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Remote Sensing and GIS for Geo-Hazards and Disasters

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (25 July 2018) | Viewed by 59137

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Prof. Dr. Jason Levy

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Disaster Preparedness and Emergency Management, University of Hawaii, 2540 Dole Street, Honolulu, HI 96822, USA
Interests: epidemiology and prevention of congenital anomalies; psychosis and affective psychosis; cancer epidemiology and prevention; molecular and human genome epidemiology; evidence synthesis related to public health and health services research
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Dear Colleagues,

Geohazards are geological and environmental conditions that may contribute to widespread damage or risk. These long-term or short-term geological processes may occur on a massive scale (e.g., landslides and tsunamis) and significantly impact regional economies.

While human activities can exacerbate these risks, the use of remote sensing and GIS for geohazards and disaster management can contribute to new insights into geohazards, their antecedent conditions, causes and impacts. The current generation of geomatics solutions can provide new opportunities for the real-time analysis and management of geohazards and disaster risks. For example, GIS can provide improved understanding into important hazards, risk and disaster questions such as: How to mitigate disaster risk? How do human activities (i.e., land use and landcover change) affect geohazards? What are the spatial impacts of climate variability and change? This Special Issue encourages papers on the use of cross-disciplinary, integrated, real-time and affordable GIS solutions suitable for hazard and disaster management. Research contributions dealing with GIS for system interoperability and scalability are particularly welcome.

Prof. Dr. Jason K. Levy
Guest Editor

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Keywords

satellite remote sensing
GIS
landslide
flood
natural hazards
sensors

Published Papers (11 papers)

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Research

16 pages, 2414 KiB

Open AccessArticle

A Model Design for Risk Assessment of Line Tripping Caused by Wildfires

by Shuzhu Shi, Chunjing Yao, Shiwei Wang and Wenjun Han

Sensors 2018, 18(6), 1941; https://doi.org/10.3390/s18061941 - 14 Jun 2018

Cited by 15 | Viewed by 3213

Abstract

A power line is particularly vulnerable to wildfires in its vicinity, and various damage including line tripping can be caused by wildfires. Using remote sensing techniques, a novel model developed to assess the risk of line tripping caused by the wildfire occurrence in high-voltage power line corridors is presented. This model mainly contains the wildfire risk assessment for power line corridors and the estimation of the probability of line tripping when a wildfire occurs in power line corridors. For the wildfire risk assessment, high-resolution satellite data, Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological data, and digital elevation model (DEM) data were employed to infer the natural factors. Human factors were also included to achieve good reliability. In the estimation of the probability of line tripping, vegetation characteristics, meteorological status, topographic conditions, and transmission line parameters were chosen as influencing factors. According to the above input variables and observed historical datasets, the risk levels for wildfire occurrence and line tripping were obtained with a logic regression approach. The experimental results demonstrate that the developed model can provide good results in predicting wildfire occurrence and line tripping for high-voltage power line corridors. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)

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27 pages, 12620 KiB

Open AccessArticle

A Service-Oriented Middleware for Integrated Management of Crowdsourced and Sensor Data Streams in Disaster Management

by Luiz Fernando F. G. de Assis, Flávio E. A. Horita, Edison P. de Freitas, Jó Ueyama and João Porto De Albuquerque

Sensors 2018, 18(6), 1689; https://doi.org/10.3390/s18061689 - 24 May 2018

Cited by 10 | Viewed by 5106

Abstract

The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. As traditional sensor networks are error-prone and difficult to maintain, the study highlights the emerging role of “citizens as sensors” as a complementary data source to increase public awareness. To this end, an interoperable, reusable middleware for managing spatial, temporal, and thematic data using Sensor Web Enablement initiative services and a processing engine was designed, implemented, and deployed. The study found that its approach provided effective sensor data-stream access, publication, and filtering in dynamic scenarios such as disaster management, as well as it enables batch and stream management integration. Also, an interoperability analytics testing of a flood citizen observatory highlighted even variable data such as those provided by the crowd can be integrated with sensor data stream. Our approach, thus, offers a mean to improve near-real-time applications. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)

