Natural Disaster Monitoring Research Articles

Coherence Change-Detection with Sentinel-1 for Natural and Anthropogenic Disaster Monitoring in Urban Areas

Rapid, reliable, and continuous information is an essential component in disaster monitoring and management. Remote sensing data could be a solution, but often cannot provide continuous data due to an absence of global coverage and weather and daylight dependency. To overcome these challenges, this study makes use of weather and day/light independent Sentinel-1 data with a global coverage to monitor localized effects of different types of disasters using the Coherence Change-Detection (CCD) technique. Coherence maps were generated from Synthetic Aperture Radar (SAR) images and used to classify areas of change and no change in six study areas. These sites are located in Syria, Puerto Rico, California, and Iran. The study areas were divided into street blocks, and the standard deviation was calculated for the coherence images for each street block over entire image stacks. The study areas were classified by land-use type to reveal the spatial variation in coherence loss after a disaster. While temporal decorrelation exhibits a general loss in coherence over time, disaster occurrence, however, indicates a significant loss in coherence after an event. The variations of each street block from the average coherence for the entire image stack, as measured by a high standard deviation after a particular disaster, is an indication of disaster induced building damage.

Exploring the Potential of Conditional Adversarial Networks for Optical and SAR Image Matching

Tasks such as the monitoring of natural disasters or the detection of change highly benefit from complementary information about an area or a specific object of interest. The required information is provided by fusing high accurate coregistered and georeferenced datasets. Aligned high-resolution optical and synthetic aperture radar (SAR) data additionally enable an absolute geolocation accuracy improvement of the optical images by extracting accurate and reliable ground control points (GCPs) from the SAR images. In this paper, we investigate the applicability of a deep learning based matching concept for the generation of precise and accurate GCPs from SAR satellite images by matching optical and SAR images. To this end, conditional generative adversarial networks (cGANs) are trained to generate SAR-like image patches from optical images. For training and testing, optical and SAR image patches are extracted from TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe. The artificially generated patches are then used to improve the conditions for three known matching approaches based on normalized cross-correlation (NCC), scale-invariant feature transform (SIFT), and binary robust invariant scalable key (BRISK), which are normally not usable for the matching of optical and SAR images. The results validate that a NCC-, SIFT-, and BRISK-based matching greatly benefit, in terms of matching accuracy and precision, from the use of the artificial templates. The comparison with two state-of-the-art optical and SAR matching approaches shows the potential of the proposed method but also revealed some challenges and the necessity for further developments.

STUDY ON PROCESS COORDINATION CAPABILITY FOR EARTH SURFACE IMAGING VIA VNREDSAT-1 OF VIETNAM AND BKA OF BELARUS

Leading companies in the field of obtaining data from Earth remote sensing from space are building Satellite constellations to increase the frequency (multiplicity) and monitor various parts of the Earth’s surface. The Satellite constellation remote sensing equipment from Airbus (France) allows daily observation of any point on the Earth’s surface with high spatial resolution, and the grouping of DigitalGlobe (USA) – with ultra-high spatial resolution. The analysis of the mutual exploitation of the national space systems of the Socialist Republic of Vietnam and the Republic of Belarus has been carried out. There have been analyzed the joint capabilities of VNREDSat-1 (Vietnam) and BKA (Belarus) Earth Observation Systems for the monitoring of natural resources, the environment and natural disasters by comparison of their space and temporal characteristics (orbit parameters and re-visit time) as well as spectral-energetic characteristics (space, spectral and radiometric resolutions). The accepted correlation of technical parameters for solving the previously mentioned tasks having into account the different phases of the mentioned satellites has been determined. The conclusions have been reinforced by the results of live experiments with archival and instant data from both EO satellites.

