individual flood services that are currently under development at DLR’s (German Aerospace Center Center for Satellite based Crisis Information (ZKI to rapidly support disaster management activities. A first-phase monitoring component of the system systematically detects potential flood events on a continental scale using daily-acquired medium spatial resolution optical data from the Moderate Resolution Imaging Spectroradiometer (MODIS. A threshold set controls the activation of the second-phase crisis component of the system, which derives flood information at higher spatial detail using a Synthetic Aperture Radar (SAR based satellite mission (TerraSAR-X. The proposed activation procedure finds use in the identification of flood situations in different spatial resolutions and in the time-critical and on demand programming of SAR satellite acquisitions at an early stage of an evolving flood situation. The automated processing chains of the MODIS (MFS and the TerraSAR-X Flood Service (TFS include data pre-processing, the computation and adaptation of global auxiliary data, thematic classification, and the subsequent dissemination of flood maps using an interactive web-client. The system is operationally demonstrated and evaluated via the monitoring two recent flood events in Russia 2013 and Albania/Montenegro 2013.

LiDAR and IFSAR-Based Flood Inundation Model Estimates for Flood-Prone Areas of Afghanistan

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Johnson, W. C.; Goldade, M. M.; Kastens, J.; Dobbs, K. E.; Macpherson, G. L.

2014-12-01

Extreme flood events are not unusual in semi-arid to hyper-arid regions of the world, and Afghanistan is no exception. Recent flashfloods and flashflood-induced landslides took nearly 100 lives and destroyed or damaged nearly 2000 homes in 12 villages within Guzargah-e-Nur district of Baghlan province in northeastern Afghanistan. With available satellite imagery, flood-water inundation estimation can be accomplished remotely, thereby providing a means to reduce the impact of such flood events by improving shared situational awareness during major flood events. Satellite orbital considerations, weather, cost, data licensing restrictions, and other issues can often complicate the acquisition of appropriately timed imagery. Given the need for tools to supplement imagery where not available, complement imagery when it is available, and bridge the gap between imagery based flood mapping and traditional hydrodynamic modeling approaches, we have developed a topographic floodplain model (FLDPLN), which has been used to identify and map river valley floodplains with elevation data ranging from 90-m SRTM to 1-m LiDAR. Floodplain "depth to flood" (DTF) databases generated by FLDPLN are completely seamless and modular. FLDPLN has been applied in Afghanistan to flood-prone areas along the northern and southern flanks of the Hindu Kush mountain range to generate a continuum of 1-m increment flood-event models up to 10 m in depth. Elevation data used in this application of FLDPLN included high-resolution, drone-acquired LiDAR (~1 m) and IFSAR (5 m; INTERMAP). Validation of the model has been accomplished using the best available satellite-derived flood inundation maps, such as those issued by Unitar's Operational Satellite Applications Programme (UNOSAT). Results provide a quantitative approach to evaluating the potential risk to urban/village infrastructure as well as to irrigation systems, agricultural fields and archaeological sites.

Interference and deception detection technology of satellite navigation based on deep learning

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Chen, Weiyi; Deng, Pingke; Qu, Yi; Zhang, Xiaoguang; Li, Yaping

2017-10-01

Satellite navigation system plays an important role in people's daily life and war. The strategic position of satellite navigation system is prominent, so it is very important to ensure that the satellite navigation system is not disturbed or destroyed. It is a critical means to detect the jamming signal to avoid the accident in a navigation system. At present, the detection technology of jamming signal in satellite navigation system is not intelligent , mainly relying on artificial decision and experience. For this issue, the paper proposes a method based on deep learning to monitor the interference source in a satellite navigation. By training the interference signal data, and extracting the features of the interference signal, the detection sys tem model is constructed. The simulation results show that, the detection accuracy of our detection system can reach nearly 70%. The method in our paper provides a new idea for the research on intelligent detection of interference and deception signal in a satellite navigation system.

Moving object detection in video satellite image based on deep learning

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Zhang, Xueyang; Xiang, Junhua

2017-11-01

Moving object detection in video satellite image is studied. A detection algorithm based on deep learning is proposed. The small scale characteristics of remote sensing video objects are analyzed. Firstly, background subtraction algorithm of adaptive Gauss mixture model is used to generate region proposals. Then the objects in region proposals are classified via the deep convolutional neural network. Thus moving objects of interest are detected combined with prior information of sub-satellite point. The deep convolution neural network employs a 21-layer residual convolutional neural network, and trains the network parameters by transfer learning. Experimental results about video from Tiantuo-2 satellite demonstrate the effectiveness of the algorithm.

Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique

OpenAIRE

Carolien Toté; Domingos Patricio; Hendrik Boogaard; Raymond van der Wijngaart; Elena Tarnavsky; Chris Funk

2015-01-01

Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Cl...

Blending satellite data and RADAR tool for rapid flood damage assessment in Agriculture: A case study in Sri Lanka

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Amarnath, Giriraj; Inada, Yoshiaki; Inoue, Ryosuke; Alahacoon, Niranga; Smakhtin, Vladimir

2014-05-01

During the catastrophic flooding it is critically important to estimate losses as it is essential for facilitating good decision making at the district, province and national levels of government and to appraise aid agencies for necessary assistance. Flood loss estimates can also be used to evaluate the cost effectiveness of alternative approaches to strengthening flood control measures. In the case of Sri Lanka there were limited knowledge and application system exist for carrying out rapid damage assessment for Agriculture in Sri Lanka. FAO has developed the tool "Rapid Agricultural Disaster Assessment Routine" (RADAR) based on theoretical approach that uses simple tools for assessing the impact on agriculture of a disastrous event. There are two knowledge bases that contain information needed for calculation of the value loss or damage. The procedure of rapid impact assessment implies the use of knowledge-bases, database and GIS. In this study, the user friendly application of RADAR system has been developed. Three components were considered including agriculture, livestock and farmers asset to estimate the losses. The application will allow estimating flood damage at various scales and this being tested at district level and specific example for the 2011 floods in Sri Lanka. In order to understand flood inundation cycle, time-series optical MODIS satellite data (2000-2011) and microwave ALOS PALSAR (2006-2011) were used to derive annual flood extent, flood duration and recurrent areas to identify flood risk and impact of seasonal flooding on agriculture. This study demonstrates how RADAR & satellite-based flood products can be effectively used for rapid damage assessment and managing the floods.

Evaluation of NWP-based Satellite Precipitation Error Correction with Near-Real-Time Model Products and Flood-inducing Storms

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Zhang, X.; Anagnostou, E. N.; Schwartz, C. S.

2017-12-01

Satellite precipitation products tend to have significant biases over complex terrain. Our research investigates a statistical approach for satellite precipitation adjustment based solely on numerical weather simulations. This approach has been evaluated in two mid-latitude (Zhang et al. 2013*1, Zhang et al. 2016*2) and three topical mountainous regions by using the WRF model to adjust two high-resolution satellite products i) National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center morphing technique (CMORPH) and ii) Global Satellite Mapping of Precipitation (GSMaP). Results show the adjustment effectively reduces the satellite underestimation of high rain rates, which provides a solid proof-of-concept for continuing research of NWP-based satellite correction. In this study we investigate the feasibility of using NCAR Real-time Ensemble Forecasts*3 for adjusting near-real-time satellite precipitation datasets over complex terrain areas in the Continental United States (CONUS) such as Olympic Peninsula, California coastal mountain ranges, Rocky Mountains and South Appalachians. The research will focus on flood-inducing storms occurred from May 2015 to December 2016 and four satellite precipitation products (CMORPH, GSMaP, PERSIANN-CCS and IMERG). The error correction performance evaluation will be based on comparisons against the gauge-adjusted Stage IV precipitation data. *1 Zhang, Xinxuan, et al. "Using NWP simulations in satellite rainfall estimation of heavy precipitation events over mountainous areas." Journal of Hydrometeorology 14.6 (2013): 1844-1858. *2 Zhang, Xinxuan, et al. "Hydrologic Evaluation of NWP-Adjusted CMORPH Estimates of Hurricane-Induced Precipitation in the Southern Appalachians." Journal of Hydrometeorology 17.4 (2016): 1087-1099. *3 Schwartz, Craig S., et al. "NCAR's experimental real-time convection-allowing ensemble prediction system." Weather and Forecasting 30.6 (2015): 1645-1654.

Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique

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Carolien Toté

2015-02-01

Full Text Available Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT v2.0, Famine Early Warning System NETwork (FEWS NET Rainfall Estimate (RFE v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS are compared to independent gauge data (2001–2012. This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique

Science.gov (United States)

Tote, Carolien; Patricio, Domingos; Boogaard, Hendrik; van der Wijngaart, Raymond; Tarnavsky, Elena; Funk, Christopher C.

2015-01-01

Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001–2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

Extreme flood event analysis in Indonesia based on rainfall intensity and recharge capacity

Science.gov (United States)

Narulita, Ida; Ningrum, Widya

2018-02-01

Indonesia is very vulnerable to flood disaster because it has high rainfall events throughout the year. Flood is categorized as the most important hazard disaster because it is causing social, economic and human losses. The purpose of this study is to analyze extreme flood event based on satellite rainfall dataset to understand the rainfall characteristic (rainfall intensity, rainfall pattern, etc.) that happened before flood disaster in the area for monsoonal, equatorial and local rainfall types. Recharge capacity will be analyzed using land cover and soil distribution. The data used in this study are CHIRPS rainfall satellite data on 0.05 ° spatial resolution and daily temporal resolution, and GSMap satellite rainfall dataset operated by JAXA on 1-hour temporal resolution and 0.1 ° spatial resolution, land use and soil distribution map for recharge capacity analysis. The rainfall characteristic before flooding, and recharge capacity analysis are expected to become the important information for flood mitigation in Indonesia.

Simultaneous Observation Data of GB-SAR/PiSAR to Detect Flooding in an Urban Area

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Manabu Watanabe

2010-01-01

Full Text Available We analyzed simultaneous observation data with ground-based synthetic aperture radar (GB-SAR and airborne SAR (PiSAR over a flood test site at which a simple house was constructed in a field. The PiSAR σ∘ under flood condition was 0.9 to 3.4 dB higher than that under nonflood condition. GB-SAR gives high spatial resolution as we could identify a single scattering component and a double bounce component from the house. GB-SAR showed that the σ∘ difference between the flooding and nonflooding conditions came from the double bounce scattering. We also confirm that the entropy is a sensitive parameter in the eigenvalue decomposition parameters, if the scattering process is dominated by the double bounce scattering. We conclude that σ∘ and entropy are a good parameter to be used to detect flooding, not only in agricultural and forest regions, but also in urban areas. We also conclude that GB-SAR is a powerful tool to supplement satellite and airborne observation, which has a relatively low spatial resolution.

Simultaneous Observation Data of GB-SAR/PiSAR to Detect Flooding in an Urban Area

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Shimada Masanobu

2010-01-01

Full Text Available Abstract We analyzed simultaneous observation data with ground-based synthetic aperture radar (GB-SAR and airborne SAR (PiSAR over a flood test site at which a simple house was constructed in a field. The PiSAR under flood condition was 0.9 to 3.4 dB higher than that under nonflood condition. GB-SAR gives high spatial resolution as we could identify a single scattering component and a double bounce component from the house. GB-SAR showed that the difference between the flooding and nonflooding conditions came from the double bounce scattering. We also confirm that the entropy is a sensitive parameter in the eigenvalue decomposition parameters, if the scattering process is dominated by the double bounce scattering. We conclude that and entropy are a good parameter to be used to detect flooding, not only in agricultural and forest regions, but also in urban areas. We also conclude that GB-SAR is a powerful tool to supplement satellite and airborne observation, which has a relatively low spatial resolution.

Flood modelling with global precipitation measurement (GPM) satellite rainfall data: a case study of Dehradun, Uttarakhand, India

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Sai Krishna, V. V.; Dikshit, Anil Kumar; Pandey, Kamal

2016-05-01

Urban expansion, water bodies and climate change are inextricably linked with each other. The macro and micro level climate changes are leading to extreme precipitation events which have severe consequences on flooding in urban areas. Flood simulations shall be helpful in demarcation of flooded areas and effective flood planning and preparedness. The temporal availability of satellite rainfall data at varying spatial scale of 0.10 to 0.50 is helpful in near real time flood simulations. The present research aims at analysing stream flow and runoff to monitor flood condition using satellite rainfall data in a hydrologic model. The satellite rainfall data used in the research was NASA's Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), which is available at 30 minutes temporal resolution. Landsat data was used for mapping the water bodies in the study area. Land use land cover (LULC) data was prepared using Landsat 8 data with maximum likelihood technique that was provided as an input to the HEC-HMS hydrological model. The research was applied to one of the urbanized cities of India, viz. Dehradun, which is the capital of Uttarakhand State. The research helped in identifying the flood vulnerability at the basin level on the basis of the runoff and various socio economic parameters using multi criteria analysis.

Identification of flooded area from satellite images using Hybrid Kohonen Fuzzy C-Means sigma classifier

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Krishna Kant Singh

2017-06-01

Full Text Available A novel neuro fuzzy classifier Hybrid Kohonen Fuzzy C-Means-σ (HKFCM-σ is proposed in this paper. The proposed classifier is a hybridization of Kohonen Clustering Network (KCN with FCM-σ clustering algorithm. The network architecture of HKFCM-σ is similar to simple KCN network having only two layers, i.e., input and output layer. However, the selection of winner neuron is done based on FCM-σ algorithm. Thus, embedding the features of both, a neural network and a fuzzy clustering algorithm in the classifier. This hybridization results in a more efficient, less complex and faster classifier for classifying satellite images. HKFCM-σ is used to identify the flooding that occurred in Kashmir area in September 2014. The HKFCM-σ classifier is applied on pre and post flooding Landsat 8 OLI images of Kashmir to detect the areas that were flooded due to the heavy rainfalls of September, 2014. The classifier is trained using the mean values of the various spectral indices like NDVI, NDWI, NDBI and first component of Principal Component Analysis. The error matrix was computed to test the performance of the method. The method yields high producer’s accuracy, consumer’s accuracy and kappa coefficient value indicating that the proposed classifier is highly effective and efficient.

Optical and Physical Methods for Mapping Flooding with Satellite Imagery

Science.gov (United States)

Fayne, Jessica Fayne; Bolten, John; Lakshmi, Venkat; Ahamed, Aakash

2016-01-01

Flood and surface water mapping is becoming increasingly necessary, as extreme flooding events worldwide can damage crop yields and contribute to billions of dollars economic damages as well as social effects including fatalities and destroyed communities (Xaio et al. 2004; Kwak et al. 2015; Mueller et al. 2016).Utilizing earth observing satellite data to map standing water from space is indispensable to flood mapping for disaster response, mitigation, prevention, and warning (McFeeters 1996; Brakenridge and Anderson 2006). Since the early 1970s(Landsat, USGS 2013), researchers have been able to remotely sense surface processes such as extreme flood events to help offset some of these problems. Researchers have demonstrated countless methods and modifications of those methods to help increase knowledge of areas at risk and areas that are flooded using remote sensing data from optical and radar systems, as well as free publically available and costly commercial datasets.

Convolutional neural network features based change detection in satellite images

Science.gov (United States)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS

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Z.-G. Zhou

2016-06-01

Full Text Available Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1 Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST. (2 Forecasting and detecting disturbances in new time series data. (3 Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI and Confidence Levels (CL. The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.

Internationally coordinated multi-mission planning is now critical to sustain the space-based rainfall observations needed for managing floods globally

International Nuclear Information System (INIS)

Reed, Patrick M; Herman, Jonathan D; Chaney, Nathaniel W; Wood, Eric F; Ferringer, Matthew P

2015-01-01

At present 4 of 10 dedicated rainfall observing satellite systems have exceeded their design life, some by more than a decade. Here, we show operational implications for flood management of a ‘collapse’ of space-based rainfall observing infrastructure as well as the high-value opportunities for a globally coordinated portfolio of satellite missions and data services. Results show that the current portfolio of rainfall missions fails to meet operational data needs for flood management, even when assuming a perfectly coordinated data product from all current rainfall-focused missions (i.e., the full portfolio). In the full portfolio, satellite-based rainfall data deficits vary across the globe and may preclude climate adaptation in locations vulnerable to increasing flood risks. Moreover, removing satellites that are currently beyond their design life (i.e., the reduced portfolio) dramatically increases data deficits globally and could cause entire high intensity flood events to be unobserved. Recovery from the reduced portfolio is possible with internationally coordinated replenishment of as few as 2 of the 4 satellite systems beyond their design life, yielding rainfall data coverages that outperform the current full portfolio (i.e., an optimized portfolio of eight satellites can outperform ten satellites). This work demonstrates the potential for internationally coordinated satellite replenishment and data services to substantially enhance the cost-effectiveness, sustainability and operational value of space-based rainfall observations in managing evolving flood risks. (letter)

Coastal flood inundation monitoring with Satellite C-band and L-band Synthetic Aperture Radar data

Science.gov (United States)

Ramsey, Elijah W.; Rangoonwala, Amina; Bannister, Terri

2013-01-01

Satellite Synthetic Aperture Radar (SAR) was evaluated as a method to operationally monitor the occurrence and distribution of storm- and tidal-related flooding of spatially extensive coastal marshes within the north-central Gulf of Mexico. Maps representing the occurrence of marsh surface inundation were created from available Advanced Land Observation Satellite (ALOS) Phased Array type L-Band SAR (PALSAR) (L-band) (21 scenes with HH polarizations in Wide Beam [100 m]) data and Environmental Satellite (ENVISAT) Advanced SAR (ASAR) (C-band) data (24 scenes with VV and HH polarizations in Wide Swath [150 m]) during 2006-2009 covering 500 km of the Louisiana coastal zone. Mapping was primarily based on a decrease in backscatter between reference and target scenes, and as an extension of previous studies, the flood inundation mapping performance was assessed by the degree of correspondence between inundation mapping and inland water levels. Both PALSAR- and ASAR-based mapping at times were based on suboptimal reference scenes; however, ASAR performance seemed more sensitive to reference-scene quality and other types of scene variability. Related to water depth, PALSAR and ASAR mapping accuracies tended to be lower when water depths were shallow and increased as water levels decreased below or increased above the ground surface, but this pattern was more pronounced with ASAR. Overall, PALSAR-based inundation accuracies averaged 84% (n = 160), while ASAR-based mapping accuracies averaged 62% (n = 245).

Detecting weather radar clutter using satellite-based nowcasting products

DEFF Research Database (Denmark)

Jensen, Thomas B.S.; Gill, Rashpal S.; Overgaard, Søren

2006-01-01

This contribution presents the initial results from experiments with detection of weather radar clutter by information fusion with satellite based nowcasting products. Previous studies using information fusion of weather radar data and first generation Meteosat imagery have shown promising results...... for the detecting and removal of clutter. Naturally, the improved spatio-temporal resolution of the Meteosat Second Generation sensors, coupled with its increased number of spectral bands, is expected to yield even better detection accuracies. Weather radar data from three C-band Doppler weather radars...... Application Facility' of EUMETSAT and is based on multispectral images from the SEVIRI sensor of the Meteosat-8 platform. Of special interest is the 'Precipitating Clouds' product, which uses the spectral information coupled with surface temperatures from Numerical Weather Predictions to assign probabilities...

Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting

Science.gov (United States)

Wardah, T.; Abu Bakar, S. H.; Bardossy, A.; Maznorizan, M.

2008-07-01

SummaryFrequent flash-floods causing immense devastation in the Klang River Basin of Malaysia necessitate an improvement in the real-time forecasting systems being used. The use of meteorological satellite images in estimating rainfall has become an attractive option for improving the performance of flood forecasting-and-warning systems. In this study, a rainfall estimation algorithm using the infrared (IR) information from the Geostationary Meteorological Satellite-5 (GMS-5) is developed for potential input in a flood forecasting system. Data from the records of GMS-5 IR images have been retrieved for selected convective cells to be trained with the radar rain rate in a back-propagation neural network. The selected data as inputs to the neural network, are five parameters having a significant correlation with the radar rain rate: namely, the cloud-top brightness-temperature of the pixel of interest, the mean and the standard deviation of the temperatures of the surrounding five by five pixels, the rate of temperature change, and the sobel operator that indicates the temperature gradient. In addition, three numerical weather prediction (NWP) products, namely the precipitable water content, relative humidity, and vertical wind, are also included as inputs. The algorithm is applied for the areal rainfall estimation in the upper Klang River Basin and compared with another technique that uses power-law regression between the cloud-top brightness-temperature and radar rain rate. Results from both techniques are validated against previously recorded Thiessen areal-averaged rainfall values with coefficient correlation values of 0.77 and 0.91 for the power-law regression and the artificial neural network (ANN) technique, respectively. An extra lead time of around 2 h is gained when the satellite-based ANN rainfall estimation is coupled with a rainfall-runoff model to forecast a flash-flood event in the upper Klang River Basin.

On the performance of satellite precipitation products in riverine flood modeling: A review

Science.gov (United States)

Maggioni, Viviana; Massari, Christian

2018-03-01

This work is meant to summarize lessons learned on using satellite precipitation products for riverine flood modeling and to propose future directions in this field of research. Firstly, the most common satellite precipitation products (SPPs) during the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) eras are reviewed. Secondly, we discuss the main errors and uncertainty sources in these datasets that have the potential to affect streamflow and runoff model simulations. Thirdly, past studies that focused on using SPPs for predicting streamflow and runoff are analyzed. As the impact of floods depends not only on the characteristics of the flood itself, but also on the characteristics of the region (population density, land use, geophysical and climatic factors), a regional analysis is required to assess the performance of hydrologic models in monitoring and predicting floods. The performance of SPP-forced hydrological models was shown to largely depend on several factors, including precipitation type, seasonality, hydrological model formulation, topography. Across several basins around the world, the bias in SPPs was recognized as a major issue and bias correction methods of different complexity were shown to significantly reduce streamflow errors. Model re-calibration was also raised as a viable option to improve SPP-forced streamflow simulations, but caution is necessary when recalibrating models with SPP, which may result in unrealistic parameter values. From a general standpoint, there is significant potential for using satellite observations in flood forecasting, but the performance of SPP in hydrological modeling is still inadequate for operational purposes.

Note on Studying Change Point of LRD Traffic Based on Li's Detection of DDoS Flood Attacking

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Zhengmin Xia

2010-01-01

Full Text Available Distributed denial-of-service (DDoS flood attacks remain great threats to the Internet. To ensure network usability and reliability, accurate detection of these attacks is critical. Based on Li's work on DDoS flood attack detection, we propose a DDoS detection method by monitoring the Hurst variation of long-range dependant traffic. Specifically, we use an autoregressive system to estimate the Hurst parameter of normal traffic. If the actual Hurst parameter varies significantly from the estimation, we assume that DDoS attack happens. Meanwhile, we propose two methods to determine the change point of Hurst parameter that indicates the occurrence of DDoS attacks. The detection rate associated with one method and false alarm rate for the other method are also derived. The test results on DARPA intrusion detection evaluation data show that the proposed approaches can achieve better detection performance than some well-known self-similarity-based detection methods.

A Decadal Historical Satellite Data and Rainfall Trend Analysis (2001–2016 for Flood Hazard Mapping in Sri Lanka

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Niranga Alahacoon

2018-03-01

Full Text Available Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE gridded rainfall data with flood maps derived from Synthetic Aperture Radar (SAR and multispectral satellite to arrive at holistic spatio-temporal patterns of floods in Sri Lanka. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR data were used to map flood extents for emergency relief operations while eight-day Moderate Resolution Imaging Spectroradiometer (MODIS surface reflectance data for the time period from 2001 to 2016 were used to map long term flood-affected areas. The inundation maps produced for rapid response were published within three hours upon the availability of satellite imagery in web platforms, with the aim of supporting a wide range of stakeholders in emergency response and flood relief operations. The aggregated time series of flood extents mapped using MODIS data were used to develop a flood occurrence map (2001–2016 for Sri Lanka. Flood hotpots identified using both optical and synthetic aperture average of 325 km2 for the years 2006–2015 and exceptional flooding in 2016 with inundation extent of approximately 1400 km2. The time series rainfall data explains increasing trend in the extreme rainfall indices with similar observation derived from satellite imagery. The results demonstrate the feasibility of using multi-sensor flood mapping approaches, which will aid Disaster Management Center (DMC and other multi-lateral agencies involved in managing rapid response operations and preparing mitigation measures.

Implementation of anomaly detection algorithms for detecting transmission control protocol synchronized flooding attacks

CSIR Research Space (South Africa)

Mkuzangwe, NNP

2015-08-01

Full Text Available This work implements two anomaly detection algorithms for detecting Transmission Control Protocol Synchronized (TCP SYN) flooding attack. The two algorithms are an adaptive threshold algorithm and a cumulative sum (CUSUM) based algorithm...

Living with floods - Household perception and satellite observations in the Barotse floodplain, Zambia

Science.gov (United States)

Cai, Xueliang; Haile, Alemseged Tamiru; Magidi, James; Mapedza, Everisto; Nhamo, Luxon

2017-08-01

The Barotse Floodplain, a designated Ramsar site, is home to thousands of indigenous people along with an extensive wetland ecosystem and food production system. Increasingly it is also a popular tourist destination with its annual Kuomboka festival which celebrates the relocation of the king and the Lozi people to higher ground before the onset of the flood season. This paper presents an integrated approach which cross validates and combines the floodplain residents' perceptions about recent floods with information on flood inundation levels derived from satellite observations. Local residents' surveys were conducted to assess farmers' perception on the flooding patterns and the impact on their livelihoods. Further, a series of flood inundation maps from 1989 to 2014 generated from remotely sensed Landsat imagery were used to assess the recent patterns of floods. Results show that the floodplain has a population of 33 thousand living in 10,849 small permeant or temporary buildings with a total cropland area of 4976 ha. The floodplain hydrology and flooding patterns have changed, confirmed by both surveys and satellite image analysis, due to catchment development and changing climate. The average annual inundated areas have increased from about 316 thousand ha in 1989-1998 to 488 thousand ha in 2005-2014. As a result the inundated cropland and houses increased from 9% to 6% in 1989 to 73% and 47% in 2014, respectively. The timing of the floods has also changed with both delaying and early onset happening more frequently. These changes cause increasing difficulties in flood forecast and preparation using indigenous knowledge, therefore creating greater damages to crops, livestock, and houses. Current floodplain management system is inadequate and new interventions are needed to help manage the floods at a systematic manner.

Smoothing of Fused Spectral Consistent Satellite Images with TV-based Edge Detection

DEFF Research Database (Denmark)

Sveinsson, Johannes; Aanæs, Henrik; Benediktsson, Jon Atli

2007-01-01

based on satellite data. Additionally, most conventional methods are loosely connected to the image forming physics of the satellite image, giving these methods an ad hoc feel. Vesteinsson et al. [1] proposed a method of fusion of satellite images that is based on the properties of imaging physics...... in a statistically meaningful way and was called spectral consistent panshapening (SCP). In this paper we improve this framework for satellite image fusion by introducing a better image prior, via data-dependent image smoothing. The dependency is obtained via total variation edge detection method.......Several widely used methods have been proposed for fusing high resolution panchromatic data and lower resolution multi-channel data. However, many of these methods fail to maintain the spectral consistency of the fused high resolution image, which is of high importance to many of the applications...

Monitoring coastal inundation with Synthetic Aperture Radar satellite data

Science.gov (United States)

Suzuoki, Yukihiro; Rangoonwala, Amina; Ramsey, Elijah W.

2011-01-01

Maps representing the presence and absence of surface inundation in the Louisiana coastal zone were created from available satellite scenes acquired by the Japanese Aerospace Exploration Agency's Advanced Land Observing Satellite and by the European Space Agency's Envisat from late 2006 through summer 2009. Detection of aboveground surface flooding relied on the well-documented and distinct signature of decreased backscatter in Synthetic Aperture Radar (SAR), which is indicative of inundated marsh in the Gulf of Mexico. Even though decreases in backscatter were distinctive, the multiplicity of possible interactions between changing flood depths and canopy height yielded complex SAR-based representations of the marshes.

Analysis of the flood extent extraction model and the natural flood influencing factors: A GIS-based and remote sensing analysis

International Nuclear Information System (INIS)

Lawal, D U; Matori, A N; Yusuf, K W; Hashim, A M; Balogun, A L

2014-01-01

Serious floods have hit the State of Perlis in 2005, 2010, as well as 2011. Perlis is situated in the northern part of Peninsula Malaysia. The floods caused great damage to properties and human lives. There are various methods used in an attempt to provide the most reliable ways to reduce the flood risk and damage to the optimum level by identifying the flood vulnerable zones. The purpose of this paper is to develop a flood extent extraction model based on Minimum Distance Algorithm and to overlay with the natural flood influencing factors considered herein in order to examine the effect of each factor in flood generation. GIS spatial database was created from a geological map, SPOT satellite image, and the topographical map. An attribute database was equally created from field investigations and historical flood areas reports of the study area. The results show a great correlation between the flood extent extraction model and the flood factors

A Cloud-Based Global Flood Disaster Community Cyber-Infrastructure: Development and Demonstration

Science.gov (United States)

Wan, Zhanming; Hong, Yang; Khan, Sadiq; Gourley, Jonathan; Flamig, Zachary; Kirschbaum, Dalia; Tang, Guoqiang

2014-01-01

Flood disasters have significant impacts on the development of communities globally. This study describes a public cloud-based flood cyber-infrastructure (CyberFlood) that collects, organizes, visualizes, and manages several global flood databases for authorities and the public in real-time, providing location-based eventful visualization as well as statistical analysis and graphing capabilities. In order to expand and update the existing flood inventory, a crowdsourcing data collection methodology is employed for the public with smartphones or Internet to report new flood events, which is also intended to engage citizen-scientists so that they may become motivated and educated about the latest developments in satellite remote sensing and hydrologic modeling technologies. Our shared vision is to better serve the global water community with comprehensive flood information, aided by the state-of-the- art cloud computing and crowdsourcing technology. The CyberFlood presents an opportunity to eventually modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters.

A FUZZY LOGIC-BASED APPROACH FOR THE DETECTION OF FLOODED VEGETATION BY MEANS OF SYNTHETIC APERTURE RADAR DATA

Directory of Open Access Journals (Sweden)

V. Tsyganskaya

2016-06-01

Full Text Available In this paper an algorithm designed to map flooded vegetation from synthetic aperture radar (SAR imagery is introduced. The approach is based on fuzzy logic which enables to deal with the ambiguity of SAR data and to integrate multiple ancillary data containing topographical information, simple hydraulic considerations and land cover information. This allows the exclusion of image elements with a backscatter value similar to flooded vegetation, to significantly reduce misclassification errors. The flooded vegetation mapping procedure is tested on a flood event that occurred in Germany over parts of the Saale catchment on January 2011 using a time series of high resolution TerraSAR-X data covering the time interval from 2009 to 2015. The results show that the analysis of multi-temporal X-band data combined with ancillary data using a fuzzy logic-based approach permits the detection of flooded vegetation areas.

Detecting Flood Variations in Shanghai over 1949–2009 with Mann-Kendall Tests and a Newspaper-Based Database

Directory of Open Access Journals (Sweden)

Shiqiang Du

2015-04-01

Full Text Available A valuable aid to assessing and managing flood risk lies in a reliable database of historical floods. In this study, a newspaper-based flood database for Shanghai (NFDS for the period 1949–2009 was developed through a systematic scanning of newspapers. After calibration and validation of the database, Mann-Kendall tests and correlation analysis were applied to detect possible changes in flood frequencies. The analysis was carried out for three different flood types: overbank flood, agricultural waterlogging, and urban waterlogging. The compiled NFDS registered 146 floods and 92% of them occurred in the flood-prone season from June to September. The statistical analyses showed that both the annual flood and the floods in June–August increased significantly. Urban waterlogging showed a very strong increasing trend, probably because of insufficient capacity of urban drainage system and impacts of rapid urbanization. By contrast, the decrease in overbank flooding and the slight increase in agricultural waterlogging were likely because of the construction of river levees and seawalls and the upgrade of agricultural drainage systems, respectively. This study demonstrated the usefulness of local newspapers in building a historical flood database and in assessing flood characterization.

Evaluation of sub daily satellite rainfall estimates through flash flood modelling in the Lower Middle Zambezi Basin

Directory of Open Access Journals (Sweden)

T. Matingo

2018-05-01

Full Text Available Flash floods are experienced almost annually in the ungauged Mbire District of the Middle Zambezi Basin. Studies related to hydrological modelling (rainfall-runoff and flood forecasting require major inputs such as precipitation which, due to shortage of observed data, are increasingly using indirect methods for estimating precipitation. This study therefore evaluated performance of CMORPH and TRMM satellite rainfall estimates (SREs for 30 min, 1 h, 3 h and daily intensities through hydrologic and flash flood modelling in the Lower Middle Zambezi Basin for the period 2013–2016. On a daily timestep, uncorrected CMORPH and TRMM show Probability of Detection (POD of 61 and 59 %, respectively, when compared to rain gauge observations. The best performance using Correlation Coefficient (CC was 70 and 60 % on daily timesteps for CMORPH and TRMM, respectively. The best RMSE for CMORPH was 0.81 % for 30 min timestep and for TRMM was 2, 11 % on 3 h timestep. For the year 2014 to 2015, the HEC-HMS (Hydrological Engineering Centre-Hydrological Modelling System daily model calibration Nash Sutcliffe efficiency (NSE for Musengezi sub catchment was 59 % whilst for Angwa it was 55 %. Angwa sub-catchment daily NSE results for the period 2015–2016 was 61 %. HEC-RAS flash flood modeling at 100, 50 and 25 year return periods for Angwa sub catchment, inundated 811 and 867 ha for TRMM rainfall simulated discharge at 3 h and daily timesteps, respectively. For CMORPH generated rainfall, the inundation was 818, 876, 890 and 891 ha at daily, 3 h, 1 h and 30 min timesteps. The 30 min time step for CMORPH effectively captures flash floods with the measure of agreement between simulated flood extent and ground control points of 69 %. For TRMM, the 3 h timestep effectively captures flash floods with coefficient of 67 %. The study therefore concludes that satellite products are most effective in capturing localized

A UAV based system for real time flash flood monitoring in desert environments using Lagrangian microsensors

KAUST Repository

Abdelkader, Mohamed; Shaqura, Mohammad; Claudel, Christian G.; Gueaieb, Wail

2013-01-01

with advance warning, for which real time monitoring is critical. While satellite-based high resolution weather forecasts can help predict floods to a certain extent, they are not reliable enough, as flood models depend on a large number of parameters

Typhoon Doksuri Flooding in 2017 - High-Resolution Inundation Mapping and Monitoring from Sentinel Satellite SAR Data

Science.gov (United States)

Nghiem, S. V.; Nguyen, D. T.

2017-12-01

In 2017, typhoons and hurricanes have inflicted catastrophic flooding across extensive regions in many countries on several continents, including Asia and North America. The U.S. Federal Emergency Management Agency (FEMA) requested urgent support for flood mapping and monitoring in an emergency response to the devastating flood situation. An innovative satellite remote sensing method, called the Depolarization Reduction Algorithm for Global Observations of inundatioN (DRAGON), has been developed and implemented for use with Sentinel synthetic aperture radar (SAR) satellite data at a resolution of 10 meters to identify, map, and monitor inundation including pre-existing water bodies and newly flooded areas. Because Sentinel SAR operates at C-band microwave frequency, it can be used for flood mapping regardless of could cover conditions typically associated with storms, and thus can provide immediate results without the need to wait for the clouds to clear out. In Southeast Asia, Typhoon Doksuri caused significant flooding across extensive regions in Vietnam and other countries in September 2017. Figure 1 presents the flood mapping result over a region around Hà Tĩnh (north central coast of Vietnam) showing flood inundated areas (in yellow) on 16 September 2017 together with pre-existing surface water (in blue) on 4 September 2017. This is just one example selected from a larger flood map covering an extensive region of about 250 km x 680 km all along the central coast of Vietnam.

Review of Tracktable for Satellite Maneuver Detection

Energy Technology Data Exchange (ETDEWEB)

Acquesta, Erin C.S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Valicka, Christopher G. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hinga, Mark B. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ehn, Carollan Beret [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2016-10-01

As a tool developed to translate geospatial data into geometrical descriptors, Tracktable offers a highly efficient means to detect anomalous flight and maritime behavior. Following the success of using geometrical descriptors for detecting anomalous trajectory behavior, the question of whether Tracktable could be used to detect satellite maneuvers arose. In answering this question, this re- port will introduce a brief description of how Tracktable has been used in the past, along with an introduction to the fundamental properties of astrodynamics for satellite trajectories. This will then allow us to compare the two problem spaces, addressing how easily the methods used by Tracktable will translate to orbital mechanics. Based on these results, we will then be able to out- line the current limitations as well as possible path forward for using Tracktable to detect satellite maneuvers.

Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique

NARCIS (Netherlands)

Tote, C.; Patricio, D.; Boogaard, H.L.; Wijngaart, van der R.; Tarnavsky, E.; Funk, C.

2015-01-01

Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and

Real-time flood extent maps based on social media

Science.gov (United States)

Eilander, Dirk; van Loenen, Arnejan; Roskam, Ruud; Wagemaker, Jurjen

2015-04-01

During a flood event it is often difficult to get accurate information about the flood extent and the people affected. This information is very important for disaster risk reduction management and crisis relief organizations. In the post flood phase, information about the flood extent is needed for damage estimation and calibrating hydrodynamic models. Currently, flood extent maps are derived from a few sources such as satellite images, areal images and post-flooding flood marks. However, getting accurate real-time or maximum flood extent maps remains difficult. With the rise of social media, we now have a new source of information with large numbers of observations. In the city of Jakarta, Indonesia, the intensity of unique flood related tweets during a flood event, peaked at 8 tweets per second during floods in early 2014. A fair amount of these tweets also contains observations of water depth and location. Our hypothesis is that based on the large numbers of tweets it is possible to generate real-time flood extent maps. In this study we use tweets from the city of Jakarta, Indonesia, to generate these flood extent maps. The data-mining procedure looks for tweets with a mention of 'banjir', the Bahasa Indonesia word for flood. It then removes modified and retweeted messages in order to keep unique tweets only. Since tweets are not always sent directly from the location of observation, the geotag in the tweets is unreliable. We therefore extract location information using mentions of names of neighborhoods and points of interest. Finally, where encountered, a mention of a length measure is extracted as water depth. These tweets containing a location reference and a water level are considered to be flood observations. The strength of this method is that it can easily be extended to other regions and languages. Based on the intensity of tweets in Jakarta during a flood event we can provide a rough estimate of the flood extent. To provide more accurate flood extend

Flood extent mapping for Namibia using change detection and thresholding with SAR

International Nuclear Information System (INIS)

Long, Stephanie; Fatoyinbo, Temilola E; Policelli, Frederick

2014-01-01

A new method for flood detection change detection and thresholding (CDAT) was used with synthetic aperture radar (SAR) imagery to delineate the extent of flooding for the Chobe floodplain in the Caprivi region of Namibia. This region experiences annual seasonal flooding and has seen a recent renewal of severe flooding after a long dry period in the 1990s. Flooding in this area has caused loss of life and livelihoods for the surrounding communities and has caught the attention of disaster relief agencies. There is a need for flood extent mapping techniques that can be used to process images quickly, providing near real-time flooding information to relief agencies. ENVISAT/ASAR and Radarsat-2 images were acquired for several flooding seasons from February 2008 to March 2013. The CDAT method was used to determine flooding from these images and includes the use of image subtraction, decision-based classification with threshold values, and segmentation of SAR images. The total extent of flooding determined for 2009, 2011 and 2012 was about 542 km 2 , 720 km 2 , and 673 km 2 respectively. Pixels determined to be flooded in vegetation were typically <0.5% of the entire scene, with the exception of 2009 where the detection of flooding in vegetation was much greater (almost one third of the total flooded area). The time to maximum flooding for the 2013 flood season was determined to be about 27 days. Landsat water classification was used to compare the results from the new CDAT with SAR method; the results show good spatial agreement with Landsat scenes. (paper)

Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses

Directory of Open Access Journals (Sweden)

Stefan Dech

2013-02-01

Full Text Available Satellite remote sensing is a valuable tool for monitoring flooding. Microwave sensors are especially appropriate instruments, as they allow the differentiation of inundated from non-inundated areas, regardless of levels of solar illumination or frequency of cloud cover in regions experiencing substantial rainy seasons. In the current study we present the longest synthetic aperture radar-based time series of flood and inundation information derived for the Mekong Delta that has been analyzed for this region so far. We employed overall 60 Envisat ASAR Wide Swath Mode data sets at a spatial resolution of 150 meters acquired during the years 2007–2011 to facilitate a thorough understanding of the flood regime in the Mekong Delta. The Mekong Delta in southern Vietnam comprises 13 provinces and is home to 18 million inhabitants. Extreme dry seasons from late December to May and wet seasons from June to December characterize people’s rural life. In this study, we show which areas of the delta are frequently affected by floods and which regions remain dry all year round. Furthermore, we present which areas are flooded at which frequency and elucidate the patterns of flood progression over the course of the rainy season. In this context, we also examine the impact of dykes on floodwater emergence and assess the relationship between retrieved flood occurrence patterns and land use. In addition, the advantages and shortcomings of ENVISAT ASAR-WSM based flood mapping are discussed. The results contribute to a comprehensive understanding of Mekong Delta flood dynamics in an environment where the flow regime is influenced by the Mekong River, overland water-flow, anthropogenic floodwater control, as well as the tides.

Detection and Mapping of the Geomorphic Effects of Flooding Using UAV Photogrammetry

Science.gov (United States)

Langhammer, Jakub; Vacková, Tereza

2018-04-01

In this paper, we present a novel technique for the objective detection of the geomorphological effects of flooding in riverbeds and floodplains using imagery acquired by unmanned aerial vehicles (UAVs, also known as drones) equipped with an panchromatic camera. The proposed method is based on the fusion of the two key data products of UAV photogrammetry, the digital elevation model (DEM), and the orthoimage, as well as derived qualitative information, which together serve as the basis for object-based segmentation and the supervised classification of fluvial forms. The orthoimage is used to calculate textural features, enabling detection of the structural properties of the image area and supporting the differentiation of features with similar spectral responses but different surface structures. The DEM is used to derive a flood depth model and the terrain ruggedness index, supporting the detection of bank erosion. All the newly derived information layers are merged with the orthoimage to form a multi-band data set, which is used for object-based segmentation and the supervised classification of key fluvial forms resulting from flooding, i.e., fresh and old gravel accumulations, sand accumulations, and bank erosion. The method was tested on the effects of a snowmelt flood that occurred in December 2015 in a montane stream in the Sumava Mountains, Czech Republic, Central Europe. A multi-rotor UAV was used to collect images of a 1-km-long and 200-m-wide stretch of meandering stream with fresh traces of fluvial activity. The performed segmentation and classification proved that the fusion of 2D and 3D data with the derived qualitative layers significantly enhanced the reliability of the fluvial form detection process. The assessment accuracy for all of the detected classes exceeded 90%. The proposed technique proved its potential for application in rapid mapping and detection of the geomorphological effects of flooding.

Characterizing Global Flood Wave Travel Times to Optimize the Utility of Near Real-Time Satellite Remote Sensing Products

Science.gov (United States)

Allen, G. H.; David, C. H.; Andreadis, K. M.; Emery, C. M.; Famiglietti, J. S.

2017-12-01

Earth observing satellites provide valuable near real-time (NRT) information about flood occurrence and magnitude worldwide. This NRT information can be used in early flood warning systems and other flood management applications to save lives and mitigate flood damage. However, these NRT products are only useful to early flood warning systems if they are quickly made available, with sufficient time for flood mitigation actions to be implemented. More specifically, NRT data latency, or the time period between the satellite observation and when the user has access to the information, must be less than the time it takes a flood to travel from the flood observation location to a given downstream point of interest. Yet the paradigm that "lower latency is always better" may not necessarily hold true in river systems due to tradeoffs between data latency and data quality. Further, the existence of statistical breaks in the global distribution of flood wave travel time (i.e. a jagged statistical distribution) would represent preferable latencies for river-observation NRT remote sensing products. Here we present a global analysis of flood wave velocity (i.e. flow celerity) and travel time. We apply a simple kinematic wave model to a global hydrography dataset and calculate flow wave celerity and travel time during bankfull flow conditions. Bankfull flow corresponds to the condition of maximum celerity and thus we present the "worst-case scenario" minimum flow wave travel time. We conduct a similar analysis with respect to the time it takes flood waves to reach the next downstream city, as well as the next downstream reservoir. Finally, we conduct these same analyses, but with regards to the technical capabilities of the planned Surface Water and Ocean Topography (SWOT) satellite mission, which is anticipated to provide waterbody elevation and extent measurements at an unprecedented spatial and temporal resolution. We validate these results with discharge records from paired

A MODIS-Based Robust Satellite Technique (RST for Timely Detection of Oil Spilled Areas

Directory of Open Access Journals (Sweden)

Teodosio Lacava

2017-02-01

Full Text Available Natural crude-oil seepages, together with the oil released into seawater as a consequence of oil exploration/production/transportation activities, and operational discharges from tankers (i.e., oil dumped during cleaning actions represent the main sources of sea oil pollution. Satellite remote sensing can be a useful tool for the management of such types of marine hazards, namely oil spills, mainly owing to the synoptic view and the good trade-off between spatial and temporal resolution, depending on the specific platform/sensor system used. In this paper, an innovative satellite-based technique for oil spill detection, based on the general robust satellite technique (RST approach, is presented. It exploits the multi-temporal analysis of data acquired in the visible channels of the Moderate Resolution Imaging Spectroradiometer (MODIS on board the Aqua satellite in order to automatically and quickly detect the presence of oil spills on the sea surface, with an attempt to minimize “false detections” caused by spurious effects associated with, for instance, cloud edges, sun/satellite geometries, sea currents, etc. The oil spill event that occurred in June 2007 off the south coast of Cyprus in the Mediterranean Sea has been considered as a test case. The resulting data, the reliability of which has been evaluated by both carrying out a confutation analysis and comparing them with those provided by the application of another independent MODIS-based method, showcase the potential of RST in identifying the presence of oil with a high level of accuracy.

A new approach for river flood extent delineation in rural and urban areas combining RADARSAT-2 imagery and flood recurrence interval data

Science.gov (United States)

Tanguy, Marion; Bernier, Monique; Chokmani, Karem

2015-04-01

When a flood hits an inhabited area, managers and services responsible for public safety need precise, reliable and up to date maps of the areas affected by the flood, in order to quickly roll out and to coordinate the adequate intervention and assistance plans required to limit the human and material damages caused by the disaster. Synthetic aperture radar (SAR) sensors are now considered as one of the most adapted tool for flood detection and mapping in a context of crisis management. Indeed, due to their capacity to acquire data night and day, in almost all meteorological conditions, SAR sensors allow the acquisition of synoptic but detailed views of the areas affected by the flood, even during the active phases of the event. Moreover, new generation sensors such as RADARSAT-2, TerraSAR-X, COSMO-SkyMed, are providing very high resolution images of the disaster (down to 1m ground resolution). Further, critical improvements have been made on the temporal repetitivity of acquisitions and on data availability, through the development of satellite constellations (i.e the four COSMO-Skymed or the Sentinel-1A and 1B satellites) and thanks to the implementation of the International Charter "Space and Major Disasters", which guarantees high priority images acquisition and delivery with 4 to 12 hours. If detection of open water flooded areas is relatively straightforward with SAR imagery, flood detection in built-up areas is often associated with important issues. Indeed, because of the side looking geometry of the SAR sensors, structures such as tall vegetation and structures parallel to the satellite direction of travel may produce shadow and layover effects, leading to important over and under-detections of flooded pixels. Besides, the numerous permanent water-surfaces like radar response areas present in built-up environments, such as parking lots, roads etc., may be mixed up with flooded areas, resulting in substantial inaccuracies in the final flood map. In spite of

Introduction to SNPP/VIIRS Flood Mapping Software Version 1.0

Science.gov (United States)

Li, S.; Sun, D.; Goldberg, M.; Sjoberg, W.; Santek, D.; Hoffman, J.