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16 pages, 9178 KiB

Open AccessArticle

Remote Sensing of Wildland Fire-Induced Risk Assessment at the Community Level

by M. Razu Ahmed, Khan Rubayet Rahaman and Quazi K. Hassan

Sensors 2018, 18(5), 1570; https://doi.org/10.3390/s18051570 - 15 May 2018

Cited by 24 | Viewed by 5312

Abstract

Wildland fires are some of the critical natural hazards that pose a significant threat to the communities located in the vicinity of forested/vegetated areas. In this paper, our overall objective was to study the structural damages due to the 2016 Horse River Fire (HRF) that happened in Fort McMurray (Alberta, Canada) by employing primarily very high spatial resolution optical satellite data, i.e., WorldView-2. Thus, our activities included the: (i) estimation of the structural damages; and (ii) delineation of the wildland-urban interface (WUI) and its associated buffers at certain intervals, and their utilization in assessing potential risks. Our proposed method of remote sensing-based estimates of the number of structural damages was compared with the ground-based information available from the Planning and Development Recovery Committee Task Force of Regional Municipality of Wood Buffalo (RMWB); and found a strong linear relationship (i.e., r² value of 0.97 with a slope of 0.97). Upon delineating the WUI and its associated buffer zones at 10 m, 30 m, 50 m, 70 m and 100 m distances; we found existence of vegetation within the 30 m buffers from the WUI for all of the damaged structures. In addition, we noticed that the relevant authorities had removed vegetation in some areas between 30 m and 70 m buffers from the WUI, which was proven to be effective in order to protect the structures in the adjacent communities. Furthermore, we mapped the wildland fire-induced vulnerable areas upon considering the WUI and its associated buffers. Our analysis revealed that approximately 30% of the areas within the buffer zones of 10 m and 30 m were vulnerable due to the presence of vegetation; in which, approximately 7% were burned during the 2016 HRF event that led the structural damages. Consequently, we suggest to remove the existing vegetation within these critical zones and also monitor the region at a regular interval in order to reduce the wildland fire-induced risk. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)

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20 pages, 30424 KiB

Open AccessArticle

Feasibility of Using Elastic Wave Velocity Monitoring for Early Warning of Rainfall-Induced Slope Failure

by Yulong Chen, Muhammad Irfan, Taro Uchimura and Ke Zhang

Sensors 2018, 18(4), 997; https://doi.org/10.3390/s18040997 - 27 Mar 2018

Cited by 23 | Viewed by 4443

Abstract

Rainfall-induced landslides are one of the most widespread slope instability phenomena posing a serious risk to public safety worldwide so that their temporal prediction is of great interest to establish effective warning systems. The objective of this study is to determine the effectiveness of elastic wave velocities in the surface layer of the slope in monitoring, prediction and early warning of landslide. The small-scale fixed and varied, and large-scale slope model tests were conducted. Analysis of the results has established that the elastic wave velocity continuously decreases in response of moisture content and deformation and there was a distinct surge in the decrease rate of wave velocity when failure was initiated. Based on the preliminary results of this analysis, the method using the change in elastic wave velocity proves superior for landslide early warning and suggests that a warning be issued at switch of wave velocity decrease rate. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)

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18 pages, 16242 KiB

Open AccessArticle

Mapping Wildfire Ignition Probability Using Sentinel 2 and LiDAR (Jerte Valley, Cáceres, Spain)

by Yolanda Sánchez Sánchez, Antonio Martínez-Graña, Fernando Santos Francés and Marina Mateos Picado

Sensors 2018, 18(3), 826; https://doi.org/10.3390/s18030826 - 09 Mar 2018

Cited by 24 | Viewed by 6150

Abstract

Wildfire is a major threat to the environment, and this threat is aggravated by different climatic and socioeconomic factors. The availability of detailed, reliable mapping and periodic and immediate updates makes wildfire prevention and extinction work more effective. An analyst protocol has been generated that allows the precise updating of high-resolution thematic maps. For this protocol, images obtained through the Sentinel 2A satellite, with a return time of five days, have been merged with Light Detection and Ranging (LiDAR) data with a density of 0.5 points/m² in order to obtain vegetation mapping with an accuracy of 88% (kappa = 0.86), which is then extrapolated to fuel model mapping through a decision tree. This process, which is fast and reliable, serves as a cartographic base for the later calculation of ignition-probability mapping. The generated cartography is a fundamental tool to be used in the decision making involved in the planning of preventive silvicultural treatments, extinguishing media distribution, infrastructure construction, etc. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)

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15 pages, 8725 KiB

Open AccessArticle

Himawari-8 Satellite Based Dynamic Monitoring of Grassland Fire in China-Mongolia Border Regions

by Li Na, Jiquan Zhang, Yulong Bao, Yongbin Bao, Risu Na, Siqin Tong and Alu Si

Sensors 2018, 18(1), 276; https://doi.org/10.3390/s18010276 - 18 Jan 2018

Cited by 28 | Viewed by 5515

Abstract

In this study, we used bands 7, 4, and 3 of the Advance Himawari Imager (AHI) data, combined with a Threshold Algorithm and a visual interpretation method to monitor the entire process of grassland fires that occurred on the China-Mongolia border regions, between 05:40 (UTC) on April 19th to 13:50 (UTC) on April 21st 2016. The results of the AHI data monitoring are evaluated by the fire point product data, the wind field data, and the environmental information data of the area in which the fire took place. The monitoring result shows that, the grassland fire burned for two days and eight hours with a total burned area of about 2708.29 km². It mainly spread from the northwest to the southeast, with a maximum burning speed of 20.9 m/s, a minimum speed of 2.52 m/s, and an average speed of about 12.07 m/s. Thus, using AHI data can not only quickly and accurately track the dynamic development of a grassland fire, but also estimate the spread speed and direction. The evaluation of fire monitoring results reveals that AHI data with high precision and timeliness can be highly consistent with the actual situation. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)

