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Inauguration of Earth Observation for Emergency Management Section
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Inauguration of Earth Observation for Emergency Management Section

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 32736

Special Issue Editor

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,

Disaster monitoring and assessment are one of the most benefited areas by the advancement of remote sensing technologies. The number of natural disasters and their effects are increasing due rapid urbanization and climate change.  Use of Earth Observation (EO) data from various sensors on board spacecraft and aircraft is increasing rapidly due to wider applicability of EO data in the timeline of disastrous events with increasing spatial resolution. EO data are used in post-disaster response, damage assessment, recovery and mitigation phases, and data collection and processing methods have advanced substantially in the recent years.

Using satellite and airborne data, regional and global environmental, economic, and societal impacts on the public health (e.g., COVID-19 pandemic) can be assessed. To discover the effects on the environment due to changes in human behavior, remote sensing data can show new trends over time. Man-made and technological hazards are events that are caused by humans and occur in or close to human settlements. They include complex emergencies, conflicts, industrial accidents, transport accidents, environmental degradation and pollution.

At the occasion of start-up of a new section“Earth Observation for Emergency Management” in the open access journal Remote Sensing (ISSN 2072-4292, IF 4.848), we intend to collect original scientific contribution using the available wide variety of remote sensed data (e.g., optical sensor, SAR, Lidar) for disaster monitoring, assessment and forecasting. Data fusion of EO data and GNSS and other sensor data is encouraged as well as the introduction of recent machine learning techniques.

This Collection offers a platform to present and discuss the development and application of remote sensing techniques toward improving our knowledge and understanding of natural hazards (e.g., earthquakes, volcanic activities, storms, floods, wildfires, and landslides) and man-made hazards and their effects to human societies and environment.

Prof. Dr. Fumio Yamazaki
Section Editor-in-Chief

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.

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Published Papers (7 papers)