2017-12-01

Near real-time satellite-derived flood maps are invaluable to river forecasters and decision-makers for disaster monitoring and relief efforts. With support from the JPSS (Joint Polar Satellite System) Proving Ground and Risk Reduction (PGRR) Program, flood detection software has been developed using Suomi-NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) imagery to automatically generate near real-time flood maps for National Weather Service (NWS) River Forecast Centers (RFC) in the USA. The software, which is called VIIRS NOAA GMU Flood Version 1.0 (hereafter referred to as VNG Flood V1.0), consists of a series of algorithms that include water detection, cloud shadow removal, terrain shadow removal, minor flood detection, water fraction retrieval, and floodwater determination. The software is designed for flood detection in any land region between 80°S and 80°N, and it has been running routinely with direct broadcast SNPP/VIIRS data at the Space Science and Engineering Center at the University of Wisconsin-Madison (UW/SSEC) and the Geographic Information Network of Alaska at the University of Alaska-Fairbanks (UAF/GINA) since 2014. Near real-time flood maps are distributed via the Unidata Local Data Manager (LDM), reviewed by river forecasters in AWIPS-II (the second generation of the Advanced Weather Interactive Processing System) and applied in flood operations. Initial feedback from operational forecasters on the product accuracy and performance has been largely positive. The software capability has also been extended to areas outside of the USA via a case-driven mode to detect major floods all over the world. Offline validation efforts include the visual inspection of over 10,000 VIIRS false-color composite images, an inter-comparison with MODIS automatic flood products and a quantitative evaluation using Landsat imagery. The steady performance from the 3-year routine process and the promising validation results

Flood Extent Mapping for Namibia Using Change Detection and Thresholding with SAR

Science.gov (United States)

Long, Stephanie; Fatoyinbo, Temilola E.; Policelli, Frederick

2014-01-01

A new method for flood detection change detection and thresholding (CDAT) was used with synthetic aperture radar (SAR) imagery to delineate the extent of flooding for the Chobe floodplain in the Caprivi region of Namibia. This region experiences annual seasonal flooding and has seen a recent renewal of severe flooding after a long dry period in the 1990s. Flooding in this area has caused loss of life and livelihoods for the surrounding communities and has caught the attention of disaster relief agencies. There is a need for flood extent mapping techniques that can be used to process images quickly, providing near real-time flooding information to relief agencies. ENVISAT/ASAR and Radarsat-2 images were acquired for several flooding seasons from February 2008 to March 2013. The CDAT method was used to determine flooding from these images and includes the use of image subtraction, decision based classification with threshold values, and segmentation of SAR images. The total extent of flooding determined for 2009, 2011 and 2012 was about 542 km2, 720 km2, and 673 km2 respectively. Pixels determined to be flooded in vegetation were typically flooding in vegetation was much greater (almost one third of the total flooded area). The time to maximum flooding for the 2013 flood season was determined to be about 27 days. Landsat water classification was used to compare the results from the new CDAT with SAR method; the results show good spatial agreement with Landsat scenes.

Comparison of floods non-stationarity detection methods: an Austrian case study

Science.gov (United States)

Salinas, Jose Luis; Viglione, Alberto; Blöschl, Günter

2016-04-01

Non-stationarities in flood regimes have a huge impact in any mid and long term flood management strategy. In particular the estimation of design floods is very sensitive to any kind of flood non-stationarity, as they should be linked to a return period, concept that can be ill defined in a non-stationary context. Therefore it is crucial when analyzing existent flood time series to detect and, where possible, attribute flood non-stationarities to changing hydroclimatic and land-use processes. This works presents the preliminary results of applying different non-stationarity detection methods on annual peak discharges time series over more than 400 gauging stations in Austria. The kind of non-stationarities analyzed include trends (linear and non-linear), breakpoints, clustering beyond stochastic randomness, and detection of flood rich/flood poor periods. Austria presents a large variety of landscapes, elevations and climates that allow us to interpret the spatial patterns obtained with the non-stationarity detection methods in terms of the dominant flood generation mechanisms.

The use of geostationary satellite based rainfall estimation and rainfall-runoff modelling for regional flash flood assessment

OpenAIRE

Suseno, Dwi Prabowo Yuga

2013-01-01

The availability of rainfall triggered hazard information such as flash flood is crucial in the flood disaster management and mitigation. However, providing that information is mainly hampered by the shortage of data because of the sparse, uneven or absence the hydrological or meteorological observation. Remote sensing techniques that make frequent observations with continuous spatial coverage provide useful information for detecting the hydrometeorological phenomena such as rainfall and floo...

Bias correction of satellite precipitation products for flood forecasting application at the Upper Mahanadi River Basin in Eastern India

Science.gov (United States)

Beria, H.; Nanda, T., Sr.; Chatterjee, C.

2015-12-01

High resolution satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF), etc., offer a promising alternative to flood forecasting in data scarce regions. At the current state-of-art, these products cannot be used in the raw form for flood forecasting, even at smaller lead times. In the current study, these precipitation products are bias corrected using statistical techniques, such as additive and multiplicative bias corrections, and wavelet multi-resolution analysis (MRA) with India Meteorological Department (IMD) gridded precipitation product,obtained from gauge-based rainfall estimates. Neural network based rainfall-runoff modeling using these bias corrected products provide encouraging results for flood forecasting upto 48 hours lead time. We will present various statistical and graphical interpretations of catchment response to high rainfall events using both the raw and bias corrected precipitation products at different lead times.

Visual attention based detection of signs of anthropogenic activities in satellite imagery

Energy Technology Data Exchange (ETDEWEB)

Skurikhin, Alexei N [Los Alamos National Laboratory

2010-10-13

With increasing deployment of satellite imaging systems, only a small fraction of collected data can be subject to expert scrutiny. We present and evaluate a two-tier approach to broad area search for signs of anthropogenic activities in high-resolution commercial satellite imagery. The method filters image information using semantically oriented interest points by combining Harris corner detection and spatial pyramid matching. The idea is that anthropogenic structures, such as rooftop outlines, fence corners, road junctions, are locally arranged in specific angular relations to each other. They are often oriented at approximately right angles to each other (which is known as rectilinearity relation). Detecting the rectilinearity provides an opportunity to highlight regions most likely to contain anthropogenic activity. This is followed by supervised classification of regions surrounding the detected corner points as man-made vs. natural scenes. We consider, in particular, a search for anthropogenic activities in uncluttered areas. In this paper, we proposed and evaluated a two-tier approach to broad area search for signs of anthropogenic activities. Results from experiments on high-resolution ({approx}0.6m) commercial satellite image data showed the potential applicability of this approach and its ability of achieving both high precision and recall rates. The main advantage of combining corner-based cueing with general object recognition is that the incorporation of domain specific knowledge even in its more general form, such as presence of comers, provides a useful cue to narrow the focus of search for signs of anthropogenic activities. Combination of comer based cueing with spatial pyramid matching addressed the issue of comer categorization. An important practical issue for further research is optimizing the balance between false positive and false negative rates. While the results presented in the paper are encouraging, the problem of an automated broad area

Developing a sustainable satellite-based environmental monitoring system In Nigeria

Science.gov (United States)

Akinyede, J. O.; Adepoju, K. A.; Akinluyi, F. O.; Anifowose, A. Y. B.

2015-10-01

Increased anthropogenic activities over the year have remained a major factor of the Earth changing environment. This phenomenon has given rise to a number of environmental degraded sites that characterize the Nigeria's landscape. The human-induced elements include gully erosion, mangrove ecosystems degradation, desertification and deforestation, particularly in the south east, Niger Delta, north east and south west of Nigeria respectively, as well as river flooding/flood plain inundation and land degradation around Kainji lake area. Because of little or no effective management measures, the attendant environmental hazards have been extremely damaging to the infrastructures and socio-economic development of the affected area. Hence, a concerted effort, through integrated and space-based research, is being intensified to manage and monitor the environment in order to restore the stability, goods and services of the environment. This has justified Nigeria's investment in its space programme, especially the launch of NigeriaSat-1, an Earth observation micro-satellite in constellation with five (5) other similar satellites, Alsat-1, China DMC, Bilsat-1, DEMOS and UK DMC belonging to Algeria, China, Turkey, Spain and United Kingdom respectively. The use of data from these satellites, particularly NigeriaSat-1, in conjunction with associated technologies has proved to be very useful in understanding the influence of both natural and human activities on the Nigeria's ecosystems and environment. The results of some researches on specific applications of Nigerian satellites are presented in this paper. Appropriate sustainable land and water resources management in the affected areas, based on Nigeria's satellite data capture and integration, are also discussed.

First evaluation of the utility of GPM precipitation in global flood monitoring

Science.gov (United States)

Wu, H.; Yan, Y.; Gao, Z.

2017-12-01

The Global Flood Monitoring System (GFMS) has been developed and used to provide real-time flood detection and streamflow estimates over the last few years with significant success shown by validation against global flood event data sets and observed streamflow variations (Wu et al., 2014). It has become a tool for various national and international organizations to appraise flood conditions in various areas, including where rainfall and hydrology information is limited. The GFMS has been using the TRMM Multi-satellite Precipitation Analysis (TMPA) as its main rainfall input. Now, with the advent of the Global Precipitation Measurement (GPM) mission there is an opportunity to significantly improve global flood monitoring and forecasting. GPM's Integrated Multi-satellitE Retrievals for GPM (IMERG) multi-satellite product is designed to take advantage of various technical advances in the field and combine that with an efficient processing system producing "early" (4 hrs) and "late" (12 hrs) products for operational use. Specifically, this study is focused on (1) understanding the difference between the new IMERG products and other existing satellite precipitation products, e.g., TMPA, CMORPH, and ground observations; (2) addressing the challenge in the usage of the IMERG for flood monitoring through hydrologic models, given that only a short period of precipitation data record has been accumulated since the lunch of GPM in 2014; and (3) comparing the statistics of flood simulation based on the DRIVE model with IMERG, TMPA, CMORPH etc. as precipitation inputs respectively. Derivation of a global threshold map is a necessary step to define flood events out of modelling results, which requires a relatively longer historic information. A set of sensitivity tests are conducted by adjusting IMERG's light, moderate, heavy rain to existing precipitation products with long-term records separately, to optimize the strategy of PDF matching. Other aspects are also examined

The Use of Water Vapor for Detecting Environments that Lead to Convectively Produced Heavy Precipitation and Flash Floods

Science.gov (United States)

Scofield, Rod; Vicente, Gilberto; Hodges, Mike

2000-01-01

This Tech Report summarizes years of study and experiences on using GOES Water vapor (6.7 micron and precipitable water) and Special Sensor Microwave Imager (SSM/1) from the Defense Meteorological Satellite Program (DMSP) derived Precipitable Water (PNAI) for detecting environments favorable for convectively produced flash floods. An emphasis is on the moisture. upper air flow, and equivalent potential temperature (Theta(sub e)) patterns that lead to devastating flood events. The 15 minute 6.7 micron water vapor imagery is essential for tracking middle to upper tropospheric disturbances that produce upward vertical motion and initiate flash flood producing systems. Water vapor imagery at 6.7 micron is also used to detect surges of upper level moisture (called tropical water vapor plumes) that have been associated with extremely heavy rainfall. Since the water vapor readily depicts lifting mechanisms and upper level moisture, water vapor imagery is often an excellent source of data for recognizing patterns of heavy precipitation and flash floods. In order to analyze the depth of the moisture, the PW aspects of the troposphere must be measured. The collocation (or nearby location) of high values ofP\\V and instability are antecedent conditions prior to the flash flood or heavy rainfall events. Knowledge of PW magnitudes have been used as thresholds for impending flash flood events, PW trends are essential in flash flood prediction. Conceptual models and water vapor products are used to study some of the characteristics of convective systems that occurred over the United States of America (USA) during the summer of 1997 and the 1997-1998 El Nino. P\\V plumes were associated with most of the \\vest coast heavy precipitation events examined during the winter season of 1997 - 1998, In another study, conducted during the summer season of 1997. results showed that the collocation of water vapor (6.7 micron) and P\\N' plumes possessed higher correlations with predicted

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

Directory of Open Access Journals (Sweden)

Biao Wang

2017-08-01

Full Text Available Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD method—a measure for the spectral distortion of change information—is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.

A UAV based system for real time flash flood monitoring in desert environments using Lagrangian microsensors

KAUST Repository

Abdelkader, Mohamed

2013-05-01

Floods are the most common natural disasters, causing thousands of casualties every year in the world. In particular, flash flood events are particularly deadly because of the short timescales on which they occur. Most casualties could be avoided with advance warning, for which real time monitoring is critical. While satellite-based high resolution weather forecasts can help predict floods to a certain extent, they are not reliable enough, as flood models depend on a large number of parameters that cannot be estimated beforehand. In this article, we present a novel flood sensing architecture to monitor large scale desert hydrological basins surrounding metropolitan areas, based on unmanned air vehicles. The system relies on Lagrangian (mobile) microsensors, that are released by a swarm of UAVs. A preliminary testbed implementing this technology is briefly described, and future research directions and problems are discussed. © 2013 IEEE.

A Global Geospatial Database of 5000+ Historic Flood Event Extents

Science.gov (United States)

Tellman, B.; Sullivan, J.; Doyle, C.; Kettner, A.; Brakenridge, G. R.; Erickson, T.; Slayback, D. A.

2017-12-01

A key dataset that is missing for global flood model validation and understanding historic spatial flood vulnerability is a global historical geo-database of flood event extents. Decades of earth observing satellites and cloud computing now make it possible to not only detect floods in near real time, but to run these water detection algorithms back in time to capture the spatial extent of large numbers of specific events. This talk will show results from the largest global historical flood database developed to date. We use the Dartmouth Flood Observatory flood catalogue to map over 5000 floods (from 1985-2017) using MODIS, Landsat, and Sentinel-1 Satellites. All events are available for public download via the Earth Engine Catalogue and via a website that allows the user to query floods by area or date, assess population exposure trends over time, and download flood extents in geospatial format.In this talk, we will highlight major trends in global flood exposure per continent, land use type, and eco-region. We will also make suggestions how to use this dataset in conjunction with other global sets to i) validate global flood models, ii) assess the potential role of climatic change in flood exposure iii) understand how urbanization and other land change processes may influence spatial flood exposure iv) assess how innovative flood interventions (e.g. wetland restoration) influence flood patterns v) control for event magnitude to assess the role of social vulnerability and damage assessment vi) aid in rapid probabilistic risk assessment to enable microinsurance markets. Authors on this paper are already using the database for the later three applications and will show examples of wetland intervention analysis in Argentina, social vulnerability analysis in the USA, and micro insurance in India.

Added value of online satellite data transmission for flood forecasting: warning systems in medium-size catchments.

Science.gov (United States)

Ruch, C; Stadler, H

2009-01-01

The present paper deals with the implementation of online data transferred via LEO satellite communication in a flood forecasting system. Although the project is ongoing, it is already recognised that the information chain: "measurement-transmission-forecast-alert" can be shortened, i.e., the flood danger can be more rapidly communicated to the population at risk. This gain is particularly valuable for medium size catchments where the concentration time (basin time of response to rainfall) is short.

Use of the Earth Observing One (EO-1) Satellite for the Namibia SensorWeb Flood Early Warning Pilot

Science.gov (United States)

Mandl, Daniel; Frye, Stuart; Cappelaere, Pat; Handy, Matthew; Policelli, Fritz; Katjizeu, McCloud; Van Langenhove, Guido; Aube, Guy; Saulnier, Jean-Francois; Sohlberg, Rob;

Decision Support for Flood Event Prediction and Monitoring

DEFF Research Database (Denmark)

Mioc, Darka; Anton, François; Liang, Gengsheng

2007-01-01

In this paper the development of Web GIS based decision support system for flood events is presented. To improve flood prediction we developed the decision support system for flood prediction and monitoring that integrates hydrological modelling and CARIS GIS. We present the methodology for data...... integration, floodplain delineation, and online map interfaces. Our Web-based GIS model can dynamically display observed and predicted flood extents for decision makers and the general public. The users can access Web-based GIS that models current flood events and displays satellite imagery and digital...... elevation model integrated with flood plain area. The system can show how the flooding prediction based on the output from hydrological modeling for the next 48 hours along the lower Saint John River Valley....

An autonomous image based approach for detecting glacial lake outburst floods

Directory of Open Access Journals (Sweden)

R. Koschitzki

2014-06-01

Full Text Available The potential danger caused by glacier margin lakes and the related risk of glacier lake outburst floods (GLOF increases constantly due to glaciers retreating in many parts of the world. Reasons for this development are on the one hand the new formation and enlargement of glacier margin lakes due to melt water. On the other hand, retreating and thinning glacier tongues lead to a decrease of the back pressure against the dammed glacier lakes. The paper describes the design of a photogrammetric GLOF monitoring system, based on monoscopic image sequence analysis for automatic detection of water level changes. The presented approach for measuring the water line in an image sequence is based on directional edge detection in LoG-filtered image data. After that, the water level is determined by a transformation of image measurements into object space based on orientation parameters of the camera and a geo-referenced lake basin model. The model can for instance be determined by photogrammetric methods after a GLOF; it may also be determined portion-wise by analysing shore lines at various water levels. Camera orientation parameters are determined by a local GPS-supported photogrammetric network. Comparing the determined water level changes with reference data provided by a water gauge, the precision is estimated in the order of one decimetre. A major challenge is the automatic detection of the water line in image sequences under varying light and visibility conditions. The paper will also discuss promising approaches such as multispectral images as well as a statistical analysis of grey value changes over short image sequences to eliminate disturbing reflections on the rough water surface.

Identification of flood-rich and flood-poor periods in flood series

Science.gov (United States)

Mediero, Luis; Santillán, David; Garrote, Luis

2015-04-01

Recently, a general concern about non-stationarity of flood series has arisen, as changes in catchment response can be driven by several factors, such as climatic and land-use changes. Several studies to detect trends in flood series at either national or trans-national scales have been conducted. Trends are usually detected by the Mann-Kendall test. However, the results of this test depend on the starting and ending year of the series, which can lead to different results in terms of the period considered. The results can be conditioned to flood-poor and flood-rich periods located at the beginning or end of the series. A methodology to identify statistically significant flood-rich and flood-poor periods is developed, based on the comparison between the expected sampling variability of floods when stationarity is assumed and the observed variability of floods in a given series. The methodology is applied to a set of long series of annual maximum floods, peaks over threshold and counts of annual occurrences in peaks over threshold series observed in Spain in the period 1942-2009. Mediero et al. (2014) found a general decreasing trend in flood series in some parts of Spain that could be caused by a flood-rich period observed in 1950-1970, placed at the beginning of the flood series. The results of this study support the findings of Mediero et al. (2014), as a flood-rich period in 1950-1970 was identified in most of the selected sites. References: Mediero, L., Santillán, D., Garrote, L., Granados, A. Detection and attribution of trends in magnitude, frequency and timing of floods in Spain, Journal of Hydrology, 517, 1072-1088, 2014.

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Directory of Open Access Journals (Sweden)

Sanggyun Lee

2016-08-01

Full Text Available Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011–2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM-bird data collected over two days during the CryoSat Validation experiment (CryoVex field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE = 0.29 m compared to thickness estimation based on existing lead detection methods (RMSE = 0.86–0.93 m. Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011–2013 and rebounded in 2014.

A SERS-active sensor based on heterogeneous gold nanostar core-silver nanoparticle satellite assemblies for ultrasensitive detection of aflatoxinB1.

Science.gov (United States)

Li, Aike; Tang, Lijuan; Song, Dan; Song, Shanshan; Ma, Wei; Xu, Liguang; Kuang, Hua; Wu, Xiaoling; Liu, Liqiang; Chen, Xin; Xu, Chuanlai

2016-01-28

A surface-enhanced Raman scattering (SERS) sensor based on gold nanostar (Au NS) core-silver nanoparticle (Ag NP) satellites was fabricated for the first time to detect aflatoxinB1 (AFB1). We constructed the SERS sensor using AFB1 aptamer (DNA1)-modified Ag satellites and a complementary sequence (DNA2)-modified Au NS core. The Raman label (ATP) was modified on the surface of Ag satellites. The SERS signal was enhanced when the satellite NP was attached to the Au core NS. The AFB1 aptamer on the surface of Ag satellites would bind to the targets when AFB1 was present in the system, Ag satellites were then removed and the SERS signal decreased. This SERS sensor showed superior specificity for AFB1 and the linear detection range was from 1 to 1000 pg mL(-1) with the limit of detection (LOD) of 0.48 pg mL(-1). The excellent recovery experiment using peanut milk demonstrated that the sensor could be applied in food and environmental detection.

Detecting potential ship objects from satellite pictures

International Nuclear Information System (INIS)

Luo, B.; Yang, C.C.; Chang, S.K.; Yang, M.C.K.

1984-01-01

Heuristic techniques are presented to detect potential ship objects from satellite pictures. These techniques utilize some noise structures of the pixel gray levels, and certain inherent features of a ship in a satellite picture. The scheme has been implemented and successfully tested on SEASAT satellite pictures. A general approach for database-oriented object detection is also suggested

Robust flood area detection using a L-band synthetic aperture radar: Preliminary application for Florida, the U.S. affected by Hurricane Irma

Science.gov (United States)

Nagai, H.; Ohki, M.; Abe, T.

2017-12-01

Urgent crisis response for a hurricane-induced flood needs urgent providing of a flood map covering a broad region. However, there is no standard threshold values for automatic flood identification from pre-and-post images obtained by satellite-based synthetic aperture radars (SARs). This problem could hamper prompt data providing for operational uses. Furthermore, one pre-flood SAR image does not always represent potential water surfaces and river flows especially in tropical flat lands which are greatly influenced by seasonal precipitation cycle. We are, therefore, developing a new method of flood mapping using PALSAR-2, an L-band SAR, which is less affected by temporal surface changes. Specifically, a mean-value image and a standard-deviation image are calculated from a series of pre-flood SAR images. It is combined with a post-flood SAR image to obtain normalized backscatter amplitude difference (NoBADi), with which a difference between a post-flood image and a mean-value image is divided by a standard-deviation image to emphasize anomalous water extents. Flooding areas are then automatically obtained from the NoBADi images as lower-value pixels avoiding potential water surfaces. We applied this method to PALSAR-2 images acquired on Sept. 8, 10, and 12, 2017, covering flooding areas in a central region of Dominican Republic and west Florida, the U.S. affected by Hurricane Irma. The output flooding outlines are validated with flooding areas manually delineated from high-resolution optical satellite images, resulting in higher consistency and less uncertainty than previous methods (i.e., a simple pre-and-post flood difference and pre-and-post coherence changes). The NoBADi method has a great potential to obtain a reliable flood map for future flood hazards, not hampered by cloud cover, seasonal surface changes, and "casual" thresholds in the flood identification process.

A Conceptual Flash Flood Early Warning System for Africa, Based on Terrestrial Microwave Links and Flash Flood Guidance

Directory of Open Access Journals (Sweden)

Joost C. B. Hoedjes

2014-04-01

Full Text Available A conceptual flash flood early warning system for developing countries is described. The system uses rainfall intensity data from terrestrial microwave communication links and the geostationary Meteosat Second Generation satellite, i.e., two systems that are already in place and operational. Flash flood early warnings are based on a combination of the Flash Flood Guidance method and a hydrological model. The system will be maintained and operated through a public-private partnership, which includes a mobile telephone operator, a national meteorological service and an emergency relief service. The mobile telephone operator acts as both the supplier of raw input data and the disseminator of early warnings. The early warning system could significantly reduce the number of fatalities due to flash floods, improve the efficiency of disaster risk reduction efforts and play an important role in strengthening the resilience to climate change of developing countries in Africa. This paper describes the system that is currently being developed for Kenya.

Framework of Jitter Detection and Compensation for High Resolution Satellites

Directory of Open Access Journals (Sweden)

Xiaohua Tong

2014-05-01

Full Text Available Attitude jitter is a common phenomenon in the application of high resolution satellites, which may result in large errors of geo-positioning and mapping accuracy. Therefore, it is critical to detect and compensate attitude jitter to explore the full geometric potential of high resolution satellites. In this paper, a framework of jitter detection and compensation for high resolution satellites is proposed and some preliminary investigation is performed. Three methods for jitter detection are presented as follows. (1 The first one is based on multispectral images using parallax between two different bands in the image; (2 The second is based on stereo images using rational polynomial coefficients (RPCs; (3 The third is based on panchromatic images employing orthorectification processing. Based on the calculated parallax maps, the frequency and amplitude of the detected jitter are obtained. Subsequently, two approaches for jitter compensation are conducted. (1 The first one is to conduct the compensation on image, which uses the derived parallax observations for resampling; (2 The second is to conduct the compensation on attitude data, which treats the influence of jitter on attitude as correction of charge-coupled device (CCD viewing angles. Experiments with images from several satellites, such as ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiaometer, LRO (Lunar Reconnaissance Orbiter and ZY-3 (ZiYuan-3 demonstrate the promising performance and feasibility of the proposed framework.

An Observation Task Chain Representation Model for Disaster Process-Oriented Remote Sensing Satellite Sensor Planning: A Flood Water Monitoring Application

Directory of Open Access Journals (Sweden)

Chao Yang

2018-03-01

Full Text Available An accurate and comprehensive representation of an observation task is a prerequisite in disaster monitoring to achieve reliable sensor observation planning. However, the extant disaster event or task information models do not fully satisfy the observation requirements for the accurate and efficient planning of remote-sensing satellite sensors. By considering the modeling requirements for a disaster observation task, we propose an observation task chain (OTChain representation model that includes four basic OTChain segments and eight-tuple observation task metadata description structures. A prototype system, namely OTChainManager, is implemented to provide functions for modeling, managing, querying, and visualizing observation tasks. In the case of flood water monitoring, we use a flood remote-sensing satellite sensor observation task for the experiment. The results show that the proposed OTChain representation model can be used in modeling process-owned flood disaster observation tasks. By querying and visualizing the flood observation task instances in the Jinsha River Basin, the proposed model can effectively express observation task processes, represent personalized observation constraints, and plan global remote-sensing satellite sensor observations. Compared with typical observation task information models or engines, the proposed OTChain representation model satisfies the information demands of the OTChain and its processes as well as impels the development of a long time-series sensor observation scheme.

Tile Drainage Expansion Detection using Satellite Soil Moisture Dynamics

Science.gov (United States)

Jacobs, J. M.; Cho, E.; Jia, X.

2017-12-01

In the past two decades, tile drainage installation has accelerated throughout the Red River of the North Basin (RRB) in parts of western Minnesota, eastern North Dakota, and a small area of northeastern South Dakota, because the flat topography and low-permeability soils in this region necessitated the removal of excess water to improve crop production. Interestingly, streamflow in the Red River has markedly increased and six of 13 major floods during the past century have occurred since the late 1990s. It has been suggested that the increase in RRB flooding could be due to change in agricultural practices, including extensive tile drainage installation. Reliable information on existing and future tile drainage installation is greatly needed to capture the rapid extension of tile drainage systems and to locate tile drainage systems in the north central U.S. including the RRB region. However, there are few reliable data of tile drainage installation records, except tile drainage permit records in the Bois de Sioux watershed (a sub-basin in southern part of the RRB where permits are required for tile drainage installation). This study presents a tile drainage expansion detection method based on a physical principle that the soil-drying rate may increase with increasing tile drainage for a given area. In order to capture the rate of change in soil drying rate with time over entire RRB (101,500 km2), two satellite-based microwave soil moisture records from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and AMSR2 were used during 2002 to 2016. In this study, a sub-watershed level (HUC10) potential tile drainage growth map was developed and the results show good agreement with tile drainage permit records of six sub-watersheds in the Bois de Sioux watershed. Future analyses will include improvement of the potential tile drainage map through additional information using optical- and thermal-based sensor products and evaluation of its

An Open-Book Modular Watershed Modeling Framework for Rapid Prototyping of GPM- based Flood Forecasting in International River Basins

Science.gov (United States)

Katiyar, N.; Hossain, F.

2006-05-01

Floods have always been disastrous for human life. It accounts for about 15 % of the total death related to natural disasters. There are around 263 transboundary river basins listed by UNESCO, wherein at least 30 countries have more than 95% of their territory locked in one or more such transboundary basins. For flood forecasting in the lower riparian nations of these International River Basins (IRBs), real-time rainfall data from upstream nations is naturally the most critical factor governing the forecasting effectiveness. However, many upstream nations fail to provide data to the lower riparian nations due to a lack of in-situ rainfall measurement infrastructure or a lack of a treaty for real-time sharing of rainfall data. A potential solution is therefore to use satellites that inherently measure rainfall across political boundaries. NASA's proposed Global Precipitation Measurement (GPM) mission appears very promising in providing this vital rainfall information under the data- limited scenario that will continue to prevail in most IRBs. However, satellite rainfall is associated with uncertainty and hence, proper characterization of the satellite rainfall error propagation in hydrologic models for flood forecasting is a critical priority that should be resolved in the coming years in anticipation of GPM. In this study, we assess an open book modular watershed modeling approach for estimating the expected error in flood forecasting related to GPM rainfall data. Our motivation stems from the critical challenge in identifying the specific IRBs that would benefit from a pre-programmed satellite-based forecasting system in anticipation of GPM. As the number of flood-prone IRBs is large, conventional data-intensive implementation of existing physically-based distributed hydrologic models on case-by-case IRBs is considered time-consuming for completing such a global assessment. A more parsimonious approach is justified at the expense of a tolerable loss of detail and

Demonstrating the Value of Near Real-time Satellite-based Earth Observations in a Research and Education Framework

Science.gov (United States)

Chiu, L.; Hao, X.; Kinter, J. L.; Stearn, G.; Aliani, M.

2017-12-01

The launch of GOES-16 series provides an opportunity to advance near real-time applications in natural hazard detection, monitoring and warning. This study demonstrates the capability and values of receiving real-time satellite-based Earth observations over a fast terrestrial networks and processing high-resolution remote sensing data in a university environment. The demonstration system includes 4 components: 1) Near real-time data receiving and processing; 2) data analysis and visualization; 3) event detection and monitoring; and 4) information dissemination. Various tools are developed and integrated to receive and process GRB data in near real-time, produce images and value-added data products, and detect and monitor extreme weather events such as hurricane, fire, flooding, fog, lightning, etc. A web-based application system is developed to disseminate near-real satellite images and data products. The images are generated with GIS-compatible format (GeoTIFF) to enable convenient use and integration in various GIS platforms. This study enhances the capacities for undergraduate and graduate education in Earth system and climate sciences, and related applications to understand the basic principles and technology in real-time applications with remote sensing measurements. It also provides an integrated platform for near real-time monitoring of extreme weather events, which are helpful for various user communities.

Flash Floods Simulation using a Physical-Based Hydrological Model at Different Hydroclimatic Regions

Science.gov (United States)

Saber, Mohamed; Kamil Yilmaz, Koray

2016-04-01

Currently, flash floods are seriously increasing and affecting many regions over the world. Therefore, this study will focus on two case studies; Wadi Abu Subeira, Egypt as arid environment, and Karpuz basin, Turkey as Mediterranean environment. The main objective of this work is to simulate flash floods at both catchments considering the hydrometeorological differences between them which in turn effect their flash flood behaviors. An integrated methodology incorporating Hydrological River Basin Environmental Assessment Model (Hydro-BEAM) and remote sensing observations was devised. Global Satellite Mapping of Precipitation (GSMAP) were compared with the rain gauge network at the target basins to estimate the bias in an effort to further use it effectively in simulation of flash floods. Based on the preliminary results of flash floods simulation on both basins, we found that runoff behaviors of flash floods are different due to the impacts of climatology, hydrological and topographical conditions. Also, the simulated surface runoff hydrographs are reasonably coincide with the simulated ones. Consequently, some mitigation strategies relying on this study could be introduced to help in reducing the flash floods disasters at different climate regions. This comparison of different climatic basins would be a reasonable implication for the potential impact of climate change on the flash floods frequencies and occurrences.

Application of Satellite Observations to Manage Natural Disasters in the Lake Victoria Basin

Science.gov (United States)

Habib, Shahid; Policelli, F.; Irwin, D.; Korme, Tesfaye; Adler, Bob; Hong, Yang

2010-01-01

multiple operational agencies within each respective country. It also requires credible in situ data such as precipitation, river flow rates and lake levels to further validate the global and regional Hood models and algorithms. This also requires a considerable amount of training and capacity building for the RCMRD experts who will help us validate the model results and eventually transition it for operational use. In a final analysis, Disaster management and humanitarian aid organizations need accurate and timely information for making decisions regarding deployment of relief teams and emergency supplies during major floods. Flood maps based on the use of satellite data have proven extremely valuable to such organizations for identifying the location, extent, and severity of these events. However, despite extraordinary efforts on the part of remote sensing data providers to rapidly deliver such maps, there is typically a delay of several days or even weeks from the on-set of flooding until such maps are available to the disaster management community. This paper summarizes efforts at NASA to address this problem through development of an integrated and automated process of a) flood forecasting b) flood detection, c) satellite data acquisition, d) rapid Hood mapping and distribution, and e) validation of Hood forecasting and detection products.

Analysis of GPS Satellite Allocation for the United States Nuclear Detonation Detection System (USNDS)

National Research Council Canada - National Science Library

Bell, Aaron

2002-01-01

...) satellites to detect atmospheric nuclear detonations. Though there are currently over 24 operational GPS satellites, USNDS ground based antennas are only capable of actively monitoring 24 satellites at a time...

An Observation Capability Semantic-Associated Approach to the Selection of Remote Sensing Satellite Sensors: A Case Study of Flood Observations in the Jinsha River Basin.

Science.gov (United States)

Hu, Chuli; Li, Jie; Lin, Xin; Chen, Nengcheng; Yang, Chao

2018-05-21

Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors.

An Observation Capability Semantic-Associated Approach to the Selection of Remote Sensing Satellite Sensors: A Case Study of Flood Observations in the Jinsha River Basin

Directory of Open Access Journals (Sweden)

Chuli Hu

2018-05-01

Full Text Available Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA ontology model that is resolved around the task-sensor-observation capability (TSOC ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors.

Introducing Multisensor Satellite Radiance-Based Evaluation for Regional Earth System Modeling

Science.gov (United States)

Matsui, T.; Santanello, J.; Shi, J. J.; Tao, W.-K.; Wu, D.; Peters-Lidard, C.; Kemp, E.; Chin, M.; Starr, D.; Sekiguchi, M.;

Detecting SYN flood attacks via statistical monitoring charts: A comparative study

KAUST Repository

Bouyeddou, Benamar

2017-12-14

Accurate detection of cyber-attacks plays a central role in safeguarding computer networks and information systems. This paper addresses the problem of detecting SYN flood attacks, which are the most popular Denial of Service (DoS) attacks. Here, we compare the detection capacity of three commonly monitoring charts namely, a Shewhart chart, a Cumulative Sum (CUSUM) control chart and exponentially weighted moving average (EWMA) chart, in detecting SYN flood attacks. The comparison study is conducted using the publicly available benchmark datasets: the 1999 DARPA Intrusion Detection Evaluation Datasets.

Pakistan flood damage mapped by UNOSAT at CERN

CERN Multimedia

Katarina Anthony

2010-01-01

As the waters recede, the Pakistan floods are attracting less attention in the world's media. But at the CERN-based headquarters of UNOSAT, the UN Institute for Training and Research Operational Satellite Application Programme, mapping the damage caused by the floods remains the top priority as the “emergency phase” is only now beginning to level off.   Flood analysis in Pakistan from 28 July to 16 September 2010. Credits: © UNOSAT UNOSAT uses impartial, objective data to assess the specifics of a disaster: What surface area has the flood covered? How many bridges and roads have been destroyed? How many areas are impenetrable? Although there are statistical answers to these questions, UNOSAT’s assessment of the damage caused by the Pakistan floods can be simply described in one word: catastrophic. The images used by UNOSAT are taken from a variety of different sources – commercial and scientific. Once a satellite takes an image, the owne...

The Matsu Wheel: A Cloud-Based Framework for Efficient Analysis and Reanalysis of Earth Satellite Imagery

Science.gov (United States)

Patterson, Maria T.; Anderson, Nicholas; Bennett, Collin; Bruggemann, Jacob; Grossman, Robert L.; Handy, Matthew; Ly, Vuong; Mandl, Daniel J.; Pederson, Shane; Pivarski, James;

Mapping the Recent US Hurricanes Triggered Flood Events in Near Real Time

Science.gov (United States)

Shen, X.; Lazin, R.; Anagnostou, E. N.; Wanik, D. W.; Brakenridge, G. R.

2017-12-01

Synthetic Aperture Radar (SAR) observations is the only reliable remote sensing data source to map flood inundation during severe weather events. Unfortunately, since state-of-art data processing algorithms cannot meet the automation and quality standard of a near-real-time (NRT) system, quality controlled inundation mapping by SAR currently depends heavily on manual processing, which limits our capability to quickly issue flood inundation maps at global scale. Specifically, most SAR-based inundation mapping algorithms are not fully automated, while those that are automated exhibit severe over- and/or under-detection errors that limit their potential. These detection errors are primarily caused by the strong overlap among the SAR backscattering probability density functions (PDF) of different land cover types. In this study, we tested a newly developed NRT SAR-based inundation mapping system, named Radar Produced Inundation Diary (RAPID), using Sentinel-1 dual polarized SAR data over recent flood events caused by Hurricanes Harvey, Irma, and Maria (2017). The system consists of 1) self-optimized multi-threshold classification, 2) over-detection removal using land-cover information and change detection, 3) under-detection compensation, and 4) machine-learning based correction. Algorithm details are introduced in another poster, H53J-1603. Good agreements were obtained by comparing the result from RAPID with visual interpretation of SAR images and manual processing from Dartmouth Flood Observatory (DFO) (See Figure 1). Specifically, the over- and under-detections that is typically noted in automated methods is significantly reduced to negligible levels. This performance indicates that RAPID can address the automation and accuracy issues of current state-of-art algorithms and has the potential to apply operationally on a number of satellite SAR missions, such as SWOT, ALOS, Sentinel etc. RAPID data can support many applications such as rapid assessment of damage

Assessment of satellite-based precipitation estimates over Paraguay

Science.gov (United States)

Oreggioni Weiberlen, Fiorella; Báez Benítez, Julián

2018-04-01

Satellite-based precipitation estimates represent a potential alternative source of input data in a plethora of meteorological and hydrological applications, especially in regions characterized by a low density of rain gauge stations. Paraguay provides a good example of a case where the use of satellite-based precipitation could be advantageous. This study aims to evaluate the version 7 of the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TMPA V7; 3B42 V7) and the version 1.0 of the purely satellite-based product of the Climate Prediction Center Morphing Technique (CMORPH RAW) through their comparison with daily in situ precipitation measurements from 1998 to 2012 over Paraguay. The statistical assessment is conducted with several commonly used indexes. Specifically, to evaluate the accuracy of daily precipitation amounts, mean error (ME), root mean square error (RMSE), BIAS, and coefficient of determination (R 2) are used, and to analyze the capability to correctly detect different precipitation intensities, false alarm ratio (FAR), frequency bias index (FBI), and probability of detection (POD) are applied to various rainfall rates (0, 0.1, 0.5, 1, 2, 5, 10, 20, 40, 60, and 80 mm/day). Results indicate that TMPA V7 has a better performance than CMORPH RAW over Paraguay. TMPA V7 has higher accuracy in the estimation of daily rainfall volumes and greater precision in the detection of wet days (> 0 mm/day). However, both satellite products show a lower ability to appropriately detect high intensity precipitation events.

An Automatic Cloud Detection Method for ZY-3 Satellite

Directory of Open Access Journals (Sweden)

CHEN Zhenwei

2015-03-01

Full Text Available Automatic cloud detection for optical satellite remote sensing images is a significant step in the production system of satellite products. For the browse images cataloged by ZY-3 satellite, the tree discriminate structure is adopted to carry out cloud detection. The image was divided into sub-images and their features were extracted to perform classification between clouds and grounds. However, due to the high complexity of clouds and surfaces and the low resolution of browse images, the traditional classification algorithms based on image features are of great limitations. In view of the problem, a prior enhancement processing to original sub-images before classification was put forward in this paper to widen the texture difference between clouds and surfaces. Afterwards, with the secondary moment and first difference of the images, the feature vectors were extended in multi-scale space, and then the cloud proportion in the image was estimated through comprehensive analysis. The presented cloud detection algorithm has already been applied to the ZY-3 application system project, and the practical experiment results indicate that this algorithm is capable of promoting the accuracy of cloud detection significantly.

Combining Space-Based and In-Situ Measurements to Track Flooding in Thailand

Science.gov (United States)

Chien, Steve; Doubleday, Joshua; Mclaren, David; Tran, Daniel; Tanpipat, Veerachai; Chitradon, Royal; Boonya-aaroonnet, Surajate; Thanapakpawin, Porranee; Khunboa, Chatchai; Leelapatra, Watis;

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

Science.gov (United States)

Tatar, Nurollah; Saadatseresht, Mohammad; Arefi, Hossein; Hadavand, Ahmad

2018-06-01

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C4 index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.

A global hydrographic array for early detection of floods and droughts

Science.gov (United States)

Brakenridge, G.; Nghiem, S.; Caquard, S.

An array of over 700 20 km-long river gaging reaches, distributed world-wide, is imaged by the SeaWinds radar scatterometer aboard QuikSCAT every 2.5 days. Strongly negative HH/VV polarity ratios indicate large amounts of surface water. We set individual reach thresholds so that the transition from bankfull to overbank river flow can be identified according to changes in this ratio. Similarly, the wide-swath MODIS optical sensors provide frequent repeat coverage of the reaches at much higher spatial resolution (250 m). In this case, several reach water surface area thresholds can be identified: low flow or drought conditions, normal in-channel flow, overbank flow, and extreme flood conditions. Sustained data collection for the reaches by both sensors allows the radar response to changing surface water to be defined, and allows evaluation of the sensitivity of the MODIS data to river discharge changes. New approaches, such as ``unmixing'' analysis of mixed water/land MODIS pixels can extend detection limits to smaller rivers and streams. It is now possible for wide-area, frequent revisit terrestrial remote sensing to provide human society with early warning of both floods and droughts and by direct observation of the runoff component of the Earth's hydrologic cycle. Examples of both radar and optical approaches towards this end are at the web sites below: http://www.dartmouth.edu/˜ floods/Modisrapidresponse.html http://www.dartmouth.edu/˜ floods/sensorweb/SensorWebindex.html http://www.dartmouth.edu/˜ floods/Quikscat/Regional2/CurrentTisza.jpg} In particular, early flood detection results are obtained over an extensive region in eastern Europe including the Tisza River basin, Romania, Hungary, and adjacent nations. Flood detection maps are updated weekly at the web site. The combination of QuikSCAT and MODIS takes advantage of the large-area coverage of these sensors together with the high temporal resolution of QuikSCAT and the high spatial resolution of MODIS

Spatial Analysis of Land Subsidence and Flood Pattern Based on DInSAR Method in Sentinel Sar Imagery and Weighting Method in Geo-Hazard Parameters Combination in North Jakarta Region

Science.gov (United States)

Prasetyo, Y.; Yuwono, B. D.; Ramadhanis, Z.

2018-02-01

The reclamation program carried out in most cities in North Jakarta is directly adjacent to the Jakarta Bay. Beside this program, the density of population and development center in North Jakarta office has increased the need for underground water excessively. As a result of these things, land subsidence in North Jakarta area is relatively high and so intense. The research methodology was developed based on the method of remote sensing and geographic information systems, expected to describe the spatial correlation between the land subsidence and flood phenomenon in North Jakarta. The DInSAR (Differential Interferometric Synthetic Aperture Radar) method with satellite image data Radar (SAR Sentinel 1A) for the years 2015 to 2016 acquisitions was used in this research. It is intended to obtain a pattern of land subsidence in North Jakarta and then combined with flood patterns. For the preparation of flood threat zoning pattern, this research has been modeling in spatial technique based on a weighted parameter of rainfall, elevation, flood zones and land use. In the final result, we have obtained a flood hazard zonation models then do the overlap against DInSAR processing results. As a result of the research, Geo-hazard modelling has a variety results as: 81% of flood threat zones consist of rural area, 12% consists of un-built areas and 7% consists of water areas. Furthermore, the correlation of land subsidence to flood risk zone is divided into three levels of suitability with 74% in high class, 22% in medium class and 4% in low class. For the result of spatial correlation area between land subsidence and flood risk zone are 77% detected in rural area, 17% detected in un-built area and 6% detected in a water area. Whereas the research product is the geo-hazard maps in North Jakarta as the basis of the spatial correlation analysis between the land subsidence and flooding phenomena.double point.

Ship Detection in Optical Satellite Image Based on RX Method and PCAnet

Science.gov (United States)

Shao, Xiu; Li, Huali; Lin, Hui; Kang, Xudong; Lu, Ting

2017-12-01

In this paper, we present a novel method for ship detection in optical satellite image based on the ReedXiaoli (RX) method and the principal component analysis network (PCAnet). The proposed method consists of the following three steps. First, the spatially adjacent pixels in optical image are arranged into a vector, transforming the optical image into a 3D cube image. By taking this process, the contextual information of the spatially adjacent pixels can be integrated to magnify the discrimination between ship and background. Second, the RX anomaly detection method is adopted to preliminarily extract ship candidates from the produced 3D cube image. Finally, real ships are further confirmed among ship candidates by applying the PCAnet and the support vector machine (SVM). Specifically, the PCAnet is a simple deep learning network which is exploited to perform feature extraction, and the SVM is applied to achieve feature pooling and decision making. Experimental results demonstrate that our approach is effective in discriminating between ships and false alarms, and has a good ship detection performance.

Land use change detection based on multi-date imagery from different satellite sensor systems

Science.gov (United States)

Stow, Douglas A.; Collins, Doretta; Mckinsey, David

1990-01-01

An empirical study is conducted to assess the accuracy of land use change detection using satellite image data acquired ten years apart by sensors with differing spatial resolutions. The primary goals of the investigation were to (1) compare standard change detection methods applied to image data of varying spatial resolution, (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice versa in the registration process, (3) determine if Landsat/Thermatic Mapper or SPOT/High Resolution Visible multispectral data provide more accurate detection of land use changes when registered to historical Landsat/MSS data. It is concluded that image ratioing of multisensor, multidate satellite data produced higher change detection accuracies than did principal components analysis, and that it is useful as a land use change enhancement method.

Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series

Directory of Open Access Journals (Sweden)

Yun-Long Kong

2018-03-01

Full Text Available A satellite image time series (SITS contains a significant amount of temporal information. By analysing this type of data, the pattern of the changes in the object of concern can be explored. The natural change in the Earth’s surface is relatively slow and exhibits a pronounced pattern. Some natural events (for example, fires, floods, plant diseases, and insect pests and human activities (for example, deforestation and urbanisation will disturb this pattern and cause a relatively profound change on the Earth’s surface. These events are usually referred to as disturbances. However, disturbances in ecosystems are not easy to detect from SITS data, because SITS contain combined information on disturbances, phenological variations and noise in remote sensing data. In this paper, a novel framework is proposed for online disturbance detection from SITS. The framework is based on long short-term memory (LSTM networks. First, LSTM networks are trained by historical SITS. The trained LSTM networks are then used to predict new time series data. Last, the predicted data are compared with real data, and the noticeable deviations reveal disturbances. Experimental results using 16-day compositions of the moderate resolution imaging spectroradiometer (MOD13Q1 illustrate the effectiveness and stability of the proposed approach for online disturbance detection.

Satellite images of the September 2013 flood event in Lyons, Colorado

Science.gov (United States)

Cole, Christopher J.; Friesen, Beverly A.; Wilds, Stanley; Noble, Suzanne; Warner, Harumi; Wilson, Earl M.

2013-01-01

The U.S. Geological Survey (USGS) Special Applications Science Center (SASC) produced an image base map showing high-resolution remotely sensed data over Lyons, Colorado—a city that was severely affected by the flood event that occurred throughout much of the Colorado Front Range in September of 2013. The 0.5-meter WorldView-2 data products were created from imagery collected by DigitalGlobe on September 13 and September 24, 2013, during and following the flood event. The images shown on this map were created to support flood response efforts, specifically for use in determining damage assessment and mitigation decisions. The raw, unprocessed imagery were orthorectified and pan-sharpened to enhance mapping accuracy and spatial resolution, and reproduced onto a cartographic base map. These maps are intended to provide a snapshot representation of post-flood ground conditions, which may be useful to decisionmakers and the general public. The SASC also provided data processing and analysis support for other Colorado flood-affected areas by creating cartographic products, geo-corrected electro-optical and radar image mosaics, and GIS water cover files for use by the Colorado National Guard, the National Park Service, the U.S. Forest Service, and the flood response community. All products for this International Charter event were uploaded to the USGS Hazards Data Distribution System (HDDS) website (http://hdds.usgs.gov/hdds2/) for distribution.