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6494 KiB

Open AccessArticle

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

by Marjan Alirezaie, Andrey Kiselev, Martin Längkvist, Franziska Klügl and Amy Loutfi

Sensors 2017, 17(11), 2545; https://doi.org/10.3390/s17112545 - 05 Nov 2017

Cited by 26 | Viewed by 4976

Abstract

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. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)

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1696 KiB

Open AccessArticle

Multipass Target Search in Natural Environments

by Michael J. Kuhlman, Michael W. Otte, Donald Sofge and Satyandra K. Gupta

Sensors 2017, 17(11), 2514; https://doi.org/10.3390/s17112514 - 02 Nov 2017

Cited by 8 | Viewed by 4058

Abstract

Consider a disaster scenario where search and rescue workers must search difficult to access buildings during an earthquake or flood. Often, finding survivors a few hours sooner results in a dramatic increase in saved lives, suggesting the use of drones for expedient rescue operations. Entropy can be used to quantify the generation and resolution of uncertainty. When searching for targets, maximizing mutual information of future sensor observations will minimize expected target location uncertainty by minimizing the entropy of the future estimate. Motion planning for multi-target autonomous search requires planning over an area with an imperfect sensor and may require multiple passes, which is hindered by the submodularity property of mutual information. Further, mission duration constraints must be handled accordingly, requiring consideration of the vehicle’s dynamics to generate feasible trajectories and must plan trajectories spanning the entire mission duration, something which most information gathering algorithms are incapable of doing. If unanticipated changes occur in an uncertain environment, new plans must be generated quickly. In addition, planning multipass trajectories requires evaluating path dependent rewards, requiring planning in the space of all previously selected actions, compounding the problem. We present an anytime algorithm for autonomous multipass target search in natural environments. The algorithm is capable of generating long duration dynamically feasible multipass coverage plans that maximize mutual information using a variety of techniques such as

ϵ

-admissible heuristics to speed up the search. To the authors’ knowledge this is the first attempt at efficiently solving multipass target search problems of such long duration. The proposed algorithm is based on best first branch and bound and is benchmarked against state of the art algorithms adapted to the problem in natural Simplex environments, gathering the most information in the given search time. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)

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13782 KiB

Open AccessArticle

Assessing Lightning and Wildfire Hazard by Land Properties and Cloud to Ground Lightning Data with Association Rule Mining in Alberta, Canada

by DongHwan Cha, Xin Wang and Jeong Woo Kim

Sensors 2017, 17(10), 2413; https://doi.org/10.3390/s17102413 - 23 Oct 2017

Cited by 8 | Viewed by 6224

Abstract

Hotspot analysis was implemented to find regions in the province of Alberta (Canada) with high frequency Cloud to Ground (CG) lightning strikes clustered together. Generally, hotspot regions are located in the central, central east, and south central regions of the study region. About 94% of annual lightning occurred during warm months (June to August) and the daily lightning frequency was influenced by the diurnal heating cycle. The association rule mining technique was used to investigate frequent CG lightning patterns, which were verified by similarity measurement to check the patterns’ consistency. The similarity coefficient values indicated that there were high correlations throughout the entire study period. Most wildfires (about 93%) in Alberta occurred in forests, wetland forests, and wetland shrub areas. It was also found that lightning and wildfires occur in two distinct areas: frequent wildfire regions with a high frequency of lightning, and frequent wild-fire regions with a low frequency of lightning. Further, the preference index (PI) revealed locations where the wildfires occurred more frequently than in other class regions. The wildfire hazard area was estimated with the CG lightning hazard map and specific land use types. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)

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18426 KiB

Open AccessArticle

Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications

by Abdulla Al-Rawabdeh, Adel Moussa, Marzieh Foroutan, Naser El-Sheimy and Ayman Habib

Sensors 2017, 17(10), 2378; https://doi.org/10.3390/s17102378 - 18 Oct 2017

Cited by 36 | Viewed by 6228

Abstract

Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)

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3406 KiB

Open AccessFeature PaperArticle

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

by M. Razu Ahmed, Khan Rubayet Rahaman, Aaron Kok and Quazi K. Hassan

Sensors 2017, 17(10), 2347; https://doi.org/10.3390/s17102347 - 14 Oct 2017

Cited by 42 | Viewed by 6662

Abstract

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., r² 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. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)

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