Rapid Disaster Mapping through Data Integration from UAVs and Multi-sensors Mounted on Investigation Platforms of NDMI, Korea

Abstract. As the recent disaster is so difficult to predict when and where it would hit, so it requires paradigm for disaster shifts from response to preparedness. In order to respond this change, NDMI has studied disaster scientific investigation (DSI) technologies for revealing systematically the root cause of disaster and protecting repetitive recurrence. The purpose of this study is to propose a convergence approach between data acquired from different types of sensors on a van-type investigation platform and UAVs of NDMI and assess their applicability for timely natural and man-made disaster mapping and monitoring. In order to evaluate its applicability for rapid disaster mapping, we pre-tested the proposed approach for NDMI site in Ulsan, Korea. For the enhancement of the direct geo-referencing accuracy of UAV imagery captured from on-board camera of DJI and the creation of more accurate map products, camera IOPs refinement and bundle adjustment were also performed with minimal GCPs. Finally, we conducted UAV data registration with LiDAR point clould for disaster mapping applications.

On the Methodology of Satellite Data Utilization in Multi-Modeling Approach for Socio-Ecological Risks Assessment Tasks: A Problem Formulation

The problem of satellite data utilization in multi-modeling approach for socio-ecological risks assessment is formally defined. Methodology of natural geo-systems modeling for variables and indicators selections is described. Observation, measurement and modeling data utilization method in the framework of multi-model approach is described. Methodology and models of risk assessment in framework of decision support approach are defined and described. Brief conclusions on efficiency of the described methodology are proposed. Proposed methodology can applied for wide range of risk-related tasks, such as natural and technological disaster monitoring, air-water-soil pollution control, crop productivity control, etc.

Understanding the decision-making process in disaster risk monitoring and early-warning: A case study within a control room in Brazil

The tasks of disaster risk monitoring and early warning are an important means of improving the efficiency of disaster response and preparedness. However, although the current works in this area have sought to provide a more accurate and better technological infrastructure of systems to support these tasks, they have failed to examine key features that may affect the decision-making. In light of this, this paper aims to provide an understanding of the decision-making process in control rooms for disaster risk monitoring and early warning. This understanding is underpinned by a conceptual framework, which has been developed in this work and describes factors that influence the decision-making. For doing so, data were collected through a series of semi-structured interviews and participatory observations and later evaluated with members of the control room of the Brazilian Center for Monitoring and Early Warning of Natural Disasters (Cemaden). The study findings provided a solid basis for designing the conceptual framework of the essential factors required by the decision-makers. These factors are separated into two groups: 1) the “dimensions” of decision-making (i.e., the type of hazard, the phase of the disaster risk, the location, and area of expertise of the operators) and the “pillars” of decision-making (i.e., the tasks, their required information, useful data sources, and the decision rule). Finally, the contributions achieved in this study may help operators to understand and propose proactive measures that could improve their decision-making, overcome uncertainties, standardize the team's decision-making, and put less pressure on operators.

Performance of Multiple Error Correction (MEC) scheme based Hybrid ARQ (HARQ) algorithm for maximizing lifetime of Wireless Sensor Network (WSN) for natural disaster monitoring

Natural disaster monitoring has become the critical area in Wireless Sensor Network to monitor the danger forthe accurate early warning to the public. The main issues such as energy-constrained and high error rates had become the utmost debates among the researchers. Hybrid ARQ was known as one of the error control protocols that outperform the existing ARQ and FEC. The existing HARQ might not be able to encounter the issues of network lifetime due to the advancement of this Sensor technology. Thus, we focused on the factors of Bit Error Rates and interference that riseto design the extension of HARQ that could lower the usage of energy and help toprolong the lifetime of the network. This paper proposed Multiple Error Correction algorithm that was based on the existing process of HARQ and presented the outcomes of implementing MEC towards the network of WSN.

