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Remote Sensing for Post-disaster Phase: Damage Assessment, Reconstruction and Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 August 2019) | Viewed by 62169

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Special Issue Editors

Prof. Dr. Fabio Dell'Acqua

E-Mail Website1 Website2
Guest Editor

Department of Electrical, Computer, Biomedical Engineering, University of Pavia, Italy - Ticinum Aerospace s.r.l., Pavia, Italy
Interests: remote sensing; Earth observation; risk management
Special Issues, Collections and Topics in MDPI journals

Mr. Ronald T. Eguchi

E-Mail Website
Guest Editor

CEO, ImageCat Inc.
Interests: remote sensing; loss estimation; exposure model development

Prof. Dr. Fumio Yamazaki

E-Mail Website1 Website2 Website3
Guest Editor

1. Chiba University, Chiba 263-8522, Japan
2. National Research Institute for Earth Science and Disaster Resilience (NIED), Tsukuba 305-0006, Japan
Interests: remote sensing; disaster management; natural hazard; stochastic mechanics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural disasters are increasing in frequency and severity in the modern world, and their impact on human lives and the economy are accelerating due to growing urbanization and increasing frequency of extreme weather events.

Disaster risk reduction (DRR) is one essential approach to curbing the toll that disasters take in terms of both human lives and financial value. However, effective intervention in the post-disaster phase also plays a major role in reducing the “disaster bill” and facilitating reconstruction. Timely and accurate data collection, and situational monitoring, can really make a difference in recovery and long-term normalization of the affected area.

Spaceborne Earth Observation (EO) has been widely applied to post-disaster response, damage assessment, recovery and mitigation, and data collection and processing methods have advanced substantially in the recent years. The overall trend towards open data favoured by major agencies and programs across the globe enable an unprecedented scale of monitoring and understanding of disaster phenomena, creating the conditions for building complete, accurate and dynamic pictures of post-disaster situations.

This Special Issue will draw from ongoing advancements and novel developments of methodologies, and best case studies, demonstrating the use of EO technology in this context. We encourage submission of both review and original research articles related to the different aspects of response and recovery. The special issue will include, but will not be limited to, the following topics:

Damage assessment and mapping using space-based and airborne Earth observation data;
Recovery monitoring;
Risk and hazard assessment;
Vulnerability and exposure information, and their use in improving damage assessment;
Insurance policies and claim vetting;
Open data and big data in risk and damage assessment;
Deeplearning in damage assessment
Crowdsourcing and participative sensing

Prof. Dr. Fabio Dell’Acqua
Mr. Ronald T. Eguchi
Prof. Dr. Fumio Yamazaki
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Natural Hazards
Risk Assessment
Damage Assessment
Recovery
Mapping
Satellites
Airborne Sensors
Data Fusion
Reconstruction Monitoring

Published Papers (12 papers)

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Research

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24 pages, 5626 KiB

Open AccessArticle

Evaluating Resilience-Centered Development Interventions with Remote Sensing

by Norman Kerle, Saman Ghaffarian, Raphael Nawrotzki, Gerald Leppert and Malte Lech

Remote Sens. 2019, 11(21), 2511; https://doi.org/10.3390/rs11212511 - 26 Oct 2019

Cited by 13 | Viewed by 5322

Abstract

Natural disasters are projected to increase in number and severity, in part due to climate change. At the same time a growing number of disaster risk reduction (DRR) and climate change adaptation measures are being implemented by governmental and non-governmental organizations, and substantial post-disaster donations are frequently pledged. At the same time there has been increasing demand for transparency and accountability, and thus evidence of those measures having a positive effect. We hypothesized that resilience-enhancing interventions should result in less damage during a hazard event, or at least quicker recovery. In this study we assessed recovery over a 3 year period of seven municipalities in the central Philippines devastated by Typhoon Haiyan in 2013. We used very high resolution optical images (<1 m), and created detailed land cover and land use maps for four epochs before and after the event, using a machine learning approach with extreme gradient boosting. The spatially and temporally highly variable recovery maps were then statistically related to detailed questionnaire data acquired by DEval in 2012 and 2016, whose principal aim was to assess the impact of a 10 year land-planning intervention program by the German agency for technical cooperation (GIZ). The survey data allowed very detailed insights into DRR-related perspectives, motivations and drivers of the affected population. To some extent they also helped to overcome the principal limitation of remote sensing, which can effectively describe but not explain the reasons for differential recovery. However, while a number of causal links between intervention parameters and reconstruction was found, the common notion that a resilient community should recover better and more quickly could not be confirmed. The study also revealed a number of methodological limitations, such as the high cost for commercial image data not matching the spatially extensive but also detailed scale of field evaluations, the remote sensing analysis likely overestimating damage and thus providing incorrect recovery metrics, and image data catalogues especially for more remote communities often being incomplete. Nevertheless, the study provides a valuable proof of concept for the synergies resulting from an integration of socio-economic survey data and remote sensing imagery for recovery assessment. Full article