Essential Technology and Application of Jitter Detection and Compensation for High Resolution Satellites

Directory of Open Access Journals (Sweden)

TONG Xiaohua

2017-10-01

Full Text Available Satellite jitter is a common and complex phenomenon for the on-orbit high resolution satellites, which may affect the mapping accuracy and quality of imagery. A framework of jitter detection and compensation integrating data processing of multiple sensors is proposed in this paper. Jitter detection is performed based on multispectral imagery, three-line-array imagery, dense ground control and attitude measurement data, and jitter compensation is conducted both on image and on attitude with the sensor model. The platform jitter of ZY-3 satellite is processed and analyzed using the proposed technology, and the results demonstrate the feasibility and reliability of jitter detection and compensation. The variation law analysis of jitter indicates that the frequencies of jitter of ZY-3 satellite hold in the range between 0.6 and 0.7 Hz, while the amplitudes of jitter of ZY-3 satellite drop from 1 pixel in the early stage to below 0.4 pixels and tend to remain stable in the following stage.

Improving the extraction of crisis information in the context of flood, fire, and landslide rapid mapping using SAR and optical remote sensing data

Science.gov (United States)

Martinis, Sandro; Clandillon, Stephen; Twele, André; Huber, Claire; Plank, Simon; Maxant, Jérôme; Cao, Wenxi; Caspard, Mathilde; May, Stéphane

2016-04-01

Optical and radar satellite remote sensing have proven to provide essential crisis information in case of natural disasters, humanitarian relief activities and civil security issues in a growing number of cases through mechanisms such as the Copernicus Emergency Management Service (EMS) of the European Commission or the International Charter 'Space and Major Disasters'. The aforementioned programs and initiatives make use of satellite-based rapid mapping services aimed at delivering reliable and accurate crisis information after natural hazards. Although these services are increasingly operational, they need to be continuously updated and improved through research and development (R&D) activities. The principal objective of ASAPTERRA (Advancing SAR and Optical Methods for Rapid Mapping), the ESA-funded R&D project being described here, is to improve, automate and, hence, speed-up geo-information extraction procedures in the context of natural hazards response. This is performed through the development, implementation, testing and validation of novel image processing methods using optical and Synthetic Aperture Radar (SAR) data. The methods are mainly developed based on data of the German radar satellites TerraSAR-X and TanDEM-X, the French satellite missions Pléiades-1A/1B as well as the ESA missions Sentinel-1/2 with the aim to better characterize the potential and limitations of these sensors and their synergy. The resulting algorithms and techniques are evaluated in real case applications during rapid mapping activities. The project is focussed on three types of natural hazards: floods, landslides and fires. Within this presentation an overview of the main methodological developments in each topic is given and demonstrated in selected test areas. The following developments are presented in the context of flood mapping: a fully automated Sentinel-1 based processing chain for detecting open flood surfaces, a method for the improved detection of flooded vegetation

FEMA DFIRM Base Flood Elevations

Data.gov (United States)

Minnesota Department of Natural Resources — The Base Flood Elevation (BFE) table is required for any digital data where BFE lines will be shown on the corresponding Flood Insurance Rate Map (FIRM). Normally,...

Base Flood Elevation (BFE) Lines

Data.gov (United States)

Department of Homeland Security — The Base Flood Elevation (BFE) table is required for any digital data where BFE lines will be shown on the corresponding Flood Insurance Rate Map (FIRM). Normally if...

Japanese Global Precipitation Measurement (GPM) mission status and application of satellite-based global rainfall map

Science.gov (United States)

Kachi, Misako; Shimizu, Shuji; Kubota, Takuji; Yoshida, Naofumi; Oki, Riko; Kojima, Masahiro; Iguchi, Toshio; Nakamura, Kenji

2010-05-01

As accuracy of satellite precipitation estimates improves and observation frequency increases, application of those data to societal benefit areas, such as weather forecasts and flood predictions, is expected, in addition to research of precipitation climatology to analyze precipitation systems. There is, however, limitation on single satellite observation in coverage and frequency. Currently, the Global Precipitation Measurement (GPM) mission is scheduled under international collaboration to fulfill various user requirements that cannot be achieved by the single satellite, like the Tropical Rainfall Measurement Mission (TRMM). The GPM mission is an international mission to achieve high-accurate and high-frequent rainfall observation over a global area. GPM is composed of a TRMM-like non-sun-synchronous orbit satellite (GPM core satellite) and constellation of satellites carrying microwave radiometer instruments. The GPM core satellite carries the Dual-frequency Precipitation Radar (DPR), which is being developed by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT), and microwave radiometer provided by the National Aeronautics and Space Administration (NASA). Development of DPR instrument is in good progress for scheduled launch in 2013, and DPR Critical Design Review has completed in July - September 2009. Constellation satellites, which carry a microwave imager and/or sounder, are planned to be launched around 2013 by each partner agency for its own purpose, and will contribute to extending coverage and increasing frequency. JAXA's future mission, the Global Change Observation Mission (GCOM) - Water (GCOM-W) satellite will be one of constellation satellites. The first generation of GCOM-W satellite is scheduled to be launched in 2011, and it carries the Advanced Microwave Scanning Radiometer 2 (AMSR2), which is being developed based on the experience of the AMSR-E on EOS Aqua satellite

Advanced Oil Spill Detection Algorithms For Satellite Based Maritime Environment Monitoring

Science.gov (United States)

Radius, Andrea; Azevedo, Rui; Sapage, Tania; Carmo, Paulo

2013-12-01

During the last years, the increasing pollution occurrence and the alarming deterioration of the environmental health conditions of the sea, lead to the need of global monitoring capabilities, namely for marine environment management in terms of oil spill detection and indication of the suspected polluter. The sensitivity of Synthetic Aperture Radar (SAR) to the different phenomena on the sea, especially for oil spill and vessel detection, makes it a key instrument for global pollution monitoring. The SAR performances in maritime pollution monitoring are being operationally explored by a set of service providers on behalf of the European Maritime Safety Agency (EMSA), which has launched in 2007 the CleanSeaNet (CSN) project - a pan-European satellite based oil monitoring service. EDISOFT, which is from the beginning a service provider for CSN, is continuously investing in R&D activities that will ultimately lead to better algorithms and better performance on oil spill detection from SAR imagery. This strategy is being pursued through EDISOFT participation in the FP7 EC Sea-U project and in the Automatic Oil Spill Detection (AOSD) ESA project. The Sea-U project has the aim to improve the current state of oil spill detection algorithms, through the informative content maximization obtained with data fusion, the exploitation of different type of data/ sensors and the development of advanced image processing, segmentation and classification techniques. The AOSD project is closely related to the operational segment, because it is focused on the automation of the oil spill detection processing chain, integrating auxiliary data, like wind information, together with image and geometry analysis techniques. The synergy between these different objectives (R&D versus operational) allowed EDISOFT to develop oil spill detection software, that combines the operational automatic aspect, obtained through dedicated integration of the processing chain in the existing open source NEST

Integration of SRTM and TRMM date into the GIS-based hydrological model for the purpose of flood modelling

Science.gov (United States)

Akbari, A.; Abu Samah, A.; Othman, F.

2012-04-01

Due to land use and climate changes, more severe and frequent floods occur worldwide. Flood simulation as the first step in flood risk management can be robustly conducted with integration of GIS, RS and flood modeling tools. The primary goal of this research is to examine the practical use of public domain satellite data and GIS-based hydrologic model. Firstly, database development process is described. GIS tools and techniques were used in the light of relevant literature to achieve the appropriate database. Watershed delineation and parameterizations were carried out using cartographic DEM derived from digital topography at a scale of 1:25 000 with 30 m cell size and SRTM elevation data at 30 m cell size. The SRTM elevation dataset is evaluated and compared with cartographic DEM. With the assistance of statistical measures such as Correlation coefficient (r), Nash-Sutcliffe efficiency (NSE), Percent Bias (PBias) or Percent of Error (PE). According to NSE index, SRTM-DEM can be used for watershed delineation and parameterization with 87% similarity with Topo-DEM in a complex and underdeveloped terrains. Primary TRMM (V6) data was used as satellite based hytograph for rainfall-runoff simulation. The SCS-CN approach was used for losses and kinematic routing method employed for hydrograph transformation through the reaches. It is concluded that TRMM estimates do not give adequate information about the storms as it can be drawn from the rain gauges. Event-based flood modeling using HEC-HMS proved that SRTM elevation dataset has the ability to obviate the lack of terrain data for hydrologic modeling where appropriate data for terrain modeling and simulation of hydrological processes is unavailable. However, TRMM precipitation estimates failed to explain the behavior of rainfall events and its resultant peak discharge and time of peak.

Application of GPR Method for Detection of Loose Zones in Flood Levee

Science.gov (United States)

Gołębiowski, Tomisław; Małysa, Tomasz

2018-02-01

In the paper the results of non-invasive georadar (GPR) surveys carried out for detection of loose zones located in the flood levee was presented. Terrain measurements were performed on the Vistula river flood levee in the village of Wawrzeńczyce near Cracow. In the investigation site, during the flood in 2010, leakages of levee were observed, so detection of inner water filtration paths was an important matter taking into account the stability of the levee during the next flood. GPR surveys had reconnaissance character, so they were carried out with the use of short-offset reflection profiling (SORP) technique and radargrams were subjected to standard signal processing. The results of surveys allowed to outline main loose zone in the levee which were the reason of leakages in 2010. Additionally gravel interbeddings in sand were detected which had an important influence, due to higher porosity of such zones, to water filtration inside of the levee. In the paper three solutions which allow to increase quality and resolution of radargrams were presented, i.e. changeable-polarisation surveys, advanced signal processing and DHA procedure.

Optomagnetic Detection of MicroRNA Based on Duplex-Specific Nuclease-Assisted Target Recycling and Multilayer Core-Satellite Magnetic Superstructures

DEFF Research Database (Denmark)

Tian, Bo; Ma, Jing; Qiu, Zhen

2017-01-01

-efficiency, and potential for bioresponsive multiplexing. Herein, we demonstrate a sensitive and rapid miRNA detection method based on optomagnetic read-out, duplex-specific nuclease (DSN)-assisted target recycling, and the use of multilayer core-satellite magnetic superstructures. Triggered by the presence of target mi...

Interactive Web-based Floodplain Simulation System for Realistic Experiments of Flooding and Flood Damage

Science.gov (United States)

Demir, I.

2013-12-01

Recent developments in web technologies make it easy to manage and visualize large data sets with general public. Novel visualization techniques and dynamic user interfaces allow users to create realistic environments, and interact with data to gain insight from simulations and environmental observations. The floodplain simulation system is a web-based 3D interactive flood simulation environment to create real world flooding scenarios. The simulation systems provides a visually striking platform with realistic terrain information, and water simulation. Students can create and modify predefined scenarios, control environmental parameters, and evaluate flood mitigation techniques. The web-based simulation system provides an environment to children and adults learn about the flooding, flood damage, and effects of development and human activity in the floodplain. The system provides various scenarios customized to fit the age and education level of the users. This presentation provides an overview of the web-based flood simulation system, and demonstrates the capabilities of the system for various flooding and land use scenarios.

Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Rupinder Singh

2017-01-01

Full Text Available In this paper, an Advanced Hybrid Intrusion Detection System (AHIDS that automatically detects the WSNs attacks is proposed. AHIDS makes use of cluster-based architecture with enhanced LEACH protocol that intends to reduce the level of energy consumption by the sensor nodes. AHIDS uses anomaly detection and misuse detection based on fuzzy rule sets along with the Multilayer Perceptron Neural Network. The Feed Forward Neural Network along with the Backpropagation Neural Network are utilized to integrate the detection results and indicate the different types of attackers (i.e., Sybil attack, wormhole attack, and hello flood attack. For detection of Sybil attack, Advanced Sybil Attack Detection Algorithm is developed while the detection of wormhole attack is done by Wormhole Resistant Hybrid Technique. The detection of hello flood attack is done by using signal strength and distance. An experimental analysis is carried out in a set of nodes; 13.33% of the nodes are determined as misbehaving nodes, which classified attackers along with a detection rate of the true positive rate and false positive rate. Sybil attack is detected at a rate of 99,40%; hello flood attack has a detection rate of 98, 20%; and wormhole attack has a detection rate of 99, 20%.

Detection of Variations of Local Irregularity of Traffic under DDOS Flood Attack

Directory of Open Access Journals (Sweden)

Ming Li

2008-01-01

Full Text Available The aim of distributed denial-of-service (DDOS flood attacks is to overwhelm the attacked site or to make its service performance deterioration considerably by sending flood packets to the target from the machines distributed all over the world. This is a kind of local behavior of traffic at the protected site because the attacked site can be recovered to its normal service state sooner or later even though it is in reality overwhelmed during attack. From a view of mathematics, it can be taken as a kind of short-range phenomenon in computer networks. In this paper, we use the Hurst parameter (H to measure the local irregularity or self-similarity of traffic under DDOS flood attack provided that fractional Gaussian noise (fGn is used as the traffic model. As flood attack packets of DDOS make the H value of arrival traffic vary significantly away from that of traffic normally arriving at the protected site, we discuss a method to statistically detect signs of DDOS flood attacks with predetermined detection probability and false alarm probability.

Testing an innovative framework for flood forecasting, monitoring and mapping in Europe

Science.gov (United States)

Dottori, Francesco; Kalas, Milan; Lorini, Valerio; Wania, Annett; Pappenberger, Florian; Salamon, Peter; Ramos, Maria Helena; Cloke, Hannah; Castillo, Carlos

2017-04-01

Between May and June 2016, France was hit by severe floods, particularly in the Loire and Seine river basins. In this work, we use this case study to test an innovative framework for flood forecasting, mapping and monitoring. More in detail, the system integrates in real-time two components of the Copernicus Emergency mapping services, namely the European Flood Awareness System and the satellite-based Rapid Mapping, with new procedures for rapid risk assessment and social media and news monitoring. We explore in detail the performance of each component of the system, demonstrating the improvements in respect to stand-alone flood forecasting and monitoring systems. We show how the performances of the forecasting component can be refined using the real-time feedback from social media monitoring to identify which areas were flooded, to evaluate the flood intensity, and therefore to correct impact estimations. Moreover, we show how the integration with impact forecast and social media monitoring can improve the timeliness and efficiency of satellite based emergency mapping, and reduce the chances of missing areas where flooding is already happening. These results illustrate how the new integrated approach leads to a better and earlier decision making and a timely evaluation of impacts.

An Unsupervised Anomalous Event Detection and Interactive Analysis Framework for Large-scale Satellite Data

Science.gov (United States)

LIU, Q.; Lv, Q.; Klucik, R.; Chen, C.; Gallaher, D. W.; Grant, G.; Shang, L.

2016-12-01

Due to the high volume and complexity of satellite data, computer-aided tools for fast quality assessments and scientific discovery are indispensable for scientists in the era of Big Data. In this work, we have developed a framework for automated anomalous event detection in massive satellite data. The framework consists of a clustering-based anomaly detection algorithm and a cloud-based tool for interactive analysis of detected anomalies. The algorithm is unsupervised and requires no prior knowledge of the data (e.g., expected normal pattern or known anomalies). As such, it works for diverse data sets, and performs well even in the presence of missing and noisy data. The cloud-based tool provides an intuitive mapping interface that allows users to interactively analyze anomalies using multiple features. As a whole, our framework can (1) identify outliers in a spatio-temporal context, (2) recognize and distinguish meaningful anomalous events from individual outliers, (3) rank those events based on "interestingness" (e.g., rareness or total number of outliers) defined by users, and (4) enable interactively query, exploration, and analysis of those anomalous events. In this presentation, we will demonstrate the effectiveness and efficiency of our framework in the application of detecting data quality issues and unusual natural events using two satellite datasets. The techniques and tools developed in this project are applicable for a diverse set of satellite data and will be made publicly available for scientists in early 2017.

Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Satellite Based on Machine Learning Approaches

Directory of Open Access Journals (Sweden)

Hyangsun Han

2015-07-01

Full Text Available As convective clouds in Northeast Asia are accompanied by various hazards related with heavy rainfall and thunderstorms, it is very important to detect convective initiation (CI in the region in order to mitigate damage by such hazards. In this study, a novel approach for CI detection using images from Meteorological Imager (MI, a payload of the Communication, Ocean, and Meteorological Satellite (COMS, was developed by improving the criteria of the interest fields of Rapidly Developing Cumulus Areas (RDCA derivation algorithm, an official CI detection algorithm for Multi-functional Transport SATellite-2 (MTSAT-2, based on three machine learning approaches—decision trees (DT, random forest (RF, and support vector machines (SVM. CI was defined as clouds within a 16 × 16 km window with the first detection of lightning occurrence at the center. A total of nine interest fields derived from visible, water vapor, and two thermal infrared images of MI obtained 15–75 min before the lightning occurrence were used as input variables for CI detection. RF produced slightly higher performance (probability of detection (POD of 75.5% and false alarm rate (FAR of 46.2% than DT (POD of 70.7% and FAR of 46.6% for detection of CI caused by migrating frontal cyclones and unstable atmosphere. SVM resulted in relatively poor performance with very high FAR ~83.3%. The averaged lead times of CI detection based on the DT and RF models were 36.8 and 37.7 min, respectively. This implies that CI over Northeast Asia can be forecasted ~30–45 min in advance using COMS MI data.

A National Assessment of Changes in Flood Exposure in the United States

Science.gov (United States)

Lam, N.; Qiang, Y.; Cai, H.; Zou, L.

2017-12-01

Analyzing flood exposure and its temporal trend is the first step toward understanding flood risk, flood hazard, and flood vulnerability. This presentation is based on a national, county-based study assessing the changes in population and urban areas in high-risk flood zones from 2001-2011 in the contiguous United States. Satellite land use land cover data, Federal Emergency Management Agency (FEMA)'s 100-year flood maps, and census data were used to extract the proportion of developed (urban) land in flood zones by county in the two time points, and indices of difference were calculated. Local Moran's I statistic was applied to identify hotspots of increase in urban area in flood zones, and geographically weighted regression was used to estimate the population in flood zones from the land cover data. Results show that in 2011, an estimate of about 25.3 million people (8.3% of the total population) lived in the high-risk flood zones. Nationally, the ratio of urban development in flood zones is less than the ratio of land in flood zones, implying that Americans were responsive to flood hazards by avoiding development in flood zones. However, this trend varied from place to place, with coastal counties having less urban development in flood zones than the inland counties. Furthermore, the contrast between coastal and inland counties increased during 2001-2011. Finally, several exceptions from the trend (hotspots) were detected, most notably New York City and Miami where significant increases in urban development in flood zones were found. This assessment provides important baseline information on the spatial patterns of flood exposure and their changes from 2001-2011. The study pinpoints regions that may need further investigations and better policy to reduce the overall flood risks. Methodologically, the study demonstrates that pixelated land cover data can be integrated with other natural and human data to investigate important societal problems. The same

A Prototype Flood Early Warning SensorWeb System for Namibia

Science.gov (United States)

Sohlberg, R. A.; Mandl, D.; Frye, S. W.; Cappelaere, P. G.; Szarzynski, J.; Policelli, F.; van Langenhove, G.

2010-12-01

During the past two years, there have been extensive floods in the country of Namibia, Africa which have affected up to a quarter of the population. Via a collaboration between a group funded by the Earth Science Technology Office (ESTO) at NASA that has been performing various SensorWeb prototyping activities for disasters, the Department of Hydrology in Namibia and the United Nations Space-based Information for Disaster and Emergency Response (UN-SPIDER) , experiments were conducted on how to apply various satellite resources integrated into a SensorWeb architecture along with in-situ sensors such as river gauges and rain gauges into a flood early warning system. The SensorWeb includes a global flood model and a higher resolution basin specific flood model. Furthermore, flood extent and status is monitored by optical and radar types of satellites and integrated via some automation. We have taken a practical approach to find out how to create a working system by selectively using the components that provide good results. The vision for the future is to combine this with the country side dwelling unit data base to create risk maps that provide specific warnings to houses within high risk areas based on near term predictions. This presentation will show some of the highlights of the effort thus far plus our future plans.

Technical note Flood map development by coupling satellite maps ...

African Journals Online (AJOL)

Flood maps are important for local authorities in designing mitigation plans to minimise damage and loss due to flooding. In recent years, flood events in the Sarawak River Basin, Malaysia have caused damage to property, loss of life and disruption of productive activities. Currently, the available flood map for Sarawak River ...

A graph-based approach to detect spatiotemporal dynamics in satellite image time series

Science.gov (United States)

Guttler, Fabio; Ienco, Dino; Nin, Jordi; Teisseire, Maguelonne; Poncelet, Pascal

2017-08-01

Enhancing the frequency of satellite acquisitions represents a key issue for Earth Observation community nowadays. Repeated observations are crucial for monitoring purposes, particularly when intra-annual process should be taken into account. Time series of images constitute a valuable source of information in these cases. The goal of this paper is to propose a new methodological framework to automatically detect and extract spatiotemporal information from satellite image time series (SITS). Existing methods dealing with such kind of data are usually classification-oriented and cannot provide information about evolutions and temporal behaviors. In this paper we propose a graph-based strategy that combines object-based image analysis (OBIA) with data mining techniques. Image objects computed at each individual timestamp are connected across the time series and generates a set of evolution graphs. Each evolution graph is associated to a particular area within the study site and stores information about its temporal evolution. Such information can be deeply explored at the evolution graph scale or used to compare the graphs and supply a general picture at the study site scale. We validated our framework on two study sites located in the South of France and involving different types of natural, semi-natural and agricultural areas. The results obtained from a Landsat SITS support the quality of the methodological approach and illustrate how the framework can be employed to extract and characterize spatiotemporal dynamics.

An operational procedure for rapid flood risk assessment in Europe

Science.gov (United States)

Dottori, Francesco; Kalas, Milan; Salamon, Peter; Bianchi, Alessandra; Alfieri, Lorenzo; Feyen, Luc

2017-07-01

The development of methods for rapid flood mapping and risk assessment is a key step to increase the usefulness of flood early warning systems and is crucial for effective emergency response and flood impact mitigation. Currently, flood early warning systems rarely include real-time components to assess potential impacts generated by forecasted flood events. To overcome this limitation, this study describes the benchmarking of an operational procedure for rapid flood risk assessment based on predictions issued by the European Flood Awareness System (EFAS). Daily streamflow forecasts produced for major European river networks are translated into event-based flood hazard maps using a large map catalogue derived from high-resolution hydrodynamic simulations. Flood hazard maps are then combined with exposure and vulnerability information, and the impacts of the forecasted flood events are evaluated in terms of flood-prone areas, economic damage and affected population, infrastructures and cities.An extensive testing of the operational procedure has been carried out by analysing the catastrophic floods of May 2014 in Bosnia-Herzegovina, Croatia and Serbia. The reliability of the flood mapping methodology is tested against satellite-based and report-based flood extent data, while modelled estimates of economic damage and affected population are compared against ground-based estimations. Finally, we evaluate the skill of risk estimates derived from EFAS flood forecasts with different lead times and combinations of probabilistic forecasts. Results highlight the potential of the real-time operational procedure in helping emergency response and management.

From TRMM to GPM: How well can heavy rainfall be detected from space?

Science.gov (United States)

Prakash, Satya; Mitra, Ashis K.; Pai, D. S.; AghaKouchak, Amir

2016-02-01

In this study, we investigate the capabilities of the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and the recently released Integrated Multi-satellitE Retrievals for GPM (IMERG) in detecting and estimating heavy rainfall across India. First, the study analyzes TMPA data products over a 17-year period (1998-2014). While TMPA and reference gauge-based observations show similar mean monthly variations of conditional heavy rainfall events, the multi-satellite product systematically overestimates its inter-annual variations. Categorical as well as volumetric skill scores reveal that TMPA over-detects heavy rainfall events (above 75th percentile of reference data), but it shows reasonable performance in capturing the volume of heavy rain across the country. An initial assessment of the GPM-based multi-satellite IMERG precipitation estimates for the southwest monsoon season shows notable improvements over TMPA in capturing heavy rainfall over India. The recently released IMERG shows promising results to help improve modeling of hydrological extremes (e.g., floods and landslides) using satellite observations.

Science and Technology in Regional Flood Disaster Pilots: A GEOSS Capacity Building Imperative

Science.gov (United States)

Frye, S. W.; Cappelaere, P. G.; Mandl, D.

2009-12-01

This paper describes activities and results of melding basic scientific research in remote sensing with applied science and technology development and infusion to implement regional flood pilot programs in Sub-Saharan Africa and the Caribbean Region. These regional flood pilots support local and national agency involvement in emergency response and humanitarian assistance activities using orbital, sub-orbital, and in-situ sensors combined with predictive models and socio-economic data to form a cohesive, interoperable set of systems that cover the full cycle of disaster mitigation, warning, response, and recovery for societal benefit. Global satellite coverage is coordinated through the Committee on Earth Observation Satellites (CEOS) in conjunction with the United Nations Space Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER). Other international non-government organizations plus regional and local agencies all play individual roles in exploring the science results, applying the observations and model outputs to form geo-referenced maps that provide improved situational awareness and environmental intelligence for disaster management. The improvements to flood forecast and nowcast outputs include higher resolution drainage and hydrology mapping, improved retrievals for microwave data for soil moisture, plus improved validation from regional ground truth databases. Flow gauge and river depth archive data from local assets provide improved validation of flood model results. Incorporation of atmospheric correction using ground truth data from calibration and validation sites enables better detection and classification of plant species identification and plant stress. Open Geospatial Consortium (OGC) standards for Sensor Web Enablement (SWE) are implemented to provide internet access to satellite tasking, data processing, and distribution/notification in addition to model outputs and other local and regional data sets

The framework of a UAS-aided flash flood modeling system for coastal regions

Science.gov (United States)

Zhang, H.; Xu, H.

2016-02-01

Flash floods cause severe economic damage and are one of the leading causes of fatalities connected with natural disasters in the Gulf Coast region. Current flash flood modeling systems rely on empirical hydrological models driven by precipitation estimates only. Although precipitation is the driving factor for flash floods, soil moisture, urban drainage system and impervious surface have been recognized to have significant impacts on the development of flash floods. We propose a new flash flooding modeling system that integrates 3-D hydrological simulation with satellite and multi-UAS observations. It will have three advantages over existing modeling systems. First, it will incorporate 1-km soil moisture data through integrating satellite images from European SMOS mission and NASA's SMAP mission. The utilization of high-resolution satellite images will provide essential information to determine antecedent soil moisture condition, which is an essential control on flood generation. Second, this system is able to adjust flood forecasting based on real-time inundation information collected by multi-UAS. A group of UAS will be deployed during storm events to capture the changing extent of flooded areas and water depth at multiple critical locations simultaneously. Such information will be transmitted to a hydrological model to validate and improve flood simulation. Third, the backbone of this system is a state-of-the-art 3-D hydrological model that assimilates the hydrological information from satellites and multi-UAS. The model is able to address surface water-groundwater interactions and reflect the effects of various infrastructures. Using Web-GIS technologies, the modeling results will be available online as interactive flood maps accessible to the public. To support the development and verification of this modeling system, surface and subsurface hydrological observations will be conducted in a number of small watersheds in the Coastal Bend region. We envision this

NATURAL HAZARD ASSESSMENT OF SW MYANMAR - A CONTRIBUTION OF REMOTE SENSING AND GIS METHODS TO THE DETECTION OF AREAS VULNERABLE TO EARTHQUAKES AND TSUNAMI / CYCLONE FLOODING

Directory of Open Access Journals (Sweden)

George Pararas-Carayannis

2009-01-01

Full Text Available Myanmar, formerly Burma, is vulnerable to several natural hazards, such as earthquakes, cyclones, floods, tsunamis and landslides. The present study focuses on geomorphologic and geologic investigations of the south-western region of the country, based on satellite data (Shuttle Radar Topography Mission-SRTM, MODIS and LANDSAT. The main objective is to detect areas vulnerable to inundation by tsunami waves and cyclone surges. Since the region is also vulnerable to earthquake hazards, it is also important to identify seismotectonic patterns, the location of major active faults, and local site conditions that may enhance ground motions and earthquake intensities. As illustrated by this study, linear, topographic features related to subsurface tectonic features become clearly visible on SRTM-derived morphometric maps and on LANDSAT imagery. The GIS integrated evaluation of LANDSAT and SRTM data helps identify areas most susceptible to flooding and inundation by tsunamis and storm surges. Additionally, land elevation maps help identify sites greater than 10 m in elevation height, that would be suitable for the building of protective tsunami/cyclone shelters.

Prompt Proxy Mapping of Flood Damaged Rice Fields Using MODIS-Derived Indices

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Youngjoo Kwak

2015-11-01

Full Text Available 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.

Satellite-based detection of volcanic sulphur dioxide from recent eruptions in Central and South America

Directory of Open Access Journals (Sweden)

D. Loyola

2008-01-01

Full Text Available Volcanic eruptions can emit large amounts of rock fragments and fine particles (ash into the atmosphere, as well as several gases, including sulphur dioxide (SO₂. These ejecta and emissions are a major natural hazard, not only to the local population, but also to the infrastructure in the vicinity of volcanoes and to aviation. Here, we describe a methodology to retrieve quantitative information about volcanic SO₂ plumes from satellite-borne measurements in the UV/Visible spectral range. The combination of a satellite-based SO₂ detection scheme and a state-of-the-art 3D trajectory model enables us to confirm the volcanic origin of trace gas signals and to estimate the plume height and the effective emission height. This is demonstrated by case-studies for four selected volcanic eruptions in South and Central America, using the GOME, SCIAMACHY and GOME-2 instruments.

Detecting SYN flood attacks via statistical monitoring charts: A comparative study

KAUST Repository

Bouyeddou, Benamar; Harrou, Fouzi; Sun, Ying; Kadri, Benamar

2017-01-01

Accurate detection of cyber-attacks plays a central role in safeguarding computer networks and information systems. This paper addresses the problem of detecting SYN flood attacks, which are the most popular Denial of Service (DoS) attacks. Here, we

Cloud detection method for Chinese moderate high resolution satellite imagery (Conference Presentation)

Science.gov (United States)

Zhong, Bo; Chen, Wuhan; Wu, Shanlong; Liu, Qinhuo

2016-10-01

Cloud detection of satellite imagery is very important for quantitative remote sensing research and remote sensing applications. However, many satellite sensors don't have enough bands for a quick, accurate, and simple detection of clouds. Particularly, the newly launched moderate to high spatial resolution satellite sensors of China, such as the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) and the wide field of view (WFV) sensor on-board the Gao Fen 1 (GF-1), only have four available bands including blue, green, red, and near infrared bands, which are far from the requirements of most could detection methods. In order to solve this problem, an improved and automated cloud detection method for Chinese satellite sensors called OCM (Object oriented Cloud and cloud-shadow Matching method) is presented in this paper. It firstly modified the Automatic Cloud Cover Assessment (ACCA) method, which was developed for Landsat-7 data, to get an initial cloud map. The modified ACCA method is mainly based on threshold and different threshold setting produces different cloud map. Subsequently, a strict threshold is used to produce a cloud map with high confidence and large amount of cloud omission and a loose threshold is used to produce a cloud map with low confidence and large amount of commission. Secondly, a corresponding cloud-shadow map is also produced using the threshold of near-infrared band. Thirdly, the cloud maps and cloud-shadow map are transferred to cloud objects and cloud-shadow objects. Cloud and cloud-shadow are usually in pairs; consequently, the final cloud and cloud-shadow maps are made based on the relationship between cloud and cloud-shadow objects. OCM method was tested using almost 200 HJ-1/CCD images across China and the overall accuracy of cloud detection is close to 90%.

Namibian Flood Early Warning SensorWeb Pilot

Science.gov (United States)

Mandl, Daniel; Policelli, Fritz; Frye, Stuart; Cappelare, Pat; Langenhove, Guido Van; Szarzynski, Joerg; Sohlberg, Rob

2010-01-01

The major goal of the Namibia SensorWeb Pilot Project is a scientifically sound, operational trans-boundary flood management decision support system for Southern African region to provide useful flood and waterborne disease forecasting tools for local decision makers. The Pilot Project established under the auspices of: Namibian Ministry of Agriculture Water and Forestry (MAWF), Department of Water Affairs; Committee on Earth Observing Satellites (CEOS), Working Group on Information Systems and Services (WGISS); and moderated by the United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER). The effort consists of identifying and prototyping technology which enables the rapid gathering and dissemination of both space-based and ground sensor data and data products for the purpose of flood disaster management and water-borne disease management.

Spatiotemporal Change Detection Using Landsat Imagery: the Case Study of Karacabey Flooded Forest, Bursa, Turkey

Science.gov (United States)

Akay, A. E.; Gencal, B.; Taş, İ.

2017-11-01

This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

An SDR based AIS receiver for satellites

DEFF Research Database (Denmark)

Larsen, Jesper Abildgaard; Mortensen, Hans Peter; Nielsen, Jens Frederik Dalsgaard

2011-01-01

For a few years now, there has been a high interest in monitoring the global ship traffic from space. A few satellite, capable of listening for ship borne AIS transponders have already been launched, and soon the AAUSAT3, carrying two different types of AIS receivers will also be launched. One...... of the AIS receivers onboard AAUSAT3 is an SDR based AIS receiver. This paper serves to describe the background of the AIS system, and how the SDR based receiver has been integrated into the AAUSAT3 satellite. Amongst some of the benefits of using an SDR based receiver is, that due to its versatility, new...... detection algorithms are easily deployed, and it is easily adapted the new proposed AIS transmission channels....

Slope mass movements on SPOT satellite images: A case of the Železniki area (W Slovenia after flash floods in September 2007

Directory of Open Access Journals (Sweden)

Mateja Jemec

2008-12-01

Full Text Available Flash floods in Slovenia, which was exposed on September 18th 2007, demanded 6 lives, several thousand houses and over one thousand kilometres of roads were damaged and more also than 50 bridges. The highest amount of rain fell at west and north-west parts of Slovenia (northern Primorska region and southern Gorenjska region,from where heavy rain spread eastwards over the central Slovenia and in east part of Slovenia. In the article we focused on area of western and north-western part of Slovenia. The aim of present research was in the first phase to describe methodology to determine landslide occurrences from satellite images before and after natural disaster on Železniki region. Second phase was based on comparison of obtained results with the existing models for prediction of slope mass movements, and finally also to determine identificability of landslide types on a satellite image.Results have shown, that the highest part of obtaining area from supervised and unsupervised classification of satellite images, are comparable with classes of landslide susceptibility, where occurrences of landslide are largest.

Flood-rich and flood-poor periods in Spain in 1942-2009

Science.gov (United States)

Mediero, Luis; Santillán, David; Garrote, Luis

2016-04-01

Several studies to detect trends in flood series at either national or trans-national scales have been conducted. Mediero et al. (2015) studied flood trends by using the longest streamflow records available in Europe. They found a decreasing trend in the Atlantic, Continental and Scandinavian regions. More specifically, Mediero et al. (2014) found a general decreasing trend in flood series in Spain in the period 1959-2009. Trends in flood series are usually detected by the Mann-Kendall test applied to a given period. However, the result of the Mann-Kendall test can change in terms of the starting and ending year of the series. Flood oscillations can occur and flood-rich and flood-poor periods could condition the results, especially when they are located at the beginning or end of the series. A methodology to identify statistically significant flood-rich and flood-poor periods is developed, based on the comparison between the expected sampling variability of floods when stationarity is assumed and the observed variability of floods in a given series. The methodology is applied to the longest series of annual maximum floods, peaks over threshold and counts of annual occurrences in peaks over threshold series observed in Spain in the period 1942-2009. A flood-rich period in 1950-1970 and a flood-poor period in 1970-1990 are identified in most of the selected sites. The generalised decreasing trend in flood series found by Mediero et al. (2014) could be explained by a flood-rich period placed at the beginning of the series and a flood-poor period located at the end of the series. References: Mediero, L., Kjeldsen, T.R., Macdonald, N., Kohnova, S., Merz, B., Vorogushyn, S., Wilson, D., Alburquerque, T., Blöschl, G., Bogdanowicz, E., Castellarin, A., Hall, J., Kobold, M., Kriauciuniene, J., Lang, M., Madsen, H., Onuşluel Gül, G., Perdigão, R.A.P., Roald, L.A., Salinas, J.L., Toumazis, A.D., Veijalainen, N., Óðinn Þórarinsson. Identification of coherent flood

Urban flood simulation based on the SWMM model

Directory of Open Access Journals (Sweden)

L. Jiang

2015-05-01

Full Text Available China is the nation with the fastest urbanization in the past decades which has caused serious urban flooding. Flood forecasting is regarded as one of the important flood mitigation methods, and is widely used in catchment flood mitigation, but is not widely used in urban flooding mitigation. This paper, employing the SWMM model, one of the widely used urban flood planning and management models, simulates the urban flooding of Dongguan City in the rapidly urbanized southern China. SWMM is first set up based on the DEM, digital map and underground pipeline network, then parameters are derived based on the properties of the subcatchment and the storm sewer conduits; the parameter sensitivity analysis shows the parameter robustness. The simulated results show that with the 1-year return period precipitation, the studied area will have no flooding, but for the 2-, 5-, 10- and 20-year return period precipitation, the studied area will be inundated. The results show the SWMM model is promising for urban flood forecasting, but as it has no surface runoff routing, the urban flooding could not be forecast precisely.

National Satellite Land Remote Sensing Data Archive

Science.gov (United States)

Faundeen, John L.; Kelly, Francis P.; Holm, Thomas M.; Nolt, Jenna E.

2013-01-01

The National Satellite Land Remote Sensing Data Archive (NSLRSDA) resides at the U.S. Geological Survey's (USGS) Earth Resources Observation and Science (EROS) Center. Through the Land Remote Sensing Policy Act of 1992, the U.S. Congress directed the Department of the Interior (DOI) to establish a permanent Government archive containing satellite remote sensing data of the Earth's land surface and to make this data easily accessible and readily available. This unique DOI/USGS archive provides a comprehensive, permanent, and impartial observational record of the planet's land surface obtained throughout more than five decades of satellite remote sensing. Satellite-derived data and information products are primary sources used to detect and understand changes such as deforestation, desertification, agricultural crop vigor, water quality, invasive plant species, and certain natural hazards such as flood extent and wildfire scars.

PROCESSING BIG REMOTE SENSING DATA FOR FAST FLOOD DETECTION IN A DISTRIBUTED COMPUTING ENVIRONMENT

Directory of Open Access Journals (Sweden)

A. Olasz

2017-07-01

Full Text Available The Earth observation (EO missions of the space agencies and space industry (ESA, NASA, national and commercial companies are evolving as never before. These missions aim to develop and launch next-generation series of satellites and sensors and often provide huge amounts of data, even free of charge, to enable novel monitoring services. The wide geospatial sector is targeted to handle new challenges to store, process and visualize these geospatial data, reaching the level of Big Data by their volume, variety, velocity, along with the need of multi-source spatio-temporal geospatial data processing. Handling and analysis of remote sensing data has always been a cumbersome task due to the ever-increasing size and frequency of collected information. This paper presents the achievements of the IQmulus EU FP7 research and development project with respect to processing and analysis of geospatial big data in the context of flood and waterlogging detection.

44 CFR 65.14 - Remapping of areas for which local flood protection systems no longer provide base flood protection.

Science.gov (United States)

2010-10-01

... local flood protection systems no longer provide base flood protection. 65.14 Section 65.14 Emergency... § 65.14 Remapping of areas for which local flood protection systems no longer provide base flood... process of restoring a flood protection system that was: (i) Constructed using Federal funds; (ii...

Early Warning System of Flood Disaster Based on Ultrasonic Sensors and Wireless Technology

Science.gov (United States)

Indrasari, W.; Iswanto, B. H.; Andayani, M.

2018-04-01

A flood disaster provides considerable losses to the people who live around the river. To mitigate losses of material due to flood disaster required an early warning system of flood disaster. For that reason, it necessary to design a system that provide alert to the people prior the flood disaster. And this paper describes development of a device for early detection system of flood disasters. This device consists of two ultrasonic sensors as a water level detector, and a water flow sensor as a water flow velocity sensor. The wireless technology and GSM is used as an information medium. The system is designed based on water level conditions in the Katulampa Dam, Bogor. Characterization of water level detector showed that the device effectively works in a range of water level of 14-250 cm, with a maximum relative error of 4.3%. Meanwhile the wireless works properly as far as 75 m, and the SMS transmission time is 8.20 second.

A Dynamic Enhancement With Background Reduction Algorithm: Overview and Application to Satellite-Based Dust Storm Detection

Science.gov (United States)

Miller, Steven D.; Bankert, Richard L.; Solbrig, Jeremy E.; Forsythe, John M.; Noh, Yoo-Jeong; Grasso, Lewis D.

2017-12-01

This paper describes a Dynamic Enhancement Background Reduction Algorithm (DEBRA) applicable to multispectral satellite imaging radiometers. DEBRA uses ancillary information about the clear-sky background to reduce false detections of atmospheric parameters in complex scenes. Applied here to the detection of lofted dust, DEBRA enlists a surface emissivity database coupled with a climatological database of surface temperature to approximate the clear-sky equivalent signal for selected infrared-based multispectral dust detection tests. This background allows for suppression of false alarms caused by land surface features while retaining some ability to detect dust above those problematic surfaces. The algorithm is applicable to both day and nighttime observations and enables weighted combinations of dust detection tests. The results are provided quantitatively, as a detection confidence factor [0, 1], but are also readily visualized as enhanced imagery. Utilizing the DEBRA confidence factor as a scaling factor in false color red/green/blue imagery enables depiction of the targeted parameter in the context of the local meteorology and topography. In this way, the method holds utility to both automated clients and human analysts alike. Examples of DEBRA performance from notable dust storms and comparisons against other detection methods and independent observations are presented.

Global Rapid Flood Mapping System with Spaceborne SAR Data

Science.gov (United States)

Yun, S. H.; Owen, S. E.; Hua, H.; Agram, P. S.; Fattahi, H.; Liang, C.; Manipon, G.; Fielding, E. J.; Rosen, P. A.; Webb, F.; Simons, M.

2017-12-01

As part of the Advanced Rapid Imaging and Analysis (ARIA) project for Natural Hazards, at NASA's Jet Propulsion Laboratory and California Institute of Technology, we have developed an automated system that produces derived products for flood extent map generation using spaceborne SAR data. The system takes user's input of area of interest polygons and time window for SAR data search (pre- and post-event). Then the system automatically searches and downloads SAR data, processes them to produce coregistered SAR image pairs, and generates log amplitude ratio images from each pair. Currently the system is automated to support SAR data from the European Space Agency's Sentinel-1A/B satellites. We have used the system to produce flood extent maps from Sentinel-1 SAR data for the May 2017 Sri Lanka floods, which killed more than 200 people and displaced about 600,000 people. Our flood extent maps were delivered to the Red Cross to support response efforts. Earlier we also responded to the historic August 2016 Louisiana floods in the United States, which claimed 13 people's lives and caused over $10 billion property damage. For this event, we made synchronized observations from space, air, and ground in close collaboration with USGS and NOAA. The USGS field crews acquired ground observation data, and NOAA acquired high-resolution airborne optical imagery within the time window of +/-2 hours of the SAR data acquisition by JAXA's ALOS-2 satellite. The USGS coordinates of flood water boundaries were used to calibrate our flood extent map derived from the ALOS-2 SAR data, and the map was delivered to FEMA for estimating the number of households affected. Based on the lessons learned from this response effort, we customized the ARIA system automation for rapid flood mapping and developed a mobile friendly web app that can easily be used in the field for data collection. Rapid automatic generation of SAR-based global flood maps calibrated with independent observations from

Sensor fault detection and recovery in satellite attitude control

Science.gov (United States)

Nasrolahi, Seiied Saeed; Abdollahi, Farzaneh

2018-04-01

This paper proposes an integrated sensor fault detection and recovery for the satellite attitude control system. By introducing a nonlinear observer, the healthy sensor measurements are provided. Considering attitude dynamics and kinematic, a novel observer is developed to detect the fault in angular rate as well as attitude sensors individually or simultaneously. There is no limit on type and configuration of attitude sensors. By designing a state feedback based control signal and Lyapunov stability criterion, the uniformly ultimately boundedness of tracking errors in the presence of sensor faults is guaranteed. Finally, simulation results are presented to illustrate the performance of the integrated scheme.

Low cost, multiscale and multi-sensor application for flooded area mapping

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D. Giordan

2018-05-01

Full Text Available Flood mapping and estimation of the maximum water depth are essential elements for the first damage evaluation, civil protection intervention planning and detection of areas where remediation is needed. In this work, we present and discuss a methodology for mapping and quantifying flood severity over floodplains. The proposed methodology considers a multiscale and multi-sensor approach using free or low-cost data and sensors. We applied this method to the November 2016 Piedmont (northwestern Italy flood. We first mapped the flooded areas at the basin scale using free satellite data from low- to medium-high-resolution from both the SAR (Sentinel-1, COSMO-Skymed and multispectral sensors (MODIS, Sentinel-2. Using very- and ultra-high-resolution images from the low-cost aerial platform and remotely piloted aerial system, we refined the flooded zone and detected the most damaged sector. The presented method considers both urbanised and non-urbanised areas. Nadiral images have several limitations, in particular in urbanised areas, where the use of terrestrial images solved this limitation. Very- and ultra-high-resolution images were processed with structure from motion (SfM for the realisation of 3-D models. These data, combined with an available digital terrain model, allowed us to obtain maps of the flooded area, maximum high water area and damaged infrastructures.

Feature Detection Systems Enhance Satellite Imagery

Science.gov (United States)

2009-01-01

In 1963, during the ninth orbit of the Faith 7 capsule, astronaut Gordon Cooper skipped his nap and took some photos of the Earth below using a Hasselblad camera. The sole flier on the Mercury-Atlas 9 mission, Cooper took 24 photos - never-before-seen images including the Tibetan plateau, the crinkled heights of the Himalayas, and the jagged coast of Burma. From his lofty perch over 100 miles above the Earth, Cooper noted villages, roads, rivers, and even, on occasion, individual houses. In 1965, encouraged by the effectiveness of NASA s orbital photography experiments during the Mercury and subsequent Gemini manned space flight missions, U.S. Geological Survey (USGS) director William Pecora put forward a plan for a remote sensing satellite program that would collect information about the planet never before attainable. By 1972, NASA had built and launched Landsat 1, the first in a series of Landsat sensors that have combined to provide the longest continuous collection of space-based Earth imagery. The archived Landsat data - 37 years worth and counting - has provided a vast library of information allowing not only the extensive mapping of Earth s surface but also the study of its environmental changes, from receding glaciers and tropical deforestation to urban growth and crop harvests. Developed and launched by NASA with data collection operated at various times by the Agency, the National Oceanic and Atmospheric Administration (NOAA), Earth Observation Satellite Company (EOSAT, a private sector partnership that became Space Imaging Corporation in 1996), and USGS, Landsat sensors have recorded flooding from Hurricane Katrina, the building boom in Dubai, and the extinction of the Aral Sea, offering scientists invaluable insights into the natural and manmade changes that shape the world. Of the seven Landsat sensors launched since 1972, Landsat 5 and Landsat 7 are still operational. Though both are in use well beyond their intended lifespans, the mid

Analysis of flood inundation in ungauged basins based on multi-source remote sensing data.

Science.gov (United States)

Gao, Wei; Shen, Qiu; Zhou, Yuehua; Li, Xin

2018-02-09

Floods are among the most expensive natural hazards experienced in many places of the world and can result in heavy losses of life and economic damages. The objective of this study is to analyze flood inundation in ungauged basins by performing near-real-time detection with flood extent and depth based on multi-source remote sensing data. Via spatial distribution analysis of flood extent and depth in a time series, the inundation condition and the characteristics of flood disaster can be reflected. The results show that the multi-source remote sensing data can make up the lack of hydrological data in ungauged basins, which is helpful to reconstruct hydrological sequence; the combination of MODIS (moderate-resolution imaging spectroradiometer) surface reflectance productions and the DFO (Dartmouth Flood Observatory) flood database can achieve the macro-dynamic monitoring of the flood inundation in ungauged basins, and then the differential technique of high-resolution optical and microwave images before and after floods can be used to calculate flood extent to reflect spatial changes of inundation; the monitoring algorithm for the flood depth combining RS and GIS is simple and easy and can quickly calculate the depth with a known flood extent that is obtained from remote sensing images in ungauged basins. Relevant results can provide effective help for the disaster relief work performed by government departments.