Environmental Data Observation System—Solam for Analysis and Interpretation of Soil Moisture Variation and Precipitation Indexes

The monitoring of urban slopes employing in-situ equipment seeks to avoid the loss of human lives midst emergency events like landslides. Cemaden (Brazilian National Centre for Monitoring and Early Warnings of Natural Disasters) has a pilot network of soil moisture and rain gauge stations located in urban slopes to understand, prevent, and mitigate landslides. Due to the large volume of data produced by this system there was a need for tools for data retrieval and visualization. This paper describes the Solam (Environmental Data Observation System) software which generates automated graphs that enable researchers to analyze and comprise data from soil moisture sensors and rain gauge stations. Those results allow to research moisture variations in six different sensor levels (from 0.5 m to 3 m deep) and precipitation indexes (hourly and accumulated rainfall) for the established period. This analysis will provide information to set critical environmental thresholds on base recorded landslides.

Correlation between Rainfall and Mass Movements in North Coast Region of Sao Paulo State, Brazil for 2014-2018

The monitoring of related hourly and accumulated rainfall index requires that critical thresholds of accumulated 72 hours rainfall are updated frequently according with the factors and local conditions (natural and anthropic) of each specific risk area. The importance of empirical methods is fundamental to confirm the relationship between rainfall intensity and accumulated rainfall with the mass movement events, in order to establish the critical threshold values. The present work performs an evaluation of the record data of mass movement events occurred in Sao Paulo State North coast region for a 4-year period (2014 to 2018) considering different mass movement characteristics (slope type, magnitude and impact level). Some rainfall values were obtained to show that within these parameters an event related to natural and anthropic features was triggered. A database was created, sorting source of information and municipalities monitored, to implement the correlation between the mass movement events and the rainfall values. To elaborate the event’s map, reliable record data of localization of the mass movement events was selected, as well as the nearest possible raingauges of CEMADEN (National Center for Monitoring and Early Warning of Natural Disasters); also the exact event triggering time, selection by the slope type, the magnitude and the impact level of the mass movement event. The rainfall values of these raingauges allowed the calculation of the accumulated rainfall index for 1, 3, 6, 24, 48, 72 and 96 hours, with the adoption of the 72 hours index for this work. The correlation graphics are divided by the slope type, the magnitude and the impact level of the mass movement event. Different critical thresholds appear, classifying such event by the influence level of triggering factors, natural and/or anthropic.

Evaluation of TRMM satellite rainfall estimates (algorithms 3B42 V7 & RT) over the Santo Antônio county (Goiás, Brazil)

The rainfall has a direct influence on the agricultural productivity, being indispensable the knowledge of its spatiotemporal behavior in order to establish trends that will assist in the management of water resources, agricultural planning, hydrological monitoring and prevention of natural disasters. Thus, this work aimed to evaluate the accuracy of the TRMM satellite precipitation estimates in relation to the recorded precipitation. For this, the rainfall data from the weather station located in the municipality of Santo Antonio de Goias - GO were used, being compared to the data of the TRMM satellite, algorithms 3B42 Version 7 and Real Time, in the period from January 1998 to October 2015. The comparison of the TRMM satellite data showed that the ten-day and monthly precipitation records of the algorithm 3B42 V_7 showed correlation values of 0.69 and 0.65, respectively, in the rainy season; in the dry season, the correlations were of 0.80 and 0.73. The ten-day concordance index ranged from 0.68 to 0.98 and the monthly concordance index ranged from 0.83 to 0.99. The algorithm 3B42 Real Time presented lower statistical results when compared to the 3B42 V_7. The satellite precipitation estimates showed both trends of overestimation and underestimation; however, the satellite data can help research in the absence of information on the rainfall in the region.