(This article belongs to the Special Issue Remote Sensing for Post-disaster Phase: Damage Assessment, Reconstruction and Monitoring)

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20 pages, 9349 KiB

Open AccessArticle

Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data

by Saman Ghaffarian, Norman Kerle, Edoardo Pasolli and Jamal Jokar Arsanjani

Remote Sens. 2019, 11(20), 2427; https://doi.org/10.3390/rs11202427 - 19 Oct 2019

Cited by 52 | Viewed by 6133

Abstract

First responders and recovery planners need accurate and quickly derived information about the status of buildings as well as newly built ones to both help victims and to make decisions for reconstruction processes after a disaster. Deep learning and, in particular, convolutional neural network (CNN)-based approaches have recently become state-of-the-art methods to extract information from remote sensing images, in particular for image-based structural damage assessment. However, they are predominantly based on manually extracted training samples. In the present study, we use pre-disaster OpenStreetMap building data to automatically generate training samples to train the proposed deep learning approach after the co-registration of the map and the satellite images. The proposed deep learning framework is based on the U-net design with residual connections, which has been shown to be an effective method to increase the efficiency of CNN-based models. The ResUnet is followed by a Conditional Random Field (CRF) implementation to further refine the results. Experimental analysis was carried out on selected very high resolution (VHR) satellite images representing various scenarios after the 2013 Super Typhoon Haiyan in both the damage and the recovery phases in Tacloban, the Philippines. The results show the robustness of the proposed ResUnet-CRF framework in updating the building map after a disaster for both damage and recovery situations by producing an overall F₁-score of 84.2%. Full article

(This article belongs to the Special Issue Remote Sensing for Post-disaster Phase: Damage Assessment, Reconstruction and Monitoring)

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21 pages, 20264 KiB

Open AccessArticle

Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

by Yusupujiang Aimaiti, Wen Liu, Fumio Yamazaki and Yoshihisa Maruyama

Remote Sens. 2019, 11(20), 2351; https://doi.org/10.3390/rs11202351 - 10 Oct 2019

Cited by 40 | Viewed by 5002

Abstract

Timely information about landslides during or immediately after an event is an invaluable source for emergency response and management. Using an active sensor, synthetic aperture radar (SAR) can capture images of the earth’s surface regardless of weather conditions and may provide a solution to the problem of mapping landslides when clouds obstruct optical imaging. The 2018 Hokkaido Eastern Iburi earthquake (M_w 6.6) and its aftershocks not only caused major damage with severe loss of life and property but also induced many landslides across the area. To gain a better understanding of the landslides induced by this earthquake, we proposed a method of landslide mapping using pre- and post-event Advanced Land Observation Satellite 2 Phased Array L-band Synthetic Aperture Radar 2 (ALOS-2 PALSAR-2) images acquired from both descending and ascending orbits. Moreover, the accuracy of the classification results was verified by comparisons with high-resolution optical images, and ground truth data (provided by GSI, Japan). The detected landslides show a good match with the reference optical images by visual comparison. The quantitative comparison results showed that a combination of the descending and ascending intensity-based landslide classification had the best accuracy with an overall accuracy and kappa coefficient of 80.1% and 0.45, respectively. Full article

(This article belongs to the Special Issue Remote Sensing for Post-disaster Phase: Damage Assessment, Reconstruction and Monitoring)

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19 pages, 8379 KiB

Open AccessArticle

Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods

by Luis Moya, Yukio Endo, Genki Okada, Shunichi Koshimura and Erick Mas

Remote Sens. 2019, 11(19), 2320; https://doi.org/10.3390/rs11192320 - 05 Oct 2019