Improving Flood Prediction By the Assimilation of Satellite Soil Moisture in Poorly Monitored Catchments.

Science.gov (United States)

Alvarez-Garreton, C. D.; Ryu, D.; Western, A. W.; Crow, W. T.; Su, C. H.; Robertson, D. E.

2014-12-01

Flood prediction in poorly monitored catchments is among the greatest challenges faced by hydrologists. To address this challenge, an increasing number of studies in the last decade have explored methods to integrate various existing observations from ground and satellites. One approach in particular, is the assimilation of satellite soil moisture (SM-DA) into rainfall-runoff models. The rationale is that satellite soil moisture (SSM) can be used to correct model soil water states, enabling more accurate prediction of catchment response to precipitation and thus better streamflow. However, there is still no consensus on the most effective SM-DA scheme and how this might depend on catchment scale, climate characteristics, runoff mechanisms, model and SSM products used, etc. In this work, an operational SM-DA scheme was set up in the poorly monitored, large (>40,000 km2), semi-arid Warrego catchment situated in eastern Australia. We assimilated passive and active SSM products into the probability distributed model (PDM) using an ensemble Kalman filter. We explored factors influencing the SM-DA framework, including relatively new techniques to remove model-observation bias, estimate observation errors and represent model errors. Furthermore, we explored the advantages of accounting for the spatial distribution of forcing and channel routing processes within the catchment by implementing and comparing lumped and semi-distributed model setups. Flood prediction is improved by SM-DA (Figure), with a 30% reduction of the average root-mean-squared difference of the ensemble prediction, a 20% reduction of the false alarm ratio and a 40% increase of the ensemble mean Nash-Sutcliffe efficiency. SM-DA skill does not significantly change with different observation error assumptions, but the skill strongly depends on the observational bias correction technique used, and more importantly, on the performance of the open-loop model before assimilation. Our findings imply that proper

Flood Response System—A Case Study

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Yogesh Kumar Singh

2017-06-01

Full Text Available Flood Response System (FRS is a network-enabled solution developed using open-source software. The system has query based flood damage assessment modules with outputs in the form of spatial maps and statistical databases. FRS effectively facilitates the management of post-disaster activities caused due to flood, like displaying spatial maps of area affected, inundated roads, etc., and maintains a steady flow of information at all levels with different access rights depending upon the criticality of the information. It is designed to facilitate users in managing information related to flooding during critical flood seasons and analyzing the extent of damage. The inputs to FRS are provided using two components: (1 a semi-automated application developed indigenously, to delineate inundated areas for Near-Real Time Flood Monitoring using Active Microwave Remote Sensing data and (2 a two-dimensional (2D hydrodynamic river model generated outputs for water depth and velocity in flooded areas for an embankment breach scenario. The 2D Hydrodynamic model, CCHE2D (Center for Computational Hydroscience and Engineering Two-Dimensional model, was used to simulate an area of 600 km2 in the flood-prone zone of the Brahmaputra basin. The resultant inundated area from the model was found to be 85% accurate when validated with post-flood optical satellite data.

Satellite-based detection of global urban heat-island temperature influence

Science.gov (United States)

Gallo, K.P.; Adegoke, Jimmy O.; Owen, T.W.; Elvidge, C.D.

2002-01-01

This study utilizes a satellite-based methodology to assess the urban heat-island influence during warm season months for over 4400 stations included in the Global Historical Climatology Network of climate stations. The methodology includes local and regional satellite retrievals of an indicator of the presence green photosynthetically active vegetation at and around the stations. The difference in local and regional samples of the normalized difference vegetation index (NDVI) is used to estimate differences in mean air temperature. Stations classified as urban averaged 0.90??C (N. Hemisphere) and 0.92??C (S. Hemisphere) warmer than the surrounding environment on the basis of the NDVI-derived temperature estimates. Additionally, stations classified as rural averaged 0.19??C (N. Hemisphere) and 0.16??C (S. Hemisphere) warmer than the surrounding environment. The NDVI-derived temperature estimates were found to be in reasonable agreement with temperature differences observed between climate stations. The results suggest that satellite-derived data sets can be used to estimate the urban heat-island temperature influence on a global basis and that a more detailed analysis of rural stations and their surrounding environment may be necessary to assure that temperature trends derived from assumed rural environments are not influenced by changes in land use/land cover. Copyright 2002 by the American Geophysical Union.

Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches

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Seongmun Sim

2018-04-01

Full Text Available Even though deicing or airframe coating technologies continue to develop, aircraft icing is still one of the critical threats to aviation. While the detection of potential icing clouds has been conducted using geostationary satellite data in the US and Europe, there is not yet a robust model that detects potential icing areas in East Asia. In this study, we proposed machine-learning-based icing detection models using data from two geostationary satellites—the Communication, Ocean, and Meteorological Satellite (COMS Meteorological Imager (MI and the Himawari-8 Advanced Himawari Imager (AHI—over Northeast Asia. Two machine learning techniques—random forest (RF and multinomial log-linear (MLL models—were evaluated with quality-controlled pilot reports (PIREPs as the reference data. The machine-learning-based models were compared to the existing models through five-fold cross-validation. The RF model for COMS MI produced the best performance, resulting in a mean probability of detection (POD of 81.8%, a mean overall accuracy (OA of 82.1%, and mean true skill statistics (TSS of 64.0%. One of the existing models, flight icing threat (FIT, produced relatively poor performance, providing a mean POD of 36.4%, a mean OA of 61.0, and a mean TSS of 9.7%. The Himawari-8 based models also produced performance comparable to the COMS models. However, it should be noted that very limited PIREP reference data were available especially for the Himawari-8 models, which requires further evaluation in the future with more reference data. The spatio-temporal patterns of the icing areas detected using the developed models were also visually examined using time-series satellite data.

Satellite-detected fluorescence reveals global physiology of ocean phytoplankton

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M. J. Behrenfeld

2009-05-01

Full Text Available Phytoplankton photosynthesis links global ocean biology and climate-driven fluctuations in the physical environment. These interactions are largely expressed through changes in phytoplankton physiology, but physiological status has proven extremely challenging to characterize globally. Phytoplankton fluorescence does provide a rich source of physiological information long exploited in laboratory and field studies, and is now observed from space. Here we evaluate the physiological underpinnings of global variations in satellite-based phytoplankton chlorophyll fluorescence. The three dominant factors influencing fluorescence distributions are chlorophyll concentration, pigment packaging effects on light absorption, and light-dependent energy-quenching processes. After accounting for these three factors, resultant global distributions of quenching-corrected fluorescence quantum yields reveal a striking consistency with anticipated patterns of iron availability. High fluorescence quantum yields are typically found in low iron waters, while low quantum yields dominate regions where other environmental factors are most limiting to phytoplankton growth. Specific properties of photosynthetic membranes are discussed that provide a mechanistic view linking iron stress to satellite-detected fluorescence. Our results present satellite-based fluorescence as a valuable tool for evaluating nutrient stress predictions in ocean ecosystem models and give the first synoptic observational evidence that iron plays an important role in seasonal phytoplankton dynamics of the Indian Ocean. Satellite fluorescence may also provide a path for monitoring climate-phytoplankton physiology interactions and improving descriptions of phytoplankton light use efficiencies in ocean productivity models.

Flood forecasting and early warning system for Dungun River Basin

International Nuclear Information System (INIS)

Hafiz, I; Sidek, L M; Basri, H; Fukami, K; Hanapi, M N; Livia, L; Nor, M D

2013-01-01

Floods can bring such disasters to the affected dweller due to loss of properties, crops and even deaths. The damages to properties and crops by the severe flooding are occurred due to the increase in the economic value of the properties as well as the extent of the flood. Flood forecasting and warning system is one of the examples of the non-structural measures which can give early warning to the affected people. People who live near the flood-prone areas will be warned so that they can evacuate themselves and their belongings before the arrival of the flood. This can considerably reduce flood loss and damage and above all, the loss of human lives. Integrated Flood Analysis System (IFAS) model is a runoff analysis model converting rainfall into runoff for a given river basin. The simulation can be done using either ground or satellite-based rainfall to produce calculated discharge within the river. The calculated discharge is used to generate the flood inundation map within the catchment area for the selected flood event using Infowork RS.

Landslide detection using very high-resolution satellite imageries

Science.gov (United States)

Suga, Yuzo; Konishi, Tomohisa

2012-10-01

The heavy rain induced by the 12th typhoon caused landslide disaster at Kii Peninsula in the middle part of Japan. We propose a quick response method for landslide disaster mapping using very high resolution (VHR) satellite imageries. Especially, Synthetic Aperture Radar (SAR) is effective because it has the capability of all weather and day/night observation. In this study, multi-temporal COSMO-SkyMed imageries were used to detect the landslide areas. It was difficult to detect the landslide areas using only backscatter change pattern derived from pre- and post-disaster COSMOSkyMed imageries. Thus, the authors adopted a correlation analysis which the moving window was selected for the correlation coefficient calculation. Low value of the correlation coefficient reflects land cover change between pre- and post-disaster imageries. This analysis is effective for the detection of landslides using SAR data. The detected landslide areas were compared with the area detected by EROS-B high resolution optical image. In addition, we have developed 3D viewing system for geospatial visualizing of the damaged area using these satellite image data with digital elevation model. The 3D viewing system has the performance of geographic measurement with respect to elevation height, area and volume calculation, and cross section drawing including landscape viewing and image layer construction using a mobile personal computer with interactive operation. As the result, it was verified that a quick response for the detection of landslide disaster at the initial stage could be effectively performed using optical and SAR very high resolution satellite data by means of 3D viewing system.

Using Information From Prior Satellite Scans to Improve Cloud Detection Near the Day-Night Terminator

Science.gov (United States)

Yost, Christopher R.; Minnis, Patrick; Trepte, Qing Z.; Palikonda, Rabindra; Ayers, Jeffrey K.; Spangenberg, Doulas A.

2012-01-01

With geostationary satellite data it is possible to have a continuous record of diurnal cycles of cloud properties for a large portion of the globe. Daytime cloud property retrieval algorithms are typically superior to nighttime algorithms because daytime methods utilize measurements of reflected solar radiation. However, reflected solar radiation is difficult to accurately model for high solar zenith angles where the amount of incident radiation is small. Clear and cloudy scenes can exhibit very small differences in reflected radiation and threshold-based cloud detection methods have more difficulty setting the proper thresholds for accurate cloud detection. Because top-of-atmosphere radiances are typically more accurately modeled outside the terminator region, information from previous scans can help guide cloud detection near the terminator. This paper presents an algorithm that uses cloud fraction and clear and cloudy infrared brightness temperatures from previous satellite scan times to improve the performance of a threshold-based cloud mask near the terminator. Comparisons of daytime, nighttime, and terminator cloud fraction derived from Geostationary Operational Environmental Satellite (GOES) radiance measurements show that the algorithm greatly reduces the number of false cloud detections and smoothes the transition from the daytime to the nighttime clod detection algorithm. Comparisons with the Geoscience Laser Altimeter System (GLAS) data show that using this algorithm decreases the number of false detections by approximately 20 percentage points.

Automatic Detection of Clouds and Shadows Using High Resolution Satellite Image Time Series

Science.gov (United States)

Champion, Nicolas

2016-06-01

Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pl

AUTOMATIC DETECTION OF CLOUDS AND SHADOWS USING HIGH RESOLUTION SATELLITE IMAGE TIME SERIES

Directory of Open Access Journals (Sweden)

N. Champion

2016-06-01

Full Text Available Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8

Trellis-coded CPM for satellite-based mobile communications

Science.gov (United States)

Abrishamkar, Farrokh; Biglieri, Ezio

1988-01-01

Digital transmission for satellite-based land mobile communications is discussed. To satisfy the power and bandwidth limitations imposed on such systems, a combination of trellis coding and continuous-phase modulated signals are considered. Some schemes based on this idea are presented, and their performance is analyzed by computer simulation. The results obtained show that a scheme based on directional detection and Viterbi decoding appears promising for practical applications.

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

Science.gov (United States)

Wolff, David B.; Fisher, Brad L.

2011-01-01

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25deg terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU

A non-stationary cost-benefit based bivariate extreme flood estimation approach

Science.gov (United States)

Qi, Wei; Liu, Junguo

2018-02-01

Cost-benefit analysis and flood frequency analysis have been integrated into a comprehensive framework to estimate cost effective design values. However, previous cost-benefit based extreme flood estimation is based on stationary assumptions and analyze dependent flood variables separately. A Non-Stationary Cost-Benefit based bivariate design flood estimation (NSCOBE) approach is developed in this study to investigate influence of non-stationarities in both the dependence of flood variables and the marginal distributions on extreme flood estimation. The dependence is modeled utilizing copula functions. Previous design flood selection criteria are not suitable for NSCOBE since they ignore time changing dependence of flood variables. Therefore, a risk calculation approach is proposed based on non-stationarities in both marginal probability distributions and copula functions. A case study with 54-year observed data is utilized to illustrate the application of NSCOBE. Results show NSCOBE can effectively integrate non-stationarities in both copula functions and marginal distributions into cost-benefit based design flood estimation. It is also found that there is a trade-off between maximum probability of exceedance calculated from copula functions and marginal distributions. This study for the first time provides a new approach towards a better understanding of influence of non-stationarities in both copula functions and marginal distributions on extreme flood estimation, and could be beneficial to cost-benefit based non-stationary bivariate design flood estimation across the world.

DEM-based Approaches for the Identification of Flood Prone Areas

Science.gov (United States)

Samela, Caterina; Manfreda, Salvatore; Nardi, Fernando; Grimaldi, Salvatore; Roth, Giorgio; Sole, Aurelia

2013-04-01

The remarkable number of inundations that caused, in the last decades, thousands of deaths and huge economic losses, testifies the extreme vulnerability of many Countries to the flood hazard. As a matter of fact, human activities are often developed in the floodplains, creating conditions of extremely high risk. Terrain morphology plays an important role in understanding, modelling and analyzing the hydraulic behaviour of flood waves. Research during the last 10 years has shown that the delineation of flood prone areas can be carried out using fast methods that relay on basin geomorphologic features. In fact, the availability of new technologies to measure surface elevation (e.g., GPS, SAR, SAR interferometry, RADAR and LASER altimetry) has given a strong impulse to the development of Digital Elevation Models (DEMs) based approaches. The identification of the dominant topographic controls on the flood inundation process is a critical research question that we try to tackle with a comparative analysis of several techniques. We reviewed four different approaches for the morphological characterization of a river basin with the aim to provide a description of their performances and to identify their range of applicability. In particular, we explored the potential of the following tools. 1) The hydrogeomorphic method proposed by Nardi et al. (2006) which defines the flood prone areas according to the water level in the river network through the hydrogeomorphic theory. 2) The linear binary classifier proposed by Degiorgis et al. (2012) which allows distinguishing flood-prone areas using two features related to the location of the site under exam with respect to the nearest hazard source. The two features, proposed in the study, are the length of the path that hydrologically connects the location under exam to the nearest element of the drainage network and the difference in elevation between the cell under exam and the final point of the same path. 3) The method by

An Agent-Based Model of Evolving Community Flood Risk.

Science.gov (United States)

Tonn, Gina L; Guikema, Seth D

2017-11-17

Although individual behavior plays a major role in community flood risk, traditional flood risk models generally do not capture information on how community policies and individual decisions impact the evolution of flood risk over time. The purpose of this study is to improve the understanding of the temporal aspects of flood risk through a combined analysis of the behavioral, engineering, and physical hazard aspects of flood risk. Additionally, the study aims to develop a new modeling approach for integrating behavior, policy, flood hazards, and engineering interventions. An agent-based model (ABM) is used to analyze the influence of flood protection measures, individual behavior, and the occurrence of floods and near-miss flood events on community flood risk. The ABM focuses on the following decisions and behaviors: dissemination of flood management information, installation of community flood protection, elevation of household mechanical equipment, and elevation of homes. The approach is place based, with a case study area in Fargo, North Dakota, but is focused on generalizable insights. Generally, community mitigation results in reduced future damage, and individual action, including mitigation and movement into and out of high-risk areas, can have a significant influence on community flood risk. The results of this study provide useful insights into the interplay between individual and community actions and how it affects the evolution of flood risk. This study lends insight into priorities for future work, including the development of more in-depth behavioral and decision rules at the individual and community level. © 2017 Society for Risk Analysis.

Estimating floodwater depths from flood inundation maps and topography

Science.gov (United States)

Cohen, Sagy; Brakenridge, G. Robert; Kettner, Albert; Bates, Bradford; Nelson, Jonathan M.; McDonald, Richard R.; Huang, Yu-Fen; Munasinghe, Dinuke; Zhang, Jiaqi

2018-01-01

Information on flood inundation extent is important for understanding societal exposure, water storage volumes, flood wave attenuation, future flood hazard, and other variables. A number of organizations now provide flood inundation maps based on satellite remote sensing. These data products can efficiently and accurately provide the areal extent of a flood event, but do not provide floodwater depth, an important attribute for first responders and damage assessment. Here we present a new methodology and a GIS-based tool, the Floodwater Depth Estimation Tool (FwDET), for estimating floodwater depth based solely on an inundation map and a digital elevation model (DEM). We compare the FwDET results against water depth maps derived from hydraulic simulation of two flood events, a large-scale event for which we use medium resolution input layer (10 m) and a small-scale event for which we use a high-resolution (LiDAR; 1 m) input. Further testing is performed for two inundation maps with a number of challenging features that include a narrow valley, a large reservoir, and an urban setting. The results show FwDET can accurately calculate floodwater depth for diverse flooding scenarios but also leads to considerable bias in locations where the inundation extent does not align well with the DEM. In these locations, manual adjustment or higher spatial resolution input is required.

Hot wet spots of Swiss buildings - detecting clusters of flood exposure

Science.gov (United States)

Röthlisberger, Veronika; Zischg, Andreas; Keiler, Margreth

2016-04-01

Where are the hotspots of flood exposure in Switzerland? There is no single answer but rather a wide range of findings depending on the databases and methods used. In principle, the analysis of flood exposure is the overlay of two spatial datasets, one on flood hazard and one on assets, e.g. buildings. The presented study aims to test a new developed approach which is based on public available Swiss data. On the hazard side, these are two different types of flood hazard maps each representing a similar return period beyond the dimensioning of structural protection systems. When it comes to assets we use nationwide harmonized data on building, namely a complete dataset of building polygons to which we assign features as volume, residents and monetary value. For the latter we apply findings of multivariate analyses of insurance data. By overlaying building polygons with the flood hazard map we identify the exposed buildings. We analyse the resulting spatial distribution of flood exposure at different levels of scales (local to regional) using administrative units (e.g. municipalities) but also artificial grids with a corresponding size (e.g. 5 000 m). The presentation focuses on the identification of hotspots highlighting the influence of the applied data and methods, e.g. local scan statistics testing intensities within and without potential clusters or log relative exposure surfaces based on kernel intensity estimates. We find a major difference of identified hotspots between absolute values and normalized values of exposure. Whereas the hotspots of flood exposure in absolute figures mirrors the underlying distribution of buildings, the hotspots of flood exposure ratios show very different pictures. We conclude that findings on flood exposure vary depending on the data and moreover the methods used and therefore need to be communicated carefully and appropriate to different stakeholders who may use the information for decision making on flood risk management.

MONITORING THE URBAN GROWTH OF DHAKA (BANGLADESH BY SATELLITE IMAGERY IN FLOODING RISK MANAGEMENT PERSPECTIVE

Directory of Open Access Journals (Sweden)

G. Bitelli

2014-01-01

The change detection procedure also showed that the flooding in October 2009 affected about 20% (115 out of 591 km2 of the entire study area; furthermore these areas became wetlands and farmland over the next three/four months.

A Comprehensive Training Data Set for the Development of Satellite-Based Volcanic Ash Detection Algorithms

Science.gov (United States)

Schmidl, Marius

2017-04-01

We present a comprehensive training data set covering a large range of atmospheric conditions, including disperse volcanic ash and desert dust layers. These data sets contain all information required for the development of volcanic ash detection algorithms based on artificial neural networks, urgently needed since volcanic ash in the airspace is a major concern of aviation safety authorities. Selected parts of the data are used to train the volcanic ash detection algorithm VADUGS. They contain atmospheric and surface-related quantities as well as the corresponding simulated satellite data for the channels in the infrared spectral range of the SEVIRI instrument on board MSG-2. To get realistic results, ECMWF, IASI-based, and GEOS-Chem data are used to calculate all parameters describing the environment, whereas the software package libRadtran is used to perform radiative transfer simulations returning the brightness temperatures for each atmospheric state. As optical properties are a prerequisite for radiative simulations accounting for aerosol layers, the development also included the computation of optical properties for a set of different aerosol types from different sources. A description of the developed software and the used methods is given, besides an overview of the resulting data sets.

Satellite-Based Sunshine Duration for Europe

Directory of Open Access Journals (Sweden)

Bodo Ahrens

2013-06-01

Full Text Available In this study, two different methods were applied to derive daily and monthly sunshine duration based on high-resolution satellite products provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT Satellite Application Facility on Climate Monitoring using data from Meteosat Second Generation (MSG SEVIRI (Spinning Enhanced Visible and Infrared Imager. The satellite products were either hourly cloud type or hourly surface incoming direct radiation. The satellite sunshine duration estimates were not found to be significantly different using the native 15-minute temporal resolution of SEVIRI. The satellite-based sunshine duration products give additional spatial information over the European continent compared with equivalent in situ-based products. An evaluation of the satellite sunshine duration by product intercomparison and against station measurements was carried out to determine their accuracy. The satellite data were found to be within ±1 h/day compared to high-quality Baseline Surface Radiation Network or surface synoptic observations (SYNOP station measurements. The satellite-based products differ more over the oceans than over land, mainly because of the treatment of fractional clouds in the cloud type-based sunshine duration product. This paper presents the methods used to derive the satellite sunshine duration products and the performance of the different retrievals. The main benefits and disadvantages compared to station-based products are also discussed.

Estimation of flood environmental effects using flood zone mapping techniques in Halilrood Kerman, Iran.

Science.gov (United States)

Boudaghpour, Siamak; Bagheri, Majid; Bagheri, Zahra

2014-01-01

High flood occurrences with large environmental damages have a growing trend in Iran. Dynamic movements of water during a flood cause different environmental damages in geographical areas with different characteristics such as topographic conditions. In general, environmental effects and damages caused by a flood in an area can be investigated from different points of view. The current essay is aiming at detecting environmental effects of flood occurrences in Halilrood catchment area of Kerman province in Iran using flood zone mapping techniques. The intended flood zone map was introduced in four steps. Steps 1 to 3 pave the way to calculate and estimate flood zone map in the understudy area while step 4 determines the estimation of environmental effects of flood occurrence. Based on our studies, wide range of accuracy for estimating the environmental effects of flood occurrence was introduced by using of flood zone mapping techniques. Moreover, it was identified that the existence of Jiroft dam in the study area can decrease flood zone from 260 hectares to 225 hectares and also it can decrease 20% of flood peak intensity. As a result, 14% of flood zone in the study area can be saved environmentally.

Sun and planet detection system for satellites. Sonnen- und Erderfassungsverfahren fuer Satelliten

Energy Technology Data Exchange (ETDEWEB)

Lange, B O; Scheit, A

1980-05-22

The invention refers to a process for the sun and planet detection system for satellites stabilised in three axes and equipped with detection sensors. The purpose of the invention is to describe such a detection system, which makes quick and reliable guiding of the satellite to its final position possible, permits the use of sensors of simple construction and of simple control laws and simple control logic. According to the invention, this problem is solved by having cumulative or alternate steps, depending on the position of the satellite relative to the sun. According to the invention they refer to the position of the sun outside the field of view for the measurement of various components and the simultaneous availability of several component values. It is particularly advantageous if only the absolutely necessary satellite manoeuvres have to be carried out, as this saves fuel and makes it possible to increase the payload or extend the satellite's life. (HWJ).

Spring floods prediction with the use of optical satellite data in Québec

Science.gov (United States)

Roy, A.; Royer, A.; Turcotte, R.

2009-04-01

The Centre d'expertise hydrique du Québec (CEHQ) operates a distributed hydrological model, which integrates a snow model, for the management of dams in the south of Québec. It appears that the estimation of the water quantity of snowmelt in spring remains a variable with a large uncertainty and induces generally to an important error in stream flow simulation. Therefore, the National snow and ice center (NSIDC) produces, from MODIS (Moderate Resolution Imaging Spectroradiometer) data, continuous and homogeneous spatial snow cover (snow/swow-free) data on the whole territory, but with a cloud contamination. This research aims to improve the prediction of spring floods and the estimation of the rate of discharge by integrating snow cover data in the CEHQ's snow model. The study is done on two watersheds: du Nord river watershed (45,8°N) and Aux Écorces river watershed (47,9°N). The snow model used in the study (SPH-AV) is an implementation developed by the CEHQ of the snowmelt model of HYDROLTEL in is hydrological forecast system to simulate the melted water. The melted water estimated is then used as input in the empirical hydrological model MOHYSE to simulate stream flow. MODIS data are considered valid only when the cloud cover on each pixel of the watersheds is less then 30%. A pixel by pixel correction is applied to the snow pack when there is a difference between satellite snow cover and modeled snow cover. In the case of model shows to much snow, a factor is applied on temperatures by iterative process (starting from the last valid MODIS data) to melt the snow. In the opposite case, the snow quantity added to the last valid MODIS data is found by iterative process so that the pixel's snow water equivalent is equal to the nonzero neighbor minimum value. The study shows, through the simulations done on the two watersheds, the interest of the use of snow/snow-free product for the operational update of snow water equivalent with the objective to improve

Mining IP to Domain Name Interactions to Detect DNS Flood Attacks on Recursive DNS Servers

Directory of Open Access Journals (Sweden)

Roberto Alonso

2016-08-01

Full Text Available The Domain Name System (DNS is a critical infrastructure of any network, and, not surprisingly a common target of cybercrime. There are numerous works that analyse higher level DNS traffic to detect anomalies in the DNS or any other network service. By contrast, few efforts have been made to study and protect the recursive DNS level. In this paper, we introduce a novel abstraction of the recursive DNS traffic to detect a flooding attack, a kind of Distributed Denial of Service (DDoS. The crux of our abstraction lies on a simple observation: Recursive DNS queries, from IP addresses to domain names, form social groups; hence, a DDoS attack should result in drastic changes on DNS social structure. We have built an anomaly-based detection mechanism, which, given a time window of DNS usage, makes use of features that attempt to capture the DNS social structure, including a heuristic that estimates group composition. Our detection mechanism has been successfully validated (in a simulated and controlled setting and with it the suitability of our abstraction to detect flooding attacks. To the best of our knowledge, this is the first time that work is successful in using this abstraction to detect these kinds of attacks at the recursive level. Before concluding the paper, we motivate further research directions considering this new abstraction, so we have designed and tested two additional experiments which exhibit promising results to detect other types of anomalies in recursive DNS servers.

Mining IP to Domain Name Interactions to Detect DNS Flood Attacks on Recursive DNS Servers.

Science.gov (United States)

Alonso, Roberto; Monroy, Raúl; Trejo, Luis A

2016-08-17

The Domain Name System (DNS) is a critical infrastructure of any network, and, not surprisingly a common target of cybercrime. There are numerous works that analyse higher level DNS traffic to detect anomalies in the DNS or any other network service. By contrast, few efforts have been made to study and protect the recursive DNS level. In this paper, we introduce a novel abstraction of the recursive DNS traffic to detect a flooding attack, a kind of Distributed Denial of Service (DDoS). The crux of our abstraction lies on a simple observation: Recursive DNS queries, from IP addresses to domain names, form social groups; hence, a DDoS attack should result in drastic changes on DNS social structure. We have built an anomaly-based detection mechanism, which, given a time window of DNS usage, makes use of features that attempt to capture the DNS social structure, including a heuristic that estimates group composition. Our detection mechanism has been successfully validated (in a simulated and controlled setting) and with it the suitability of our abstraction to detect flooding attacks. To the best of our knowledge, this is the first time that work is successful in using this abstraction to detect these kinds of attacks at the recursive level. Before concluding the paper, we motivate further research directions considering this new abstraction, so we have designed and tested two additional experiments which exhibit promising results to detect other types of anomalies in recursive DNS servers.

Base Flood Elevation

Data.gov (United States)

Earth Data Analysis Center, University of New Mexico — The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision...

Sustainability-Based Flood Hazard Mapping of the Swannanoa River Watershed

Directory of Open Access Journals (Sweden)

Ebrahim Ahmadisharaf

2017-09-01

Full Text Available An integrated framework is presented for sustainability-based flood hazard mapping of the Swannanoa River watershed in the state of North Carolina, U.S. The framework uses a hydrologic model for rainfall–runoff transformation, a two-dimensional unsteady hydraulic model flood simulation and a GIS-based multi-criteria decision-making technique for flood hazard mapping. Economic, social, and environmental flood hazards are taken into account. The importance of each hazard is quantified through a survey to the experts. Utilizing the proposed framework, sustainability-based flood hazard mapping is performed for the 100-year design event. As a result, the overall flood hazard is provided in each geographic location. The sensitivity of the overall hazard with respect to the weights of the three hazard components were also investigated. While the conventional flood management approach is to assess the environmental impacts of mitigation measures after a set of feasible options are selected, the presented framework incorporates the environmental impacts into the analysis concurrently with the economic and social influences. Thereby, it provides a more sustainable perspective of flood management and can greatly help the decision makers to make better-informed decisions by clearly understanding the impacts of flooding on economy, society and environment.

A Cascading Storm-Flood-Landslide Guidance System: Development and Application in China

Science.gov (United States)

Zeng, Ziyue; Tang, Guoqiang; Long, Di; Ma, Meihong; Hong, Yang

2016-04-01

Flash floods and landslides, triggered by storms, often interact and cause cascading effects on human lives and property. Satellite remote sensing data has significant potential use in analysis of these natural hazards. As one of the regions continuously affected by severe flash floods and landslides, Yunnan Province, located in Southwest China, has a complex mountainous hydrometeorology and suffers from frequent heavy rainfalls from May through to late September. Taking Yunnan as a test-bed, this study proposed a Cascading Storm-Flood-Landslide Guidance System to progressively analysis and evaluate the risk of the multi-hazards based on multisource satellite remote sensing data. First, three standardized rainfall amounts (average daily amount in flood seasons, maximum 1h and maximum 6h amount) from the products of Topical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) were used as rainfall indicators to derive the StorM Hazard Index (SMHI). In this process, an integrated approach of the Analytic Hierarchy Process (AHP) and the Information-Entropy theory was adopted to determine the weight of each indicator. Then, land cover and vegetation cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) products, soil type from the Harmonized World Soil Database (HWSD) soil map, and slope from the Shuttle Radar Topography Mission (SRTM) data were add as semi-static geo-topographical indicators to derive the Flash Flood Hazard Index (FFHI). Furthermore, three more relevant landslide-controlling indicators, including elevation, slope angle and soil text were involved to derive the LandSlide Hazard Index (LSHI). Further inclusion of GDP, population and prevention measures as vulnerability indicators enabled to consecutively predict the risk of storm to flash flood and landslide, respectively. Consequently, the spatial patterns of the hazard indices show that the southeast of Yunnan has more possibility to encounter with storms

Rapid Flood Map Generation from Spaceborne SAR Observations

Science.gov (United States)

Yun, S. H.; Liang, C.; Manipon, G.; Jung, J.; Gurrola, E. M.; Owen, S. E.; Hua, H.; Agram, P. S.; Webb, F.; Sacco, G. F.; Rosen, P. A.; Simons, M.

2016-12-01

The Advanced Rapid Imaging and Analysis (ARIA) team has responded to the January 2016 US Midwest Floods along the Mississippi River. Daily teleconferences with FEMA, NOAA, NGA, and USGS, provided information on precipitation and flood crest migration, based on which we coordinated with the Japanese Aerospace Exploration Agency (JAXA) through NASA headquarters for JAXA's ALOS-2 timely tasking over two paths. We produced flood extent maps using ALOS-2 SM3 mode Level 1.5 data that were provided through the International Charter and stored at the US Geological Survey's Hazards Data Distribution System (HDDS) archive. On January 6, the first four frames (70 km x 240 km) were acquired, which included the City of Memphis. We registered post-event SAR images to pre-event images, applied radiometric calibration, took a logarithm of the ratio of the two images. Two thresholds were applied to represent flooded areas that became open water (colored in blue) and flooded areas with tall vegetation (colored in red). The second path was acquired on January 11 further down along the Mississippi River. Seven frames (70 km x 420 km) were acquired and flood maps were created in the similar fashion. The maps were delivered to the FEMA as well as posted on ARIA's public website. The FEMA stated that SAR provides inspection priority for optical imagery and ground response. The ALOS-2 data and the products have been a very important source of information during this response as the flood crest has moved down stream. The SAR data continue to be an important resource during times when optical observations are often not useful. In close collaboration with FEMA and USGS, we also work on other flood events including June 2016 China Floods using European Space Agency's (ESA's) Sentienl-1 data, to produce flood extent maps and identify algorithmic needs and ARIA system's requirements to automate and rapidly produce and deliver flood maps for future events. With the addition of Sentinel-1B

Linking flood peak, flood volume and inundation extent: a DEM-based approach

Science.gov (United States)

Rebolho, Cédric; Furusho-Percot, Carina; Blaquière, Simon; Brettschneider, Marco; Andréassian, Vazken

2017-04-01

Traditionally, flood inundation maps are computed based on the Shallow Water Equations (SWE) in one or two dimensions, with various simplifications that have proved to give good results. However, the complexity of the SWEs often requires a numerical resolution which can need long computing time, as well as detailed cross section data: this often results in restricting these models to rather small areas abundant with high quality data. This, along with the necessity for fast inundation mapping, are the reason why rapid inundation models are being designed, working for (almost) any river with a minimum amount of data and, above all, easily available data. Our model tries to follow this path by using a 100m DEM over France from which are extracted a drainage network and the associated drainage areas. It is based on two pre-existing methods: (1) SHYREG (Arnaud et al.,2013), a regionalized approach used to calculate the 2-year and 10-year flood quantiles (used as approximated bankfull flow and maximum discharge, respectively) for each river pixel of the DEM (below a 10 000 km2 drainage area) and (2) SOCOSE (Mailhol,1980), which gives, amongst other things, an empirical formula of a characteristic flood duration (for each pixel) based on catchment area, average precipitation and temperature. An overflow volume for each river pixel is extracted from a triangular shaped synthetic hydrograph designed with SHYREG quantiles and SOCOSE flood duration. The volume is then spread from downstream to upstream one river pixel at a time. When the entire hydrographic network is processed, the model stops and generates a map of potential inundation area associated with the 10-year flood quantile. Our model can also be calibrated using past-events inundation maps by adjusting two parameters, one which modifies the overflow duration, and the other, equivalent to a minimum drainage area for river pixels to be flooded. Thus, in calibration on a sample of 42 basins, the first draft of the

Development of Integrated Flood Analysis System for Improving Flood Mitigation Capabilities in Korea

Science.gov (United States)

Moon, Young-Il; Kim, Jong-suk

2016-04-01

Recently, the needs of people are growing for a more safety life and secure homeland from unexpected natural disasters. Flood damages have been recorded every year and those damages are greater than the annual average of 2 trillion won since 2000 in Korea. It has been increased in casualties and property damages due to flooding caused by hydrometeorlogical extremes according to climate change. Although the importance of flooding situation is emerging rapidly, studies related to development of integrated management system for reducing floods are insufficient in Korea. In addition, it is difficult to effectively reduce floods without developing integrated operation system taking into account of sewage pipe network configuration with the river level. Since the floods result in increasing damages to infrastructure, as well as life and property, structural and non-structural measures should be urgently established in order to effectively reduce the flood. Therefore, in this study, we developed an integrated flood analysis system that systematized technology to quantify flood risk and flood forecasting for supporting synthetic decision-making through real-time monitoring and prediction on flash rain or short-term rainfall by using radar and satellite information in Korea. Keywords: Flooding, Integrated flood analysis system, Rainfall forecasting, Korea Acknowledgments This work was carried out with the support of "Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ011686022015)" Rural Development Administration, Republic of Korea

14 CFR 141.91 - Satellite bases.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 3 2010-01-01 2010-01-01 false Satellite bases. 141.91 Section 141.91... OTHER CERTIFICATED AGENCIES PILOT SCHOOLS Operating Rules § 141.91 Satellite bases. The holder of a... assistant chief instructor is designated for each satellite base, and that assistant chief instructor is...

Advanced flooding-based routing protocols for underwater sensor networks

OpenAIRE

Isufi, E.; Dol, H.; Leus, G.J.T.

2016-01-01

Flooding-based protocols are a reliable solution to deliver packets in underwater sensor networks. However, these protocols potentially involve all the nodes in the forwarding process. Thus, the performance and energy efficiency are not optimal. In this work, we propose some advances of a flooding-based protocol with the goal to improve the performance and the energy efficiency. The first idea considers the node position information in order to reduce the number of relays that may apply flood...

Global Near Real-Time MODIS and Landsat Flood Mapping and Product Delivery

Science.gov (United States)

Policelli, F. S.; Slayback, D. A.; Tokay, M. M.; Brakenridge, G. R.

2014-12-01

Flooding is the most destructive, frequent, and costly natural disaster faced by modern society, and is increasing in frequency and damage (deaths, displacements, and financial costs) as populations increase and climate change generates more extreme weather events. When major flooding events occur, the disaster management community needs frequently updated and easily accessible information to better understand the extent of flooding and coordinate response efforts. With funding from NASA's Applied Sciences program, we developed and are now operating a near real-time global flood mapping system to help provide flood extent information within 24-48 hours of events. The principal element of the system applies a water detection algorithm to MODIS imagery, which is processed by the LANCE (Land Atmosphere Near real-time Capability for EOS) system at NASA Goddard within a few hours of satellite overpass. Using imagery from both the Terra (10:30 AM local time overpass) and Aqua (1:30 PM) platforms allows the system to deliver an initial daily assessment of flood extent by late afternoon, and more robust assessments after accumulating cloud-free imagery over several days. Cloud cover is the primary limitation in detecting surface water from MODIS imagery. Other issues include the relatively coarse scale of the MODIS imagery (250 meters) for some events, the difficulty of detecting flood waters in areas with continuous canopy cover, confusion of shadow (cloud or terrain) with water, and accurately identifying detected water as flood as opposed to normal water extent. We are working on improvements to address these limitations. We have also begun delivery of near real time water maps at 30 m resolution from Landsat imagery. Although Landsat is not available daily globally, but only every 8 days if imagery from both operating platforms (Landsat 7 and 8) is accessed, it can provide useful higher resolution data on water extent when a clear acquisition coincides with an active

GSM and web-based flood monitoring system

International Nuclear Information System (INIS)

Pagatpat, J C; Arellano, A C; Gerasta, O J

2015-01-01

The purpose of this project is to develop a local real-time river flood monitoring and warning system for the selected communities near Mandulog River. This study focuses only on the detection and early warning alert system (via website and/or cell phone text messages) that alerts local subscribers of potential flood events. Furthermore, this system is interactive wherein all non-registered subscribers could inquire the actual water level of the desired area location they want to monitor. An estimated time a particular river waterway will overflow is also included in the analyses. The hardware used in the design is split into several parts namely: the water level detector, GSM module, and microcontroller development board. (paper)

Highly Enhanced Risk Management Emergency Satellite

DEFF Research Database (Denmark)

Dalmeir, Michael; Gataullin, Yunir; Indrajit, Agung

HERMES (Highly Enhanced Risk Management Emergency Satellite) is potential European satellite mission for global flood management, being implemented by Technical University Munich and European Space Agency. With its main instrument - a reliable and precise Synthetic Aperture Radar (SAR) antenna...

Satellite Imagery Assisted Road-Based Visual Navigation System

Science.gov (United States)

Volkova, A.; Gibbens, P. W.

2016-06-01

There is a growing demand for unmanned aerial systems as autonomous surveillance, exploration and remote sensing solutions. Among the key concerns for robust operation of these systems is the need to reliably navigate the environment without reliance on global navigation satellite system (GNSS). This is of particular concern in Defence circles, but is also a major safety issue for commercial operations. In these circumstances, the aircraft needs to navigate relying only on information from on-board passive sensors such as digital cameras. An autonomous feature-based visual system presented in this work offers a novel integral approach to the modelling and registration of visual features that responds to the specific needs of the navigation system. It detects visual features from Google Earth* build a feature database. The same algorithm then detects features in an on-board cameras video stream. On one level this serves to localise the vehicle relative to the environment using Simultaneous Localisation and Mapping (SLAM). On a second level it correlates them with the database to localise the vehicle with respect to the inertial frame. The performance of the presented visual navigation system was compared using the satellite imagery from different years. Based on comparison results, an analysis of the effects of seasonal, structural and qualitative changes of the imagery source on the performance of the navigation algorithm is presented. * The algorithm is independent of the source of satellite imagery and another provider can be used

Cross-validation Methodology between Ground and GPM Satellite-based Radar Rainfall Product over Dallas-Fort Worth (DFW) Metroplex

Science.gov (United States)

Chen, H.; Chandrasekar, V.; Biswas, S.

2015-12-01

Over the past two decades, a large number of rainfall products have been developed based on satellite, radar, and/or rain gauge observations. However, to produce optimal rainfall estimation for a given region is still challenging due to the space time variability of rainfall at many scales and the spatial and temporal sampling difference of different rainfall instruments. In order to produce high-resolution rainfall products for urban flash flood applications and improve the weather sensing capability in urban environment, the center for Collaborative Adaptive Sensing of the Atmosphere (CASA), in collaboration with National Weather Service (NWS) and North Central Texas Council of Governments (NCTCOG), has developed an urban radar remote sensing network in DFW Metroplex. DFW is the largest inland metropolitan area in the U.S., that experiences a wide range of natural weather hazards such as flash flood and hailstorms. The DFW urban remote sensing network, centered by the deployment of eight dual-polarization X-band radars and a NWS WSR-88DP radar, is expected to provide impacts-based warning and forecasts for benefit of the public safety and economy. High-resolution quantitative precipitation estimation (QPE) is one of the major goals of the development of this urban test bed. In addition to ground radar-based rainfall estimation, satellite-based rainfall products for this area are also of interest for this study. Typical example is the rainfall rate product produced by the Dual-frequency Precipitation Radar (DPR) onboard Global Precipitation Measurement (GPM) Core Observatory satellite. Therefore, cross-comparison between ground and space-based rainfall estimation is critical to building an optimal regional rainfall system, which can take advantages of the sampling differences of different sensors. This paper presents the real-time high-resolution QPE system developed for DFW urban radar network, which is based upon the combination of S-band WSR-88DP and X

NEAR REAL-TIME AUTOMATIC MARINE VESSEL DETECTION ON OPTICAL SATELLITE IMAGES

Directory of Open Access Journals (Sweden)

G. Máttyus

2013-05-01

Full Text Available Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation. Optical satellite images (OSI can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000 × 16000 pixels image on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.

Near Real-Time Automatic Marine Vessel Detection on Optical Satellite Images

Science.gov (United States)

Máttyus, G.

2013-05-01

Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR) satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI) can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation). Optical satellite images (OSI) can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width) and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000 × 16000 pixels image) on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.

Application research for 4D technology in flood forecasting and evaluation

Science.gov (United States)

Li, Ziwei; Liu, Yutong; Cao, Hongjie

1998-08-01

In order to monitor the region which disaster flood happened frequently in China, satisfy the great need of province governments for high accuracy monitoring and evaluated data for disaster and improve the efficiency for repelling disaster, under the Ninth Five-year National Key Technologies Programme, the method was researched for flood forecasting and evaluation using satellite and aerial remoted sensed image and land monitor data. The effective and practicable flood forecasting and evaluation system was established and DongTing Lake was selected as the test site. Modern Digital photogrammetry, remote sensing and GIS technology was used in this system, the disastrous flood could be forecasted and loss can be evaluated base on '4D' (DEM -- Digital Elevation Model, DOQ -- Digital OrthophotoQuads, DRG -- Digital Raster Graph, DTI -- Digital Thematic Information) disaster background database. The technology of gathering and establishing method for '4D' disaster environment background database, application technology for flood forecasting and evaluation based on '4D' background data and experimental results for DongTing Lake test site were introduced in detail in this paper.

Active and Passive Remote Sensing Data Time Series for Flood Detection and Surface Water Mapping

Science.gov (United States)

Bioresita, Filsa; Puissant, Anne; Stumpf, André; Malet, Jean-Philippe

2017-04-01

Split Based Approach (MSBA) is used in order to focus on surface water areas automatically and facilitate the estimation of class models for water and non-water areas. A Finite Mixture Model is employed as the underlying statistical model to produce probabilistic maps. Subsequently, bilateral filtering is applied to take into account spatial neighborhood relationships in the generation of final map. The elimination of shadows effect is performed in a post-processing step. The processing chain is tested on three case studies. The first case is a flood event in central Ireland, the second case is located in Yorkshire county / Great Britain, and the third test case covers a recent flood event in northern Italy. The tests showed that the modified SBA step and the Finite Mixture Models can be applied for the automatic surface water detection in a variety of test cases. An evaluation again Copernicus products derived from very-high resolution imagery was performed, and showed a high overall accuracy and F-measure of the obtained maps. This evaluation also showed that the use of probability maps and bilateral filtering improved the accuracy of classification results significantly. Based on this quantitative evaluation, it is concluded that the processing chain can be applied for flood mapping from Sentinel-1 data. To estimate robust statistical distributions the method requires sufficient surface waters areas in the observed zone and sufficient contrast between surface waters and other land use classes. Ongoing research addresses the fusion of Sentinel-1 and passive remote sensing data (e.g. Sentinel-2) in order to reduce the current shortcomings in the developed processing chain. In this work, fusion is performed at the feature level to better account for the difference image properties of SAR and optical sensors. Further, the processing chain is currently being optimized in terms of calculation time for a further integration as a flood mapping service on the A2S (Alsace Aval

An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring.

Science.gov (United States)

Alirezaie, Marjan; Kiselev, Andrey; Längkvist, Martin; Klügl, Franziska; Loutfi, Amy

2017-11-05

This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment-central Stockholm-in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as "find all regions close to schools and far from the flooded area". The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.

An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring

Directory of Open Access Journals (Sweden)

Marjan Alirezaie

2017-11-01

Full Text Available This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.

Use of NOAA-N satellites for land/water discrimination and flood monitoring

Science.gov (United States)

Tappan, G.; Horvath, N. C.; Doraiswamy, P. C.; Engman, T.; Goss, D. W. (Principal Investigator)

1983-01-01

A tool for monitoring the extent of major floods was developed using data collected by the NOAA-6 advanced very high resolution radiometer (AVHRR). A basic understanding of the spectral returns in AVHRR channels 1 and 2 for water, soil, and vegetation was reached using a large number of NOAA-6 scenes from different seasons and geographic locations. A look-up table classifier was developed based on analysis of the reflective channel relationships for each surface feature. The classifier automatically separated land from water and produced classification maps which were registered for a number of acquisitions, including coverage of a major flood on the Parana River of Argentina.

Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

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Jinyin Ye

2016-01-01

Full Text Available TIGGE (THORPEX International Grand Global Ensemble was a major part of the THORPEX (Observing System Research and Predictability Experiment. It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.

RainyDay: An Online, Open-Source Tool for Physically-based Rainfall and Flood Frequency Analysis

Science.gov (United States)

Wright, D.; Yu, G.; Holman, K. D.