Satellite observation of coastline change around Hay Point, Australia before and after tropical cyclone “Debbie”

On March 28, 2017, tropical cyclone “Debbie” made landfall on the coast of the state of Queensland in Australia. This was the most powerful tropical cyclone to hit Australia in the past 6 years. Debbie has resulted in at least five deaths and caused rail damage, power outages and flooding according to relevant reports. This cyclone halted railway transportation and several mines in Queensland had to be closed, including the coal exports terminals of Abbot Point, Dalrymple Bay and Hay Point. In a short period, Debbie resulted in a strong hydro-dynamical response. Subsequently, Debbie caused considerable coastline movement and significantly modified the coastal topography. The position of a coastline is controlled by tide level and coastal topography. Hence, we should take tide level variation into consideration to avoid its impact on the determination of coastline position. To locate the position of coastline and assess its temporal change around Hay Point, we collected tide level data from Rosslyn Bay station near the study area. Based on the tide level data, two Landsat-8 Operational Land Imager (OLI) images showing similar tide levels before and after Debbie were obtained from the United States Geological Survey (USGS) Earth Explorer website. Landsat scenes used in this paper from Landsat Surface Reflectance Climate Data Records (CDR) had been pre-processed to L1TP level (Precision and Terrain Correction). The widely used Normalized Difference Water Index (NDWI) was used to enhance water information. This water index is able to enlarge the discrimination between surface water and other types of land cover. The optimal threshold was manually determined using the frequency histogram of the NDWI image. We applied the optimal threshold to produce a land-water map. Then, inland water was filled to remove its effect on the land-ocean map. Finally, manual checking and editing were implemented in ArcGIS10.2 to ensure the quality of land-ocean maps before and after Debbie. We used the produced land-ocean map to represent the position of the coastline. Two land-ocean maps were overlaid and subtracted to generate the coastline change map. The net change of land area per 1 km×1 km cell along the coastline was calculated to characterize the spatial pattern of coastline change after cyclone Debbie. Our results suggest that the coastline significantly expanded to the ocean after the landfall of Debbie with a net land gain of 54.5 km2 along the coast around Hay Point, Australia. In our study area, 98.2% of the coastline exhibited expansion to the ocean and only 1.8% of the coastline retreated to the land. Note that we may slightly underestimate the net land gain because some regions in the study area were covered by cloud/shadow during observations. We also observed a significant increase of suspended sediment concentration around some coasts using visual judgement, which is likely to be associated with the coastline expansion around the coastal area near Hay Point. Timely and accurate information on coastline position and its change is essential to the management of sudden siltation of ports and waterways, and the subsequent resumption of the operation of wharves. Such datasets can also be helpful to develop and validate storm surge-wave-sediment numerical models. This paper used a simple application to demonstrate the great potential of satellite technology for natural disaster monitoring and assessment. In the future, more attention should be paid to the use of multi-source remotely sensed data to conduct continuous monitoring of coastline dynamics with higher spatial resolution and temporal frequency.

Object-based cloud detection of multitemporal high-resolution stationary satellite images

Satellite remote sensing that utilizes highly accurate cloud detection is important for monitoring natural disasters. The GaoFen-4, China’s first high-resolution stationary satellite, was recently launched and acquires imagery at a spatial resolution of 50 m and a high temporal resolution (up to 10 min). An object-based cloud detection method was conducted for a time series of GaoFen-4 images. The cloudy objects were obtained from the individual images, and the outlier detection of multiple temporal objects was further processed for refinement. In the initial cloud detection, the objects were segmented by the mean-shift algorithm, and their morphological features were extracted by extended attribute profiles. The threshold-detected cloudy objects were trained according to spectral and morphological features, and the initial objects were classified as cloudy or clear by a regularized least-squares classifier. Furthermore, the medians and standard deviations of the classified cloudy and clear objects were calculated and subsequently refined by the outlier detection of multiple temporal images. The clear object features deviated more than a multiple of standard deviations from the medians of the clear objects that were classified as cloudy objects. Additionally, the refined clear objects were obtained by a similar outlier detection method. Flood event monitoring using GaoFen-4 images showed that the average overall accuracy of the initial cloud detection was 83.4% and increased to 93.3% after refinement. This object-based cloud detection method was insensitive to variations in land objects and can effectively improve cloud detection within small or thin areas, which can be helpful for the monitoring of natural disasters.

