An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic

Abstract. An algorithm developed to map flooded areas from synthetic aperture radar imagery is presented in this paper. It is conceived to be inserted in the operational flood management system of the Italian Civil Protection and can be used in an almost automatic mode or in an interactive mode, depending on the user's needs. The approach is based on the fuzzy logic that is used to integrate theoretical knowledge about the radar return from inundated areas taken into account by means of three electromagnetic scattering models, with simple hydraulic considerations and contextual information. This integration aims at allowing a user to cope with situations, such as the presence of vegetation in the flooded area, in which inundation mapping from satellite radars represents a difficult task. The algorithm is designed to work with radar data at L, C, and X frequency bands and employs also ancillary data, such as a land cover map and a digital elevation model. The flood mapping procedure is tested on an inundation that occurred in Albania on January 2010 using COSMO-SkyMed very high resolution X-band SAR data

Flood Risk Mapping Using GIS and Multi-Criteria Analysis at Nanga Pinoh West Kalimantan Area

Flood is one of the disasters that often hit various regions in Indonesia, specifically in West Kalimantan. The floods in Nanga Pinoh District, Melawi Regency, submerged 18 villages and thousands of houses. Therefore, this study aimed to map flood risk areas in Nanga Pinoh and their environmental impact. Secondary data on the slope, total rainfall, flow density, soil type, and land cover analyzed with the multi-criteria GIS analysis were used. The results showed that the location had low, medium, and high risks. It was found that areas with high, prone, medium, and low risk class are 1,515.95 ha, 30,194.92 ha, 21,953.80 ha, and 3.14 ha, respectively. These findings implied that the GIS approach and multi-criteria analysis are effective tools for flood risk maps and helpful in anticipating greater losses and mitigating the disasters

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18% respectively

Mapping the Land-Use Suitability for Urban Sprawl Using Remote Sensing and GIS Under Different Scenarios

Urbanization is one of the important issues in fast developing countries, such as China, Turkey, Brazil, and South Africa. Therefore, sustainable urbanization strategies come into question while designing the cities. In this point, land-use suitability mapping for urban areas is of importance. Spatial information sciences, such as geographical information systems (GIS) and remote sensing are applied widely for mapping land-use suitability. In this study, Van City, which is the most crowded city in eastern Turkey, was evaluated by applying three different scenarios called ecological, economic, and sustainable. The multi-criteria evaluation technique was used in GIS environment in the mapping stage. Distance from roads, distance from urban boundary, hillshade, slope, elevation, land-use cover, and land-use ability factors were used as inputs in the analysis stage. The weights of each input factor were calculated according to urban change dynamics between 2002 and 2015. As a result of the study, the weighting approach using the natural change dynamics of Van City has a great potential to define objective weights. In addition, Van City was developed orderly on agricultural lands and grasslands, and it was not a sustainable development for the region because the main income is still agriculture and animal production, so a new strategy was designed in a sustainable scenario to prevent agriculture and grassland area loss in a mutual benefit between nature and human

IoT-based Lava Flood Early Warning System with Rainfall Intensity Monitoring and Disaster Communication Technology

A lava flood disaster is a volcanic hazard that often occurs when heavy rains are happening at the top of a volcano. This flood carries volcanic material from upstream to downstream of the river, affecting populous areas located quite far from the volcano peak. Therefore, an advanced early warning system of cold lava floods is inarguably vital. This paper aims to present a reliable, remote, Early Warning System (EWS) specifically designed for lava flood detection, along with its disaster communication system. The proposed system consists of two main subsystems: lava flood detection and disaster communication systems. It utilizes a modified automatic rain gauge; a novel configured vibration sensor; Fuzzy Tree Decision algorithm; ESP microcontrollers that support IoT, and disaster communication tools (WhatsApp, SMS, radio communication). According to the experiment results, the prototype of rainfall detection using the tipping bucket rain gauge sensor can measure heavy and moderate rainfall intensities with 81.5% accuracy. Meanwhile, the prototype of earthquake vibration detection using a geophone sensor can remove noise from car vibrations with a Kalman filter and measure vibrations in high and medium intensity with an accuracy of 89.5%. Measurements from sensors are sent to the webserver. The disaster mitigation team uses data from the webserver to evacuate residents using the disaster communication method. The proposed system was successfully implemented in Mount Merapi, Indonesia, coordinated with the local Disaster Deduction Risk (DDR) forum. Doi: 10.28991/esj-2021-SP1-011 Full Text: PD