2017-12-01

Flood frequency analysis in ungaged or changing watersheds typically requires rainfall intensity-duration-frequency (IDF) curves combined with hydrologic models. IDF curves only depict point-scale rainfall depth, while true rainstorms exhibit complex spatial and temporal structures. Floods result from these rainfall structures interacting with watershed features such as land cover, soils, and variable antecedent conditions as well as river channel processes. Thus, IDF curves are traditionally combined with a variety of "design storm" assumptions such as area reduction factors and idealized rainfall space-time distributions to translate rainfall depths into inputs that are suitable for flood hydrologic modeling. The impacts of such assumptions are relatively poorly understood. Meanwhile, modern precipitation estimates from gridded weather radar, grid-interpolated rain gages, satellites, and numerical weather models provide more realistic depictions of rainfall space-time structure. Usage of such datasets for rainfall and flood frequency analysis, however, are hindered by relatively short record lengths. We present RainyDay, an open-source stochastic storm transposition (SST) framework for generating large numbers of realistic rainfall "scenarios." SST "lengthens" the rainfall record by temporal resampling and geospatial transposition of observed storms to extract space-time information from regional gridded rainfall data. Relatively short (10-15 year) records of bias-corrected radar rainfall data are sufficient to estimate rainfall and flood events with much longer recurrence intervals including 100-year and 500-year events. We describe the SST methodology as implemented in RainyDay and compare rainfall IDF results from RainyDay to conventional estimates from NOAA Atlas 14. Then, we demonstrate some of the flood frequency analysis properties that are possible when RainyDay is integrated with a distributed hydrologic model, including robust estimation of flood

Near Real-Time Flood Monitoring and Impact Assessment Systems. Chapter 6; [Case Study: 2011 Flooding in Southeast Asia

Science.gov (United States)

Ahamed, Aakash; Bolten, John; Doyle, Colin; Fayne, Jessica

2016-01-01

Floods are the costliest natural disaster, causing approximately 6.8 million deaths in the twentieth century alone. Worldwide economic flood damage estimates in 2012 exceed $19 Billion USD. Extended duration floods also pose longer term threats to food security, water, sanitation, hygiene, and community livelihoods, particularly in developing countries. Projections by the Intergovernmental Panel on Climate Change (IPCC) suggest that precipitation extremes, rainfall intensity, storm intensity, and variability are increasing due to climate change. Increasing hydrologic uncertainty will likely lead to unprecedented extreme flood events. As such, there is a vital need to enhance and further develop traditional techniques used to rapidly assess flooding and extend analytical methods to estimate impacted population and infrastructure. Measuring flood extent in situ is generally impractical, time consuming, and can be inaccurate. Remotely sensed imagery acquired from space-borne and airborne sensors provides a viable platform for consistent and rapid wall-to-wall monitoring of large flood events through time. Terabytes of freely available satellite imagery are made available online each day by NASA, ESA, and other international space research institutions. Advances in cloud computing and data storage technologies allow researchers to leverage these satellite data and apply analytical methods at scale. Repeat-survey earth observations help provide insight about how natural phenomena change through time, including the progression and recession of floodwaters. In recent years, cloud-penetrating radar remote sensing techniques (e.g., Synthetic Aperture Radar) and high temporal resolution imagery platforms (e.g., MODIS and its 1-day return period), along with high performance computing infrastructure, have enabled significant advances in software systems that provide flood warning, assessments, and hazard reduction potential. By incorporating social and economic data

A near real-time satellite-based global drought climate data record

International Nuclear Information System (INIS)

AghaKouchak, Amir; Nakhjiri, Navid

2012-01-01

Reliable drought monitoring requires long-term and continuous precipitation data. High resolution satellite measurements provide valuable precipitation information on a quasi-global scale. However, their short lengths of records limit their applications in drought monitoring. In addition to this limitation, long-term low resolution satellite-based gauge-adjusted data sets such as the Global Precipitation Climatology Project (GPCP) one are not available in near real-time form for timely drought monitoring. This study bridges the gap between low resolution long-term satellite gauge-adjusted data and the emerging high resolution satellite precipitation data sets to create a long-term climate data record of droughts. To accomplish this, a Bayesian correction algorithm is used to combine GPCP data with real-time satellite precipitation data sets for drought monitoring and analysis. The results showed that the combined data sets after the Bayesian correction were a significant improvement compared to the uncorrected data. Furthermore, several recent major droughts such as the 2011 Texas, 2010 Amazon and 2010 Horn of Africa droughts were detected in the combined real-time and long-term satellite observations. This highlights the potential application of satellite precipitation data for regional to global drought monitoring. The final product is a real-time data-driven satellite-based standardized precipitation index that can be used for drought monitoring especially over remote and/or ungauged regions. (letter)

Interdisciplinary Approach for Assessment of Continental River Flood Risk: A Case Study of the Czech Republic

Science.gov (United States)

Ushiyama, Tomoki; Kwak, Youngjoo; Ledvinka, Ondřej; Iwami, Yoichi; Danhelka, Jan

2017-04-01

In this research, GIS-based hydrological model-driven approach produces the distribution of continent-level flood risk based on national-level GIS data. In order to reveal flood hazard, exposure, and vulnerability in a large river basin, the system employs the simplified model such as GFiD2M (Global Flood inundation Depth 2-dimension Model) to calculate the differential inundation depth and the economic loss by pixel-based statistical processing, considering climate and socioeconomic scenarios, the representative concentration pathways emissions and the shared socioeconomic pathways, despite current limitations of data collections and poor data availability. We need new approaches to seek the possibility of its national-scale application, so that the framework can bring (1) improved flood inundation map (i.e., discharge, depth, velocity) using rainfall runoff inundation model, based on the in-situ data (rain-gauge and water level), validated with Earth Observation data, i.e., MODIS, (2) advanced flood forecasting using radar and satellite observed rainfall for national-level operational hydrological observations, (3) potential economic impact with the effect of flood hazard and risk under climate and socioeconomic changes based on rainfall from general circulation model. The preliminary examinations showed the better possibility of a nation-wide application for integrated flood risk management. At the same time, the hazard and risk model were also validated against event-based flood inundation of a national-level flood in the Czech Republic. Within the Czech Republic, although radar rainfall data have been used in operational hydrology for some time, there are also other products capable of warning us about the potential risk of floods. For instance, images from Europe's Sentinel satellites have not been evaluated for their use in Czech hydrology. This research is at the very beginning of a validation and its evaluation, focusing mainly on heavy rainfall and

Cyber Surveillance for Flood Disasters

Directory of Open Access Journals (Sweden)

Shi-Wei Lo

2015-01-01

Full Text Available Regional heavy rainfall is usually caused by the influence of extreme weather conditions. Instant heavy rainfall often results in the flooding of rivers and the neighboring low-lying areas, which is responsible for a large number of casualties and considerable property loss. The existing precipitation forecast systems mostly focus on the analysis and forecast of large-scale areas but do not provide precise instant automatic monitoring and alert feedback for individual river areas and sections. Therefore, in this paper, we propose an easy method to automatically monitor the flood object of a specific area, based on the currently widely used remote cyber surveillance systems and image processing methods, in order to obtain instant flooding and waterlogging event feedback. The intrusion detection mode of these surveillance systems is used in this study, wherein a flood is considered a possible invasion object. Through the detection and verification of flood objects, automatic flood risk-level monitoring of specific individual river segments, as well as the automatic urban inundation detection, has become possible. The proposed method can better meet the practical needs of disaster prevention than the method of large-area forecasting. It also has several other advantages, such as flexibility in location selection, no requirement of a standard water-level ruler, and a relatively large field of view, when compared with the traditional water-level measurements using video screens. The results can offer prompt reference for appropriate disaster warning actions in small areas, making them more accurate and effective.

Case studies of extended model-based flood forecasting: prediction of dike strength and flood impacts

Science.gov (United States)

Stuparu, Dana; Bachmann, Daniel; Bogaard, Tom; Twigt, Daniel; Verkade, Jan; de Bruijn, Karin; de Leeuw, Annemargreet

2017-04-01

Flood forecasts, warning and emergency response are important components in flood risk management. Most flood forecasting systems use models to translate weather predictions to forecasted discharges or water levels. However, this information is often not sufficient for real time decisions. A sound understanding of the reliability of embankments and flood dynamics is needed to react timely and reduce the negative effects of the flood. Where are the weak points in the dike system? When, how much and where the water will flow? When and where is the greatest impact expected? Model-based flood impact forecasting tries to answer these questions by adding new dimensions to the existing forecasting systems by providing forecasted information about: (a) the dike strength during the event (reliability), (b) the flood extent in case of an overflow or a dike failure (flood spread) and (c) the assets at risk (impacts). This work presents three study-cases in which such a set-up is applied. Special features are highlighted. Forecasting of dike strength. The first study-case focusses on the forecast of dike strength in the Netherlands for the river Rhine branches Waal, Nederrijn and IJssel. A so-called reliability transformation is used to translate the predicted water levels at selected dike sections into failure probabilities during a flood event. The reliability of a dike section is defined by fragility curves - a summary of the dike strength conditional to the water level. The reliability information enhances the emergency management and inspections of embankments. Ensemble forecasting. The second study-case shows the setup of a flood impact forecasting system in Dumfries, Scotland. The existing forecasting system is extended with a 2D flood spreading model in combination with the Delft-FIAT impact model. Ensemble forecasts are used to make use of the uncertainty in the precipitation forecasts, which is useful to quantify the certainty of a forecasted flood event. From global

Coupling Analysis of Heat Island Effects, Vegetation Coverage and Urban Flood in Wuhan

Science.gov (United States)

Liu, Y.; Liu, Q.; Fan, W.; Wang, G.

2018-04-01

In this paper, satellite image, remote sensing technique and geographic information system technique are main technical bases. Spectral and other factors comprehensive analysis and visual interpretation are main methods. We use GF-1 and Landsat8 remote sensing satellite image of Wuhan as data source, and from which we extract vegetation distribution, urban heat island relative intensity distribution map and urban flood submergence range. Based on the extracted information, through spatial analysis and regression analysis, we find correlations among heat island effect, vegetation coverage and urban flood. The results show that there is a high degree of overlap between of urban heat island and urban flood. The area of urban heat island has buildings with little vegetation cover, which may be one of the reasons for the local heavy rainstorms. Furthermore, the urban heat island has a negative correlation with vegetation coverage, and the heat island effect can be alleviated by the vegetation to a certain extent. So it is easy to understand that the new industrial zones and commercial areas which under constructions distribute in the city, these land surfaces becoming bare or have low vegetation coverage, can form new heat islands easily.

Flood hazard assessment in areas prone to flash flooding

Science.gov (United States)

Kvočka, Davor; Falconer, Roger A.; Bray, Michaela

2016-04-01

Contemporary climate projections suggest that there will be an increase in the occurrence of high-intensity rainfall events in the future. These precipitation extremes are usually the main cause for the emergence of extreme flooding, such as flash flooding. Flash floods are among the most unpredictable, violent and fatal natural hazards in the world. Furthermore, it is expected that flash flooding will occur even more frequently in the future due to more frequent development of extreme weather events, which will greatly increase the danger to people caused by flash flooding. This being the case, there will be a need for high resolution flood hazard maps in areas susceptible to flash flooding. This study investigates what type of flood hazard assessment methods should be used for assessing the flood hazard to people caused by flash flooding. Two different types of flood hazard assessment methods were tested: (i) a widely used method based on an empirical analysis, and (ii) a new, physically based and experimentally calibrated method. Two flash flood events were considered herein, namely: the 2004 Boscastle flash flood and the 2007 Železniki flash flood. The results obtained in this study suggest that in the areas susceptible to extreme flooding, the flood hazard assessment should be conducted using methods based on a mechanics-based analysis. In comparison to standard flood hazard assessment methods, these physically based methods: (i) take into account all of the physical forces, which act on a human body in floodwater, (ii) successfully adapt to abrupt changes in the flow regime, which often occur for flash flood events, and (iii) rapidly assess a flood hazard index in a relatively short period of time.

A Global Drought and Flood Catalogue for the past 100 years

Science.gov (United States)

Sheffield, J.; He, X.; Peng, L.; Pan, M.; Fisher, C. K.; Wood, E. F.

2017-12-01

Extreme hydrological events cause the most impacts of natural hazards globally, impacting on a wide range of sectors including, most prominently, agriculture, food security and water availability and quality, but also on energy production, forestry, health, transportation and fisheries. Understanding how floods and droughts intersect, and have changed in the past provides the basis for understanding current risk and how it may change in the future. To do this requires an understanding of the mechanisms associated with events and therefore their predictability, attribution of long-term changes in risk, and quantification of projections of changes in the future. Of key importance are long-term records of relevant variables so that risk can be quantified more accurately, given the growing acknowledgement that risk is not stationary under long-term climate variability and climate change. To address this, we develop a catalogue of drought and flood events based on land surface and hydrodynamic modeling, forced by a hybrid meteorological dataset that draws from the continuity and coverage of reanalysis, and satellite datasets, merged with global gauge databases. The meteorological dataset is corrected for temporal inhomogeneities, spurious trends and variable inter-dependencies to ensure long-term consistency, as well as realistic representation of short-term variability and extremes. The VIC land surface model is run for the past 100 years at 0.25-degree resolution for global land areas. The VIC runoff is then used to drive the CaMa-Flood hydrodynamic model to obtain information on flood inundation risk. The model outputs are compared to satellite based estimates of flood and drought conditions and the observational flood record. The data are analyzed in terms of the spatio-temporal characteristics of large-scale flood and drought events with a particular focus on characterizing the long-term variability in risk. Significant changes in risk occur on multi-decadal time

Monitoring and Mapping the Hurricane Harvey Flooding in Houston, Texas.

Science.gov (United States)

Balaji Bhaskar, M. S.

2017-12-01

Monitoring and Mapping the Hurricane Harvey Flooding in Houston, Texas.Urban flooding is a hazard that causes major destruction and loss of life. High intense precipitation events have increased significantly in Houston, Texas in recent years resulting in frequent river and bayou flooding. Many of the historical storm events such as Allison, Rita and Ike have caused several billion dollars in losses for the Houston-Galveston Region. A category 4 Hurricane Harvey made landfall on South Texas resulting in heavy precipitation from Aug 25 to 29 of 2017. About 1 trillion gallons of water fell across Harris County over a 4-day period. This amount of water covers Harris County's 1,800 square miles with an average of 33 inches of water. The long rain event resulted in an average 40inch rainfall across the area in several rain gauges and the maximum rainfall of 49.6 inches was recorded near Clear Creek. The objectives of our study are to 1) Process the Geographic Information System (GIS) and satellite data from the pre and post Hurricane Harvey event in Houston, Texas and 2) Analyze the satellite imagery to map the nature and pattern of the flooding in Houston-Galveston Region. The GIS data of the study area was downloaded and processed from the various publicly available resources such as Houston Galveston Area Council (HGAC), Texas Commission of Environmental Quality (TCEQ) and Texas Natural Resource Information Systems (TNRIS). The satellite data collected soon after the Harvey flooding event were downloaded and processed using the ERDAS image processing software. The flood plain areas surrounding the Brazos River, Buffalo Bayou and the Addicks Barker reservoirs showed severe inundation. The different watershed areas affected by the catastrophic flooding in the wake of Hurricane Harvey were mapped and compared with the pre flooding event.

Socio-economic Impact Analysis for Near Real-Time Flood Detection in the Lower Mekong River Basin

Science.gov (United States)

Oddo, P.; Ahamed, A.; Bolten, J. D.

2017-12-01

Flood events pose a severe threat to communities in the Lower Mekong River Basin. The combination of population growth, urbanization, and economic development exacerbate the impacts of these flood events. Flood damage assessments are frequently used to quantify the economic losses in the wake of storms. These assessments are critical for understanding the effects of flooding on the local population, and for informing decision-makers about future risks. Remote sensing systems provide a valuable tool for monitoring flood conditions and assessing their severity more rapidly than traditional post-event evaluations. The frequency and severity of extreme flood events are projected to increase, further illustrating the need for improved flood monitoring and impact analysis. In this study we implement a socio-economic damage model into a decision support tool with near real-time flood detection capabilities (NASA's Project Mekong). Surface water extent for current and historical floods is found using multispectral Moderate-resolution Imaging Spectroradiometer (MODIS) 250-meter imagery and the spectral Normalized Difference Vegetation Index (NDVI) signatures of permanent water bodies (MOD44W). Direct and indirect damages to populations, infrastructure, and agriculture are assessed using the 2011 Southeast Asian flood as a case study. Improved land cover and flood depth assessments result in a more refined understanding of losses throughout the Mekong River Basin. Results suggest that rapid initial estimates of flood impacts can provide valuable information to governments, international agencies, and disaster responders in the wake of extreme flood events.

Ranking coastal flood protection designs from engineered to nature-based

NARCIS (Netherlands)

Nat, van der A.; Vellinga, P.; Leemans, R.; Slobbe, van E.

2016-01-01

Compared to traditional hard engineering, nature-based flood protection can be more cost effective, use up less raw materials, increase system adaptability and present opportunities to improve ecosystem functioning. However, high flood safety standards cause the need to combine nature-based

Flash floods warning technique based on wireless communication networks data

Science.gov (United States)

David, Noam; Alpert, Pinhas; Messer, Hagit

2010-05-01

Flash floods can occur throughout or subsequent to rainfall events, particularly in cases where the precipitation is of high-intensity. Unfortunately, each year these floods cause severe property damage and heavy casualties. At present, there are no sufficient real time flash flood warning facilities found to cope with this phenomenon. Here we show the tremendous potential of flash floods advanced warning based on precipitation measurements of commercial microwave links. As was recently shown, wireless communication networks supply high resolution precipitation measurements at ground level while often being situated in flood prone areas, covering large parts of these hazardous regions. We present the flash flood warning potential of the wireless communication system for two different cases when floods occurred at the Judean desert and at the northern Negev in Israel. In both cases, an advanced warning regarding the hazard could have been announced based on this system. • This research was supported by THE ISRAEL SCIENCE FOUNDATION (grant No. 173/08). This work was also supported by a grant from the Yeshaya Horowitz Association, Jerusalem. Additional support was given by the PROCEMA-BMBF project and by the GLOWA-JR BMBF project.

Social media for disaster response during floods

Science.gov (United States)

Eilander, D.; van de Vries, C.; Baart, F.; van Swol, R.; Wagemaker, J.; van Loenen, A.

2015-12-01

During floods it is difficult to obtain real-time accurate information about the extent and severity of the hazard. This information is very important for disaster risk reduction management and crisis relief organizations. Currently, real-time information is derived from few sources such as field reports, traffic camera's, satellite images and areal images. However, getting a real-time and accurate picture of the situation on the ground remains difficult. At the same time, people affected by natural hazards increasingly share their observations and their needs through digital media. Unlike conventional monitoring systems, Twitter data contains a relatively large number of real-time ground truth observations representing both physical hazard characteristics and hazard impacts. In the city of Jakarta, Indonesia, the intensity of unique flood related tweets during a flood event, peaked at almost 900 tweets per minute during floods in early 2015. Flood events around the world in 2014/2015 yielded large numbers of flood related tweets: from Philippines (85.000) to Pakistan (82.000) to South-Korea (50.000) to Detroit (20.000). The challenge here is to filter out useful content from this cloud of data, validate these observations and convert them to readily usable information. In Jakarta, flood related tweets often contain information about the flood depth. In a pilot we showed that this type of information can be used for real-time mapping of the flood extent by plotting these observations on a Digital Elevation Model. Uncertainties in the observations were taken into account by assigning a probability to each observation indicating its likelihood to be correct based on statistical analysis of the total population of tweets. The resulting flood maps proved to be correct for about 75% of the neighborhoods in Jakarta. Further cross-validation of flood related tweets against (hydro-) meteorological data is to likely improve the skill of the method.

The Use of Geospatial Technologies in Flood Hazard Mapping and Assessment: Case Study from River Evros

Science.gov (United States)

Mentzafou, Angeliki; Markogianni, Vasiliki; Dimitriou, Elias

2017-02-01

Many scientists link climate change to the increase of the extreme weather phenomena frequency, which combined with land use changes often lead to disasters with severe social and economic effects. Especially floods as a consequence of heavy rainfall can put vulnerable human and natural systems such as transboundary wetlands at risk. In order to meet the European Directive 2007/60/EC requirements for the development of flood risk management plans, the flood hazard map of Evros transboundary watershed was produced after a grid-based GIS modelling method that aggregates the main factors related to the development of floods: topography, land use, geology, slope, flow accumulation and rainfall intensity. The verification of this tool was achieved through the comparison between the produced hazard map and the inundation maps derived from the supervised classification of Landsat 5 and 7 satellite imageries of four flood events that took place at Evros delta proximity, a wetland of international importance. The comparison of the modelled output (high and very high flood hazard areas) with the extent of the inundated areas as mapped from the satellite data indicated the satisfactory performance of the model. Furthermore, the vulnerability of each land use against the flood events was examined. Geographically Weighted Regression has also been applied between the final flood hazard map and the major factors in order to ascertain their contribution to flood events. The results accredited the existence of a strong relationship between land uses and flood hazard indicating the flood susceptibility of the lowlands and agricultural land. A dynamic transboundary flood hazard management plan should be developed in order to meet the Flood Directive requirements for adequate and coordinated mitigation practices to reduce flood risk.

Analysis of initial changes in the proteins of soybean root tip under flooding stress using gel-free and gel-based proteomic techniques.

Science.gov (United States)

Yin, Xiaojian; Sakata, Katsumi; Nanjo, Yohei; Komatsu, Setsuko

2014-06-25

Flooding has a severe negative effect on soybean cultivation in the early stages of growth. To obtain a better understanding of the response mechanisms of soybean to flooding stress, initial changes in root tip proteins under flooding were analyzed using two proteomic techniques. Two-day-old soybeans were treated with flooding for 3, 6, 12, and 24h. The weight of soybeans increased during the first 3h of flooding, but root elongation was not observed. Using gel-based and gel-free proteomic techniques, 115 proteins were identified in root tips, of which 9 proteins were commonly detected by both methods. The 71 proteins identified by the gel-free proteomics were analyzed by a hierarchical clustering method based on induction levels during the flooding, and the proteins were divided into 5 clusters. Additional interaction analysis of the proteins revealed that ten proteins belonging to cluster I formed the center of a protein interaction network. mRNA expression analysis of these ten proteins showed that citrate lyase and heat shock protein 70 were down-regulated, whereas calreticulin was up-regulated in initial phase of flooding. These results suggest that flooding stress to soybean induces calcium-related signal transduction, which might play important roles in the early responses to flooding. Flooding has a severe negative effect on soybean cultivation, particularly in the early stages of growth. To better understand the response mechanisms of soybean to the early stages of flooding stress, two proteomic techniques were used. Two-day-old soybeans were treated without or with flooding for 3, 6, 12, and 24h. The fresh weight of soybeans increased during the first 3h of flooding stress, but the growth then slowed and no root elongation was observed. Using gel-based and gel-free proteomic techniques, 115 proteins were identified in root tips, of which 9 proteins were commonly detected by both methods. The 71 proteins identified by the gel-free proteomics were analyzed

Storm-surge flooding on the Yukon-Kuskokwim Delta, Alaska

Science.gov (United States)

Terenzi, John; Ely, Craig R.; Jorgenson, M. Torre

2014-01-01

Coastal regions of Alaska are regularly affected by intense storms of ocean origin, the frequency and intensity of which are expected to increase as a result of global climate change. The Yukon-Kuskokwim Delta (YKD), situated in western Alaska on the eastern edge of the Bering Sea, is one of the largest deltaic systems in North America. Its low relief makes it especially susceptible to storm-driven flood tides and increases in sea level. Little information exists on the extent of flooding caused by storm surges in western Alaska and its effects on salinization, shoreline erosion, permafrost thaw, vegetation, wildlife, and the subsistence-based economy. In this paper, we summarize storm flooding events in the Bering Sea region of western Alaska during 1913 – 2011 and map both the extent of inland flooding caused by autumn storms on the central YKD, using Radarsat-1 and MODIS satellite imagery, and the drift lines, using high-resolution IKONOS satellite imagery and field surveys. The largest storm surges occurred in autumn and were associated with high tides and strong (> 65 km hr-1) southwest winds. Maximum inland extent of flooding from storm surges was 30.3 km in 2005, 27.4 km in 2006, and 32.3 km in 2011, with total flood area covering 47.1%, 32.5%, and 39.4% of the 6730 km2 study area, respectively. Peak stages for the 2005 and 2011 storms were 3.1 m and 3.3 m above mean sea level, respectively—almost as high as the 3.5 m amsl elevation estimated for the largest storm observed (in November 1974). Several historically abandoned village sites lie within the area of inundation of the largest flood events. With projected sea level rise, large storms are expected to become more frequent and cover larger areas, with deleterious effects on freshwater ponds, non-saline habitats, permafrost, and landscapes used by nesting birds and local people.

Developing a Global Database of Historic Flood Events to Support Machine Learning Flood Prediction in Google Earth Engine

Science.gov (United States)

Tellman, B.; Sullivan, J.; Kettner, A.; Brakenridge, G. R.; Slayback, D. A.; Kuhn, C.; Doyle, C.

2016-12-01

There is an increasing need to understand flood vulnerability as the societal and economic effects of flooding increases. Risk models from insurance companies and flood models from hydrologists must be calibrated based on flood observations in order to make future predictions that can improve planning and help societies reduce future disasters. Specifically, to improve these models both traditional methods of flood prediction from physically based models as well as data-driven techniques, such as machine learning, require spatial flood observation to validate model outputs and quantify uncertainty. A key dataset that is missing for flood model validation is a global historical geo-database of flood event extents. Currently, the most advanced database of historical flood extent is hosted and maintained at the Dartmouth Flood Observatory (DFO) that has catalogued 4320 floods (1985-2015) but has only mapped 5% of these floods. We are addressing this data gap by mapping the inventory of floods in the DFO database to create a first-of- its-kind, comprehensive, global and historical geospatial database of flood events. To do so, we combine water detection algorithms on MODIS and Landsat 5,7 and 8 imagery in Google Earth Engine to map discrete flood events. The created database will be available in the Earth Engine Catalogue for download by country, region, or time period. This dataset can be leveraged for new data-driven hydrologic modeling using machine learning algorithms in Earth Engine's highly parallelized computing environment, and we will show examples for New York and Senegal.

A new automatic SAR-based flood mapping application hosted on the European Space Agency's grid processing on demand fast access to imagery environment

Science.gov (United States)

Hostache, Renaud; Chini, Marco; Matgen, Patrick; Giustarini, Laura

2013-04-01

There is a clear need for developing innovative processing chains based on earth observation (EO) data to generate products supporting emergency response and flood management at a global scale. Here an automatic flood mapping application is introduced. The latter is currently hosted on the Grid Processing on Demand (G-POD) Fast Access to Imagery (Faire) environment of the European Space Agency. The main objective of the online application is to deliver flooded areas using both recent and historical acquisitions of SAR data in an operational framework. It is worth mentioning that the method can be applied to both medium and high resolution SAR images. The flood mapping application consists of two main blocks: 1) A set of query tools for selecting the "crisis image" and the optimal corresponding pre-flood "reference image" from the G-POD archive. 2) An algorithm for extracting flooded areas using the previously selected "crisis image" and "reference image". The proposed method is a hybrid methodology, which combines histogram thresholding, region growing and change detection as an approach enabling the automatic, objective and reliable flood extent extraction from SAR images. The method is based on the calibration of a statistical distribution of "open water" backscatter values inferred from SAR images of floods. Change detection with respect to a pre-flood reference image helps reducing over-detection of inundated areas. The algorithms are computationally efficient and operate with minimum data requirements, considering as input data a flood image and a reference image. Stakeholders in flood management and service providers are able to log onto the flood mapping application to get support for the retrieval, from the rolling archive, of the most appropriate pre-flood reference image. Potential users will also be able to apply the implemented flood delineation algorithm. Case studies of several recent high magnitude flooding events (e.g. July 2007 Severn River flood

Flash Flood Detection in Urban Cities Using Ultrasonic and Infrared Sensors

KAUST Repository

Mousa, Mustafa; Zhang, Xiangliang; Claudel, Christian

2016-01-01

Floods are the most common type of natural disaster. Often leading to loss of lives and properties in the thousands yearly. Among these events, urban flash floods are particularly deadly because of the short timescales on which they occur, and because of the population density of cities. Since most flood casualties are caused by a lack of information on the impending flood (type, location, severity), sensing these events is critical to generate accurate and detailed warnings and short term forecasts. However, no dedicated flash flood sensing systems, that could monitor the propagation of flash floods, in real time, currently exist in cities. In the present paper, firstly a new sensing device that can simultaneously monitor urban flash floods and traffic congestion has been presented. This sensing device is based on the combination of ultrasonic range-finding with remote temperature sensing, and can sense both phenomena with a high degree of accuracy, using a combination of L1-regularized reconstruction and artificial neural networks to process measurement data. Secondly, corresponding algorithms have been implemented on a low-power wireless sensor platform, and their performance in water level estimation in a 6 months test involving four different sensors is illustrated. The results demonstrate that urban water levels can be reliably estimated with error less than 2 cm, and that the preprocessing and machine learning schemes can run in real-time on currently available wireless sensor platforms.

Flash Flood Detection in Urban Cities Using Ultrasonic and Infrared Sensors

KAUST Repository

Mousa, Mustafa

2016-07-19

Floods are the most common type of natural disaster. Often leading to loss of lives and properties in the thousands yearly. Among these events, urban flash floods are particularly deadly because of the short timescales on which they occur, and because of the population density of cities. Since most flood casualties are caused by a lack of information on the impending flood (type, location, severity), sensing these events is critical to generate accurate and detailed warnings and short term forecasts. However, no dedicated flash flood sensing systems, that could monitor the propagation of flash floods, in real time, currently exist in cities. In the present paper, firstly a new sensing device that can simultaneously monitor urban flash floods and traffic congestion has been presented. This sensing device is based on the combination of ultrasonic range-finding with remote temperature sensing, and can sense both phenomena with a high degree of accuracy, using a combination of L1-regularized reconstruction and artificial neural networks to process measurement data. Secondly, corresponding algorithms have been implemented on a low-power wireless sensor platform, and their performance in water level estimation in a 6 months test involving four different sensors is illustrated. The results demonstrate that urban water levels can be reliably estimated with error less than 2 cm, and that the preprocessing and machine learning schemes can run in real-time on currently available wireless sensor platforms.

Flood protection structure detection with Lidar: examples on French Mediterranean rivers and coastal areas

Directory of Open Access Journals (Sweden)

Trmal Céline

2016-01-01

Full Text Available This paper aims at presenting different topographic analysis conducted with GIS software in order to detect flood protection structures, natural or artificial, in river floodplains but also in coastal zones. Those computations are relevant because of the availability of high-resolution lidar digital terrain model (DTM. An automatic detection permits to map the footprint of those structures. Then detailed mapping of structure crest is achieved by implementing a least cost path analysis on DTM but also on other terrain aspects such as the curvature. On coastal zones, the analysis is going further by identifying flood protected areas and the level of protection regarding sea level. This article is illustrated by examples on French Mediterranean rivers and coastal areas.

Flood Risk Assessment in Urban Areas Based on Spatial Analytics and Social Factors

Directory of Open Access Journals (Sweden)

Costas Armenakis

2017-11-01

Full Text Available Flood maps alone are not sufficient to determine and assess the risks to people, property, infrastructure, and services due to a flood event. Simply put, the risk is almost zero to minimum if the flooded region is “empty” (i.e., unpopulated, has not properties, no industry, no infrastructure, and no socio-economic activity. High spatial resolution Earth Observation (EO data can contribute to the generation and updating of flood risk maps based on several aspects including population, economic development, and critical infrastructure, which can enhance a city’s flood mitigation and preparedness planning. In this case study for the Don River watershed, Toronto, the flood risk is determined and flood risk index maps are generated by implementing a methodology for estimating risk based on the geographic coverage of the flood hazard, vulnerability of people, and the exposure of large building structures to flood water. Specifically, the spatial flood risk index maps have been generated through analytical spatial modeling which takes into account the areas in which a flood hazard is expected to occur, the terrain’s morphological characteristics, socio-economic parameters based on demographic data, and the density of large building complexes. Generated flood risk maps are verified through visual inspection with 3D city flood maps. Findings illustrate that areas of higher flood risk coincide with areas of high flood hazard and social and building exposure vulnerability.

Crowdsourcing detailed flood data

Science.gov (United States)

Walliman, Nicholas; Ogden, Ray; Amouzad*, Shahrzhad

2015-04-01

Over the last decade the average annual loss across the European Union due to flooding has been 4.5bn Euros, but increasingly intense rainfall, as well as population growth, urbanisation and the rising costs of asset replacements, may see this rise to 23bn Euros a year by 2050. Equally disturbing are the profound social costs to individuals, families and communities which in addition to loss of lives include: loss of livelihoods, decreased purchasing and production power, relocation and migration, adverse psychosocial effects, and hindrance of economic growth and development. Flood prediction, management and defence strategies rely on the availability of accurate information and flood modelling. Whilst automated data gathering (by measurement and satellite) of the extent of flooding is already advanced it is least reliable in urban and physically complex geographies where often the need for precise estimation is most acute. Crowdsourced data of actual flood events is a potentially critical component of this allowing improved accuracy in situations and identifying the effects of local landscape and topography where the height of a simple kerb, or discontinuity in a boundary wall can have profound importance. Mobile 'App' based data acquisition using crowdsourcing in critical areas can combine camera records with GPS positional data and time, as well as descriptive data relating to the event. This will automatically produce a dataset, managed in ArcView GIS, with the potential for follow up calls to get more information through structured scripts for each strand. Through this local residents can provide highly detailed information that can be reflected in sophisticated flood protection models and be core to framing urban resilience strategies and optimising the effectiveness of investment. This paper will describe this pioneering approach that will develop flood event data in support of systems that will advance existing approaches such as developed in the in the UK

Spatiotemporal hazard mapping of a flood event "migration" in a transboundary river basin as an operational tool in flood risk management

Science.gov (United States)

Perrou, Theodora; Papastergios, Asterios; Parcharidis, Issaak; Chini, Marco

2017-10-01

Flood disaster is one of the heaviest disasters in the world. It is necessary to monitor and evaluate the flood disaster in order to mitigate the consequences. As floods do not recognize borders, transboundary flood risk management is imperative in shared river basins. Disaster management is highly dependent on early information and requires data from the whole river basin. Based on the hypothesis that the flood events over the same area with same magnitude have almost identical evolution, it is crucial to develop a repository database of historical flood events. This tool, in the case of extended transboundary river basins, could constitute an operational warning system for the downstream area. The utility of SAR images for flood mapping, was demonstrated by previous studies but the SAR systems in orbit were not characterized by high operational capacity. Copernicus system will fill this gap in operational service for risk management, especially during emergency phase. The operational capabilities have been significantly improved by newly available satellite constellation, such as the Sentinel-1A AB mission, which is able to provide systematic acquisitions with a very high temporal resolution in a wide swath coverage. The present study deals with the monitoring of a transboundary flood event in Evros basin. The objective of the study is to create the "migration story" of the flooded areas on the basis of the evolution in time for the event occurred from October 2014 till May 2015. Flood hazard maps will be created, using SAR-based semi-automatic algorithms and then through the synthesis of the related maps in a GIS-system, a spatiotemporal thematic map of the event will be produced. The thematic map combined with TanDEM-X DEM, 12m/pixel spatial resolution, will define the non- affected areas which is a very useful information for the emergency planning and emergency response phases. The Sentinels meet the main requirements to be an effective and suitable

Multitemporal satellite change detection investigations for documentation and valorization of cultural landscape

Science.gov (United States)

Lasaponara, R.; Masini, n.

2012-04-01

archaeological prospection. Int J Remote Sens 27: 3607-3614. Lasaponara R, Masini N (2006b) Identification of archaeological buried remains based on Normalized Difference Vegetation Index (NDVI) from Quickbird satellite data. IEEE Geosci Remote S 3(3): 325-328. Lasaponara R, Masini N (2007a) Detection of archaeological crop marks by using satellite QuickBird multispectral imagery. J Archaeol Sci 34: 214-21. Lasaponara R, Masini N (2007b) Improving satellite Quickbird - based identification of landscape archaeological features trough tasselled cup transformation and PCA. 21st CIPA Symposium, Atene, 1-6 giugno 2007. Lasaponara R, Masini N (2010) Facing the archaeological looting in Peru by local spatial autocorrelation statistics of Very high resolution satellite imagery. In: Taniar D et al (Eds), Proceedings of ICSSA, The 2010 International Conference on Computational Science and its Application (Fukuoka-Japan, March 23 - 26, 2010), Springer, Berlin, 261-269. Lasaponara R, Masini N (2011) Satellite Remote Sensing in Archaeology : past, present and future. J Archaeol Sc 38: 1995-2002. Lasaponara R, Masini N, Rizzo E, Orefici G (2011) New discoveries in the Piramide Naranjada in Cahuachi (Peru) using satellite, Ground Probing Radar and magnetic investigations. J Archaeol Sci 38: 2031-2039. Lasaponara R, Masini N, Scardozzi G (2008) Satellite based archaeological research in ancient territory of Hierapolis. 1st International EARSeL Workshop. Advances in Remote Sensing for Archaeology and Cultural Heritage Management", CNR, Rome, September 30-October 4, Aracne, Rome, pp.11-16. Lillesand T M, Kiefer R W (2000) Remote Sensing and Image interpretation. John Wiley and Sons, New York. Masini N, Lasaponara R (2006) Satellite-based recognition of landscape archaeological features related to ancient human transformation. J Geophys Eng 3: 230-235, doi:10.1088/1742-2132/3/3/004. Masini N, Lasaponara R (2007) Investigating the spectral capability of QuickBird data to detect archaeological

Flash flood modeling with the MARINE hydrological distributed model

Science.gov (United States)

Estupina-Borrell, V.; Dartus, D.; Ababou, R.

2006-11-01

Flash floods are characterized by their violence and the rapidity of their occurrence. Because these events are rare and unpredictable, but also fast and intense, their anticipation with sufficient lead time for warning and broadcasting is a primary subject of research. Because of the heterogeneities of the rain and of the behavior of the surface, spatially distributed hydrological models can lead to a better understanding of the processes and so on they can contribute to a better forecasting of flash flood. Our main goal here is to develop an operational and robust methodology for flash flood forecasting. This methodology should provide relevant data (information) about flood evolution on short time scales, and should be applicable even in locations where direct observations are sparse (e.g. absence of historical and modern rainfalls and streamflows in small mountainous watersheds). The flash flood forecast is obtained by the physically based, space-time distributed hydrological model "MARINE'' (Model of Anticipation of Runoff and INondations for Extreme events). This model is presented and tested in this paper for a real flash flood event. The model consists in two steps, or two components: the first component is a "basin'' flood module which generates flood runoff in the upstream part of the watershed, and the second component is the "stream network'' module, which propagates the flood in the main river and its subsidiaries. The basin flash flood generation model is a rainfall-runoff model that can integrate remotely sensed data. Surface hydraulics equations are solved with enough simplifying hypotheses to allow real time exploitation. The minimum data required by the model are: (i) the Digital Elevation Model, used to calculate slopes that generate runoff, it can be issued from satellite imagery (SPOT) or from French Geographical Institute (IGN); (ii) the rainfall data from meteorological radar, observed or anticipated by the French Meteorological Service (M�

a Novel Ship Detection Method for Large-Scale Optical Satellite Images Based on Visual Lbp Feature and Visual Attention Model

Science.gov (United States)

Haigang, Sui; Zhina, Song

2016-06-01

Reliably ship detection in optical satellite images has a wide application in both military and civil fields. However, this problem is very difficult in complex backgrounds, such as waves, clouds, and small islands. Aiming at these issues, this paper explores an automatic and robust model for ship detection in large-scale optical satellite images, which relies on detecting statistical signatures of ship targets, in terms of biologically-inspired visual features. This model first selects salient candidate regions across large-scale images by using a mechanism based on biologically-inspired visual features, combined with visual attention model with local binary pattern (CVLBP). Different from traditional studies, the proposed algorithm is high-speed and helpful to focus on the suspected ship areas avoiding the separation step of land and sea. Largearea images are cut into small image chips and analyzed in two complementary ways: Sparse saliency using visual attention model and detail signatures using LBP features, thus accordant with sparseness of ship distribution on images. Then these features are employed to classify each chip as containing ship targets or not, using a support vector machine (SVM). After getting the suspicious areas, there are still some false alarms such as microwaves and small ribbon clouds, thus simple shape and texture analysis are adopted to distinguish between ships and nonships in suspicious areas. Experimental results show the proposed method is insensitive to waves, clouds, illumination and ship size.

Predicting geomorphically-induced flood risk for the Nepalese Terai communities

Science.gov (United States)

Dingle, Elizabeth; Creed, Maggie; Attal, Mikael; Sinclair, Hugh; Mudd, Simon; Borthwick, Alistair; Dugar, Sumit; Brown, Sarah

2017-04-01

Rivers sourced from the Himalaya irrigate the Indo-Gangetic Plain via major river networks that support 10% of the global population. However, many of these rivers are also the source of devastating floods. During the 2014 Karnali River floods in west Nepal, the Karnali rose to around 16 m at Chisapani (where it enters the Indo-Gangetic Plain), 1 m higher than the previous record in 1983; the return interval for this event was estimated to be 1000 years. Flood risk may currently be underestimated in this region, primarily because changes to the channel bed are not included when identifying areas at risk of flooding from events of varying recurrence intervals. Our observations in the field, corroborated by satellite imagery, show that river beds are highly mobile and constantly evolve through each monsoon. Increased bed levels due to sediment aggradation decreases the capacity of the river, increasing significantly the risk of devastating flood events; we refer to these as 'geomorphically-induced floods'. Major, short-lived episodes of sediment accumulation in channels are caused by stochastic variability in sediment flux generated by storms, earthquakes and glacial outburst floods from upstream parts of the catchment. Here, we generate a field-calibrated, geomorphic flood risk model for varying upstream scenarios, and predict changing flood risk for the Karnali River. A numerical model is used to carry out a sensitivity analysis of changes in channel geometry (particularly aggradation or degradation) based on realistic flood scenarios. In these scenarios, water and sediment discharge are varied within a range of plausible values, up to extreme sediment and water fluxes caused by widespread landsliding and/or intense monsoon precipitation based on existing records. The results of this sensitivity analysis will be used to inform flood hazard maps of the Karnali River floodplain and assess the vulnerability of the populations in the region.

Direct Satellite Data Acquisition and its Application for Large -scale Monitoring Projects in Russia

Science.gov (United States)

Gershenzon, O.

2011-12-01

ScanEx RDC created an infrastructure (ground stations network) to acquire and process remote sensing data from different satellites: Terra, Aqua, Landsat, IRS-P5/P6, SPOT 4/5, FORMOSAT-2, EROS A/B, RADARSAT-1/2, ENVISAT-1. It owns image archives from these satellites as well as from SPOT-2 and CARTOSAT-2. ScanEx RDC builds and delivers remote sensing ground stations (working with up to 15 satellites); and owns the ground stations network to acquire data for Russia and surrounding territory. ScanEx stations are the basic component in departmental networks of remote sensing data acquisition for different state authorities (Roshydromet, Ministry of Natural Recourses, Emercom) and University- based remote sensing data acquisition and processing centers in Russia and abroad. ScanEx performs large-scale projects in collaboration with government agencies to monitor forests, floods, fires, sea surface pollution, and ice situation in Northern Russia. During 2010-2011 ScanEx conducted daily monitoring of wild fires in Russia detecting and registering thermal anomalies using data from Terra, Aqua, Landsat and SPOT satellites. Detailed SPOT 4/5 data is used to analyze burnt areas and to assess damage caused by fire. Satellite data along with other information about fire situation in Russia was daily updated and published via free-access Internet geoportal. A few projects ScanEx conducted together with environmental NGO. Project "Satellite monitoring of Especially Protected Natural Areas of Russia and its results visualization on geoportal was conducted in cooperation with NGO "Transparent World". The project's goal was to observe natural phenomena and economical activity, including illegal, by means of Earth remote sensing data. Monitoring is based on multi-temporal optical space imagery of different spatial resolution. Project results include detection of anthropogenic objects that appeared in the vicinity or even within the border of natural territories, that have never been

Passivity Based Nonlinear Attitude Control of the Rømer Satellite

DEFF Research Database (Denmark)

Quottrup, Michael Melholt; Krogh-Sørensen, J.; Wisniewski, Rafal

2001-01-01

This paper suggests nonlinear attitude control of the Danish satellite Rømer. This satellite will be designed to fulfil two scientific objectives: The observation of stellar oscillations and the detection and localisation of gamma-ray bursts. The satellite will be equipped with a tetrahedron...

Detecting aircrafts from satellite images using saliency and conical ...

Indian Academy of Sciences (India)

Samik Banerjee

automatically detect all kinds of interesting targets in satellite images. .... which is used for text and image categorization, has been also introduced for object ...... 3.4 GHz processor, 32 GB RAM and Windows 7 (64 bit). Operating System. 6.

Assessment of nature-based flood defences' implementation potential : development and application of a game theory based method

NARCIS (Netherlands)

Janssen, S.K.H.; Hermans, L.M.

2017-01-01

Nature-based flood defence (NBFD) by means of vegetated foreshores is an innovative flood protection strategy. In contrasts with traditional hard structures it combines nature and flood protection functions and employs natural dynamics. Introducing such an innovation into actual flood protection

Use of Space Technology in Flood Mitigation (Western Province, Zambia)

Science.gov (United States)

Mulando, A.

2001-05-01

Disasters, by definition are events that appear suddenly and with little warning. They are usually short lived, with extreme events bringing death, injury and destruction of buildings and communications. Their aftermath can be as damaging as their physical effects through destruction of sanitation and water supplies, destruction of housing and breakdown of transport for food, temporary shelter and emergency services. Since floods are one of the natural disasters which endanger both life and property, it becomes vital to know its extents and where the hazards exists. Flood disasters manifest natural processes on a larger scale and information provided by Remote Sensing is a most appropriate input to analysis of actual events and investigations of potential risks. An analytical and qualitative image processing and interpretation of Remotely Sensed data as well as other data such as rainfall, population, settlements not to mention but a few should be used to derive good mitigation strategies. Since mitigation is the cornerstone of emergency management, it therefore becomes a sustained action that will reduce or eliminate long term risks to people and property from natural hazards such as floods and their effects. This will definitely involve keeping of homes and other sensitive structures away from flood plains. Promotion of sound land use planning based on this known hazard, "FLOODS" is one such form of mitigation that can be applied in flood affected areas within flood plain. Therefore future mitigation technologies and procedures should increasingly be based on the use of flood extent information provided by Remote Sensing Satellites like the NOAA AVHRR as well as information on the designated flood hazard and risk areas.

Remote Sensing-Based Quantification of the Impact of Flash Flooding on the Rice Production: A Case Study over Northeastern Bangladesh.

Science.gov (United States)

Ahmed, M Razu; Rahaman, Khan Rubayet; Kok, Aaron; Hassan, Quazi K

2017-10-14

The northeastern region of Bangladesh often experiences flash flooding during the pre-harvesting period of the boro rice crop, which is the major cereal crop in the country. In this study, our objective was to delineate the impact of the 2017 flash flood (that initiated on 27 March 2017) on boro rice using multi-temporal Landsat-8 OLI and MODIS data. Initially, we opted to use Landsat-8 OLI data for mapping the damages; however, during and after the flooding event the acquisition of cloud free images were challenging. Thus, we used this data to map the cultivated boro rice acreage considering the planting to mature stages of the crop. Also, in order to map the extent of the damaged boro area, we utilized MODIS data as their 16-day composites provided cloud free information. Our results indicated that both the cultivated and damaged boro area estimates based on satellite data had strong relationships while compared to the ground-based estimates (i.e., r ² values approximately 0.92 for both cases, and RMSE of 18,374 and 9380 ha for cultivated and damaged areas, respectively). Finally, we believe that our study would be critical for planning and ensuring food security for the country.

Wide area change detection with satellite imagery for locating underground nuclear testing

International Nuclear Information System (INIS)

Canty, M.J.; Jasani, B.; Schlittenhardt, J.