Cited by 17 | Viewed by 3502

Abstract

Synthetic aperture radar (SAR) images have been used to map flooded areas with great success. Flooded areas are often identified by detecting changes between a pair of images recorded before and after a certain flood. During the 2018 Western Japan Floods, the change detection method generated significant misclassifications for agricultural targets. To evaluate whether such a situation could be repeated in future events, this paper examines and identifies the causes of the misclassifications. We concluded that the errors occurred because of the following. (i) The use of only a single pair of SAR images from before and after the floods. (ii) The unawareness of the dynamics of the backscattering intensity through time in agricultural areas. (iii) The effect of the wavelength on agricultural targets. Furthermore, it is highly probable that such conditions might occur in future events. Our conclusions are supported by a field survey of 35 paddy fields located within the misclassified area and the analysis of Sentinel-1 time series data. In addition, in this paper, we propose a new parameter, which we named “conditional coherence”, that can be of help to overcome the referred issue. The new parameter is based on the physical mechanism of the backscattering on flooded and non-flooded agricultural targets. The performance of the conditional coherence as an input of discriminant functions to identify flooded and non-flooded agricultural targets is reported as well. Full article

(This article belongs to the Special Issue Remote Sensing for Post-disaster Phase: Damage Assessment, Reconstruction and Monitoring)

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17 pages, 5865 KiB

Open AccessArticle

Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data

by Wen Liu, Fumio Yamazaki and Yoshihisa Maruyama

Remote Sens. 2019, 11(19), 2292; https://doi.org/10.3390/rs11192292 - 01 Oct 2019

Cited by 15 | Viewed by 3597

Abstract

A series of earthquakes hit Kumamoto Prefecture, Japan, continuously over a period of two days in April 2016. The earthquakes caused many landslides and numerous surface ruptures. In this study, two sets of the pre- and post-event airborne Lidar data were applied to detect landslides along the Futagawa fault. First, the horizontal displacements caused by the crustal displacements were removed by a subpixel registration. Then, the vertical displacements were calculated by averaging the vertical differences in 100-m grids. The erosions and depositions in the corrected vertical differences were extracted using the thresholding method. Slope information was applied to remove the vertical differences caused by collapsed buildings. Then, the linked depositions were identified from the erosions according to the aspect information. Finally, the erosion and its linked deposition were identified as a landslide. The results were verified using truth data from field surveys and image interpretation. Both the pair of digital surface models acquired over a short period and the pair of digital terrain models acquired over a 10-year period showed good potential for detecting 70% of landslides. Full article

(This article belongs to the Special Issue Remote Sensing for Post-disaster Phase: Damage Assessment, Reconstruction and Monitoring)

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30 pages, 14037 KiB

Open AccessArticle

Decreasing Trend of Geohazards Induced by the 2008 Wenchuan Earthquake Inferred from Time Series NDVI Data

by Zhongyun Ni, Zhenyu Yang, Weile Li, Yinbing Zhao and Zhengwei He

Remote Sens. 2019, 11(19), 2192; https://doi.org/10.3390/rs11192192 - 20 Sep 2019

Cited by 20 | Viewed by 2855

Abstract

The occurrence of aftershocks and geohazards (landslides, collapses, and debris flows) decreases with time following a major earthquake. The 12 May 2008 Wenchuan Earthquake in Sichuan, China, provides the opportunity to characterize the subsequent spatiotemporal evolution of geohazards. Following the 12 May 2008 Wenchuan Earthquake, the incidence of geohazards first increased sharply, representing a “post-earthquake effect”, before starting to decrease. We compared the spatial distribution of the area affected by vegetation damage (AVD) triggered by large and medium-scale geohazards (LMG). We studied the interval prior to the 12 May 2008 Wenchuan Earthquake (2001–2007), the co-seismic period (2008), and the post-earthquake interval (2009–2016) and characterized the trend of decreasing geohazards at a macro scale. In vegetated areas, geohazards often seriously damage the vegetation, resulting in pronounced contrasts with the surrounding surface in terms of color tone, texture, morphology, and Normalized Difference Vegetation Index (NDVI) which are evident in remote sensing images (RSI). In principle, it is possible to use the strong positive correlation between AVD and geohazards to determine indirectly the resulting vegetation and to monitor its spatiotemporal evolution. In this study we attempted to characterize the process of geohazard evolution in the region affected by the 12 May 2008 Wenchuan Earthquake during 2001–2016. Our approach was to analyze the characteristics of areas with reduced vegetation coverage caused by LMG. Our principal findings are as follows: (i) Before the Wenchuan Earthquake (during 2001–2007), there was no evidence for a linear increase in the number of LMG with time; thus, the geological environment was relatively stable and the geohazards were mainly induced by rainfall events. (ii) The 12 May 2008 Wenchuan Earthquake was the main cause of a surge in geohazards in 2008, with the characteristics of seismogenic faults and strong aftershocks determining the spatial distribution of geohazards. (iii) Following the 12 May 2008 Wenchuan Earthquake (during 2009–2016) the incidence of geohazards exhibited an oscillating pattern of attenuation, with a decreasing trend of higher-grade seismic intensity. The intensity of geohazards was related to rainfall and seismogenic faults, and also to the number, magnitude and depth of new earthquakes following the 12 May 2008 Wenchuan Earthquake. Our results provide a new perspective on the temporal pattern of attenuation of seismic geohazards, with implications for disaster prevention and mitigation and ecological restoration in the areas affected by the 12 May 2008 Wenchuan Earthquake. Full article