Design and Development of COA-Leach: A well-organized Energy Hypothesize Constructed on Energy Price Demonstrating in Wireless Sensor Network

Wireless Sensor Network (WSN) consists of huge collection of small, battery-powered sensor nodes. Advancements in the WSN give a new scope and dimension for implementing WSN in real-time applications. WSN is inexpensively implemented in the desired area to monitor space, things, and the interaction between the things and encompassing space such as surveillance, intelligent alarm, pipeline and location monitoring, natural disaster monitoring, etc. For processing, transmission and aggregation of data for the aforementioned monitoring functions, nodes consume more energy. This becomes a daunting challenge for WSN, since WSNs are limited with the limited power, processing time and storage capacity. In order to overcome resource constraints, authors of this article proposed a new comprehensive energy postulate that depends on the energy cost modeling named Cyclic Optimized Approach–LEACH (COA-LEACH). The model is constructed through thorough examinations of energy spend in a working WSN for deployment. The decision made in the deployment of nodes explicitly affects the sensing quality as well as the energy consumption of the entire network. Authors mainly focused on the self-rearrange for identification of the neighbor, which is useful for the energy conservation. The proposed generalized model is validated by experimented through spotting toxic gas and also authors in sighted the energy requirement for both the existing and proposed energy model.

Measurement of small co-seismic deformation field from multi-temporal SAR interferometry: application to the 19 September 2004 Huntoon Valley earthquake

ABSTRACTInterferometric synthetic aperture (InSAR) has been widely applied to natural disaster monitoring. However, it has limitations due to the influence of noise sources such as atmospheric and topographic artefacts, data processing errors, etc. In particular, atmospheric effect is one of the most prominent noise sources in InSAR for the monitoring of small magnitude deformations. In this paper, we proposed an efficient multi-temporal InSAR (MTInSAR) approach to measure small co-seismic deformations by minimizing atmospheric anomalies. This approach was applied to investigate the 18 September 2004 earthquake over Huntoon Valley, California, using 13 ascending and 22 descending ENVISAT synthetic aperture radar (SAR) images. The results showed that the co-seismic deformation was ±1.5 and ±1.0 cm in the horizontal and vertical directions, respectively. The earthquake source parameters were estimated using an elastic dislocation source from the ascending and descending acquisitions. The root mean square errors between the observed and modelled deformations were improved by the proposed MTInSAR approach to about 3.8 and 1.8 mm from about 4.0 and 5.2 mm in the ascending and descending orbits, respectively. It means that the MTInSAR approach presented herein remarkably improved the measurement performance of a small co-seismic deformation.

Flood Monitoring Using Satellite-Based RGB Composite Imagery and Refractive Index Retrieval in Visible and Near-Infrared Bands

Satellite remote sensing provides significant information for the monitoring of natural disasters. Recently, on a global scale, floods have been increasing both in frequency and in magnitude. In order to map the inundation area, flooding events are investigated using unique RGB composite imagery based on the MODIS surface reflectance (MOD09GA) data obtained from the Terra satellite, which is used to visualize and analyze these events. This study proposes using an RGB combination of MODIS band 6 (1.64 μm), band 5 (1.24 μm), and band 2 (0.86 μm) data from the visible and the near-infrared spectral ranges to map flood events. The flooding events that were investigated in this study occurred on 25 October 2015 along the Pampanga River in the Philippines, and on 28 July 2016 along the Poyang and Dongting Lakes in China. In the case of the Pampanga River, the inundated areas were estimated with surface reflectance (R) thresholds of 0.0 ≤ R6 ≤ 0.102, 0.0 ≤ R5 ≤ 0.138, and 0.03 ≤ R2 ≤ 0.148 for MODIS bands 6, 5, and 2, respectively, which were determined using Otsu’s method. The total inundated area was estimated to be 487.75 km2. This estimate was indirectly compared with the results obtained from SENTINEL-1A Synthetic Aperture Radar (SAR) data. The total inundated area on 26 October 2015 for the case of the Pampanga River was estimated to be 486.37 km2 using histogram analysis based on Otsu’s method. For the flooding case in China, the total estimated inundated area using MODIS RGB imagery on 28 July 2016 and SAR on 3 August 2016 was 1148.25 km2 and 1110.096 km2, respectively. In addition, RGB imagery results using MODIS 6-5-2 bands were supported by the refractive index retrieval along the inundation area. A threshold of 1.6 for the real part of the complex refractive index allows for the discrimination between the flooded and non-flooded areas using the Hong and ASH approximations. This study shows that the RGB composite techniques using advanced sensors with more bands and higher spatio-temporal resolutions, and supported by the refractive index retrieval method, are useful for estimating flood events.