2001-01-01

With the advent of high resolution optical imagery from commercial earth observation satellites, the use of remote sensing data for verification of nuclear non-proliferation agreements is becoming increasingly attractive. Non-governmental organizations are routinely publishing high-quality imagery of sensitive nuclear installations round the world, and international verification authorities, such as the International Atomic Energy Agency (IAEA) or the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO), will also want to make use, directly or indirectly, of this additional open source of information. Exact location of the sites of underground nuclear explosions is a task eminently suited to satellite imagery. Here both moderate resolutions for detecting signals in very large testing ranges as well as high resolution images for exact interpretation play important roles. We describe in our paper a particularly sensitive change detection procedure for bitemporal, multispectral satellite imagery which can be used to locate the spall zone of underground nuclear explosions with commercial satellite imagery. The method is based on the multivariate alteration detection (MAD) technique of Nielsen et al. Linear combinations of the spectral channels in two images of the same scene are chosen so as to minimize their positive correlation. This leads to a series of difference images - the so-called MAD components - which are mutually orthogonal (uncorrelated) and ordered according to decreasing variance in their pixel intensities. Since interesting changes in man-made structures may contribute minimally to the overall variance (as the latter may be dominated for instance by seasonal vegetation differences) it is often the case that such changes turn up in a higher order MAD component. This is because they will be uncorrelated with seasonal vegetation changes, stochastic image noise or other major contributions to the overall change signal. This in fact is one of the

Volumetric Forest Change Detection Through Vhr Satellite Imagery

Science.gov (United States)

Akca, Devrim; Stylianidis, Efstratios; Smagas, Konstantinos; Hofer, Martin; Poli, Daniela; Gruen, Armin; Sanchez Martin, Victor; Altan, Orhan; Walli, Andreas; Jimeno, Elisa; Garcia, Alejandro

2016-06-01

Quick and economical ways of detecting of planimetric and volumetric changes of forest areas are in high demand. A research platform, called FORSAT (A satellite processing platform for high resolution forest assessment), was developed for the extraction of 3D geometric information from VHR (very-high resolution) imagery from satellite optical sensors and automatic change detection. This 3D forest information solution was developed during a Eurostars project. FORSAT includes two main units. The first one is dedicated to the geometric and radiometric processing of satellite optical imagery and 2D/3D information extraction. This includes: image radiometric pre-processing, image and ground point measurement, improvement of geometric sensor orientation, quasiepipolar image generation for stereo measurements, digital surface model (DSM) extraction by using a precise and robust image matching approach specially designed for VHR satellite imagery, generation of orthoimages, and 3D measurements in single images using mono-plotting and in stereo images as well as triplets. FORSAT supports most of the VHR optically imagery commonly used for civil applications: IKONOS, OrbView - 3, SPOT - 5 HRS, SPOT - 5 HRG, QuickBird, GeoEye-1, WorldView-1/2, Pléiades 1A/1B, SPOT 6/7, and sensors of similar type to be expected in the future. The second unit of FORSAT is dedicated to 3D surface comparison for change detection. It allows users to import digital elevation models (DEMs), align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes between epochs. To this end our 3D surface matching method LS3D is being used. FORSAT is a single source and flexible forest information solution with a very competitive price/quality ratio, allowing expert and non-expert remote sensing users to monitor forests in three and four dimensions from VHR optical imagery for many forest information needs. The capacity and benefits of FORSAT have been tested in

Time series analysis of infrared satellite data for detecting thermal anomalies: a hybrid approach

Science.gov (United States)

Koeppen, W. C.; Pilger, E.; Wright, R.

2011-07-01

We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust Satellite Techniques (RST) algorithm. Using test sites at Anatahan and Kīlauea volcanoes, the hybrid time series approach detected ~15% more thermal anomalies than MODVOLC with very few, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf of Mexico as an end-member scenario representing very persistent thermal anomalies. At Cantarell, the hybrid algorithm showed only a slight improvement, but it did identify flares that were undetected by MODVOLC. We estimate that at least 80 MODIS images for each calendar month are required to create good reference images necessary for the time series analysis of the hybrid algorithm. The improved performance of the new algorithm over MODVOLC will result in the detection of low temperature thermal anomalies that will be useful in improving our ability to document Earth's volcanic eruptions, as well as detecting low temperature thermal precursors to larger eruptions.

Detection and assessment of flood susceptible irrigation networks in Licab, Nueva Ecija, Philippines using LiDAR DTM

Science.gov (United States)

Alberto, R. T.; Hernando, P. J. C.; Tagaca, R. C.; Celestino, A. B.; Palado, G. C.; Camaso, E. E.; Damian, G. B.

2017-09-01

Climate change has wide-ranging effects on the environment and socio-economic and related sectors which includes water resources, agriculture and food security, human health, terrestrial ecosystems, coastal zones and biodiversity. Farmers are under pressure to the changing weather and increasing unpredictable water supply. Because of rainfall deficiencies, artificial application of water has been made through irrigation. Irrigation is a basic determinant of agriculture because its inadequacies are the most powerful constraints on the increase of agricultural production. Irrigation networks are permanent and temporary conduits that supply water to agricultural areas from an irrigation source. Detection of irrigation networks using LiDAR DTM, and flood susceptible assessment of irrigation networks could give baseline information on the development and management of sustainable agriculture. Map Gully Depth (MGD) in Whitebox GAT was used to generate the potential irrigation networks. The extracted MGD was overlaid in ArcGIS as guide in the digitization of potential irrigation networks. A flood hazard map was also used to identify the flood susceptible irrigation networks in the study area. The study was assessed through field validation of points which were generated using random sampling method. Results of the study showed that most of the detected irrigation networks have low to moderate susceptibility to flooding while the rest have high susceptibility to flooding which is due to shifting weather. These irrigation networks may cause flood when it overflows that could also bring huge damage to rice and other agricultural areas.

Parts-based geophysical inversion with application to water flooding interface detection and geological facies detection

Science.gov (United States)

Zhang, Junwei

I built parts-based and manifold based mathematical learning model for the geophysical inverse problem and I applied this approach to two problems. One is related to the detection of the oil-water encroachment front during the water flooding of an oil reservoir. In this application, I propose a new 4D inversion approach based on the Gauss-Newton approach to invert time-lapse cross-well resistance data. The goal of this study is to image the position of the oil-water encroachment front in a heterogeneous clayey sand reservoir. This approach is based on explicitly connecting the change of resistivity to the petrophysical properties controlling the position of the front (porosity and permeability) and to the saturation of the water phase through a petrophysical resistivity model accounting for bulk and surface conductivity contributions and saturation. The distributions of the permeability and porosity are also inverted using the time-lapse resistivity data in order to better reconstruct the position of the oil water encroachment front. In our synthetic test case, we get a better position of the front with the by-products of porosity and permeability inferences near the flow trajectory and close to the wells. The numerical simulations show that the position of the front is recovered well but the distribution of the recovered porosity and permeability is only fair. A comparison with a commercial code based on a classical Gauss-Newton approach with no information provided by the two-phase flow model fails to recover the position of the front. The new approach could be also used for the time-lapse monitoring of various processes in both geothermal fields and oil and gas reservoirs using a combination of geophysical methods. A paper has been published in Geophysical Journal International on this topic and I am the first author of this paper. The second application is related to the detection of geological facies boundaries and their deforation to satisfy to geophysica

Flood occurrence mapping of the middle Mahakam lowland area using satellite radar

Directory of Open Access Journals (Sweden)

H. Hidayat

2012-07-01

Full Text Available Floodplain lakes and peatlands in the middle Mahakam lowland area are considered as ecologically important wetland in East Kalimantan, Indonesia. However, due to a lack of data, the hydrological functioning of the region is still poorly understood. Among remote sensing techniques that can increase data availability, radar is well-suitable for the identification, mapping, and measurement of tropical wetlands, for its cloud unimpeded sensing and night and day operation. Here we aim to extract flood extent and flood occurrence information from a series of radar images of the middle Mahakam lowland area. We explore the use of Phased Array L-band Synthetic Aperture Radar (PALSAR imagery for observing flood inundation dynamics by incorporating field water level measurements. Water level measurements were carried out along the river, in lakes and in peatlands, using pressure transducers. For validation of the open water flood occurrence map, bathymetry measurements were carried out in the main lakes. A series of PALSAR images covering the middle and lower Mahakam area in the years 2007 through 2010 were collected. A fully inundated region can be easily recognized on radar images from a dark signature. Open water flood occurrence was mapped using a threshold value taken from radar backscatter of the permanently inundated river and lakes areas. Radar backscatter intensity analysis of the vegetated floodplain area revealed consistently high backscatter values, indicating flood inundation under forest canopy. We used those values as the threshold for flood occurrence mapping in the vegetated area.

Data assimilation of citizen collected information for real-time flood hazard mapping

Science.gov (United States)

Sayama, T.; Takara, K. T.

2017-12-01

Many studies in data assimilation in hydrology have focused on the integration of satellite remote sensing and in-situ monitoring data into hydrologic or land surface models. For flood predictions also, recent studies have demonstrated to assimilate remotely sensed inundation information with flood inundation models. In actual flood disaster situations, citizen collected information including local reports by residents and rescue teams and more recently tweets via social media also contain valuable information. The main interest of this study is how to effectively use such citizen collected information for real-time flood hazard mapping. Here we propose a new data assimilation technique based on pre-conducted ensemble inundation simulations and update inundation depth distributions sequentially when local data becomes available. The propose method is composed by the following two-steps. The first step is based on weighting average of preliminary ensemble simulations, whose weights are updated by Bayesian approach. The second step is based on an optimal interpolation, where the covariance matrix is calculated from the ensemble simulations. The proposed method was applied to case studies including an actual flood event occurred. It considers two situations with more idealized one by assuming continuous flood inundation depth information is available at multiple locations. The other one, which is more realistic case during such a severe flood disaster, assumes uncertain and non-continuous information is available to be assimilated. The results show that, in the first idealized situation, the large scale inundation during the flooding was estimated reasonably with RMSE effective. Nevertheless, the applications of the proposed data assimilation method demonstrated a high potential of this method for assimilating citizen collected information for real-time flood hazard mapping in the future.

Leveraging Trillions of Pixels for Flood Mitigation Decisions Support in the Rio Salado Basin, Argentina

Science.gov (United States)

Sullivan, J.; Routh, D.; Tellman, B.; Doyle, C.; Tomlin, J. N.

2017-12-01

The Rio Salado River Basin in Argentina is an economically important region that generates 25-30 percent of Argentina's grain and meat production. Between 2000-2011, floods in the basin caused nearly US$4.5 billion in losses and affected 5.5 million people. With the goal of developing cost-efficient flood monitoring and prediction capabilities in the Rio Salado Basin to support decision making, Cloud to Street is developing satellite based analytics to cover information gaps and improve monitoring capacity. This talk will showcase the Flood Risk Dashboard developed by Cloud to Street to support monitoring and decision-making at the level of provincial and national water management agencies in the Rio Salado Watershed. The Dashboard is based on analyzing thousands of MODIS, Landsat, and Sentinel scenes in Google Earth Engine to reconstruct the spatial history of flooding in the basin. The tool, iteratively designed with the end-user, shows a history of floodable areas with specific return times, exposed land uses and population, precipitation hyetographs, and spatial and temporal flood trends in the basin. These trends are used to understand both the impact of past flood mitigation investments (i.e. wetland reconstruction) and identify shifting flood risks. Based on this experience, we will also describe best practices on making remote sensing "flood dashboards" for water agencies.

Remote sensing analysis for flood risk management in urban sprawl contexts

Directory of Open Access Journals (Sweden)

Francesca Franci

2015-07-01

Full Text Available Remote sensing can play a key role in risk assessment and management, especially when several concurrent factors coexist, such as a predisposition to natural disasters and the urban sprawl, spreading over highly vulnerable areas. In this context, multitemporal analysis can provide decision-makers with tools and information to reduce the impacts of disasters (e.g. flooding and to encourage a sustainable development. The present work focuses on the employment of multispectral satellite imagery to produce multitemporal land use/cover maps for the city of Dhaka, which is subject to frequent flooding events. In particular, the evaluation of the urban growth, the analysis of the annual dynamics of flooding and the study of the 2004 catastrophic event were performed. For the change-detection procedure, Landsat images were used. These images allow the quantification of the very rapid growth of the metropolis, with an increase in built-up areas from 75 to 111 km2. The image of 2009 showed that an ordinary flood affects about 115 km2 (on a studied area of 591 km2. On the other hand, the analysis of the 2004 extreme flooding event, performed on a wider area, showed that the affected lands added up to 750 km2 (on about 3845 km2.

Prototypes of risk-based flood forecasting systems in the Netherlands and Italy

Directory of Open Access Journals (Sweden)

Bachmann D.

2016-01-01

Full Text Available Flood forecasting, warning and emergency response are important components of flood management. Currently, the model-based prediction of discharge and/or water level in a river is common practice for operational flood forecasting. Based on the prediction of these values decisions about specific emergency measures are made within emergency response. However, the information provided for decision support is often restricted to pure hydrological or hydraulic aspects of a flood. Information about weak sections within the flood defences, flood prone areas and assets at risk in the protected areas are rarely used in current early warning and response systems. This information is often available for strategic planning, but is not in an appropriate format for operational purposes. This paper presents the extension of existing flood forecasting systems with elements of strategic flood risk analysis, such as probabilistic failure analysis, two dimensional flood spreading simulation and the analysis of flood impacts and consequences. This paper presents the first results from two prototype applications of the new developed concept: The first prototype is applied to the Rotterdam area situated in the western part of the Netherlands. The second pilot study focusses on a rural area between the cities of Mantua and Ferrara along the Po river (Italy.

Flood extent and water level estimation from SAR using data-model integration

Science.gov (United States)

Ajadi, O. A.; Meyer, F. J.

2017-12-01

Synthetic Aperture Radar (SAR) images have long been recognized as a valuable data source for flood mapping. Compared to other sources, SAR's weather and illumination independence and large area coverage at high spatial resolution supports reliable, frequent, and detailed observations of developing flood events. Accordingly, SAR has the potential to greatly aid in the near real-time monitoring of natural hazards, such as flood detection, if combined with automated image processing. This research works towards increasing the reliability and temporal sampling of SAR-derived flood hazard information by integrating information from multiple SAR sensors and SAR modalities (images and Interferometric SAR (InSAR) coherence) and by combining SAR-derived change detection information with hydrologic and hydraulic flood forecast models. First, the combination of multi-temporal SAR intensity images and coherence information for generating flood extent maps is introduced. The application of least-squares estimation integrates flood information from multiple SAR sensors, thus increasing the temporal sampling. SAR-based flood extent information will be combined with a Digital Elevation Model (DEM) to reduce false alarms and to estimate water depth and flood volume. The SAR-based flood extent map is assimilated into the Hydrologic Engineering Center River Analysis System (Hec-RAS) model to aid in hydraulic model calibration. The developed technology is improving the accuracy of flood information by exploiting information from data and models. It also provides enhanced flood information to decision-makers supporting the response to flood extent and improving emergency relief efforts.

Development of Deep Learning Based Data Fusion Approach for Accurate Rainfall Estimation Using Ground Radar and Satellite Precipitation Products

Science.gov (United States)

Chen, H.; Chandra, C. V.; Tan, H.; Cifelli, R.; Xie, P.

2016-12-01

Rainfall estimation based on onboard satellite measurements has been an important topic in satellite meteorology for decades. A number of precipitation products at multiple time and space scales have been developed based upon satellite observations. For example, NOAA Climate Prediction Center has developed a morphing technique (i.e., CMORPH) to produce global precipitation products by combining existing space based rainfall estimates. The CMORPH products are essentially derived based on geostationary satellite IR brightness temperature information and retrievals from passive microwave measurements (Joyce et al. 2004). Although the space-based precipitation products provide an excellent tool for regional and global hydrologic and climate studies as well as improved situational awareness for operational forecasts, its accuracy is limited due to the sampling limitations, particularly for extreme events such as very light and/or heavy rain. On the other hand, ground-based radar is more mature science for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization technique and further enhanced by urban scale radar networks. Therefore, ground radars are often critical for providing local scale rainfall estimation and a "heads-up" for operational forecasters to issue watches and warnings as well as validation of various space measurements and products. The CASA DFW QPE system, which is based on dual-polarization X-band CASA radars and a local S-band WSR-88DP radar, has demonstrated its excellent performance during several years of operation in a variety of precipitation regimes. The real-time CASA DFW QPE products are used extensively for localized hydrometeorological applications such as urban flash flood forecasting. In this paper, a neural network based data fusion mechanism is introduced to improve the satellite-based CMORPH precipitation product by taking into account the ground radar measurements. A deep learning system is

An Enhanced Satellite-Based Algorithm for Detecting and Tracking Dust Outbreaks by Means of SEVIRI Data

Directory of Open Access Journals (Sweden)

Francesco Marchese

2017-05-01

Full Text Available Dust outbreaks are meteorological phenomena of great interest for scientists and authorities (because of their impact on the climate, environment, and human activities, which may be detected, monitored, and characterized from space using different methods and procedures. Among the recent dust detection algorithms, the RSTDUST multi-temporal technique has provided good results in different geographic areas (e.g., Mediterranean basin; Arabian Peninsula, exhibiting a better performance than traditional split window methods, in spite of some limitations. In this study, we present an optimized configuration of this technique, which better exploits data provided by Spinning Enhanced Visible and Infrared Imager (SEVIRI aboard Meteosat Second Generation (MSG satellites to address those issues (e.g., sensitivity reduction over arid and semi-arid regions; dependence on some meteorological clouds. Three massive dust events affecting Europe and the Mediterranean basin in May 2008/2010 are analysed in this work, using information provided by some independent and well-established aerosol products to assess the achieved results. The study shows that the proposed algorithm, christened eRSTDUST (i.e., enhanced RSTDUST, which provides qualitative information about dust outbreaks, is capable of increasing the trade-off between reliability and sensitivity. The results encourage further experimentations of this method in other periods of the year, also exploiting data provided by different satellite sensors, for better evaluating the advantages arising from the use of this dust detection technique in operational scenarios.

Flood Risk Analysis in Lower Part of Markham River Based on Multi-Criteria Decision Approach (MCDA

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Sailesh Samanta

2016-08-01

Full Text Available Papua New Guinea is blessed with a plethora of enviable natural resources, but at the same time it is also cursed by quite a few natural disasters like volcanic eruptions, earthquakes, landslide, floods, droughts etc. Floods happen to be a natural process of maintaining the health of the rivers and depth of its thalweg; it saves the river from becoming morbid while toning up the fertility of the riverine landscape. At the same time, from human perspective, all these ecological goodies are nullified when flood is construed overwhelmingly as one of the most devastating events in respect to social and economic consequences. The present investigation was tailored to assess the use of multi-criteria decision approach (MCDA in inland flood risk analysis. Categorization of possible flood risk zones was accomplished using geospatial data sets, like elevation, slope, distance to river, and land use/land cover, which were derived from digital elevation model (DEM and satellite image, respectively. A pilot study area was selected in the lower part of Markham River in Morobe Province, Papua New Guinea. The study area is bounded by 146°31′ to 146°58′ east and 6°33′ to 6°46′ south; covers an area of 758.30 km2. The validation of a flood hazard risk map was carried out using past flood records in the study area. This result suggests that MCDA within GIS techniques is very useful in accurate and reliable flood risk analysis and mapping. This approach is convenient for the assessment of flood in any region, specifically in no-data regions, and can be useful for researchers and planners in flood mitigation strategies.

DETECTION OF BARCHAN DUNES IN HIGH RESOLUTION SATELLITE IMAGES

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M. A. Azzaoui

2016-06-01

Full Text Available Barchan dunes are the fastest moving sand dunes in the desert. We developed a process to detect barchans dunes on High resolution satellite images. It consisted of three steps, we first enhanced the image using histogram equalization and noise reduction filters. Then, the second step proceeds to eliminate the parts of the image having a texture different from that of the barchans dunes. Using supervised learning, we tested a coarse to fine textural analysis based on Kolomogorov Smirnov test and Youden’s J-statistic on co-occurrence matrix. As an output we obtained a mask that we used in the next step to reduce the search area. In the third step we used a gliding window on the mask and check SURF features with SVM to get barchans dunes candidates. Detected barchans dunes were considered as the fusion of overlapping candidates. The results of this approach were very satisfying in processing time and precision.

Fast Simulation of Large-Scale Floods Based on GPU Parallel Computing

OpenAIRE

Qiang Liu; Yi Qin; Guodong Li

2018-01-01

Computing speed is a significant issue of large-scale flood simulations for real-time response to disaster prevention and mitigation. Even today, most of the large-scale flood simulations are generally run on supercomputers due to the massive amounts of data and computations necessary. In this work, a two-dimensional shallow water model based on an unstructured Godunov-type finite volume scheme was proposed for flood simulation. To realize a fast simulation of large-scale floods on a personal...

Satellite-Based Precipitation Datasets

Science.gov (United States)

Munchak, S. J.; Huffman, G. J.

2017-12-01

Of the possible sources of precipitation data, those based on satellites provide the greatest spatial coverage. There is a wide selection of datasets, algorithms, and versions from which to choose, which can be confusing to non-specialists wishing to use the data. The International Precipitation Working Group (IPWG) maintains tables of the major publicly available, long-term, quasi-global precipitation data sets (http://www.isac.cnr.it/ ipwg/data/datasets.html), and this talk briefly reviews the various categories. As examples, NASA provides two sets of quasi-global precipitation data sets: the older Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and current Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG). Both provide near-real-time and post-real-time products that are uniformly gridded in space and time. The TMPA products are 3-hourly 0.25°x0.25° on the latitude band 50°N-S for about 16 years, while the IMERG products are half-hourly 0.1°x0.1° on 60°N-S for over 3 years (with plans to go to 16+ years in Spring 2018). In addition to the precipitation estimates, each data set provides fields of other variables, such as the satellite sensor providing estimates and estimated random error. The discussion concludes with advice about determining suitability for use, the necessity of being clear about product names and versions, and the need for continued support for satellite- and surface-based observation.

Satellite-based laser windsounder

International Nuclear Information System (INIS)

Schultz, J.F.; Czuchlewski, S.J.; Quick, C.R.

1997-01-01

This is the final report of a one-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The project''s primary objective is to determine the technical feasibility of using satellite-based laser wind sensing systems for detailed study of winds, aerosols, and particulates around and downstream of suspected proliferation facilities. Extensive interactions with the relevant operational organization resulted in enthusiastic support and useful guidance with respect to measurement requirements and priorities. Four candidate wind sensing techniques were evaluated, and the incoherent Doppler technique was selected. A small satellite concept design study was completed to identify the technical issues inherent in a proof-of-concept small satellite mission. Use of a Mach-Zehnder interferometer instead of a Fabry-Perot would significantly simplify the optical train and could reduce weight, and possibly power, requirements with no loss of performance. A breadboard Mach-Zehnder interferometer-based system has been built to verify these predictions. Detailed plans were made for resolving other issues through construction and testing of a ground-based lidar system in collaboration with the University of Wisconsin, and through numerical lidar wind data assimilation studies

Construction of Polarimetric Radar-Based Reference Rain Maps for the Iowa Flood Studies Campaign

Science.gov (United States)

Petersen, Walt; Krajewski, Witek; Wolff, David; Gatlin, Patrick

2015-04-01

The Global Precipitation Measurement (GPM) Mission Iowa Flood Studies (IFloodS) campaign was conducted in central and northeastern Iowa during the months of April-June, 2013. Specific science objectives for IFloodS included quantification of uncertainties in satellite and ground-based estimates of precipitation, 4-D characterization of precipitation physical processes and associated parameters (e.g., size distributions, water contents, types, structure etc.), assessment of the impact of precipitation estimation uncertainty and physical processes on hydrologic predictive skill, and refinement of field observations and data analysis approaches as they pertain to future GPM integrated hydrologic validation and related field studies. In addition to field campaign archival of raw and processed satellite data (including precipitation products), key ground-based platforms such as the NASA NPOL S-band and D3R Ka/Ku-band dual-polarimetric radars, University of Iowa X-band dual-polarimetric radars, a large network of paired rain gauge platforms, and a large network of 2D Video and Parsivel disdrometers were deployed. In something of a canonical approach, the radar (NPOL in particular), gauge and disdrometer observational assets were deployed to create a consistent high-quality distributed (time and space sampling) radar-based ground "reference" rainfall dataset, with known uncertainties, that could be used for assessing the satellite-based precipitation products at a range of space/time scales. Subsequently, the impact of uncertainties in the satellite products could be evaluated relative to the ground-benchmark in coupled weather, land-surface and distributed hydrologic modeling frameworks as related to flood prediction. Relative to establishing the ground-based "benchmark", numerous avenues were pursued in the making and verification of IFloodS "reference" dual-polarimetric radar-based rain maps, and this study documents the process and results as they pertain specifically

Construction of Polarimetric Radar-Based Reference Rain Maps for the Iowa Flood Studies Campaign

Science.gov (United States)

Petersen, Walter; Wolff, David; Krajewski, Witek; Gatlin, Patrick

2015-01-01

The Global Precipitation Measurement (GPM) Mission Iowa Flood Studies (IFloodS) campaign was conducted in central and northeastern Iowa during the months of April-June, 2013. Specific science objectives for IFloodS included quantification of uncertainties in satellite and ground-based estimates of precipitation, 4-D characterization of precipitation physical processes and associated parameters (e.g., size distributions, water contents, types, structure etc.), assessment of the impact of precipitation estimation uncertainty and physical processes on hydrologic predictive skill, and refinement of field observations and data analysis approaches as they pertain to future GPM integrated hydrologic validation and related field studies. In addition to field campaign archival of raw and processed satellite data (including precipitation products), key ground-based platforms such as the NASA NPOL S-band and D3R Ka/Ku-band dual-polarimetric radars, University of Iowa X-band dual-polarimetric radars, a large network of paired rain gauge platforms, and a large network of 2D Video and Parsivel disdrometers were deployed. In something of a canonical approach, the radar (NPOL in particular), gauge and disdrometer observational assets were deployed to create a consistent high-quality distributed (time and space sampling) radar-based ground "reference" rainfall dataset, with known uncertainties, that could be used for assessing the satellite-based precipitation products at a range of space/time scales. Subsequently, the impact of uncertainties in the satellite products could be evaluated relative to the ground-benchmark in coupled weather, land-surface and distributed hydrologic modeling frameworks as related to flood prediction. Relative to establishing the ground-based "benchmark", numerous avenues were pursued in the making and verification of IFloodS "reference" dual-polarimetric radar-based rain maps, and this study documents the process and results as they pertain specifically

A Geometry-Based Cycle Slip Detection and Repair Method with Time-Differenced Carrier Phase (TDCP for a Single Frequency Global Position System (GPS + BeiDou Navigation Satellite System (BDS Receiver

Directory of Open Access Journals (Sweden)

Chuang Qian

2016-12-01

Full Text Available As the field of high-precision applications based on carriers continues to expand, the development of low-cost, small, modular receivers and their application in diverse scenarios and situations with complex data quality has increased the requirements of carrier-phase data preprocessing. A new geometry-based cycle slip detection and repair method based on Global Position System (GPS + BeiDou Navigation Satellite System (BDS is proposed. The method uses a Time-differenced Carrier Phase (TDCP model, which eliminates the Inner-System Bias (ISB between GPS and BDS, and it is conducive to the effective combination of GPS and BDS. It avoids the interference of the noise of the pseudo-range with cycle slip detection, while the cycle slips are preserved as integers. This method does not limit the receiver frequency number, and it is applicable to single-frequency data. The process is divided into two steps to detect and repair cycle slip. The first step is cycle slip detection, using the Improved Local Analysis Method (ILAM to find satellites that have cycle slips; The second step is to repair the cycle slips, including estimating the float solution of changes in ambiguities at the satellites that have cycle slips with the least squares method and the integer solution of the cycle slips by rounding. In the process of rounding, in addition to the success probability, a decimal test is carried out to validate the result. Finally, experiments with filed test data are carried out to prove the effectiveness of this method. The results show that the detectable cycle slips number with GPS + BDS is much greater than that with GPS. The method can also detect the non-integer outliers while fixing the cycle slip. The maximum decimal bias in repair is less than that with GPS. It implies that this method takes full advantages of multi-system.

GIS-BASED PREDICTION OF HURRICANE FLOOD INUNDATION

Energy Technology Data Exchange (ETDEWEB)

JUDI, DAVID [Los Alamos National Laboratory; KALYANAPU, ALFRED [Los Alamos National Laboratory; MCPHERSON, TIMOTHY [Los Alamos National Laboratory; BERSCHEID, ALAN [Los Alamos National Laboratory

2007-01-17

A simulation environment is being developed for the prediction and analysis of the inundation consequences for infrastructure systems from extreme flood events. This decision support architecture includes a GIS-based environment for model input development, simulation integration tools for meteorological, hydrologic, and infrastructure system models and damage assessment tools for infrastructure systems. The GIS-based environment processes digital elevation models (30-m from the USGS), land use/cover (30-m NLCD), stream networks from the National Hydrography Dataset (NHD) and soils data from the NRCS (STATSGO) to create stream network, subbasins, and cross-section shapefiles for drainage basins selected for analysis. Rainfall predictions are made by a numerical weather model and ingested in gridded format into the simulation environment. Runoff hydrographs are estimated using Green-Ampt infiltration excess runoff prediction and a 1D diffusive wave overland flow routing approach. The hydrographs are fed into the stream network and integrated in a dynamic wave routing module using the EPA's Storm Water Management Model (SWMM) to predict flood depth. The flood depths are then transformed into inundation maps and exported for damage assessment. Hydrologic/hydraulic results are presented for Tropical Storm Allison.

Performance Evaluation of Three Different High Resolution Satellite Images in Semi-Automatic Urban Illegal Building Detection

Science.gov (United States)

Khalilimoghadama, N.; Delavar, M. R.; Hanachi, P.

2017-09-01

The problem of overcrowding of mega cities has been bolded in recent years. To meet the need of housing this increased population, which is of great importance in mega cities, a huge number of buildings are constructed annually. With the ever-increasing trend of building constructions, we are faced with the growing trend of building infractions and illegal buildings (IBs). Acquiring multi-temporal satellite images and using change detection techniques is one of the proper methods of IB monitoring. Using the type of satellite images with different spatial and spectral resolutions has always been an issue in efficient detection of the building changes. In this research, three bi-temporal high-resolution satellite images of IRS-P5, GeoEye-1 and QuickBird sensors acquired from the west of metropolitan area of Tehran, capital of Iran, in addition to city maps and municipality property database were used to detect the under construction buildings with improved performance and accuracy. Furthermore, determining the employed bi-temporal satellite images to provide better performance and accuracy in the case of IB detection is the other purpose of this research. The Kappa coefficients of 70 %, 64 %, and 68 % were obtained for producing change image maps using GeoEye-1, IRS-P5, and QuickBird satellite images, respectively. In addition, the overall accuracies of 100 %, 6 %, and 83 % were achieved for IB detection using the satellite images, respectively. These accuracies substantiate the fact that the GeoEye-1 satellite images had the best performance among the employed images in producing change image map and detecting the IBs.

Flood Finder: Mobile-based automated water level estimation and mapping during floods

International Nuclear Information System (INIS)

Pongsiriyaporn, B; Jariyavajee, C; Laoharawee, N; Narkthong, N; Pitichat, T; Goldin, S E

2014-01-01

Every year, Southeast Asia faces numerous flooding disasters, resulting in very high human and economic loss. Responding to a sudden flood is difficult due to the lack of accurate and up-to- date information about the incoming water status. We have developed a mobile application called Flood Finder to solve this problem. Flood Finder allows smartphone users to measure, share and search for water level information at specified locations. The application uses image processing to compute the water level from a photo taken by users. The photo must be of a known reference object with a standard size. These water levels are more reliable and consistent than human estimates since they are derived from an algorithmic measuring function. Flood Finder uploads water level readings to the server, where they can be searched and mapped by other users via the mobile phone app or standard browsers. Given the widespread availability of smartphones in Asia, Flood Finder can provide more accurate and up-to-date information for better preparation for a flood disaster as well as life safety and property protection

Estimating Coastal Lagoon Tidal Flooding and Repletion with Multidate ASTER Thermal Imagery

Directory of Open Access Journals (Sweden)

Thomas R. Allen

2012-10-01

Full Text Available Coastal lagoons mix inflowing freshwater and tidal marine waters in complex spatial patterns. This project sought to detect and measure temperature and spatial variability of flood tides for a constricted coastal lagoon using multitemporal remote sensing. Advanced Spaceborne Thermal Emission Radiometer (ASTER thermal infrared data provided estimates of surface temperature for delineation of repletion zones in portions of Chincoteague Bay, Virginia. ASTER high spatial resolution sea-surface temperature imagery in conjunction with in situ observations and tidal predictions helped determine the optimal seasonal data for analyses. The selected time series ASTER satellite data sets were analyzed at different tidal phases and seasons in 2004–2006. Skin surface temperatures of ocean and estuarine waters were differentiated by flood tidal penetration and ebb flows. Spatially variable tidal flood penetration was evaluated using discrete seed-pixel area analysis and time series Principal Components Analysis. Results from these techniques provide spatial extent and variability dynamics of tidal repletion, flushing, and mixing, important factors in eutrophication assessment, water quality and resource monitoring, and application of hydrodynamic modeling for coastal estuary science and management.

Integrating adaptive behaviour in large-scale flood risk assessments: an Agent-Based Modelling approach

Science.gov (United States)

Haer, Toon; Aerts, Jeroen

2015-04-01

Between 1998 and 2009, Europe suffered over 213 major damaging floods, causing 1126 deaths, displacing around half a million people. In this period, floods caused at least 52 billion euro in insured economic losses making floods the most costly natural hazard faced in Europe. In many low-lying areas, the main strategy to cope with floods is to reduce the risk of the hazard through flood defence structures, like dikes and levees. However, it is suggested that part of the responsibility for flood protection needs to shift to households and businesses in areas at risk, and that governments and insurers can effectively stimulate the implementation of individual protective measures. However, adaptive behaviour towards flood risk reduction and the interaction between the government, insurers, and individuals has hardly been studied in large-scale flood risk assessments. In this study, an European Agent-Based Model is developed including agent representatives for the administrative stakeholders of European Member states, insurers and reinsurers markets, and individuals following complex behaviour models. The Agent-Based Modelling approach allows for an in-depth analysis of the interaction between heterogeneous autonomous agents and the resulting (non-)adaptive behaviour. Existing flood damage models are part of the European Agent-Based Model to allow for a dynamic response of both the agents and the environment to changing flood risk and protective efforts. By following an Agent-Based Modelling approach this study is a first contribution to overcome the limitations of traditional large-scale flood risk models in which the influence of individual adaptive behaviour towards flood risk reduction is often lacking.

Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast

International Nuclear Information System (INIS)

Escrig, H.; Batlles, F.J.; Alonso, J.; Baena, F.M.; Bosch, J.L.; Salbidegoitia, I.B.; Burgaleta, J.I.

2013-01-01

Considering that clouds are the greatest causes to solar radiation blocking, short term cloud forecasting can help power plant operation and therefore improve benefits. Cloud detection, classification and motion vector determination are key to forecasting sun obstruction by clouds. Geostationary satellites provide cloud information covering wide areas, allowing cloud forecast to be performed for several hours in advance. Herein, the methodology developed and tested in this study is based on multispectral tests and binary cross correlations followed by coherence and quality control tests over resulting motion vectors. Monthly synthetic surface albedo image and a method to reject erroneous correlation vectors were developed. Cloud classification in terms of opacity and height of cloud top is also performed. A whole-sky camera has been used for validation, showing over 85% of agreement between the camera and the satellite derived cloud cover, whereas error in motion vectors is below 15%. - Highlights: ► A methodology for detection, classification and movement of clouds is presented. ► METEOSAT satellite images are used to obtain a cloud mask. ► The prediction of cloudiness is estimated with 90% in overcast conditions. ► Results for partially covered sky conditions showed a 75% accuracy. ► Motion vectors are estimated from the clouds with a success probability of 86%

Radar-based Flood Warning System for Houston, Texas and Its Performance Evaluation

Science.gov (United States)

Fang, N.; Bedient, P.

2009-12-01

Houston has a long history of flooding problems as a serious nature. For instance, Houstonians suffered from severe flood inundation during Tropical Storm Allison in 2001 and Hurricane Ike in 2008. Radar-based flood warning systems as non-structural tools to provide accurate and timely warnings to the public and private entities are greatly needed for urban areas prone to flash floods. Fortunately, the advent of GIS, radar-based rainfall estimation using NEXRAD, and real-time delivery systems on the internet have allowed flood alert systems to provide important advanced warning of impending flood conditions. Thus, emergency personnel can take proper steps to mitigate against catastrophic losses. The Rice and Texas Medical Center (TMC) Flood Alert System (FAS2) has been delivering warning information with 2 to 3 hours of lead time to facility personnel in a readily understood format for more than 40 events since 1997. The system performed well during these major rainfall events with R square value of 93%. The current system has been improved by incorporating a new hydraulic prediction tool - FloodPlain Map Library (FPML). The FPML module aims to provide visualized information such as floodplain maps and water surface elevations instead of just showing hydrographs in real time based on NEXRAD radar rainfall data. During Hurricane Ike (September, 2008), FAS2 successfully provided precise and timely flood warning information to TMC with the peak flow difference of 3.6% and the volume difference of 5.6%; timing was excellent for this double-peaked event. With the funding from the Texas Department of Transportation, a similar flood warning system has been developed at a critical transportation pass along Highway 288 in Houston, Texas. In order to enable emergency personnel to begin flood preparation with as much lead time as possible, FAS2 is being used as a prototype to develop warning system for other flood-prone areas such as City of Sugar Land.

Global trends in satellite-based emergency mapping

Science.gov (United States)

Voigt, Stefan; Giulio-Tonolo, Fabio; Lyons, Josh; Kučera, Jan; Jones, Brenda; Schneiderhan, Tobias; Platzeck, Gabriel; Kaku, Kazuya; Hazarika, Manzul Kumar; Czaran, Lorant; Li, Suju; Pedersen, Wendi; James, Godstime Kadiri; Proy, Catherine; Muthike, Denis Macharia; Bequignon, Jerome; Guha-Sapir, Debarati

2016-01-01

Over the past 15 years, scientists and disaster responders have increasingly used satellite-based Earth observations for global rapid assessment of disaster situations. We review global trends in satellite rapid response and emergency mapping from 2000 to 2014, analyzing more than 1000 incidents in which satellite monitoring was used for assessing major disaster situations. We provide a synthesis of spatial patterns and temporal trends in global satellite emergency mapping efforts and show that satellite-based emergency mapping is most intensively deployed in Asia and Europe and follows well the geographic, physical, and temporal distributions of global natural disasters. We present an outlook on the future use of Earth observation technology for disaster response and mitigation by putting past and current developments into context and perspective.

SHADOW DETECTION FROM VERY HIGH RESOLUTON SATELLITE IMAGE USING GRABCUT SEGMENTATION AND RATIO-BAND ALGORITHMS

Directory of Open Access Journals (Sweden)

N. M. S. M. Kadhim

2015-03-01

Full Text Available Very-High-Resolution (VHR satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-the-art shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour, the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates

Shadow Detection from Very High Resoluton Satellite Image Using Grabcut Segmentation and Ratio-Band Algorithms

Science.gov (United States)

Kadhim, N. M. S. M.; Mourshed, M.; Bray, M. T.

2015-03-01

Very-High-Resolution (VHR) satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-the-art shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour), the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates significant performance of

Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

Science.gov (United States)

Kit, Oleksandr; Lüdeke, Matthias

2013-09-01

This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.

Integrating Data Streams from in-situ Measurements, Social Networks and Satellite Earth Observation to Augment Operational Flood Monitoring and Forecasting: the 2017 Hurricane Season in the Americas as a Large-scale Test Case

Science.gov (United States)

Matgen, P.; Pelich, R.; Brangbour, E.; Bruneau, P.; Chini, M.; Hostache, R.; Schumann, G.; Tamisier, T.

2017-12-01

Hurricanes Harvey, Irma and Maria generated large streams of heterogeneous data, coming notably from three main sources: imagery (satellite and aircraft), in-situ measurement stations and social media. Interpreting these data streams brings critical information to develop, validate and update prediction models. The study addresses existing gaps in the joint extraction of disaster risk information from multiple data sources and their usefulness for reducing the predictive uncertainty of large-scale flood inundation models. Satellite EO data, most notably the free-of-charge data streams generated by the Copernicus program, provided a wealth of high-resolution imagery covering the large areas affected. Our study is focussing on the mapping of flooded areas from a sequence of Sentinel-1 SAR imagery using a classification algorithm recently implemented on the European Space Agency's Grid Processing On Demand environment. The end-to-end-processing chain provided a fast access to all relevant imagery and an effective processing for near-real time analyses. The classification algorithm was applied on pairs of images to rapidly and automatically detect, record and disseminate all observable changes of water bodies. Disaster information was also retrieved from photos as well as texts contributed on social networks and the study shows how this information may complement EO and in-situ data and augment information content. As social media data are noisy and difficult to geo-localize, different techniques are being developed to automatically infer associated semantics and geotags. The presentation provides a cross-comparison between the hazard information obtained from the three data sources. We provide examples of how the generated database of geo-localized disaster information was finally integrated into a large-scale hydrodynamic model of the Colorado River emptying into the Matagorda Bay on the Gulf of Mexico in order to reduce its predictive uncertainty. We describe the

Enhancement of global flood damage assessments using building material based vulnerability curves

Science.gov (United States)

Englhardt, Johanna; de Ruiter, Marleen; de Moel, Hans; Aerts, Jeroen

2017-04-01

This study discusses the development of an enhanced approach for flood damage and risk assessments using vulnerability curves that are based on building material information. The approach draws upon common practices in earthquake vulnerability assessments, and is an alternative for land-use or building occupancy approach in flood risk assessment models. The approach is of particular importance for studies where there is a large variation in building material, such as large scale studies or studies in developing countries. A case study of Ethiopia is used to demonstrate the impact of the different methodological approaches on direct damage assessments due to flooding. Generally, flood damage assessments use damage curves for different land-use or occupancy types (i.e. urban or residential and commercial classes). However, these categories do not necessarily relate directly to vulnerability of damage by flood waters. For this, the construction type and building material may be more important, as is used in earthquake risk assessments. For this study, we use building material classification data of the PAGER1 project to define new building material based vulnerability classes for flood damage. This approach will be compared to the widely applied land-use based vulnerability curves such as used by De Moel et al. (2011). The case of Ethiopia demonstrates and compares the feasibility of this novel flood vulnerability method on a country level which holds the potential to be scaled up to a global level. The study shows that flood vulnerability based on building material also allows for better differentiation between flood damage in urban and rural settings, opening doors to better link to poverty studies when such exposure data is available. Furthermore, this new approach paves the road to the enhancement of multi-risk assessments as the method enables the comparison of vulnerability across different natural hazard types that also use material-based vulnerability curves

Evaluation of Refuge Life Risk using Geographical and Social Grid-Models with Satellite-Based House Ratio and Flood Depth by Tsunami Simulation

Science.gov (United States)

Kaneko, D.; Hosoyamada, T.

2017-12-01

evacuation measures against life risk have been created using satellite land-cover classification data in the city. The two models using tsunami-flooding simulation have been used to design the disaster prevention hill. The results obtained contribute to preparing the society for disaster prevention measures or reconstruction after tsunami disasters.

NWP-Based Adjustment of IMERG Precipitation for Flood-Inducing Complex Terrain Storms: Evaluation over CONUS

Directory of Open Access Journals (Sweden)

Xinxuan Zhang

2018-04-01

Full Text Available This paper evaluates the use of precipitation forecasts from a numerical weather prediction (NWP model for near-real-time satellite precipitation adjustment based on 81 flood-inducing heavy precipitation events in seven mountainous regions over the conterminous United States. The study is facilitated by the National Center for Atmospheric Research (NCAR real-time ensemble forecasts (called model, the Integrated Multi-satellitE Retrievals for GPM (IMERG near-real-time precipitation product (called raw IMERG and the Stage IV multi-radar/multi-sensor precipitation product (called Stage IV used as a reference. We evaluated four precipitation datasets (the model forecasts, raw IMERG, gauge-adjusted IMERG and model-adjusted IMERG through comparisons against Stage IV at six-hourly and event length scales. The raw IMERG product consistently underestimated heavy precipitation in all study regions, while the domain average rainfall magnitudes exhibited by the model were fairly accurate. The model exhibited error in the locations of intense precipitation over inland regions, however, while the IMERG product generally showed correct spatial precipitation patterns. Overall, the model-adjusted IMERG product performed best over inland regions by taking advantage of the more accurate rainfall magnitude from NWP and the spatial distribution from IMERG. In coastal regions, although model-based adjustment effectively improved the performance of the raw IMERG product, the model forecast performed even better. The IMERG product could benefit from gauge-based adjustment, as well, but the improvement from model-based adjustment was consistently more significant.

A Novel Flood Forecasting Method Based on Initial State Variable Correction

Directory of Open Access Journals (Sweden)

Kuang Li

2017-12-01

Full Text Available The influence of initial state variables on flood forecasting accuracy by using conceptual hydrological models is analyzed in this paper and a novel flood forecasting method based on correction of initial state variables is proposed. The new method is abbreviated as ISVC (Initial State Variable Correction. The ISVC takes the residual between the measured and forecasted flows during the initial period of the flood event as the objective function, and it uses a particle swarm optimization algorithm to correct the initial state variables, which are then used to drive the flood forecasting model. The historical flood events of 11 watersheds in south China are forecasted and verified, and important issues concerning the ISVC application are then discussed. The study results show that the ISVC is effective and applicable in flood forecasting tasks. It can significantly improve the flood forecasting accuracy in most cases.

Action-based flood forecasting for triggering humanitarian action

Science.gov (United States)

Coughlan de Perez, Erin; van den Hurk, Bart; van Aalst, Maarten K.; Amuron, Irene; Bamanya, Deus; Hauser, Tristan; Jongma, Brenden; Lopez, Ana; Mason, Simon; Mendler de Suarez, Janot; Pappenberger, Florian; Rueth, Alexandra; Stephens, Elisabeth; Suarez, Pablo; Wagemaker, Jurjen; Zsoter, Ervin

2016-09-01

Too often, credible scientific early warning information of increased disaster risk does not result in humanitarian action. With financial resources tilted heavily towards response after a disaster, disaster managers have limited incentive and ability to process complex scientific data, including uncertainties. These incentives are beginning to change, with the advent of several new forecast-based financing systems that provide funding based on a forecast of an extreme event. Given the changing landscape, here we demonstrate a method to select and use appropriate forecasts for specific humanitarian disaster prevention actions, even in a data-scarce location. This action-based forecasting methodology takes into account the parameters of each action, such as action lifetime, when verifying a forecast. Forecasts are linked with action based on an understanding of (1) the magnitude of previous flooding events and (2) the willingness to act "in vain" for specific actions. This is applied in the context of the Uganda Red Cross Society forecast-based financing pilot project, with forecasts from the Global Flood Awareness System (GloFAS). Using this method, we define the "danger level" of flooding, and we select the probabilistic forecast triggers that are appropriate for specific actions. Results from this methodology can be applied globally across hazards and fed into a financing system that ensures that automatic, pre-funded early action will be triggered by forecasts.

Hydrologic Simulation in Mediterranean flood prone Watersheds using high-resolution quality data

Science.gov (United States)

Eirini Vozinaki, Anthi; Alexakis, Dimitrios; Pappa, Polixeni; Tsanis, Ioannis

2015-04-01

Flooding is a significant threat causing lots of inconveniencies in several societies, worldwide. The fact that the climatic change is already happening, increases the flooding risk, which is no longer a substantial menace to several societies and their economies. The improvement of spatial-resolution and accuracy of the topography and land use data due to remote sensing techniques could provide integrated flood inundation simulations. In this work hydrological analysis of several historic flood events in Mediterranean flood prone watersheds (island of Crete/Greece) takes place. Satellite images of high resolution are elaborated. A very high resolution (VHR) digital elevation model (DEM) is produced from a GeoEye-1 0.5-m-resolution satellite stereo pair and is used for floodplain management and mapping applications such as watershed delineation and river cross-section extraction. Sophisticated classification algorithms are implemented for improving Land Use/ Land Cover maps accuracy. In addition, soil maps are updated with means of Radar satellite images. The above high-resolution data are innovatively used to simulate and validate several historical flood events in Mediterranean watersheds, which have experienced severe flooding in the past. The hydrologic/hydraulic models used for flood inundation simulation in this work are HEC-HMS and HEC-RAS. The Natural Resource Conservation Service (NRCS) curve number (CN) approach is implemented to account for the effect of LULC and soil on the hydrologic response of the catchment. The use of high resolution data provides detailed validation results and results of high precision, accordingly. Furthermore, the meteorological forecasting data, which are also combined to the simulation model results, manage the development of an integrated flood forecasting and early warning system tool, which is capable of confronting or even preventing this imminent risk. The research reported in this paper was fully supported by the

Status Update on the GPM Ground Validation Iowa Flood Studies (IFloodS) Field Experiment

Science.gov (United States)

Petersen, Walt; Krajewski, Witold

2013-04-01

The overarching objective of integrated hydrologic ground validation activities supporting the Global Precipitation Measurement Mission (GPM) is to provide better understanding of the strengths and limitations of the satellite products, in the context of hydrologic applications. To this end, the GPM Ground Validation (GV) program is conducting the first of several hydrology-oriented field efforts: the Iowa Flood Studies (IFloodS) experiment. IFloodS will be conducted in the central to northeastern part of Iowa in Midwestern United States during the months of April-June, 2013. Specific science objectives and related goals for the IFloodS experiment can be summarized as follows: 1. Quantify the physical characteristics and space/time variability of rain (rates, DSD, process/"regime") and map to satellite rainfall retrieval uncertainty. 2. Assess satellite rainfall retrieval uncertainties at instantaneous to daily time scales and evaluate propagation/impact of uncertainty in flood-prediction. 3. Assess hydrologic predictive skill as a function of space/time scales, basin morphology, and land use/cover. 4. Discern the relative roles of rainfall quantities such as rate and accumulation as compared to other factors (e.g. transport of water in the drainage network) in flood genesis. 5. Refine approaches to "integrated hydrologic GV" concept based on IFloodS experiences and apply to future GPM Integrated GV field efforts. These objectives will be achieved via the deployment of the NASA NPOL S-band and D3R Ka/Ku-band dual-polarimetric radars, University of Iowa X-band dual-polarimetric radars, a large network of paired rain gauge platforms with attendant soil moisture and temperature probes, a large network of both 2D Video and Parsivel disdrometers, and USDA-ARS gauge and soil-moisture measurements (in collaboration with the NASA SMAP mission). The aforementioned measurements will be used to complement existing operational WSR-88D S-band polarimetric radar measurements

Effective delineation of urban flooded areas based on aerial ortho-photo imagery

Science.gov (United States)

Zhang, Ying; Guindon, Bert; Raymond, Don; Hong, Gang

2016-10-01

The combination of rapid global urban growth and climate change has resulted in increased occurrence of major urban flood events across the globe. The distribution of flooded area is one of the key information layers for applications of emergency planning and response management. While SAR systems and technologies have been widely used for flood area delineation, radar images suffer from range ambiguities arising from corner reflection effects and shadowing in dense urban settings. A new mapping framework is proposed for the extraction and quantification of flood extent based on aerial optical multi-spectral imagery and ancillary data. This involves first mapping of flood areas directly visible to the sensor. Subsequently, the complete area of submergence is estimated from this initial mapping and inference techniques based on baseline data such as land cover and GIS information such as available digital elevation models. The methodology has been tested and proven effective using aerial photography for the case of the 2013 flood in Calgary, Canada.