(This article belongs to the Special Issue Remote Sensing for Post-disaster Phase: Damage Assessment, Reconstruction and Monitoring)

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27 pages, 14402 KiB

Open AccessArticle

Time Series GIS Map Dataset of Demolished Buildings in Mashiki Town after the 2016 Kumamoto, Japan Earthquake

by Yuzuru Kushiyama and Masashi Matsuoka

Remote Sens. 2019, 11(19), 2190; https://doi.org/10.3390/rs11192190 - 20 Sep 2019

Cited by 2 | Viewed by 3080

Abstract

After a large-scale disaster, many damaged buildings are demolished and treated as disaster waste. Though the weight of disaster waste was estimated two months after the 2016 earthquake in Kumamoto, Japan, the estimated weight was significantly different from the result when the disaster waste disposal was completed in March 2018. The amount of disaster waste generated is able to be estimated by an equation by multiplying the total number of severely damaged and partially damaged buildings by the coefficient of generated weight per building. We suppose that the amount of disaster waste would be affected by the conditions of demolished buildings, namely, the areas and typologies of building structures, but this has not yet been clarified. Therefore, in this study, we aimed to use geographic information system (GIS) map data to create a time series GIS map dataset with labels of demolished and remaining buildings in Mashiki town for the two-year period prior to the completion of the disaster waste disposal. We used OpenStreetMap (OSM) data as the base data and time series SPOT images observed in the two years following the Kumamoto earthquake to label all demolished and remaining buildings in the GIS map dataset. To effectively label the approximately 16,000 buildings in Mashiki town, we calculated an indicator that shows the possibility of the buildings to be classified as the remaining and demolished buildings from a change of brightness in SPOT images. We classified 5701 demolished buildings from 16,106 buildings, as of March 2018, by visual interpretation of the SPOT and Pleiades images with reference to this indicator. We verified that the number of demolished buildings was almost the same as the number reported by Mashiki municipality. Moreover, we assessed the accuracy of our proposed method: The F-measure was higher than 0.9 using the training dataset, which was verified by a field survey and visual interpretation, and included the labels of the 55 demolished and 55 remaining buildings. We also assessed the accuracy of the proposed method by applying it to all the labels in the OSM dataset, but the F-measure was 0.579. If we applied test data including balanced labels of the other 100 demolished and 100 remaining buildings, which were other than the training data, the F-measure was 0.790 calculated from the SPOT image of 25 March 2018. Our proposed method performed better for the balanced classification but not for imbalanced classification. We studied the examples of image characteristics of correct and incorrect estimation by thresholding the indicator. Full article

(This article belongs to the Special Issue Remote Sensing for Post-disaster Phase: Damage Assessment, Reconstruction and Monitoring)

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24 pages, 42060 KiB

Open AccessArticle

Rapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A Imagery

by Anh Phan, Duong N. Ha, Chuc D. Man, Thuy T. Nguyen, Hung Q. Bui and Thanh T. N. Nguyen

Remote Sens. 2019, 11(17), 2034; https://doi.org/10.3390/rs11172034 - 29 Aug 2019