Multisensor systems and flood risk management. Application to the Danube Delta using radar and hyperspectral imagery

International audienceAt the beginning of the 21st century, flood risk still represents a major world threat ( 60% of natural disasters are initiated by storms ) and the climate warming might even accentuate this phenomenon in the future. In Europe, despite all the policies in place and the measures taken during the past decades, large floods have occurred almost every year. The news regularly confirms this reality and the serious threat posed by flood risks in Europe. This paper presents an application to the Danube Delta exploiting radar imagery ENVISAT/ASAR and hyperspectral imagery CHRIS/PROBA for mapping flooded and floodable areas during the events of spring 2006. The uses of multisensor systems, such as radar and hyperspectral imagers, contribute to a better comprehension of floods in this wetland, their impacts, and risk management and sustainable development in the delta. In the section Risk management, this paper approaches the methodological aspects related to the characterization of the flood hazard whereas in the section Forecasting we will focus on the knowledge and modeling of the Land cover. The method of kernels, particularly adapted to the highlighting of the special-temporal variations - Support Vector Machine - and the methods based on the principle of the vague logic ( object-oriented classifications ) will be implemented so as to obtain the information plan of the spatial data.En ce début de 21e siècle, le risque d'inondation constitue encore le risque majeur au monde ( avec les tempêtes, les inondations représentent 60% des catastrophes naturelles ) et le réchauffement climatique pourrait encore renforcer ce phénomène à l'avenir. En Europe, malgré toutes les politiques et les mesures prises, au cours des dernières décennies, de grandes inondations ont lieu quasiment chaque année. Les actualités confirment régulièrement la réalité et la prégnance du risque d'inondation en Europe. Cet article présente une application concernant le risque d'inondation durant les événements du printemps 2006 dans le delta du Danube en exploitant des images radar ENVISAT/ASAR et l'imagerie hyperspectrale CHRIS/PROBA en matière d'analyse et de cartographie des zones inondées et de la classe de l'inondable. L'utilisation couplée des techniques spatiales ( radar et hyperspectrale ) pourrait contribuer à une meilleure compréhension des phénomènes liés aux inondations dans le Delta du Danube, ainsi qu'à la gestion de ce risque dans le delta et à son développement durable. Dans la partie Gestion du risque, ce travail aborde des aspects méthodologiques liés à la caractérisation de l'aléa de l'inondation tandis que dans la partie Prévision, la connaissance et la modélisation de l'Occupation du sol seront abordés. Des méthodes des noyaux ( kernels ), adaptées en particulier à la mise en évidence des variations spatio-temporelles - Suport Vector Machine - ainsi que des méthodes basées sur le principe de la logique floue ( classifications orientées objet ) sont mis en place afin d'obtenir le plan d'information des données spatiales

Assessing Building Vulnerability to Tsunami Hazard Using Integrative Remote Sensing and GIS Approaches