Application of InSAR Modeling for Monitoring of Territories Under Landslides Risk in Armenia

In this article we describe works (that were carried out in the frames of NATO Project: NATO CLG: ESP.EAP.CLG. 983876) that have done for creating DEM for selected areas of Armenia with high level of risk where natural disasters such as grounds sedimentation, landslides, mudflows, earthquakes periodically happen. In the future the obtained DEM will be used to create regional system for the monitoring of natural disasters (to control topographic changes on the territories with high level of risk) in the territory of Armenia using tools such as InSAR modeling, systems of mathematical interpretation of digital models, GIS technologies and mathematical models for processing satellite images. The results of monitoring will be periodically presented to local authorities to take measures in order to prevent probable natural disasters impact on population and communications.

FPGA implementation of the principal component analysis algorithm for dimensionality reduction of hyperspectral images

Remotely sensed hyperspectral imaging is a very active research area, with numerous contributions in the recent scientific literature. The analysis of these images represents an extremely complex procedure from a computational point of view, mainly due to the high dimensionality of the data and the inherent complexity of the state-of-the-art algorithms for processing hyperspectral images. This computational cost represents a significant disadvantage in applications that require real-time response, such as fire tracing, prevention and monitoring of natural disasters, chemical spills, and other environmental pollution. Many of these algorithms consider, as one of their fundamental stages to fully process a hyperspectral image, a dimensionality reduction in order to remove noise and redundant information in the hyperspectral images under analysis. Therefore, it is possible to significantly reduce the size of the images, and hence, alleviate data storage requirements. However, this step is not exempt of computationally complex matrix operations, such as the computation of the eigenvalues and the eigenvectors of large and dense matrices. Hence, for the aforementioned applications in which prompt replies are mandatory, this dimensionality reduction must be considerably accelerated, typically through the utilization of high-performance computing platforms. For this purpose, reconfigurable hardware solutions such as field-programmable gate arrays have been consolidated during the last years as one of the standard choices for the fast processing of hyperspectral remotely sensed images due to their smaller size, weight and power consumption when compared with other high-performance computing systems. In this paper, we propose the implementation in reconfigurable hardware of the principal component analysis (PCA) algorithm to carry out the dimensionality reduction in hyperspectral images. Experimental results demonstrate that our hardware version of the PCA algorithm significantly outperforms a commercial software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing. Furthermore, our implementation exhibits real-time performance with regard to the time that the targeted hyperspectral instrument takes to collect the image data.

BEYOND - Building Capacity for a Centre of Excellence for EO-based monitoring of Natural Disasters - FP7 Project

BEYOND - Building Capacity for a Centre of Excellence for EO-based monitoring of Natural Disasters - FP7 Project

Embedded Implementation of VHR Satellite Image Segmentation.

Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage.

Errata: Inverse synthetic aperture radar imaging of targets with complex motions based on modified chirp rate–quadratic chirp rate distribution for cubic phase signal

The <i>Journal of Applied Remote Sensing</i> (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous other commercial and scientific applications.