Cited by 20 | Viewed by 5425

Abstract

The Red River Delta (RRD), including 11 provinces, is one of the four largest rice-growing areas in Vietnam. Tropical storms often occur and cause serious flooding from May to October annually in the RRD, which strongly affects the productivity of the summer–autumn rice, one of two main rice crops. Therefore, the rapid assessment of damaged rice area by flooding inundation is critical for farmers and the government. In this study, we proposed a methodology for quick estimation of rice areas damaged by flooding using Sentinel 1A (S1A) imagery. Firstly, the latest rice map was produced. Then, a Near Real-Time (NRT) flood map, which is estimated from S1A images at the closest time to a flooding event, was generated by excluding the yearly permanent map from the temporal water map. Our experiment was conducted for the assessment of damaged rice area by flooding from the tropical storm named Son-Tinh, which happened on 19–21 July 2018. A Support Vector Machine (SVM) classifier was applied on time-series of S1A VV with VH data (VVVH) to obtain a rice map for the winter-spring season of 2018 with 90.5% Overall Accuracy (OA) and 2.37% difference (12,544 ha) from the General Statistics Office (GSO) of Vietnam’s reports for the whole region. Then, the Otsu thresholding method was applied for permanent water surface extraction and NRT flood mapping. The estimated damaged area was compared to available provincial and communal statistics for validation and further analysis. Right after the Son-Tinh storm, the estimation of inundated rice was approximately 50% of the total rice area in the RRD (271,092 ha). As a result, rice damage level strongly corresponds to the inundation period. In addition, the rice-flooding frequency map over the RRD was estimated to show rice fields suffering a high risk of flooding during the rainy season in the RRD. Our experiment’s results highlight the potential of using Synthetic-Aperture Radar (SAR) imagery for fast monitoring and assessment of paddy rice areas affected by flooding at a large scale in the RRD region. Full article

(This article belongs to the Special Issue Remote Sensing for Post-disaster Phase: Damage Assessment, Reconstruction and Monitoring)

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20 pages, 11412 KiB

Open AccessArticle

A Comparative Study of Texture and Convolutional Neural Network Features for Detecting Collapsed Buildings After Earthquakes Using Pre- and Post-Event Satellite Imagery

by Min Ji, Lanfa Liu, Runlin Du and Manfred F. Buchroithner

Remote Sens. 2019, 11(10), 1202; https://doi.org/10.3390/rs11101202 - 21 May 2019

Cited by 63 | Viewed by 5980

Abstract

The accurate and quick derivation of the distribution of damaged building must be considered essential for the emergency response. With the success of deep learning, there is an increasing interest to apply it for earthquake-induced building damage mapping, and its performance has not been compared with conventional methods in detecting building damage after the earthquake. In the present study, the performance of grey-level co-occurrence matrix texture and convolutional neural network (CNN) features were comparatively evaluated with the random forest classifier. Pre- and post-event very high-resolution (VHR) remote sensing imagery were considered to identify collapsed buildings after the 2010 Haiti earthquake. Overall accuracy (OA), allocation disagreement (AD), quantity disagreement (QD), Kappa, user accuracy (UA), and producer accuracy (PA) were used as the evaluation metrics. The results showed that the CNN feature with random forest method had the best performance, achieving an OA of 87.6% and a total disagreement of 12.4%. CNNs have the potential to extract deep features for identifying collapsed buildings compared to the texture feature with random forest method by increasing Kappa from 61.7% to 69.5% and reducing the total disagreement from 16.6% to 14.1%. The accuracy for identifying buildings was improved by combining CNN features with random forest compared with the CNN approach. OA increased from 85.9% to 87.6%, and the total disagreement reduced from 14.1% to 12.4%. The results indicate that the learnt CNN features can outperform texture features for identifying collapsed buildings using VHR remotely sensed space imagery. Full article

(This article belongs to the Special Issue Remote Sensing for Post-disaster Phase: Damage Assessment, Reconstruction and Monitoring)

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20 pages, 15435 KiB

Open AccessEditor’s ChoiceArticle

Post-Disaster Recovery Assessment with Machine Learning-Derived Land Cover and Land Use Information

by Mohammadreza Sheykhmousa, Norman Kerle, Monika Kuffer and Saman Ghaffarian

Remote Sens. 2019, 11(10), 1174; https://doi.org/10.3390/rs11101174 - 17 May 2019