Risk and vulnerability assessment for natural hazards is of high interest. Various methods focusing on building vulnerability assessment have been developed ranging from simple approaches to sophisticated ones depending on the objectives of the study, the availability of data and technology. In-situ assessment methods have been widely used to measure building vulnerability to various types of hazards while remote sensing methods, specifically developed for assessing building vulnerability to tsunami hazard, are still very limited. The combination of remote sensing approaches with in-situ methods offers unique opportunities to overcome limitations of in-situ assessments. The main objective of this research is to develop remote sensing techniques in assessing building vulnerability to tsunami hazard as one of the key elements of risk assessment. The research work has been performed in the framework of the GITEWS (German-Indonesian Tsunami Early Warning System) project. This research contributes to two major components of tsunami risk assessment: (1) the provision of infrastructure vulnerability information as an important element in the exposure assessment; (2) tsunami evacuation modelling which is a critical element for assessing immediate response and capability to evacuate as part of the coping capacity analysis. The newly developed methodology is based on the combination of in-situ measurements and remote sensing techniques in a so-called “bottom-up remote sensing approach”. Within this approach, basic information was acquired by in-situ data collection (bottom level), which was then used as input for further analysis in the remote sensing approach (upper level). The results of this research show that a combined in-situ measurement and remote sensing approach can be successfully employed to assess and classify buildings into 4 classes based on their level of vulnerability to tsunami hazard with an accuracy of more than 80 percent. Statistical analysis successfully revealed key spatial parameters which were regarded to link parameters between in-situ and remote sensing approach such as size, height, shape, regularity, orientation, and accessibility. The key spatial parameters and their specified threshold values were implemented in a decision tree algorithm for developing a remote sensing rule-set of building vulnerability classification. A big number of buildings in the study area (Cilacap city, Indonesia) were successfully classified into the building vulnerability classes. The categorization ranges from high to low vulnerable buildings (A to C) and includes also a category of buildings which are potentially suitable for tsunami vertical evacuation (VE). A multi-criteria analysis was developed that incorporates three main components for vulnerability assessment: stability, tsunami resistance and accessibility. All the defined components were configured in a decision tree algorithm by applying weighting, scoring and threshold definition based on the building sample data. Stability components consist of structure parameters, which are closely related to the building stability against earthquake energy. Building stability needs to be analyzed because most of tsunami events in Indonesia are preceded by major earthquakes. Stability components analysis was applied in the first step of the newly developed decision tree algorithm to evaluate the building stability when earthquake strikes. Buildings with total scores below the defined threshold of stability were classified as the most vulnerable class A. Such the buildings have a high probability of being damaged after earthquake events. The remaining buildings with total scores above the defined threshold of stability were further analyzed using tsunami components and accessibility components to classify them into the vulnerability classes B, C and VE respectively. This research is based on very high spatial resolution satellite images (QuickBird) and object-based image analysis. Object-based image analysis is was chosen, because it allows the formulation of rule-sets based on image objects instead of pixels, which has significant advantages especially for the analysis of very high resolution satellite images. In the pre-processing stage, three image processing steps were performed: geometric correction, pan-sharpening and filtering. Adaptive Local Sigma and Morphological Opening filter techniques were applied as basis for the subsequent building edge detection. The data pre-processing significantly increased the accuracy of the following steps of image classification. In the next step image segmentation was developed to extract adequate image objects to be used for further classification. Image classification was carried out by grouping resulting objects into desired classes based on the derived object features. A single object was assigned by its feature characteristics calculated in the segmentation process. The characteristic features of an object - which were grouped into spectral signature, shape, size, texture, and neighbouring relations - were analysed, selected and semantically modelled to classify objects into object classes. Fuzzy logic algorithm and object feature separation analysis was performed to set the member¬ship values of objects that were grouped into particular classes. Finally this approach successfully detected and mapped building objects in the study area with their spatial attributes which provide base information for building vulnerability classification. A building vulnerability classification rule-set has been developed in this research and successfully applied to categorize building vulnerability classes. The developed approach was applied for Cilacap city, Indonesia. In order to analyze the transferability of this newly developed approach, the algorithm was also applied to Padang City, Indonesia. The results showed that the developed methodology is in general transferable. However, it requires some adaptations (e.g. thresholds) to provide accurate results. The results of this research show that Cilacap City is very vulnerable to tsunami hazard. Class A (very vulnerable) buildings cover the biggest portion of area in Cilacap City (63%), followed by class C (28%), class VE (6%) and class B (3%). Preventive measures should be carried out for the purpose of disaster risk reduction, especially for people living in such the most vulnerable buildings. Finally, the results were applied for tsunami evacuation modeling. The buildings, which were categorized as potential candidates for vertical evacuation, were selected and a GIS approach was applied to model evacuation time and evacuation routes. The results of this analysis provide important inputs to the disaster management authorities for future evacuation planning and disaster mitigation