A web GIS based integrated flood assessment modeling tool for coastal urban watersheds

Science.gov (United States)

Kulkarni, A. T.; Mohanty, J.; Eldho, T. I.; Rao, E. P.; Mohan, B. K.

2014-03-01

Urban flooding has become an increasingly important issue in many parts of the world. In this study, an integrated flood assessment model (IFAM) is presented for the coastal urban flood simulation. A web based GIS framework has been adopted to organize the spatial datasets for the study area considered and to run the model within this framework. The integrated flood model consists of a mass balance based 1-D overland flow model, 1-D finite element based channel flow model based on diffusion wave approximation and a quasi 2-D raster flood inundation model based on the continuity equation. The model code is written in MATLAB and the application is integrated within a web GIS server product viz: Web Gram Server™ (WGS), developed at IIT Bombay, using Java, JSP and JQuery technologies. Its user interface is developed using open layers and the attribute data are stored in MySQL open source DBMS. The model is integrated within WGS and is called via Java script. The application has been demonstrated for two coastal urban watersheds of Navi Mumbai, India. Simulated flood extents for extreme rainfall event of 26 July, 2005 in the two urban watersheds of Navi Mumbai city are presented and discussed. The study demonstrates the effectiveness of the flood simulation tool in a web GIS environment to facilitate data access and visualization of GIS datasets and simulation results.

Flood forecasting and warning systems in Pakistan

International Nuclear Information System (INIS)

Ali Awan, Shaukat

2004-01-01

Meteorologically, there are two situations which may cause three types of floods in Indus Basin in Pakistan: i) Meteorological Situation for Category-I Floods when the seasonal low is a semi permanent weather system situated over south eastern Balochistan, south western Punjab, adjoining parts of Sindh get intensified and causes the moisture from the Arabian Sea to be brought up to upper catchments of Chenab and Jhelum rivers. (ii) Meteorological Situation for Category-11 and Category-111 Floods, which is linked with monsoon low/depression. Such monsoon systems originate in Bay of Bengal region and then move across India in general west/north westerly direction arrive over Rajasthan or any of adjoining states of India. Flood management in Pakistan is multi-functional process involving a number of different organizations. The first step in the process is issuance of flood forecast/warning, which is performed by Pakistan Meteorological Department (PMD) utilizing satellite cloud pictures and quantitative precipitation measurement radar data, in addition to the conventional weather forecasting facilities. For quantitative flood forecasting, hydrological data is obtained through the Provincial Irrigation Department and WAPDA. Furthermore, improved rainfall/runoff and flood routing models have been developed to provide more reliable and explicit flood information to a flood prone population.(Author)

Mapping Infected Area after a Flash-Flooding Storm Using Multi Criteria Analysis and Spectral Indices

Science.gov (United States)

Al-Akad, S.; Akensous, Y.; Hakdaoui, M.

2017-11-01

This research article is summarize the applications of remote sensing and GIS to study the urban floods risk in Al Mukalla. Satellite acquisition of a flood event on October 2015 in Al Mukalla (Yemen) by using flood risk mapping techniques illustrate the potential risk present in this city. Satellite images (The Landsat and DEM images data were atmospherically corrected, radiometric corrected, and geometric and topographic distortions rectified.) are used for flood risk mapping to afford a hazard (vulnerability) map. This map is provided by applying image-processing techniques and using geographic information system (GIS) environment also the application of NDVI, NDWI index, and a method to estimate the flood-hazard areas. Four factors were considered in order to estimate the spatial distribution of the hazardous areas: flow accumulation, slope, land use, geology and elevation. The multi-criteria analysis, allowing to deal with vulnerability to flooding, as well as mapping areas at the risk of flooding of the city Al Mukalla. The main object of this research is to provide a simple and rapid method to reduce and manage the risks caused by flood in Yemen by take as example the city of Al Mukalla.

Rain detection over land surfaces using passive microwave satellite data

NARCIS (Netherlands)

Bauer, P.; Burose, D.; Schulz, J.

2002-01-01

An algorithm is presented for the detection of surface rainfall using passive microwave measurements by satellite radiometers. The technique consists of a two-stage approach to distinguish precipitation signatures from other effects: (1) Contributions from slowly varying parameters (surface type and

The Skeletal Muscle Satellite Cell

Science.gov (United States)

2011-01-01

The skeletal muscle satellite cell was first described and named based on its anatomic location between the myofiber plasma and basement membranes. In 1961, two independent studies by Alexander Mauro and Bernard Katz provided the first electron microscopic descriptions of satellite cells in frog and rat muscles. These cells were soon detected in other vertebrates and acquired candidacy as the source of myogenic cells needed for myofiber growth and repair throughout life. Cultures of isolated myofibers and, subsequently, transplantation of single myofibers demonstrated that satellite cells were myogenic progenitors. More recently, satellite cells were redefined as myogenic stem cells given their ability to self-renew in addition to producing differentiated progeny. Identification of distinctively expressed molecular markers, in particular Pax7, has facilitated detection of satellite cells using light microscopy. Notwithstanding the remarkable progress made since the discovery of satellite cells, researchers have looked for alternative cells with myogenic capacity that can potentially be used for whole body cell-based therapy of skeletal muscle. Yet, new studies show that inducible ablation of satellite cells in adult muscle impairs myofiber regeneration. Thus, on the 50th anniversary since its discovery, the satellite cell’s indispensable role in muscle repair has been reaffirmed. PMID:22147605

Object-Based Assessment of Satellite Precipitation Products

Directory of Open Access Journals (Sweden)

Jingjing Li

2016-06-01

Full Text Available An object-based verification approach is employed to assess the performance of the commonly used high-resolution satellite precipitation products: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN, Climate Prediction center MORPHing technique (CMORPH, and Tropical Rainfall Measurement Mission (TRMM Multi-Satellite Precipitation Analysis (TMPA 3B42RT. The evaluation of the satellite precipitation products focuses on the skill of depicting the geometric features of the localized precipitation areas. Seasonal variability of the performances of these products against the ground observations is investigated through the examples of warm and cold seasons. It is found that PERSIANN is capable of depicting the orientation of the localized precipitation areas in both seasons. CMORPH has the ability to capture the sizes of the localized precipitation areas and performs the best in the overall assessment for both seasons. 3B42RT is capable of depicting the location of the precipitation areas for both seasons. In addition, all of the products perform better on capturing the sizes and centroids of precipitation areas in the warm season than in the cold season, while they perform better on depicting the intersection area and orientation in the cold season than in the warm season. These products are more skillful on correctly detecting the localized precipitation areas against the observations in the warm season than in the cold season.

Oil Spill Disasters Detection and Monitoring by RST Analysis of Optical Satellite Radiances: the Case of Deepwater Horizon Platform in the Gulf of Mexico

Science.gov (United States)

Pergola, N.; Grimaldi, S. C.; Coviello, I.; Faruolo, M.; Lacava, T.; Tramutoli, V.

2010-12-01

Marine oil spill disasters may have devastating effects on the marine and coastal environment. For monitoring and mitigation purposes, timely detection and continuously updated information on polluted areas are required. Satellite remote sensing can give a significant contribution in such a direction. Nowadays, SAR (Synthetic Aperture Radar) technology has been recognized as the most efficient for oil spill detection and mapping, thanks to the high spatial resolution and all-time/all-weather capability of the present operational sensors. Anyway, the present SARs revisiting time does not allow for a rapid detection and a near real-time monitoring of these phenomena at global scale. Passive optical sensors, on board meteorological satellites, thanks to their high temporal resolution (from a few hours to 15 minutes, depending on the characteristics of the platform/sensor), may represent, at this moment, a suitable SAR alternative/complement for oil spill detection and monitoring. Up to now, some techniques, based on optical satellite data, have been proposed for “a posteriori” mapping of already known oil spill discharges. On the other hand, reliable satellite methods for an automatic and timely detection of oil spills, for surveillance and warning purposes, are still currently missing. Recently, an innovative technique for automatic and near real time oil spill detection and monitoring has been proposed. The technique is based on the general RST (Robust Satellite Technique) approach which exploits multi-temporal satellite records in order to obtain a former characterization of the measured signal, in terms of expected value and natural variability, providing a further identification of signal anomalies by an automatic, unsupervised change detection step. Results obtained by using AVHRR (Advanced Very High Resolution Radiometer) Thermal Infrared data, in different geographic areas and observational conditions, demonstrated excellent detection capabilities both in

SALIENCY BASED SEGMENTATION OF SATELLITE IMAGES

Directory of Open Access Journals (Sweden)

A. Sharma

2015-03-01

Full Text Available Saliency gives the way as humans see any image and saliency based segmentation can be eventually helpful in Psychovisual image interpretation. Keeping this in view few saliency models are used along with segmentation algorithm and only the salient segments from image have been extracted. The work is carried out for terrestrial images as well as for satellite images. The methodology used in this work extracts those segments from segmented image which are having higher or equal saliency value than a threshold value. Salient and non salient regions of image become foreground and background respectively and thus image gets separated. For carrying out this work a dataset of terrestrial images and Worldview 2 satellite images (sample data are used. Results show that those saliency models which works better for terrestrial images are not good enough for satellite image in terms of foreground and background separation. Foreground and background separation in terrestrial images is based on salient objects visible on the images whereas in satellite images this separation is based on salient area rather than salient objects.

Flood Risk Assessment Based On Security Deficit Analysis

Science.gov (United States)

Beck, J.; Metzger, R.; Hingray, B.; Musy, A.

Risk is a human perception: a given risk may be considered as acceptable or unac- ceptable depending on the group that has to face that risk. Flood risk analysis of- ten estimates economic losses from damages, but neglects the question of accept- able/unacceptable risk. With input from land use managers, politicians and other stakeholders, risk assessment based on security deficit analysis determines objects with unacceptable risk and their degree of security deficit. Such a risk assessment methodology, initially developed by the Swiss federal authorities, is illustrated by its application on a reach of the Alzette River (Luxembourg) in the framework of the IRMA-SPONGE FRHYMAP project. Flood risk assessment always involves a flood hazard analysis, an exposed object vulnerability analysis, and an analysis combing the results of these two previous analyses. The flood hazard analysis was done with the quasi-2D hydraulic model FldPln to produce flood intensity maps. Flood intensity was determined by the water height and velocity. Object data for the vulnerability analysis, provided by the Luxembourg government, were classified according to their potential damage. Potential damage is expressed in terms of direct, human life and secondary losses. A thematic map was produced to show the object classification. Protection goals were then attributed to the object classes. Protection goals are assigned in terms of an acceptable flood intensity for a certain flood frequency. This is where input from land use managers and politicians comes into play. The perception of risk in the re- gion or country influences the protection goal assignment. Protection goals as used in Switzerland were used in this project. Thematic maps showing the protection goals of each object in the case study area for a given flood frequency were produced. Com- parison between an object's protection goal and the intensity of the flood that touched the object determine the acceptability of the risk and the

Flood Insurance Rate Maps and Base Flood Elevations, FIRM, DFIRM, BFE, Federal Emergency Management Agency (FEMA) - Flood Insurance Rate Maps (FIRM), Published in 2011, 1:1200 (1in=100ft) scale, Polk County Government.

Data.gov (United States)

NSGIC Local Govt | GIS Inventory — Flood Insurance Rate Maps and Base Flood Elevations, FIRM, DFIRM, BFE dataset current as of 2011. Federal Emergency Management Agency (FEMA) - Flood Insurance Rate...

Satellite skill in detecting extreme episodes in near-surface air quality

Science.gov (United States)

Ruiz, D. J.; Prather, M. J.

2017-12-01

Ozone (O3) contributes to ambient air pollution, adversely affecting public health, agriculture, and ecosystems. Reliable, long-term, densely distributed surface networks are required to establish the scale, intensity and repeatability of major pollution events (designated here in a climatological sense as air quality extremes, AQX as defined in Schnell's work). Regrettably, such networks are only available for North America (NA) and Europe (EU), which does not include many populated regions where the deaths associated with air pollution exposure are alarmingly high. Directly measuring surface pollutants from space without lidar is extremely difficult. Mapping of daily pollution events requires cross-track nadir scanners and these have limited sensitivity to surface O3 levels. This work examines several years of coincident surface and OMI satellite measurements over NA-EU, in combination with a chemistry-transport model (CTM) hindcast of that period to understand how the large-scale AQX episodes may extend into the free troposphere and thus be more amenable to satellite mapping. We show how extreme NA-EU episodes are measured from OMI and then look for such patterns over other polluted regions of the globe. We gather individual high-quality O3 surface site measurements from these other regions, to check on our satellite detection. Our approach with global satellite detection would avoid issues associated with regional variations in seasonality, chemical regime, data product biases; and it does not require defining a separate absolute threshold for each data product (surface site and satellite). This also enables coherent linking of the extreme events into large-scale pollution episodes whose magnitude evolves over 100's of km for several days. Tools used here include the UC Irvine CTM, which shows that much of the O3 surface variability is lost at heights above 2 km, but AQX local events are readily seen in a 0-3 km column average. The OMI data are taken from X

PLASTIC AND GLASS GREENHOUSES DETECTION AND DELINEATION FROM WORLDVIEW-2 SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

D. Koc-San

2016-06-01

Full Text Available Greenhouse detection using remote sensing technologies is an important research area for yield estimation, sustainable development, urban and rural planning and management. An approach was developed in this study for the detection and delineation of greenhouse areas from high resolution satellite imagery. Initially, the candidate greenhouse patches were detected using supervised classification techniques. For this purpose, Maximum Likelihood (ML, Random Forest (RF, and Support Vector Machines (SVM classification techniques were applied and compared. Then, sieve filter and morphological operations were performed for improving the classification results. Finally, the obtained candidate plastic and glass greenhouse areas were delineated using boundary tracing and Douglas Peucker line simplification algorithms. The proposed approach was implemented in the Kumluca district of Antalya, Turkey utilizing pan-sharpened WorldView-2 satellite imageries. Kumluca is the prominent district of Antalya with greenhouse cultivation and includes both plastic and glass greenhouses intensively. When the greenhouse classification results were analysed, it can be stated that the SVM classification provides most accurate results and RF classification follows this. The SVM classification overall accuracy was obtained as 90.28%. When the greenhouse boundary delineation results were considered, the plastic greenhouses were delineated with 92.11% accuracy, while glass greenhouses were delineated with 80.67% accuracy. The obtained results indicate that, generally plastic and glass greenhouses can be detected and delineated successfully from WorldView-2 satellite imagery.

Precipitation Analysis at Fine Time Scales Using Multiple Satellites: Real-time and Research Products and Applications

Science.gov (United States)

Adler, Robert; Huffman, George; Bolvin, David; Nelkin, Eric; Curtis, Scott; Pierce, Harold

2004-01-01

Quasi-global precipitation analyses at fine time scales (3-hr) are described. TRMM observations (radar and passive microwave) are used to calibrate polar-orbit microwave observations from SSM/I (and other satellites instruments, including AMSR and AMSU) and geosynchronous IR observations. The individual data sets are then merged using a priority order based on quality to form the Multi-satellite Precipitation Analysis (MPA). Raingauge information is used to help constrain the satellite-based estimates over land. The TRMM standard research product (Version 6 3B-42 of the TRMM products) will be available for the entire TRMM period (January 1998-present) in 2004. The real-time version of this merged product has been produced over the past two years and is available on the U.S. TRMM web site (trmm.gsfc.nasa.gov) at 0.25" latitude-longitude resolution over the latitude range from 5O"N-5O0S. Validation of daily totals indicates good results, with limitations noted in mid-latitude winter over land and regions of shallow, orographic precipitation. Various applications of these estimates are described, including: 1) detecting potential floods in near real-time; 2) analyzing Indian Ocean precipitation variations related to the initiation of El Nino; 3) determining characteristics of the African monsoon; and 4) analysis of diurnal variations.

Equalization and detection for digital communication over nonlinear bandlimited satellite communication channels. Ph.D. Thesis

Science.gov (United States)

Gutierrez, Alberto, Jr.

1995-01-01

This dissertation evaluates receiver-based methods for mitigating the effects due to nonlinear bandlimited signal distortion present in high data rate satellite channels. The effects of the nonlinear bandlimited distortion is illustrated for digitally modulated signals. A lucid development of the low-pass Volterra discrete time model for a nonlinear communication channel is presented. In addition, finite-state machine models are explicitly developed for a nonlinear bandlimited satellite channel. A nonlinear fixed equalizer based on Volterra series has previously been studied for compensation of noiseless signal distortion due to a nonlinear satellite channel. This dissertation studies adaptive Volterra equalizers on a downlink-limited nonlinear bandlimited satellite channel. We employ as figure of merits performance in the mean-square error and probability of error senses. In addition, a receiver consisting of a fractionally-spaced equalizer (FSE) followed by a Volterra equalizer (FSE-Volterra) is found to give improvement beyond that gained by the Volterra equalizer. Significant probability of error performance improvement is found for multilevel modulation schemes. Also, it is found that probability of error improvement is more significant for modulation schemes, constant amplitude and multilevel, which require higher signal to noise ratios (i.e., higher modulation orders) for reliable operation. The maximum likelihood sequence detection (MLSD) receiver for a nonlinear satellite channel, a bank of matched filters followed by a Viterbi detector, serves as a probability of error lower bound for the Volterra and FSE-Volterra equalizers. However, this receiver has not been evaluated for a specific satellite channel. In this work, an MLSD receiver is evaluated for a specific downlink-limited satellite channel. Because of the bank of matched filters, the MLSD receiver may be high in complexity. Consequently, the probability of error performance of a more practical

How frequently will the Surface Water and Ocean Topography (SWOT) observe floods?

Science.gov (United States)

Frasson, R. P. M.; Schumann, G.

2017-12-01

The SWOT mission will measure river width and water surface elevations of rivers wider than 100 m. As the data gathered by this mission will be freely available, it can be of great use for flood modeling, especially in areas where streamgage networks are exceedingly sparse, or when data sharing barriers prevent the timely access to information. Despite having world-wide coverage, SWOT's temporal sampling is limited, with most locations being revisited once or twice every 21 days. Our objective is to evaluate which fraction of world-wide floods SWOT will observe and how many observations per event the satellite will likely obtain. We take advantage of the extensive database of floods constructed by the Dartmouth Flood Observatory, who, since 1985, searches through news sources and governmental agencies, and more recently remote sensing imagery for flood information, including flood duration, location and affected area. We cross-referenced the flood locations in the DFO archive with the SWOT prototype prior database of river centerlines and the anticipated satellite's orbit to identify how many of the SWOT swaths were located within 10 km, 20 km, and 50 km from a flood centroid. Subsequently, we estimated the probability that SWOT would have at least one observation of a flood event per distance bin by multiplying the number of swaths in the distance bin by the flood duration divided by the SWOT orbit repeat period. Our analysis contemplated 132 world-wide floods recorded between May 2016 and May 2017. From these, 29, 52, and 86 floods had at least a 50% probability of having one overpass within 10 km, 20 km, and 50 km respectively. Moreover, after excluding flood events with no river centerlines within 10 km of its centroid, the average number of swaths within 10 km of a flood centroid was 1.79, indicating that in the 37 flood events that were likely caused by river flooding, at least one measurement was guaranteed to happen during the event.

Establishing a rainfall threshold for flash flood warnings based on the DFFG method in Yunnan province, China

Science.gov (United States)

Ma, M.; Wang, H.; Chen, Y.; Tang, G.; Hong, Z.; Zhang, K.; Hong, Y.

2017-12-01

appropriate threshold for flash flood warnings. Finally, the article highlights the importance of accurately simulating the hydrological processes and high-resolution satellite rainfall data on the accurate forecasting of rainfall triggered flash flood events.

Discharge estimation in arid areas with the help of optical satellite data

Science.gov (United States)

Mett, M.; Aufleger, M.

2009-04-01

The MENA region is facing severe water scarcity. Overexploitation of groundwater resources leads to an ongoing drawdown of the water tables, salinisation and desertification of vast areas. To make matters worse enormous birth-rates, economic growth and refugees from conflict areas let the need for water explode. In the context of climate change this situation will even worsen and armed conflicts are within the bounds of possibility. To ease water scarcity many innovative techniques like artificial groundwater recharge are being developed or already state of the art. But missing hydrological information (for instance discharge data) often prevents design and efficient operation of such measures. Especially in poor countries hydrological measuring devices like gage stations are often missing, in a bad status or professionals of the water sector are absent. This leads to the paradox situation that in many arid regions water resources are indeed available but they cannot be utilised because they are not known. Nowadays different approaches are being designed to obtain hydrological information from perennial river systems with the help of satellite techniques. Mostly they are based on hydraulic parameters like river dimensions, roughness and water levels which can be derived from satellite data. By using conventional flow formulas and additional field investigations the discharge can be estimated. Another methodology derived information about maximum flow depth and flow width from optical sensors of high resolution to calculate discharge of the rivers whilst the flood. Attempts to derive discharge information from structural components of the river and fluviomorphologic changes due to changing flow regimes are in the focus of recent research. One attempt used Synthetic Aperture Radar (SAR) data to estimate discharge in braided river systems. Other attempts used airborne SAR imagery to obtain information about sinuosity and total river width of perennial braided river

Optimisation of Lagrangian Flash Flood Microsensors Dropped by Unmanned Aerial Vehicle

KAUST Repository

Abdulaal, Mohammed

2014-05-01

Abstract Physical Sciences and Engineering Division Mechanical Engineering Department Master of Science Optimisation of Lagrangian Flash Flood Microsensors Dropped by Unmanned Aerial Vehicle by Mohammed Abdulaal Floods are the most common natural disasters, causing thousands of casualties every year in the world. In particular, ash ood events are particularly deadly because of the short timescales on which they occur. Classical sensing solutions such as xed wireless sensor networks or satellite imagery are either too expensive or too inaccurate. Nevertheless, Unmanned Aerial Vehicles equipped with mobile microsensors could be capable of sensing ash oods in real time for a low overall cost, saving lives and greatly improving the e ciency of the emergency response. Using ood simulation data, we show that this system could be used to detect ash oods. We also present an ongoing implementation of this system using 3D printed sensors and sensor delivery systems on a UAV testbed as well as some preliminary results.

Lower hybrid wave cavities detected by the FREJA satellite

International Nuclear Information System (INIS)

Pecseli, H.L.; Iranpour, K.; Holter, Oe.; Lybekk, B.; Holtet J.; Truelsen, J.; Holback, B.

1994-12-01

Localized electrostatic wave packets in the frequency region of lower-hybrid waves have been detected by the instruments on the FREJA satellite. These waves are usually associated with local density depletions indicating that the structures can be interpreted as wave filled cavities. The basic features of the observations are discussed. Based on simple statistical arguments it is attempted to present some characteristics which have to be accommodated within an ultimate theory describing the observed wave phenomena. An explanation in terms of collapse of nonlinear lower-hybrid waves is discussed in particular. It is argued that such a model seems inapplicable, at least in its simplest form, by providing time and length scales which are not in agreement with observations. Alternatives to this model are presented. 24 refs., 8 figs

An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

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Chandi Witharana

2016-04-01

Full Text Available The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We systematically gauged the segmentation quality, classification accuracy, and the reproducibility of fuzzy rules. A master ruleset was developed based on one study site and it was re-tasked “without adaptation” and “with adaptation” on candidate image scenes comprising guano stains. Our results suggest that object-based methods incorporating the spectral, textural, spatial, and contextual characteristics of guano are capable of successfully detecting guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while adapted rules produced comparable or superior results compared to the reference image. This work provides a road map to an operational “image-to-assessment pipeline” that will enable Antarctic wildlife researchers to seamlessly integrate VHSR imagery into on-demand penguin population census.

DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

Science.gov (United States)

Kalia, S.; Li, S.; Ganguly, S.; Nemani, R. R.

2017-12-01

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remotesensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud/shadow mask from geostationary satellite data iscritical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds, which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classifycloud/shadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoder-decoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multi-spectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

Towards a Flood Severity Index

Science.gov (United States)

Kettner, A.; Chong, A.; Prades, L.; Brakenridge, G. R.; Muir, S.; Amparore, A.; Slayback, D. A.; Poungprom, R.

2017-12-01

Flooding is the most common natural hazard worldwide, affecting 21 million people every year. In the immediate moments following a flood event, humanitarian actors like the World Food Program need to make rapid decisions ( 72 hrs) on how to prioritize affected areas impacted by such an event. For other natural disasters like hurricanes/cyclones and earthquakes, there are industry-recognized standards on how the impacted areas are to be classified. Shake maps, quantifying peak ground motion, from for example the US Geological Survey are widely used for assessing earthquakes. Similarly, cyclones are tracked by Joint Typhoon Warning Center (JTWC) and Global Disaster Alert and Coordination System (GDACS) who release storm nodes and tracks (forecasted and actual), with wind buffers and classify the event according to the Saffir-Simpson Hurricane Wind Scale. For floods, the community is usually able to acquire unclassified data of the flood extent as identified from satellite imagery. Most often no water discharge hydrograph is available to classify the event into recurrence intervals simply because there is no gauging station, or the gauging station was unable to record the maximum discharge due to overtopping or flood damage. So, the question remains: How do we methodically turn a flooded area into classified areas of different gradations of impact? Here, we present a first approach towards developing a global applicable flood severity index. The flood severity index is set up such that it considers relatively easily obtainable physical parameters in a short period of time like: flood frequency (relating the current flood to historical events) and magnitude, as well as land cover, slope, and where available pre-event simulated flood depth. The scale includes categories ranging from very minor flooding to catastrophic flooding. We test and evaluate the postulated classification scheme against a set of past flood events. Once a severity category is determined, socio

Flood Damage and Loss Estimation for Iowa on Web-based Systems using HAZUS

Science.gov (United States)

Yildirim, E.; Sermet, M. Y.; Demir, I.

2016-12-01

Importance of decision support systems for flood emergency response and loss estimation increases with its social and economic impacts. To estimate the damage of the flood, there are several software systems available to researchers and decision makers. HAZUS-MH is one of the most widely used desktop program, developed by FEMA (Federal Emergency Management Agency), to estimate economic loss and social impacts of disasters such as earthquake, hurricane and flooding (riverine and coastal). HAZUS used loss estimation methodology and implements through geographic information system (GIS). HAZUS contains structural, demographic, and vehicle information across United States. Thus, it allows decision makers to understand and predict possible casualties and damage of the floods by running flood simulations through GIS application. However, it doesn't represent real time conditions because of using static data. To close this gap, an overview of a web-based infrastructure coupling HAZUS and real time data provided by IFIS (Iowa Flood Information System) is presented by this research. IFIS is developed by the Iowa Flood Center, and a one-stop web-platform to access community-based flood conditions, forecasts, visualizations, inundation maps and flood-related data, information, and applications. Large volume of real-time observational data from a variety of sensors and remote sensing resources (radars, rain gauges, stream sensors, etc.) and flood inundation models are staged on a user-friendly maps environment that is accessible to the general public. Providing cross sectional analyses between HAZUS-MH and IFIS datasets, emergency managers are able to evaluate flood damage during flood events easier and more accessible in real time conditions. With matching data from HAZUS-MH census tract layer and IFC gauges, economical effects of flooding can be observed and evaluated by decision makers. The system will also provide visualization of the data by using augmented reality for

Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity

Directory of Open Access Journals (Sweden)

S. A. Henson

2010-02-01

Full Text Available Global climate change is predicted to alter the ocean's biological productivity. But how will we recognise the impacts of climate change on ocean productivity? The most comprehensive information available on its global distribution comes from satellite ocean colour data. Now that over ten years of satellite-derived chlorophyll and productivity data have accumulated, can we begin to detect and attribute climate change-driven trends in productivity? Here we compare recent trends in satellite ocean colour data to longer-term time series from three biogeochemical models (GFDL, IPSL and NCAR. We find that detection of climate change-driven trends in the satellite data is confounded by the relatively short time series and large interannual and decadal variability in productivity. Thus, recent observed changes in chlorophyll, primary production and the size of the oligotrophic gyres cannot be unequivocally attributed to the impact of global climate change. Instead, our analyses suggest that a time series of ~40 years length is needed to distinguish a global warming trend from natural variability. In some regions, notably equatorial regions, detection times are predicted to be shorter (~20–30 years. Analysis of modelled chlorophyll and primary production from 2001–2100 suggests that, on average, the climate change-driven trend will not be unambiguously separable from decadal variability until ~2055. Because the magnitude of natural variability in chlorophyll and primary production is larger than, or similar to, the global warming trend, a consistent, decades-long data record must be established if the impact of climate change on ocean productivity is to be definitively detected.

Exploitation of Documented Historical Floods for Achieving Better Flood Defense

Directory of Open Access Journals (Sweden)

Slobodan Kolaković

2016-01-01

Full Text Available Establishing Base Flood Elevation for a stream network corresponding to a big catchment is feasible by interdisciplinary approach, involving stochastic hydrology, river hydraulics, and computer aided simulations. A numerical model calibrated by historical floods has been exploited in this study. The short presentation of the catchment of the Tisza River in this paper is followed by the overview of historical floods which hit the region in the documented period of 130 years. Several well documented historical floods provided opportunity for the calibration of the chosen numerical model. Once established, the model could be used for investigation of different extreme flood scenarios and to establish the Base Flood Elevation. The calibration has shown that the coefficient of friction in case of the Tisza River is dependent both on the actual water level and on the preceding flood events. The effect of flood plain maintenance as well as the activation of six potential detention ponds on flood mitigation has been examined. Furthermore, the expected maximum water levels have also been determined for the case if the ever observed biggest 1888 flood hit the region again. The investigated cases of flood superposition highlighted the impact of tributary Maros on flood mitigation along the Tisza River.

Investigation of Flood Risk Assessment in Inaccessible Regions using Multiple Remote Sensing and Geographic Information Systems

Science.gov (United States)

Lim, J.; Lee, K. S.

2017-12-01

Flooding is extremely dangerous when a river overflows to inundate an urban area. From 1995 to 2016, North Korea (NK) experienced annual extensive damage to life and property almost each year due to a levee breach resulting from typhoons and heavy rainfall during the summer monsoon season. Recently, Hoeryeong City (2016) experienced heavy rainfall during typhoon Lionrock and the resulting flood killed and injured many people (68,900) and destroyed numerous buildings and settlements (11,600). The NK state media described it as the biggest national disaster since 1945. Thus, almost all annual repeat occurrences of floods in NK have had a serious impact, which makes it necessary to figure out the extent of floods in restoring the damaged environment. In addition, traditional hydrological model is impractical to delineate Flood Damaged Areas (FDAs) in NK due to the inaccessibility. Under such a situation, multiple optical Remote Sensing (RS) and radar RS along with a Geographic Information System (GIS)-based spatial analysis were utilized in this study (1) to develop modelling FDA delineation using multiple RS and GIS methods and (2) to conduct flood risk assessment in NK. Interpreting high-resolution web-based satellite imagery were also implemented to confirm the results of the study. From the study result, it was found that (1) on August 30th, 2016, an area of 117.2 km2 (8.6%) at Hoeryeong City was inundated. Most floods occurred in flat areas with a lower and middle stream order. (2) In the binary logistic regression model applied in this study, the distance from the nearest stream map and landform map variables are important factors to delineate FDAs because these two factors reflect heterogeneous mountainous NK topography. (3) Total annual flood risk of study area is estimated to be ₩454.13 million NKW ($504,417.24 USD, and ₩576.53 million SKW). The risk of the confluence of the Tumen River and Hoeryeong stream appears to be the highest. (4) High resolution

Analysis of land cover change impact on flood events using remote sensing, GIS and hydrological models: a case study of the Nyando River Basin in Kenya

International Nuclear Information System (INIS)

Olang, L. O.

2009-01-01

In this study, land cover changes in the Nyando River basin (3500 km 2 ) of Kenya were analyzed and their impact of floods quantified. Three Landsat satellite images for 1973, 1986 and 2000 were acquired, processed and classified based on seven major land cover classes prevalent in the basin using a hybrid of supervised and non supervised classification procedures. The detected land cover changes, together with a DEM and a soil map of the basin, were then used to estimate physically based parameters for the selected hydrological models. The models were then used to estimate local and flood peak discharges and volumes arising from selected storm events for each state of the classified land cover dataset. To further understand how changes in the land cover may impact on the flood hydrology, three scenarios that represent quite extreme alternatives were formulated to study the possible bandwidth during floods. Land cover classification results revealed immense land degradation over the span of study. Forests reduced by an area of 488 km 2 representing a 20% decline, while agricultural fields expanded by 581 km 2 representing a 16% increase over the same period of time (1973-2000). Hydrological modeling results indicated that the basin underwent significant increase in the peak discharge value. The flood peak discharges in the whole basin were noted to have increased by at least 16% over the period of 1973 -2000.Flood volumes were also noted to have increased by at least 10% over the same period of time. (author) [de

A new automatic synthetic aperture radar-based flood mapping application hosted on the European Space Agency's Grid Processing of Demand Fast Access to Imagery environment

Science.gov (United States)

Matgen, Patrick; Giustarini, Laura; Hostache, Renaud

2012-10-01

This paper introduces an automatic flood mapping application that is hosted on the Grid Processing on Demand (GPOD) Fast Access to Imagery (Faire) environment of the European Space Agency. The main objective of the online application is to deliver operationally flooded areas using both recent and historical acquisitions of SAR data. Having as a short-term target the flooding-related exploitation of data generated by the upcoming ESA SENTINEL-1 SAR mission, the flood mapping application consists of two building blocks: i) a set of query tools for selecting the "crisis image" and the optimal corresponding "reference image" from the G-POD archive and ii) an algorithm for extracting flooded areas via change detection using the previously selected "crisis image" and "reference image". Stakeholders in flood management and service providers are able to log onto the flood mapping application to get support for the retrieval, from the rolling archive, of the most appropriate reference image. Potential users will also be able to apply the implemented flood delineation algorithm. The latter combines histogram thresholding, region growing and change detection as an approach enabling the automatic, objective and reliable flood extent extraction from SAR images. Both algorithms are computationally efficient and operate with minimum data requirements. The case study of the high magnitude flooding event that occurred in July 2007 on the Severn River, UK, and that was observed with a moderateresolution SAR sensor as well as airborne photography highlights the performance of the proposed online application. The flood mapping application on G-POD can be used sporadically, i.e. whenever a major flood event occurs and there is a demand for SAR-based flood extent maps. In the long term, a potential extension of the application could consist in systematically extracting flooded areas from all SAR images acquired on a daily, weekly or monthly basis.

Research on the new type of multi-functional satellite system for space debris detection

Science.gov (United States)

Guo, Linghua; Fu, Qiang; Jiang, Huilin; Xu, Xihe

2017-05-01

With the rapid development of space exploration and utilization, orbital debris increases dramatically, leading to great threat to human space activities and spacecraft security. In this paper, a new type of multi-functional space debris satellite system (MSDS) was put forward, which shared main optical system, and possessed functions of multidimensional information detection, polarized remote sensing and high rate transmission. The MSDS system can meet the requirements of detection and identification for the small orbital debris which is 1000km faraway, as well as the requirements of the data transmission by 50 Mbps to 2.5 Gbps@200-1000 km. At the same time, by the method of satellite orbital maneuver and attitude adjusting, the orbital debris information that is real-time, complex and refined, allweather can be acquired and transmitted by the new system. Such new type of multifunctional satellite system can provide important and effective technology for international orbital debris detection.

Ground test of satellite constellation based quantum communication

OpenAIRE

Liao, Sheng-Kai; Yong, Hai-Lin; Liu, Chang; Shentu, Guo-Liang; Li, Dong-Dong; Lin, Jin; Dai, Hui; Zhao, Shuang-Qiang; Li, Bo; Guan, Jian-Yu; Chen, Wei; Gong, Yun-Hong; Li, Yang; Lin, Ze-Hong; Pan, Ge-Sheng

2016-01-01

Satellite based quantum communication has been proven as a feasible way to achieve global scale quantum communication network. Very recently, a low-Earth-orbit (LEO) satellite has been launched for this purpose. However, with a single satellite, it takes an inefficient 3-day period to provide the worldwide connectivity. On the other hand, similar to how the Iridium system functions in classic communication, satellite constellation (SC) composed of many quantum satellites, could provide global...

Integration of Grid and Sensor Web for Flood Monitoring and Risk Assessment from Heterogeneous Data

Science.gov (United States)

Kussul, Nataliia; Skakun, Sergii; Shelestov, Andrii

2013-04-01

Over last decades we have witnessed the upward global trend in natural disaster occurrence. Hydrological and meteorological disasters such as floods are the main contributors to this pattern. In recent years flood management has shifted from protection against floods to managing the risks of floods (the European Flood risk directive). In order to enable operational flood monitoring and assessment of flood risk, it is required to provide an infrastructure with standardized interfaces and services. Grid and Sensor Web can meet these requirements. In this paper we present a general approach to flood monitoring and risk assessment based on heterogeneous geospatial data acquired from multiple sources. To enable operational flood risk assessment integration of Grid and Sensor Web approaches is proposed [1]. Grid represents a distributed environment that integrates heterogeneous computing and storage resources administrated by multiple organizations. SensorWeb is an emerging paradigm for integrating heterogeneous satellite and in situ sensors and data systems into a common informational infrastructure that produces products on demand. The basic Sensor Web functionality includes sensor discovery, triggering events by observed or predicted conditions, remote data access and processing capabilities to generate and deliver data products. Sensor Web is governed by the set of standards, called Sensor Web Enablement (SWE), developed by the Open Geospatial Consortium (OGC). Different practical issues regarding integration of Sensor Web with Grids are discussed in the study. We show how the Sensor Web can benefit from using Grids and vice versa. For example, Sensor Web services such as SOS, SPS and SAS can benefit from the integration with the Grid platform like Globus Toolkit. The proposed approach is implemented within the Sensor Web framework for flood monitoring and risk assessment, and a case-study of exploiting this framework, namely the Namibia SensorWeb Pilot Project, is

Applying the Flood Vulnerability Index as a Knowledge base for flood risk assessment

NARCIS (Netherlands)

Balica, S-F.

2012-01-01

Floods are one of the most common and widely distributed natural risks to life and property worldwide. An important part of modern flood risk management is to evaluate vulnerability to floods. This evaluation can be done only by using a parametric approach. Worldwide there is a need to enhance our

Region-Based Building Rooftop Extraction and Change Detection

Science.gov (United States)

Tian, J.; Metzlaff, L.; d'Angelo, P.; Reinartz, P.

2017-09-01

Automatic extraction of building changes is important for many applications like disaster monitoring and city planning. Although a lot of research work is available based on 2D as well as 3D data, an improvement in accuracy and efficiency is still needed. The introducing of digital surface models (DSMs) to building change detection has strongly improved the resulting accuracy. In this paper, a post-classification approach is proposed for building change detection using satellite stereo imagery. Firstly, DSMs are generated from satellite stereo imagery and further refined by using a segmentation result obtained from the Sobel gradients of the panchromatic image. Besides the refined DSMs, the panchromatic image and the pansharpened multispectral image are used as input features for mean-shift segmentation. The DSM is used to calculate the nDSM, out of which the initial building candidate regions are extracted. The candidate mask is further refined by morphological filtering and by excluding shadow regions. Following this, all segments that overlap with a building candidate region are determined. A building oriented segments merging procedure is introduced to generate a final building rooftop mask. As the last step, object based change detection is performed by directly comparing the building rooftops extracted from the pre- and after-event imagery and by fusing the change indicators with the roof-top region map. A quantitative and qualitative assessment of the proposed approach is provided by using WorldView-2 satellite data from Istanbul, Turkey.

REGION-BASED BUILDING ROOFTOP EXTRACTION AND CHANGE DETECTION

Directory of Open Access Journals (Sweden)

J. Tian

2017-09-01

Full Text Available Automatic extraction of building changes is important for many applications like disaster monitoring and city planning. Although a lot of research work is available based on 2D as well as 3D data, an improvement in accuracy and efficiency is still needed. The introducing of digital surface models (DSMs to building change detection has strongly improved the resulting accuracy. In this paper, a post-classification approach is proposed for building change detection using satellite stereo imagery. Firstly, DSMs are generated from satellite stereo imagery and further refined by using a segmentation result obtained from the Sobel gradients of the panchromatic image. Besides the refined DSMs, the panchromatic image and the pansharpened multispectral image are used as input features for mean-shift segmentation. The DSM is used to calculate the nDSM, out of which the initial building candidate regions are extracted. The candidate mask is further refined by morphological filtering and by excluding shadow regions. Following this, all segments that overlap with a building candidate region are determined. A building oriented segments merging procedure is introduced to generate a final building rooftop mask. As the last step, object based change detection is performed by directly comparing the building rooftops extracted from the pre- and after-event imagery and by fusing the change indicators with the roof-top region map. A quantitative and qualitative assessment of the proposed approach is provided by using WorldView-2 satellite data from Istanbul, Turkey.

Frequency and seasonality of flash floods in Slovenia

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Trobec Tajan

2017-01-01

Full Text Available The purpose of this paper is to assess and analyse the dynamics of flash flooding events in Slovenia. The paper examines in particular the frequency of flash floods and their seasonal distribution. The methodology is based on the analysis of historical records and modern flood data. The results of a long-term frequency analysis of 138 flash floods that occurred between 1550 and 2015 are presented. Because of the lack of adequate historical flood data prior to 1950 the main analysis is based on data for the periodbetween1951 and2015, while the analysis of data for the period between1550 and1950 is added as a supplement to the main analysis. Analysis of data for the period after 1950 shows that on average 1.3 flash floods occur each year in Slovenia. The linear trend for the number of flash floods is increasing but is not statistically significant. Despite the fact that the majority of Slovenian rivers have one of the peaks in spring and one of the lows in summer, 90% of flash floods actually occur during meteorological summer or autumn - i.e. between June and November, which shows that discharge regimes and flood regimes are not necessarily related. Because of the lack of flood records from the more distant past as well as the large variability of flash flood events in the last several decades, we cannot provide a definitive answer to the question about possible changes in their frequency and seasonality by relying solely on the detected trends. Nevertheless, considering the results of analysis and future climate change scenarios the frequency of flash floods in Slovenia could increase while the period of flash flood occurrence could be extended.

An automated fog monitoring system for the Indo-Gangetic Plains based on satellite measurements

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Patil, Dinesh; Chourey, Reema; Rizvi, Sarwar; Singh, Manoj; Gautam, Ritesh

2016-05-01

Fog is a meteorological phenomenon that causes reduction in regional visibility and affects air quality, thus leading to various societal and economic implications, especially disrupting air and rail transportation. The persistent and widespread winter fog impacts the entire the Indo-Gangetic Plains (IGP), as frequently observed in satellite imagery. The IGP is a densely populated region in south Asia, inhabiting about 1/6th of the world's population, with a strong upward pollution trend. In this study, we have used multi-spectral radiances and aerosol/cloud retrievals from Terra/Aqua MODIS data for developing an automated web-based fog monitoring system over the IGP. Using our previous and existing methodologies, and ongoing algorithm development for the detection of fog and retrieval of associated microphysical properties (e.g. fog droplet effective radius), we characterize the widespread fog detection during both daytime and nighttime. Specifically, for the night time fog detection, the algorithm employs a satellite-based bi-spectral brightness temperature difference technique between two spectral channels: MODIS band-22 (3.9μm) and band-31 (10.75μm). Further, we are extending our algorithm development to geostationary satellites, for providing continuous monitoring of the spatial-temporal variation of fog. We anticipate that the ongoing and future development of a fog monitoring system would be of assistance to air, rail and vehicular transportation management, as well as for dissemination of fog information to government agencies and general public. The outputs of fog detection algorithm and related aerosol/cloud parameters are operationally disseminated via http://fogsouthasia.com/.

Coupling impervious surface rate derived from satellite remote sensing with distributed hydrological model for highly urbanized watershed flood forecasting

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Dong, L.

2017-12-01

Abstract: The original urban surface structure changed a lot because of the rapid development of urbanization. Impermeable area has increased a lot. It causes great pressure for city flood control and drainage. Songmushan reservoir basin with high degree of urbanization is taken for an example. Pixel from Landsat is decomposed by Linear spectral mixture model and the proportion of urban area in it is considered as impervious rate. Based on impervious rate data before and after urbanization, an physically based distributed hydrological model, Liuxihe Model, is used to simulate the process of hydrology. The research shows that the performance of the flood forecasting of high urbanization area carried out with Liuxihe Model is perfect and can meet the requirement of the accuracy of city flood control and drainage. The increase of impervious area causes conflux speed more quickly and peak flow to be increased. It also makes the time of peak flow advance and the runoff coefficient increase. Key words: Liuxihe Model; Impervious rate; City flood control and drainage; Urbanization; Songmushan reservoir basin

Landslide and Flood Warning System Prototypes based on Wireless Sensor Networks

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Hloupis, George; Stavrakas, Ilias; Triantis, Dimos

2010-05-01

Wireless sensor networks (WSNs) are one of the emerging areas that received great attention during the last few years. This is mainly due to the fact that WSNs have provided scientists with the capability of developing real-time monitoring systems equipped with sensors based on Micro-Electro-Mechanical Systems (MEMS). WSNs have great potential for many applications in environmental monitoring since the sensor nodes that comprised from can host several MEMS sensors (such as temperature, humidity, inertial, pressure, strain-gauge) and transducers (such as position, velocity, acceleration, vibration). The resulting devices are small and inexpensive but with limited memory and computing resources. Each sensor node contains a sensing module which along with an RF transceiver. The communication is broadcast-based since the network topology can change rapidly due to node failures [1]. Sensor nodes can transmit their measurements to central servers through gateway nodes without any processing or they make preliminary calculations locally in order to produce results that will be sent to central servers [2]. Based on the above characteristics, two prototypes using WSNs are presented in this paper: A Landslide detection system and a Flood warning system. Both systems sent their data to central processing server where the core of processing routines exists. Transmission is made using Zigbee and IEEE 802.11b protocol but is capable to use VSAT communication also. Landslide detection system uses structured network topology. Each measuring node comprises of a columnar module that is half buried to the area under investigation. Each sensing module contains a geophone, an inclinometer and a set of strain gauges. Data transmitted to central processing server where possible landslide evolution is monitored. Flood detection system uses unstructured network topology since the failure rate of sensor nodes is expected higher. Each sensing module contains a custom water level sensor

A Bayesian decision approach to rainfall thresholds based flood warning

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M. L. V. Martina

2006-01-01

Full Text Available Operational real time flood forecasting systems generally require a hydrological model to run in real time as well as a series of hydro-informatics tools to transform the flood forecast into relatively simple and clear messages to the decision makers involved in flood defense. The scope of this paper is to set forth the possibility of providing flood warnings at given river sections based on the direct comparison of the quantitative precipitation forecast with critical rainfall threshold values, without the need of an on-line real time forecasting system. This approach leads to an extremely simplified alert system to be used by non technical stakeholders and could also be used to supplement the traditional flood forecasting systems in case of system failures. The critical rainfall threshold values, incorporating the soil moisture initial conditions, result from statistical analyses using long hydrological time series combined with a Bayesian utility function minimization. In the paper, results of an application of the proposed methodology to the Sieve river, a tributary of the Arno river in Italy, are given to exemplify its practical applicability.