Cited by 36 | Viewed by 7981

Abstract

Post-disaster recovery (PDR) is a complex, long-lasting, resource intensive, and poorly understood process. PDR goes beyond physical reconstruction (physical recovery) and includes relevant processes such as economic and social (functional recovery) processes. Knowing the size and location of the places that positively or negatively recovered is important to effectively support policymakers to help readjust planning and resource allocation to rebuild better. Disasters and the subsequent recovery are mainly expressed through unique land cover and land use changes (LCLUCs). Although LCLUCs have been widely studied in remote sensing, their value for recovery assessment has not yet been explored, which is the focus of this paper. An RS-based methodology was created for PDR assessment based on multi-temporal, very high-resolution satellite images. Different trajectories of change were analyzed and evaluated, i.e., transition patterns (TPs) that signal positive or negative recovery. Experimental analysis was carried out on three WorldView-2 images acquired over Tacloban city, Philippines, which was heavily affected by Typhoon Haiyan in 2013. Support vector machine, a robust machine learning algorithm, was employed with texture features extracted from the grey level co-occurrence matrix and local binary patterns. Although classification results for the images before and four years after the typhoon show high accuracy, substantial uncertainties mark the results for the immediate post-event image. All land cover (LC) and land use (LU) classified maps were stacked, and only changes related to TPs were extracted. The final products are LC and LU recovery maps that quantify the PDR process at the pixel level. It was found that physical and functional recovery can be mainly explained through the LCLUC information. In addition, LC and LU-based recovery maps support a general and a detailed recovery understanding, respectively. It is therefore suggested to use the LC and LU-based recovery maps to monitor and support the short and the long-term recovery, respectively. Full article

(This article belongs to the Special Issue Remote Sensing for Post-disaster Phase: Damage Assessment, Reconstruction and Monitoring)

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19 pages, 11708 KiB

Open AccessArticle

Fusion Analysis of Optical Satellite Images and Digital Elevation Model for Quantifying Volume in Debris Flow Disaster

by Hiroyuki Miura

Remote Sens. 2019, 11(9), 1096; https://doi.org/10.3390/rs11091096 - 08 May 2019

Cited by 15 | Viewed by 5490

Abstract

Rapid identification of affected areas and volumes in a large-scale debris flow disaster is important for early-stage recovery and debris management planning. This study introduces a methodology for fusion analysis of optical satellite images and digital elevation model (DEM) for simplified quantification of volumes in a debris flow event. The LiDAR data, the pre- and post-event Sentinel-2 images and the pre-event DEM in Hiroshima, Japan affected by the debris flow disaster on July 2018 are analyzed in this study. Erosion depth by the debris flows is empirically modeled from the pre- and post-event LiDAR-derived DEMs. Erosion areas are detected from the change detection of the satellite images and the DEM-based debris flow propagation analysis by providing predefined sources. The volumes and their pattern are estimated from the detected erosion areas by multiplying the empirical erosion depth. The result of the volume estimations show good agreement with the LiDAR-derived volumes. Full article

(This article belongs to the Special Issue Remote Sensing for Post-disaster Phase: Damage Assessment, Reconstruction and Monitoring)

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Other

Jump to: Research

14 pages, 4617 KiB

Open AccessLetter

Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions

by Francesco Nex, Diogo Duarte, Anne Steenbeek and Norman Kerle

Remote Sens. 2019, 11(3), 287; https://doi.org/10.3390/rs11030287 - 01 Feb 2019

Cited by 81 | Viewed by 6269

Abstract

The timely and efficient generation of detailed damage maps is of fundamental importance following disaster events to speed up first responders’ (FR) rescue activities and help trapped victims. Several works dealing with the automated detection of building damages have been published in the last decade. The increasingly widespread availability of inexpensive UAV platforms has also driven their recent adoption for rescue operations (i.e., search and rescue). Their deployment, however, remains largely limited to visual image inspection by skilled operators, limiting their applicability in time-constrained real conditions. This paper proposes a new solution to autonomously map building damages with a commercial UAV in near real-time. The solution integrates different components that allow the live streaming of the images on a laptop and their processing on the fly. Advanced photogrammetric techniques and deep learning algorithms are combined to deliver a true-orthophoto showing the position of building damages, which are already processed by the time the UAV returns to base. These algorithms have been customized to deliver fast results, fulfilling the near real-time requirements. The complete solution has been tested in different conditions, and received positive feedback by the FR involved in the EU funded project INACHUS. Two realistic pilot tests are described in the paper. The achieved results show the great potential of the presented approach, how close the proposed solution is to FR’ expectations, and where more work is still needed. Full article

(This article belongs to the Special Issue Remote Sensing for Post-disaster Phase: Damage Assessment, Reconstruction and Monitoring)

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