Detection of temporarily flooded vegetation using time series of dual polarised C-band synthetic aperture radar data

The intense research of the last decades in the field of flood monitoring has shown that microwave sensors provide valuable information about the spatial and temporal flood extent. The new generation of satellites, such as the Sentinel-1 (S-1) constellation, provide a unique, temporally high-resolution detection of the earth's surface and its environmental changes. This opens up new possibilities for accurate and rapid flood monitoring that can support operational applications. Due to the observation of the earth's surface from space, large-scale flood events and their spatiotemporal changes can be monitored. This requires the adaptation of existing or the development of new algorithms, which on the one hand enable precise and computationally efficient flood detection and on the other hand can process a large amounts of data. In order to capture the entire extent of the flood area, it is essential to detect temporary flooded vegetation (TFV) areas in addition to the open water areas. The disregard of temporary flooded vegetation areas can lead to severe underestimation of the extent and volume of the flood. Under certain system and environmental conditions, Synthetic Aperture Radar (SAR) can be utilized to extract information from under the vegetation cover. Due to multiple backscattering of the SAR signal between the water surface and the vegetation, the flooded vegetation areas are mostly characterized by increased backscatter values. Using this information in combination with a continuous monitoring of the earth's surface by the S-1 satellites, characteristic time series-based patterns for temporary flooded vegetation can be identified. This combination of information provides the foundation for the time series approach presented here. This work provides a comprehensive overview of the relevant sensor and environmental parameters and their impact on the SAR signal regarding temporary open water (TOW) and TFV areas. In addition, existing methods for the derivation of flooded vegetation are reviewed and their benefits, limitations, methodological trends and potential research needs for this area are identified and assessed. The focus of the work lies in the development of a SAR and time series-based approach for the improved extraction of flooded areas by the supplementation of TFV and on the provision of a precise and rapid method for the detection of the entire flood extent. The approach developed in this thesis allows for the precise extraction of large-scale flood areas using dual-polarized C-band time series data and additional information such as topography and urban areas. The time series features include the characteristic variations (decrease and/or increase of backscatter values) on the flood date for the flood-related classes compared to the whole time series. These features are generated individually for each available polarization (VV, VH) and their ratios (VV/VH, VV-VH, VV+VV). The generation of the time series features was performed by Z-transform for each image element, taking into account the backscatter values on the flood date and the mean value and standard deviation of the backscatter values from the nonflood dates. This allowed the comparison of backscatter intensity changes between the image elements. The time series features constitute the foundation for the hierarchical threshold method for deriving flood-related classes. Using the Random Forest algorithm, the importance of the time series data for the individual flood-related classes was analyzed and evaluated. The results showed that the dual-polarized time series features are particularly relevant for the derivation of TFV. However, this may differ depending on the vegetation type and other environmental conditions. The analyses based on S-1 data in Namibia, Greece/Turkey and China during large-scale floods show the effectiveness of the method presented here in terms of classification accuracy. Theiv supplementary integration of temporary flooded vegetation areas and the use of additional information resulted in a significant improvement in the detection of the entire flood extent. It could be shown that a comparably high classification accuracy (~ 80%) was achieved for the flood extent in each of study areas. The transferability of the approach due to the application of a single time series feature regarding the derivation of open water areas could be confirmed for all study areas. Considering the seasonal component by using time series data, the seasonal variability of the backscatter signal for vegetation can be detected. This allows for an improved differentiation between flooded and non-flooded vegetation areas. Simultaneously, changes in the backscatter signal can be assigned to changes in the environmental conditions, since on the one hand a time series of the same image element is considered and on the other hand the sensor parameters do not change due to the same acquisition geometry. Overall, the proposed time series approach allows for a considerable improvement in the derivation of the entire flood extent by supplementing the TOW areas with the TFV areas