Development of method for evaluating estimated inundation area by using river flood analysis based on multiple flood scenarios

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Ono, T.; Takahashi, T.

2017-12-01

Non-structural mitigation measures such as flood hazard map based on estimated inundation area have been more important because heavy rains exceeding the design rainfall frequently occur in recent years. However, conventional method may lead to an underestimation of the area because assumed locations of dike breach in river flood analysis are limited to the cases exceeding the high-water level. The objective of this study is to consider the uncertainty of estimated inundation area with difference of the location of dike breach in river flood analysis. This study proposed multiple flood scenarios which can set automatically multiple locations of dike breach in river flood analysis. The major premise of adopting this method is not to be able to predict the location of dike breach correctly. The proposed method utilized interval of dike breach which is distance of dike breaches placed next to each other. That is, multiple locations of dike breach were set every interval of dike breach. The 2D shallow water equations was adopted as the governing equation of river flood analysis, and the leap-frog scheme with staggered grid was used. The river flood analysis was verified by applying for the 2015 Kinugawa river flooding, and the proposed multiple flood scenarios was applied for the Akutagawa river in Takatsuki city. As the result of computation in the Akutagawa river, a comparison with each computed maximum inundation depth of dike breaches placed next to each other proved that the proposed method enabled to prevent underestimation of estimated inundation area. Further, the analyses on spatial distribution of inundation class and maximum inundation depth in each of the measurement points also proved that the optimum interval of dike breach which can evaluate the maximum inundation area using the minimum assumed locations of dike breach. In brief, this study found the optimum interval of dike breach in the Akutagawa river, which enabled estimated maximum inundation area

AUTOMATIC CLOUD DETECTION FROM MULTI-TEMPORAL SATELLITE IMAGES: TOWARDS THE USE OF PLÉIADES TIME SERIES

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N. Champion

2012-08-01

Full Text Available Contrary to aerial images, satellite images are often affected by the presence of clouds. Identifying and removing these clouds is one of the primary steps to perform when processing satellite images, as they may alter subsequent procedures such as atmospheric corrections, DSM production or land cover classification. The main goal of this paper is to present the cloud detection approach, developed at the French Mapping agency. Our approach is based on the availability of multi-temporal satellite images (i.e. time series that generally contain between 5 and 10 images and is based on a region-growing procedure. Seeds (corresponding to clouds are firstly extracted through a pixel-to-pixel comparison between the images contained in time series (the presence of a cloud is here assumed to be related to a high variation of reflectance between two images. Clouds are then delineated finely using a dedicated region-growing algorithm. The method, originally designed for panchromatic SPOT5-HRS images, is tested in this paper using time series with 9 multi-temporal satellite images. Our preliminary experiments show the good performances of our method. In a near future, the method will be applied to Pléiades images, acquired during the in-flight commissioning phase of the satellite (launched at the end of 2011. In that context, this is a particular goal of this paper to show to which extent and in which way our method can be adapted to this kind of imagery.

Validation of an Innovative Satellite-Based UV Dosimeter

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Morelli, Marco; Masini, Andrea; Simeone, Emilio; Khazova, Marina

2016-08-01

We present an innovative satellite-based UV (ultraviolet) radiation dosimeter with a mobile app interface that has been validated by exploiting both ground-based measurements and an in-vivo assessment of the erythemal effects on some volunteers having a controlled exposure to solar radiation.Both validations showed that the satellite-based UV dosimeter has a good accuracy and reliability needed for health-related applications.The app with this satellite-based UV dosimeter also includes other related functionalities such as the provision of safe sun exposure time updated in real-time and end exposure visual/sound alert. This app will be launched on the global market by siHealth Ltd in May 2016 under the name of "HappySun" and available both for Android and for iOS devices (more info on http://www.happysun.co.uk).Extensive R&D activities are on-going for further improvement of the satellite-based UV dosimeter's accuracy.

Providing Evidence-Based, Intelligent Support for Flood Resilient Planning and Policy: The PEARL Knowledge Base

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George Karavokiros

2016-09-01

Full Text Available While flood risk is evolving as one of the most imminent natural hazards and the shift from a reactive decision environment to a proactive one sets the basis of the latest thinking in flood management, the need to equip decision makers with necessary tools to think about and intelligently select options and strategies for flood management is becoming ever more pressing. Within this context, the Preparing for Extreme and Rare Events in Coastal Regions (PEARL intelligent knowledge-base (PEARL KB of resilience strategies is presented here as an environment that allows end-users to navigate from their observed problem to a selection of possible options and interventions worth considering within an intuitive visual web interface assisting advanced interactivity. Incorporation of real case studies within the PEARL KB enables the extraction of (evidence-based lessons from all over the word, while the KB’s collection of methods and tools directly supports the optimal selection of suitable interventions. The Knowledge-Base also gives access to the PEARL KB Flood Resilience Index (FRI tool, which is an online tool for resilience assessment at a city level available to authorities and citizens. We argue that the PEARL KB equips authorities with tangible and operational tools that can improve strategic and operational flood risk management by assessing and eventually increasing resilience, while building towards the strengthening of risk governance. The online tools that the PEARL KB gives access to were demonstrated and tested in the city of Rethymno, Greece.

A generalized Grubbs-Beck test statistic for detecting multiple potentially influential low outliers in flood series

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Cohn, T.A.; England, J.F.; Berenbrock, C.E.; Mason, R.R.; Stedinger, J.R.; Lamontagne, J.R.

2013-01-01

he Grubbs-Beck test is recommended by the federal guidelines for detection of low outliers in flood flow frequency computation in the United States. This paper presents a generalization of the Grubbs-Beck test for normal data (similar to the Rosner (1983) test; see also Spencer and McCuen (1996)) that can provide a consistent standard for identifying multiple potentially influential low flows. In cases where low outliers have been identified, they can be represented as “less-than” values, and a frequency distribution can be developed using censored-data statistical techniques, such as the Expected Moments Algorithm. This approach can improve the fit of the right-hand tail of a frequency distribution and provide protection from lack-of-fit due to unimportant but potentially influential low flows (PILFs) in a flood series, thus making the flood frequency analysis procedure more robust.

Impact of Uncertainty Characterization of Satellite Rainfall Inputs and Model Parameters on Hydrological Data Assimilation with the Ensemble Kalman Filter for Flood Prediction

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Vergara, H. J.; Kirstetter, P.; Hong, Y.; Gourley, J. J.; Wang, X.

2013-12-01

the sensitivities of the EnKF is revealed examining the impact of uncertainty representation on the assimilation process and subsequent forecasts. The methodology is tested on the hydrologic modeling of a basin subject to recurrent flooding events triggered by landfalling tropical systems. An ensemble framework for flood forecasting is employed to simulate the hydrologic processes using different levels of model structural complexity. Tropical Rainfall Measuring Mission (TRMM)-based QPE are used to force the hydrologic modeling system. A radar-based QPE product is utilized as a reference to estimate errors in satellite rainfall estimates and to characterize the uncertainty in model inputs. This set-up contributes to the development of methodologies to understand and characterize the error in satellite rainfall in a hydrologic modeling framework. Consequently, the results from this study will provide guidance for the integration of other remote-sensing observations through data assimilation for a more complete picture of the hydrometeorological phenomena of interest.

CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE

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K.-Y. Lee

2016-06-01

Full Text Available Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012 uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+ and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate

Cloud Detection of Optical Satellite Images Using Support Vector Machine

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Lee, Kuan-Yi; Lin, Chao-Hung

2016-06-01

Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection

Integrating hydrodynamic models and COSMO-SkyMed derived products for flood damage assessment

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Giuffra, Flavio; Boni, Giorgio; Pulvirenti, Luca; Pierdicca, Nazzareno; Rudari, Roberto; Fiorini, Mattia

2015-04-01

Floods are the most frequent weather disasters in the world and probably the most costly in terms of social and economic losses. They may have a strong impact on infrastructures and health because the range of possible damages includes casualties, loss of housing and destruction of crops. Presently, the most common approach for remotely sensing floods is the use of synthetic aperture radar (SAR) images. Key features of SAR data for inundation mapping are the synoptic view, the capability to operate even in cloudy conditions and during both day and night time and the sensitivity of the microwave radiation to water. The launch of a new generation of instruments, such as TerraSAR-X and COSMO-SkyMed (CSK) allows producing near real time flood maps having a spatial resolution in the order of 1-5 m. Moreover, the present (CSK) and upcoming (Sentinel-1) constellations permit the acquisition of radar data characterized by a short revisit time (in the order of some hours for CSK), so that the production of frequent inundation maps can be envisaged. Nonetheless, gaps might be present in the SAR-derived flood maps because of the limited area imaged by SAR; moreover, the detection of floodwater may be complicated by the presence of very dense vegetation or urban settlements. Hence the need to complement SAR-derived flood maps with the outputs of physical models. Physical models allow delivering to end users very useful information for a complete flood damage assessment, such as data on water depths and flow directions, which cannot be directly derived from satellite remote sensing images. In addition, the flood extent predictions of hydraulic models can be compared to SAR-derived inundation maps to calibrate the models, or to fill the aforementioned gaps that can be present in the SAR-derived maps. Finally, physical models enable the construction of risk scenarios useful for emergency managers to take their decisions and for programming additional SAR acquisitions in order to

A Space Based Solar Power Satellite System

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Engel, J. M.; Polling, D.; Ustamujic, F.; Yaldiz, R.; et al.

2002-01-01

(SPoTS) supplying other satellites with energy. SPoTS is due to be commercially viable and operative in 2020. of Technology designed the SPoTS during a full-time design period of six weeks as a third year final project. The team, organized according to the principles of systems engineering, first conducted a literature study on space wireless energy transfer to select the most suitable candidates for use on the SPoTS. After that, several different system concepts have been generated and evaluated, the most promising concept being worked out in greater detail. km altitude. Each SPoTS satellite has a 50m diameter inflatable solar collector that focuses all received sunlight. Then, the received sunlight is further redirected by means of four pointing mirrors toward four individual customer satellites. A market-analysis study showed, that providing power to geo-stationary communication satellites during their eclipse would be most beneficial. At arrival at geo-stationary orbit, the focused beam has expended to such an extent that its density equals one solar flux. This means that customer satellites can continue to use their regular solar arrays during their eclipse for power generation, resulting in a satellite battery mass reduction. the customer satellites in geo-stationary orbit, the transmitted energy beams needs to be pointed with very high accuracy. Computations showed that for this degree of accuracy, sensors are needed, which are not mainstream nowadays. Therefore further research must be conducted in this area in order to make these high-accuracy-pointing systems commercially attractive for use on the SPoTS satellites around 2020. Total 20-year system lifetime cost for 18 SPoT satellites are estimated at approximately USD 6 billion [FY2001]. In order to compete with traditional battery-based satellite power systems or possible ground based wireless power transfer systems the price per kWh for the customer must be significantly lower than the present one

A Model to Partly but Reliably Distinguish DDOS Flood Traffic from Aggregated One

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Ming Li

2012-01-01

Full Text Available Reliable distinguishing DDOS flood traffic from aggregated traffic is desperately desired by reliable prevention of DDOS attacks. By reliable distinguishing, we mean that flood traffic can be distinguished from aggregated one for a predetermined probability. The basis to reliably distinguish flood traffic from aggregated one is reliable detection of signs of DDOS flood attacks. As is known, reliably distinguishing DDOS flood traffic from aggregated traffic becomes a tough task mainly due to the effects of flash-crowd traffic. For this reason, this paper studies reliable detection in the underlying DiffServ network to use static-priority schedulers. In this network environment, we present a method for reliable detection of signs of DDOS flood attacks for a given class with a given priority. There are two assumptions introduced in this study. One is that flash-crowd traffic does not have all priorities but some. The other is that attack traffic has all priorities in all classes, otherwise an attacker cannot completely achieve its DDOS goal. Further, we suppose that the protected site is equipped with a sensor that has a signature library of the legitimate traffic with the priorities flash-crowd traffic does not have. Based on those, we are able to reliably distinguish attack traffic from aggregated traffic with the priorities that flash-crowd traffic does not have according to a given detection probability.

Ship detection in satellite imagery using rank-order greyscale hit-or-miss transforms

Energy Technology Data Exchange (ETDEWEB)

Harvey, Neal R [Los Alamos National Laboratory; Porter, Reid B [Los Alamos National Laboratory; Theiler, James [Los Alamos National Laboratory

2010-01-01

Ship detection from satellite imagery is something that has great utility in various communities. Knowing where ships are and their types provides useful intelligence information. However, detecting and recognizing ships is a difficult problem. Existing techniques suffer from too many false-alarms. We describe approaches we have taken in trying to build ship detection algorithms that have reduced false alarms. Our approach uses a version of the grayscale morphological Hit-or-Miss transform. While this is well known and used in its standard form, we use a version in which we use a rank-order selection for the dilation and erosion parts of the transform, instead of the standard maximum and minimum operators. This provides some slack in the fitting that the algorithm employs and provides a method for tuning the algorithm's performance for particular detection problems. We describe our algorithms, show the effect of the rank-order parameter on the algorithm's performance and illustrate the use of this approach for real ship detection problems with panchromatic satellite imagery.

Mapping Daily and Maximum Flood Extents at 90-m Resolution During Hurricanes Harvey and Irma Using Passive Microwave Remote Sensing

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Galantowicz, J. F.; Picton, J.; Root, B.

2017-12-01

Passive microwave remote sensing can provided a distinct perspective on flood events by virtue of wide sensor fields of view, frequent observations from multiple satellites, and sensitivity through clouds and vegetation. During Hurricanes Harvey and Irma, we used AMSR2 (Advanced Microwave Scanning Radiometer 2, JAXA) data to map flood extents starting from the first post-storm rain-free sensor passes. Our standard flood mapping algorithm (FloodScan) derives flooded fraction from 22-km microwave data (AMSR2 or NASA's GMI) in near real time and downscales it to 90-m resolution using a database built from topography, hydrology, and Global Surface Water Explorer data and normalized to microwave data footprint shapes. During Harvey and Irma we tested experimental versions of the algorithm designed to map the maximum post-storm flood extent rapidly and made a variety of map products available immediately for use in storm monitoring and response. The maps have several unique features including spanning the entire storm-affected area and providing multiple post-storm updates as flood water shifted and receded. From the daily maps we derived secondary products such as flood duration, maximum flood extent (Figure 1), and flood depth. In this presentation, we describe flood extent evolution, maximum extent, and local details as detected by the FloodScan algorithm in the wake of Harvey and Irma. We compare FloodScan results to other available flood mapping resources, note observed shortcomings, and describe improvements made in response. We also discuss how best-estimate maps could be updated in near real time by merging FloodScan products and data from other remote sensing systems and hydrological models.

Advancing satellite-based solar power forecasting through integration of infrared channels for automatic detection of coastal marine inversion layer

Energy Technology Data Exchange (ETDEWEB)

Kostylev, Vladimir; Kostylev, Andrey; Carter, Chris; Mahoney, Chad; Pavlovski, Alexandre; Daye, Tony [Green Power Labs Inc., Dartmouth, NS (Canada); Cormier, Dallas Eugene; Fotland, Lena [San Diego Gas and Electric Co., San Diego, CA (United States)

2012-07-01

The marine atmospheric boundary layer is a layer or cool, moist maritime air with the thickness of a few thousand feet immediately below a temperature inversion. In coastal areas as moist air rises from the ocean surface, it becomes trapped and is often compressed into fog above which a layer of stratus clouds often forms. This phenomenon is common for satellite-based solar radiation monitoring and forecasting. Hour ahead satellite-based solar radiation forecasts are commonly using visible spectrum satellite images, from which it is difficult to automatically differentiate low stratus clouds and fog from high altitude clouds. This provides a challenge for cloud motion tyracking and cloud cover forecasting. San Diego Gas and Electric {sup registered} (SDG and E {sup registered}) Marine Layer Project was undertaken to obtain information for integration with PV forecasts, and to develop a detailed understanding of long-term benefits from forecasting Marine Layer (ML) events and their effects on PV production. In order to establish climatological ML patterns, spatial extent and distribution of marine layer, we analyzed visible and IR spectrum satellite images (GOES WEST) archive for the period of eleven years (2000 - 2010). Historical boundaries of marine layers impact were established based on the cross-classification of visible spectrum (VIS) and infrared (IR) images. This approach is successfully used by us and elsewhere for evaluating cloud albedo in common satellite-based techniques for solar radiation monitoring and forecasting. The approach allows differentiation of cloud cover and helps distinguish low laying fog which is the main consequence of marine layer formation. ML occurrence probability and maximum extent inland was established for each hour and day of the analyzed period and seasonal/patterns were described. SDG and E service area is the most affected region by ML events with highest extent and probability of ML occurrence. Influence of ML was the

Flooding and Flood Management

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Brooks, K.N.; Fallon, J.D.; Lorenz, D.L.; Stark, J.R.; Menard, Jason; Easter, K.W.; Perry, Jim

2011-01-01

Floods result in great human disasters globally and nationally, causing an average of $4 billion of damages each year in the United States. Minnesota has its share of floods and flood damages, and the state has awarded nearly $278 million to local units of government for flood mitigation projects through its Flood Hazard Mitigation Grant Program. Since 1995, flood mitigation in the Red River Valley has exceeded $146 million. Considerable local and state funding has been provided to manage and mitigate problems of excess stormwater in urban areas, flooding of farmlands, and flood damages at road crossings. The cumulative costs involved with floods and flood mitigation in Minnesota are not known precisely, but it is safe to conclude that flood mitigation is a costly business. This chapter begins with a description of floods in Minneosta to provide examples and contrasts across the state. Background material is presented to provide a basic understanding of floods and flood processes, predication, and management and mitigation. Methods of analyzing and characterizing floods are presented because they affect how we respond to flooding and can influence relevant practices. The understanding and perceptions of floods and flooding commonly differ among those who work in flood forecasting, flood protection, or water resource mamnagement and citizens and businesses affected by floods. These differences can become magnified following a major flood, pointing to the need for better understanding of flooding as well as common language to describe flood risks and the uncertainty associated with determining such risks. Expectations of accurate and timely flood forecasts and our ability to control floods do not always match reality. Striving for clarity is important in formulating policies that can help avoid recurring flood damages and costs.

Contribution of Earth Observation data to flood risk mapping in the framework of the NATO SFP 'TIGRU' Project

International Nuclear Information System (INIS)

Stancalie, Gheorghe; Alecu, Corina; Craciunescul, Vasile; Diamandi, Andrei; Oancea, Simona; Brakenridge, Robert G.

2004-01-01

An important contribution of Earth Observation (EO) derived information in the topic of managing flooding connected phenomena could, be envisaged at the level of mapping aspects. EO satellites can provide necessary information for flood hazard and vulnerability assessment and mapping, which are directly used in the decision-making process. The EO data-derived information of the land cover/land use is important because it makes possible periodical updating and comparisons, and thus contribute to characterize the human presence and to provide elements on the vulnerability aspects, as well as the evaluation of the impact of the flooding. In order to obtain high-level thematic products the data extracted from the EO images must be integrated with other non-space ancillary data (topographical, penological, meteorological data) and hydrologic/hydraulic models outputs. This approach may be used in different phases of establishing the sensitive areas such as: the management of the database-built up from the ensemble of the spatially geo-referenced information; the elaboration of the risk indices from morpho-hydro graphical, meteorological and hydrological data; the interfacing with the models in order to improve their compatibility with input data; recovery of results and the possibility to work out scenarios; presentation of results as synthesis maps easy to access and interpret, additionally adequate to be combined with other information layouts resulted from the GIS database. The paper presents the specific methods, developed in the framework of the NATO SfP 'TIGRU' project 'Monitoring of extreme flood events in Romania and Hungary using EO data' for deriving satellite-based applications and products for flood risk mapping. The study area is situated in the Crisul Alb - Crisul Negru - K6r6s transboundary basin, crossing the Romanian - Hungarian border. Using the optical and microwave data supplied by the new satellite sensors (U.S. DMSP/Quikscat, LANDSAT-7/TM, EOS

Rainfall frequency analysis for ungauged sites using satellite precipitation products

Science.gov (United States)

Gado, Tamer A.; Hsu, Kuolin; Sorooshian, Soroosh

2017-11-01

The occurrence of extreme rainfall events and their impacts on hydrologic systems and society are critical considerations in the design and management of a large number of water resources projects. As precipitation records are often limited or unavailable at many sites, it is essential to develop better methods for regional estimation of extreme rainfall at these partially-gauged or ungauged sites. In this study, an innovative method for regional rainfall frequency analysis for ungauged sites is presented. The new method (hereafter, this is called the RRFA-S) is based on corrected annual maximum series obtained from a satellite precipitation product (e.g., PERSIANN-CDR). The probability matching method (PMM) is used here for bias correction to match the CDF of satellite-based precipitation data with the gauged data. The RRFA-S method was assessed through a comparative study with the traditional index flood method using the available annual maximum series of daily rainfall in two different regions in USA (11 sites in Colorado and 18 sites in California). The leave-one-out cross-validation technique was used to represent the ungauged site condition. Results of this numerical application have found that the quantile estimates obtained from the new approach are more accurate and more robust than those given by the traditional index flood method.

Leo satellite-based telecommunication network concepts

Science.gov (United States)

Aiken, John G.; Swan, Peter A.; Leopold, Ray J.

1991-01-01

Design considerations are discussed for Low Earth Orbit (LEO) satellite based telecommunications networks. The satellites are assumed to be connected to each other via intersatellite links. They are connected to the end user either directly or through gateways to other networks. Frequency reuse, circuit switching, packet switching, call handoff, and routing for these systems are discussed by analogy with terrestrial cellular (mobile radio) telecommunication systems.

Fine-tuning satellite-based rainfall estimates

Science.gov (United States)

Harsa, Hastuadi; Buono, Agus; Hidayat, Rahmat; Achyar, Jaumil; Noviati, Sri; Kurniawan, Roni; Praja, Alfan S.

2018-05-01

Rainfall datasets are available from various sources, including satellite estimates and ground observation. The locations of ground observation scatter sparsely. Therefore, the use of satellite estimates is advantageous, because satellite estimates can provide data on places where the ground observations do not present. However, in general, the satellite estimates data contain bias, since they are product of algorithms that transform the sensors response into rainfall values. Another cause may come from the number of ground observations used by the algorithms as the reference in determining the rainfall values. This paper describe the application of bias correction method to modify the satellite-based dataset by adding a number of ground observation locations that have not been used before by the algorithm. The bias correction was performed by utilizing Quantile Mapping procedure between ground observation data and satellite estimates data. Since Quantile Mapping required mean and standard deviation of both the reference and the being-corrected data, thus the Inverse Distance Weighting scheme was applied beforehand to the mean and standard deviation of the observation data in order to provide a spatial composition of them, which were originally scattered. Therefore, it was possible to provide a reference data point at the same location with that of the satellite estimates. The results show that the new dataset have statistically better representation of the rainfall values recorded by the ground observation than the previous dataset.

On the importance of analyzing flood defense failures

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Özer Işıl Ece

2016-01-01

Full Text Available Flood defense failures are rare events but when they do occur lead to significant amounts of damage. The defenses are usually designed for rather low-frequency hydraulic loading and as such typically at least high enough to prevent overflow. When they fail, flood defenses like levees built with modern design codes usually either fail due to wave overtopping or geotechnical failure mechanisms such as instability or internal erosion. Subsequently geotechnical failures could trigger an overflow leading for the breach to grow in size Not only the conditions relevant for these failure mechanisms are highly uncertain, also the model uncertainty in geomechanical, internal erosion models, or breach models are high compared to other structural models. Hence, there is a need for better validation and calibration of models or, in other words, better insight in model uncertainty. As scale effects typically play an important role and full-scale testing is challenging and costly, historic flood defense failures can be used to provide insights into the real failure processes and conditions. The recently initiated SAFElevee project at Delft University of Technology aims to exploit this source of information by performing back analysis of levee failures at different level of detail. Besides detailed process based analyses, the project aims to investigate spatial and temporal patterns in deformation as a function of the hydrodynamic loading using satellite radar interferometry (i.e. PS-InSAR in order to examine its relation with levee failure mechanisms. The project aims to combine probabilistic approaches with the mechanics of the various relevant failure mechanisms to reduce model uncertainty and propose improvements to assessment and design models. This paper describes the approach of the study to levee breach analysis and the use of satellites for breach initiation analysis, both adopted within the SAFElevee project.

From extended integrity monitoring to the safety evaluation of satellite-based localisation system

International Nuclear Information System (INIS)

Legrand, Cyril; Beugin, Julie; Marais, Juliette; Conrard, Blaise; El-Koursi, El-Miloudi; Berbineau, Marion

2016-01-01

Global Navigation Satellite Systems (GNSS) such as GPS, already used in aeronautics for safety-related applications, can play a major role in railway safety by allowing a train to locate itself safely. However, in order to implement this positioning solution in any embedded system, its performances must be evaluated according to railway standards. The evaluation of GNSS performances is not based on the same attributes class than RAMS evaluation. Face to these diffculties, we propose to express the integrity attribute, performance of satellite-based localisation. This attribute comes from aeronautical standards and for a hybridised GNSS with inertial system. To achieve this objective, the integrity attribute must be extended to this kind of system and algorithms initially devoted to GNSS integrity monitoring only must be adapted. Thereafter, the formalisation of this integrity attribute permits us to analyse the safety quantitatively through the probabilities of integrity risk and wrong-side failure. In this paper, after an introductory discussion about the use of localisation systems in railway safety context together with integrity issues, a particular integrity monitoring is proposed and described. The detection events of this algorithm permit us to conclude about safety level of satellite-based localisation system.

Fast Simulation of Large-Scale Floods Based on GPU Parallel Computing

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Qiang Liu

2018-05-01

Full Text Available Computing speed is a significant issue of large-scale flood simulations for real-time response to disaster prevention and mitigation. Even today, most of the large-scale flood simulations are generally run on supercomputers due to the massive amounts of data and computations necessary. In this work, a two-dimensional shallow water model based on an unstructured Godunov-type finite volume scheme was proposed for flood simulation. To realize a fast simulation of large-scale floods on a personal computer, a Graphics Processing Unit (GPU-based, high-performance computing method using the OpenACC application was adopted to parallelize the shallow water model. An unstructured data management method was presented to control the data transportation between the GPU and CPU (Central Processing Unit with minimum overhead, and then both computation and data were offloaded from the CPU to the GPU, which exploited the computational capability of the GPU as much as possible. The parallel model was validated using various benchmarks and real-world case studies. The results demonstrate that speed-ups of up to one order of magnitude can be achieved in comparison with the serial model. The proposed parallel model provides a fast and reliable tool with which to quickly assess flood hazards in large-scale areas and, thus, has a bright application prospect for dynamic inundation risk identification and disaster assessment.

Large-scale derived flood frequency analysis based on continuous simulation

Science.gov (United States)

Dung Nguyen, Viet; Hundecha, Yeshewatesfa; Guse, Björn; Vorogushyn, Sergiy; Merz, Bruno

2016-04-01

There is an increasing need for spatially consistent flood risk assessments at the regional scale (several 100.000 km2), in particular in the insurance industry and for national risk reduction strategies. However, most large-scale flood risk assessments are composed of smaller-scale assessments and show spatial inconsistencies. To overcome this deficit, a large-scale flood model composed of a weather generator and catchments models was developed reflecting the spatially inherent heterogeneity. The weather generator is a multisite and multivariate stochastic model capable of generating synthetic meteorological fields (precipitation, temperature, etc.) at daily resolution for the regional scale. These fields respect the observed autocorrelation, spatial correlation and co-variance between the variables. They are used as input into catchment models. A long-term simulation of this combined system enables to derive very long discharge series at many catchment locations serving as a basic for spatially consistent flood risk estimates at the regional scale. This combined model was set up and validated for major river catchments in Germany. The weather generator was trained by 53-year observation data at 528 stations covering not only the complete Germany but also parts of France, Switzerland, Czech Republic and Australia with the aggregated spatial scale of 443,931 km2. 10.000 years of daily meteorological fields for the study area were generated. Likewise, rainfall-runoff simulations with SWIM were performed for the entire Elbe, Rhine, Weser, Donau and Ems catchments. The validation results illustrate a good performance of the combined system, as the simulated flood magnitudes and frequencies agree well with the observed flood data. Based on continuous simulation this model chain is then used to estimate flood quantiles for the whole Germany including upstream headwater catchments in neighbouring countries. This continuous large scale approach overcomes the several

Sediment Trapping by Emerged Channel Bars in the Lowermost Mississippi River during a Major Flood

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Bo Wang

2015-11-01

Full Text Available The formation of channel bars has been recognized as the most significant sediment response to the highly trained Mississippi River (MR. However, no quantitative study exists on the dynamics of emerged channel bars and associated sediment accumulation in the last 500-kilometer reach of the MR from the Gulf of Mexico outlet, also known as the lowermost Mississippi River. Such knowledge is especially critical for riverine sediment management to impede coastal land loss in the Mississippi River Delta. In this study, we utilized a series of satellite images taken from August 2010 to January 2012 to assess the changes in surface area and volume of three large emerged channel bars in the lowermost MR following an unprecedented spring flood in 2011. River stage data were collected to develop a rating curve of surface areas detected by satellite images with flow conditions for each of the three bars. A uniform geometry associated with the areal change was assumed to estimate the bar volume changes. Our study reveals that the 2011 spring flood increased the surface area of the bars by 3.5% to 11.1%, resulting in a total surface increase of 7.3%, or 424,000 m2. Based on the surface area change, we estimated a total bar volume increase of 4.4%, or 1,219,900 m3. This volume increase would be equivalent to a sediment trapping of approximately 1.0 million metric tons, assuming a sediment bulk density of 1.2 metric tons per cubic meter. This large quantity of sediment is likely an underestimation because of the neglect of subaqueous bar area change and the assumption of a uniform geometry in volume estimation. Nonetheless, the results imply that channel bars in the lowermost MR are capable of capturing a substantial amount of sediment during floods, and that a thorough assessment of their long-term change can provide important insights into sediment trapping in the lowermost MR as well as the feasibility of proposed river sediment diversions.

Integrating Entropy-Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard.

Science.gov (United States)

Liu, Rui; Chen, Yun; Wu, Jianping; Gao, Lei; Barrett, Damian; Xu, Tingbao; Li, Xiaojuan; Li, Linyi; Huang, Chang; Yu, Jia

2017-04-01

Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net-water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics-based entropy method. The weighted indices were input into the WNB-based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image-based sampling and validation, cell-by-cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood-related environmental hazard studies. © 2016 Society for Risk Analysis.

Impact of the Three-Gorges Dam and water transfer project on Changjiang floods

Science.gov (United States)

Nakayama, Tadanobu; Shankman, David

2013-01-01

Increasing frequency of severe floods on the middle and lower Changjiang (Yangtze) River during the past few decades can be attributed to both abnormal monsoon rainfall and landscape changes that include extensive deforestation affecting river sedimentation, and shrinking lakes and levee construction that reduced the areas available for floodwater storage. The Three-Gorges Dam (TGD) and the South-to-North Water Transfer Project (SNWTP) will also affect frequency and intensity of severe floods in the Poyang Lake region of the middle Changjiang. Process-based National Integrated Catchment-based Eco-hydrology (NICE) model predicts that the TGD will increase flood risk during the early summer monsoon against the original justifications for building the dam, relating to complex river-lake-groundwater interactions. Several scenarios predict that morphological change will increase flood risk around the lake. This indicates the importance of managing both flood discharge and sediment deposition for the entire basin. Further, the authors assessed the impact of sand mining in the lake after its prohibition on the Changjiang, and clarified that alternative scenario of sand mining in lakes currently disconnected from the mainstream would reduce the flood risk to a greater extent than intensive dredging along junction channel. Because dry biomasses simulated by the model were linearly related to the Time-Integrated Normalized Difference Vegetation Index (TINDVI) estimated from satellite images, its decadal gradient during 1982-1999 showed a spatially heterogeneous distribution and generally decreasing trends beside the lakes, indicating that the increases in lake reclamation and the resultant decrease in rice productivity are closely related to the hydrologic changes. This integrated approach could help to minimize flood damage and promote better decisions addressing sustainable development.

Main flood peaks in the medieval Carpathian Basin (1000-1500): Annual and decadal overview

Science.gov (United States)

Kiss, Andrea

2013-04-01

The analysis of over 140 reported floods is mainly based on contemporary legal evidence (charters), partly on other types of contemporary documentary evidence. Majority of sources contains data on individual flood events (i.e. occurrence, seasonality, magnitude). Concerning main flood peaks, evidence on annual and multi-annual (decadal, multi-decadal) level is also available. Despite data increase in the 13th century, only in the 14th-15th centuries documentation is representative enough to draw further conclusions. Apart from secondary flood peaks (probably in the mid-13th century and the turn of the 13th-14th centuries), three main periods with high flood frequencies are detected: 1330s-1350s, 1390s-1430s, and the late 1480s-1490s (continuing in the early 16th century). The first major flood peak was primarily reported in the eastern Carpathian Basin (the Tisa catchment), and can be characterised by a number of high-intensity flood events (with 1342-1343 in centre). During the second major, prolonged flood peak of 1390s-1430s, and that of the third, late 15th century one the importance of floods occurred on the Danube and in the Danube catchment area has to be as well highlighted. Moreover, in the first half of the 15th century long-term hydrological problems (prolonged high water-level and high flood frequency problems) can be identified. In some cases high flood-frequency periods were accompanied by documented hydromorphological impacts and some impacts on society can be also detected. Results show good agreement with the decadal precipitation reconstruction based on speleothem investigations carried out in North-Hungary.

Integrated Flood Forecast and Virtual Dam Operation System for Water Resources and Flood Risk Management

Science.gov (United States)

Shibuo, Yoshihiro; Ikoma, Eiji; Lawford, Peter; Oyanagi, Misa; Kanauchi, Shizu; Koudelova, Petra; Kitsuregawa, Masaru; Koike, Toshio

2014-05-01

While availability of hydrological- and hydrometeorological data shows growing tendency and advanced modeling techniques are emerging, such newly available data and advanced models may not always be applied in the field of decision-making. In this study we present an integrated system of ensemble streamflow forecast (ESP) and virtual dam simulator, which is designed to support river and dam manager's decision making. The system consists of three main functions: real time hydrological model, ESP model, and dam simulator model. In the real time model, the system simulates current condition of river basins, such as soil moisture and river discharges, using LSM coupled distributed hydrological model. The ESP model takes initial condition from the real time model's output and generates ESP, based on numerical weather prediction. The dam simulator model provides virtual dam operation and users can experience impact of dam control on remaining reservoir volume and downstream flood under the anticipated flood forecast. Thus the river and dam managers shall be able to evaluate benefit of priori dam release and flood risk reduction at the same time, on real time basis. Furthermore the system has been developed under the concept of data and models integration, and it is coupled with Data Integration and Analysis System (DIAS) - a Japanese national project for integrating and analyzing massive amount of observational and model data. Therefore it has advantage in direct use of miscellaneous data from point/radar-derived observation, numerical weather prediction output, to satellite imagery stored in data archive. Output of the system is accessible over the web interface, making information available with relative ease, e.g. from ordinary PC to mobile devices. We have been applying the system to the Upper Tone region, located northwest from Tokyo metropolitan area, and we show application example of the system in recent flood events caused by typhoons.

Comparing the Performance of Commonly Available Digital Elevation Models in GIS-based Flood Simulation

Science.gov (United States)

Ybanez, R. L.; Lagmay, A. M. A.; David, C. P.

2016-12-01

With climatological hazards increasing globally, the Philippines is listed as one of the most vulnerable countries in the world due to its location in the Western Pacific. Flood hazards mapping and modelling is one of the responses by local government and research institutions to help prepare for and mitigate the effects of flood hazards that constantly threaten towns and cities in floodplains during the 6-month rainy season. Available digital elevation maps, which serve as the most important dataset used in 2D flood modelling, are limited in the Philippines and testing is needed to determine which of the few would work best for flood hazards mapping and modelling. Two-dimensional GIS-based flood modelling with the flood-routing software FLO-2D was conducted using three different available DEMs from the ASTER GDEM, the SRTM GDEM, and the locally available IfSAR DTM. All other parameters kept uniform, such as resolution, soil parameters, rainfall amount, and surface roughness, the three models were run over a 129-sq. kilometer watershed with only the basemap varying. The output flood hazard maps were compared on the basis of their flood distribution, extent, and depth. The ASTER and SRTM GDEMs contained too much error and noise which manifested as dissipated and dissolved hazard areas in the lower watershed where clearly delineated flood hazards should be present. Noise on the two datasets are clearly visible as erratic mounds in the floodplain. The dataset which produced the only feasible flood hazard map is the IfSAR DTM which delineates flood hazard areas clearly and properly. Despite the use of ASTER and SRTM with their published resolution and accuracy, their use in GIS-based flood modelling would be unreliable. Although not as accessible, only IfSAR or better datasets should be used for creating secondary products from these base DEM datasets. For developing countries which are most prone to hazards, but with limited choices for basemaps used in hazards

Ice Sheet Change Detection by Satellite Image Differencing

Science.gov (United States)

Bindschadler, Robert A.; Scambos, Ted A.; Choi, Hyeungu; Haran, Terry M.

2010-01-01

Differencing of digital satellite image pairs highlights subtle changes in near-identical scenes of Earth surfaces. Using the mathematical relationships relevant to photoclinometry, we examine the effectiveness of this method for the study of localized ice sheet surface topography changes using numerical experiments. We then test these results by differencing images of several regions in West Antarctica, including some where changes have previously been identified in altimeter profiles. The technique works well with coregistered images having low noise, high radiometric sensitivity, and near-identical solar illumination geometry. Clouds and frosts detract from resolving surface features. The ETM(plus) sensor on Landsat-7, ALI sensor on EO-1, and MODIS sensor on the Aqua and Terra satellite platforms all have potential for detecting localized topographic changes such as shifting dunes, surface inflation and deflation features associated with sub-glacial lake fill-drain events, or grounding line changes. Availability and frequency of MODIS images favor this sensor for wide application, and using it, we demonstrate both qualitative identification of changes in topography and quantitative mapping of slope and elevation changes.

Natural disaster reduction applications of the Chinese small satellite constellation for environment and disaster monitoring and forecasting

Science.gov (United States)

Liu, Sanchao; Fan, Yida; Gao, Maofang

2013-10-01

The Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting (SSCEDMF) is an important component of Chinese satellites earth observation system. The first stage of SSCEDMF is composed by "2+1" satellites. The 2 optical satellites (HJ-1-A and HJ-1-B) and 1 S band microwave satellite (HJ-1-C) were successful launched on September 6, 2008 and November 19, 2012 respectively. This article introduced SSCEDMF characteristic and the disaster reduction application system and satellites on-orbit test works, and also analyzed the application capacity in natural disasters included flood, ice flooding, wild fire, severely drought, snow disasters, large area landslide and debris flow, sea ice, earthquake recovering, desertification and plant diseases and insect pests. Furthermore, we show some cases of China's and other countries' new natural disasters forecasting, monitoring, assessment and recovery construction.

Virtual reality in urban water management: communicating urban flooding with particle-based CFD simulations.

Science.gov (United States)

Winkler, Daniel; Zischg, Jonatan; Rauch, Wolfgang

2018-01-01

For communicating urban flood risk to authorities and the public, a realistic three-dimensional visual display is frequently more suitable than detailed flood maps. Virtual reality could also serve to plan short-term flooding interventions. We introduce here an alternative approach for simulating three-dimensional flooding dynamics in large- and small-scale urban scenes by reaching out to computer graphics. This approach, denoted 'particle in cell', is a particle-based CFD method that is used to predict physically plausible results instead of accurate flow dynamics. We exemplify the approach for the real flooding event in July 2016 in Innsbruck.

From flood protection to flood risk management: condition-based and performance-based regulations in German water law

NARCIS (Netherlands)

Hartmann, T.; Albrecht, J.

2014-01-01

In many European countries, a paradigm shift from technically oriented flood protection to a holistic approach of flood risk management is taking place. In Germany, this approach is currently being implemented after several amendments of the Federal Water Act. The paradigm shift is also reflected in

LAKE ICE DETECTION IN LOW-RESOLUTION OPTICAL SATELLITE IMAGES

Directory of Open Access Journals (Sweden)

M. Tom

2018-05-01

Full Text Available Monitoring and analyzing the (decreasing trends in lake freezing provides important information for climate research. Multi-temporal satellite images are a natural data source to survey ice on lakes. In this paper, we describe a method for lake ice monitoring, which uses low spatial resolution (250 m–1000 m satellite images to determine whether a lake is frozen or not. We report results on four selected lakes in Switzerland: Sihl, Sils, Silvaplana and St. Moritz. These lakes have different properties regarding area, altitude, surrounding topography and freezing frequency, describing cases of medium to high difficulty. Digitized Open Street Map (OSM lake outlines are back-projected on to the image space after generalization. As a pre-processing step, the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images. We define the lake ice detection as a two-class (frozen, non-frozen semantic segmentation problem. Several spectral channels of the multi-spectral satellite data are used, both reflective and emissive (thermal. Only the cloud-free (clean pixels which lie completely inside the lake are analyzed. The most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with non-linear support vector machine (SVM. We show experimentally that this straight-forward approach works well with both MODIS and VIIRS satellite imagery. Moreover, we show that the algorithm produces consistent results when tested on data from multiple winters.

Lake Ice Detection in Low-Resolution Optical Satellite Images

Science.gov (United States)

Tom, M.; Kälin, U.; Sütterlin, M.; Baltsavias, E.; Schindler, K.

2018-05-01

Monitoring and analyzing the (decreasing) trends in lake freezing provides important information for climate research. Multi-temporal satellite images are a natural data source to survey ice on lakes. In this paper, we describe a method for lake ice monitoring, which uses low spatial resolution (250 m-1000 m) satellite images to determine whether a lake is frozen or not. We report results on four selected lakes in Switzerland: Sihl, Sils, Silvaplana and St. Moritz. These lakes have different properties regarding area, altitude, surrounding topography and freezing frequency, describing cases of medium to high difficulty. Digitized Open Street Map (OSM) lake outlines are back-projected on to the image space after generalization. As a pre-processing step, the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images. We define the lake ice detection as a two-class (frozen, non-frozen) semantic segmentation problem. Several spectral channels of the multi-spectral satellite data are used, both reflective and emissive (thermal). Only the cloud-free (clean) pixels which lie completely inside the lake are analyzed. The most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with non-linear support vector machine (SVM). We show experimentally that this straight-forward approach works well with both MODIS and VIIRS satellite imagery. Moreover, we show that the algorithm produces consistent results when tested on data from multiple winters.

Precipitation Analysis at Fine Time Scales using TRMM and Other Satellites: Realtime and Research Products and Applications

Science.gov (United States)

Adler, Robert; Huffman, George; Bolvin, David; Nelkin, Eric; Curtis, Scott; Pierce, Harold; Gu, Guojon

2004-01-01

Quasi-global precipitation analyses at fine time scales (3-hr) are described. TRMM observations (radar and passive microwave) are used to calibrate polar-orbit microwave observations from SSM/I (and other satellites instruments, including AMSR and AMSU) and geosynchronous IR observations. The individual data sets are then merged using a priority order based on quality to form the TRMM Multi-satellite Precipitation Analysis (MPA). Raingauge information is used to help constrain the satellite-based estimates over land. The TRMM standard research product (Version 6 3B-42 of the TRMM products) will be available for the entire TRMM period (January 1998-present) by the end of 2004. The real-time version of this merged product has been produced over the past two years and is available on the U.S. TRMM web site (trmm.gsfc.nasa.gov) at 0.25 deg latitude-longitude resolution over the latitude range from 50 deg N-50 deg S. Validation of daily totals indicates good results, with limitations noted in mid-latitude winter over land and regions of shallow, orographic precipitation. Various applications of these estimates are described, including: 1) detecting potential floods in near real-time; 2) analyzing Indian Ocean precipitation variations related to the initiation of El Nino; 3) determining characteristics of the African monsoon; and 4) analysis of diurnal variations.

Detection of the Velocity Shear Effect on the Spatial Distributions of the Galactic Satellites in Isolated Systems

Science.gov (United States)

Lee, Jounghun; Choi, Yun-Young

2015-02-01

We report a detection of the effect of the large-scale velocity shear on the spatial distributions of the galactic satellites around the isolated hosts. Identifying the isolated galactic systems, each of which consists of a single host galaxy and its satellites, from the Seventh Data Release of the Sloan Digital Sky Survey and reconstructing linearly the velocity shear field in the local universe, we measure the alignments between the relative positions of the satellites from their isolated hosts and the principal axes of the local velocity shear tensors projected onto the plane of sky. We find a clear signal that the galactic satellites in isolated systems are located preferentially along the directions of the minor principal axes of the large-scale velocity shear field. Those galactic satellites that are spirals, are brighter, are located at distances larger than the projected virial radii of the hosts, and belong to the spiral hosts yield stronger alignment signals, which implies that the alignment strength depends on the formation and accretion epochs of the galactic satellites. It is also shown that the alignment strength is quite insensitive to the cosmic web environment, as well as the size and luminosity of the isolated hosts. Although this result is consistent with the numerical finding of Libeskind et al. based on an N-body experiment, owing to the very low significance of the observed signals, it remains inconclusive whether or not the velocity shear effect on the satellite distribution is truly universal.

DETECTION OF THE VELOCITY SHEAR EFFECT ON THE SPATIAL DISTRIBUTIONS OF THE GALACTIC SATELLITES IN ISOLATED SYSTEMS

International Nuclear Information System (INIS)

Lee, Jounghun; Choi, Yun-Young

2015-01-01

We report a detection of the effect of the large-scale velocity shear on the spatial distributions of the galactic satellites around the isolated hosts. Identifying the isolated galactic systems, each of which consists of a single host galaxy and its satellites, from the Seventh Data Release of the Sloan Digital Sky Survey and reconstructing linearly the velocity shear field in the local universe, we measure the alignments between the relative positions of the satellites from their isolated hosts and the principal axes of the local velocity shear tensors projected onto the plane of sky. We find a clear signal that the galactic satellites in isolated systems are located preferentially along the directions of the minor principal axes of the large-scale velocity shear field. Those galactic satellites that are spirals, are brighter, are located at distances larger than the projected virial radii of the hosts, and belong to the spiral hosts yield stronger alignment signals, which implies that the alignment strength depends on the formation and accretion epochs of the galactic satellites. It is also shown that the alignment strength is quite insensitive to the cosmic web environment, as well as the size and luminosity of the isolated hosts. Although this result is consistent with the numerical finding of Libeskind et al. based on an N-body experiment, owing to the very low significance of the observed signals, it remains inconclusive whether or not the velocity shear effect on the satellite distribution is truly universal

A risk-based approach to flood management decisions in a nonstationary world

Science.gov (United States)

Rosner, Ana; Vogel, Richard M.; Kirshen, Paul H.

2014-03-01

Traditional approaches to flood management in a nonstationary world begin with a null hypothesis test of "no trend" and its likelihood, with little or no attention given to the likelihood that we might ignore a trend if it really existed. Concluding a trend exists when it does not, or rejecting a trend when it exists are known as type I and type II errors, respectively. Decision-makers are poorly served by statistical and/or decision methods that do not carefully consider both over- and under-preparation errors, respectively. Similarly, little attention is given to how to integrate uncertainty in our ability to detect trends into a flood management decision context. We show how trend hypothesis test results can be combined with an adaptation's infrastructure costs and damages avoided to provide a rational decision approach in a nonstationary world. The criterion of expected regret is shown to be a useful metric that integrates the statistical, economic, and hydrological aspects of the flood management problem in a nonstationary world.

Application of SVM on satellite images to detect hotspots in Jharia coal field region of India

Energy Technology Data Exchange (ETDEWEB)

Gautam, R.S.; Singh, D.; Mittal, A.; Sajin, P. [Indian Institute for Technology, Roorkee (India)