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Research

16 pages, 26580 KiB  
Article
Using Night Lights from Space to Assess Areas Impacted by the 2023 Turkey Earthquake
by Noam Levin
Remote Sens. 2023, 15(8), 2120; https://doi.org/10.3390/rs15082120 - 17 Apr 2023
Cited by 13 | Viewed by 8330
Abstract
The 6 February 2023 earthquakes that hit south-eastern Turkey were amongst the deadliest in the past century. Here, we report the ability to map and quantify areas impacted by these earthquakes using changes in nighttime lights, as mapped by NASA’s VIIRS/DNB sensor. We [...] Read more.
The 6 February 2023 earthquakes that hit south-eastern Turkey were amongst the deadliest in the past century. Here, we report the ability to map and quantify areas impacted by these earthquakes using changes in nighttime lights, as mapped by NASA’s VIIRS/DNB sensor. We show the correspondence between the 7.8 magnitude earthquake and impacted areas, located in cities and towns, mostly along the fault line, in areas where macroseismic intensity values were higher than 7. We verified the darkening of night lights as recorded by VIIRS using the new SDGSAT-1 Glimmer multispectral nighttime sensor, as well as by comparing changes in nighttime lights with reports on damaged buildings. The ability to rapidly map impacted areas from space using nighttime lights is of key importance for prioritizing and directing emergency and rescue services globally. Full article
(This article belongs to the Special Issue Inauguration of Earth Observation for Emergency Management Section)
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19 pages, 5157 KiB  
Article
New Approach for Photogrammetric Rock Slope Premonitory Movements Monitoring
by Mª Amparo Núñez-Andrés, Albert Prades-Valls, Gerard Matas, Felipe Buill and Nieves Lantada
Remote Sens. 2023, 15(2), 293; https://doi.org/10.3390/rs15020293 - 4 Jan 2023
Cited by 8 | Viewed by 2315
Abstract
An automated, fixed-location, continuous time-lapse camera system was developed to analyze the existence of rockfall precursory movements and quantify volume changes after detachments. It was implemented to monitor the basaltic formation on which Castellfollit de la Roca village is built. Due to the [...] Read more.
An automated, fixed-location, continuous time-lapse camera system was developed to analyze the existence of rockfall precursory movements and quantify volume changes after detachments. It was implemented to monitor the basaltic formation on which Castellfollit de la Roca village is built. Due to the geometrical conditions of the area, the camera system consists of three digital cameras managed by a control unit that contains a Raspberry Pi 4 microprocessor. Images taken every day are sent to a server for processing. A workflow has been developed to work with a set of images with an irregular time interval to detect precursor movement. The first step consists of matching the images with a reference master image and filtering the vegetation to improve the process using a mask obtained by a green leaf index (GLI) index. Then, the adjusted images are used for a forward-backward correlation process carried out to detect movements. If movement is detected, a 3D model is built using structure from motion (SfM) to quantify the movements. The system has been working since September 2021. During this period, movements from 0.01 to 0.5 m and several rockfalls of a small volume have been detected. Full article
(This article belongs to the Special Issue Inauguration of Earth Observation for Emergency Management Section)
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20 pages, 9065 KiB  
Article
Use of Multi-Temporal LiDAR Data to Extract Collapsed Buildings and to Monitor Their Removal Process after the 2016 Kumamoto Earthquake
by Fumio Yamazaki, Wen Liu and Kei Horie
Remote Sens. 2022, 14(23), 5970; https://doi.org/10.3390/rs14235970 - 25 Nov 2022
Cited by 4 | Viewed by 2752
Abstract
This study demonstrates the use of multi-temporal LiDAR data to extract collapsed buildings and to monitor their removal process in Minami-Aso village, Kumamoto prefecture, Japan, after the April 2016 Kumamoto earthquake. By taking the difference in digital surface models (DSMs) acquired at pre- [...] Read more.
This study demonstrates the use of multi-temporal LiDAR data to extract collapsed buildings and to monitor their removal process in Minami-Aso village, Kumamoto prefecture, Japan, after the April 2016 Kumamoto earthquake. By taking the difference in digital surface models (DSMs) acquired at pre- and post-event times, collapsed buildings were extracted and the results were compared with damage survey data by the municipal government and aerial optical images. Approximately 40% of severely damaged buildings showed a reduction in the average height within a reduced building footprint between the pre- and post-event DSMs. Comparing the removal process of buildings in the post-event periods with the damage classification result from the municipal government, the damage level was found to affect judgements by the owners regarding demolition and removal. Full article
(This article belongs to the Special Issue Inauguration of Earth Observation for Emergency Management Section)
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22 pages, 13117 KiB  
Article
Impact of Hurricane Harvey on the Upper Texas Coast: Using Airborne Lidar Data Sets with UAV-Derived Topographic Data to Monitor Change and Track Recovery
by Sara S. Rojas, Shuhab D. Khan and Aydin Shahtakhtinskiy
Remote Sens. 2022, 14(21), 5357; https://doi.org/10.3390/rs14215357 - 26 Oct 2022
Cited by 3 | Viewed by 2343
Abstract
The frequency of hurricanes and tropical storms is increasing; for example, there were a record-breaking 31 named storms during the 2020 Atlantic hurricane season. Texas has historically been susceptible to hurricanes and tropical storms; however, Hurricane Harvey in 2017 was the highest category [...] Read more.
The frequency of hurricanes and tropical storms is increasing; for example, there were a record-breaking 31 named storms during the 2020 Atlantic hurricane season. Texas has historically been susceptible to hurricanes and tropical storms; however, Hurricane Harvey in 2017 was the highest category storm event to cross Texas since 2000. Our regional change analysis used 2016 and 2018 lidar-derived elevation models with 1 m spatial resolution to determine above-sea level changes due to Hurricane Harvey. The upper Texas coast experienced shoreline erosion, with local depositional events occurring on the southeastern sides of jetties and groins. Incidents of dune washout and overwash fans were present along the barrier islands of the upper Texas coast, as well as erosion to foredune complexes and a decrease in dune heights. As of March 2018, recovery is visible through berm buildup and backbeach aggradation. Our multiyear analysis (above sea level) of four sites within Galveston and Follett’s Islands determined the immediate impact of Harvey (2016–2017) and followed recovery until March 2019. The multiyear analysis determined that all four sites experienced varying levels of recovery by 2018. UAV surveys conducted in 2022 showed potential in acquiring topographic data for comparison with 2019 beach-dune conditions. Full article
(This article belongs to the Special Issue Inauguration of Earth Observation for Emergency Management Section)
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21 pages, 5635 KiB  
Article
Coastal Vulnerability Assessment of Bali Province, Indonesia Using Remote Sensing and GIS Approaches
by Amandangi Wahyuning Hastuti, Masahiko Nagai and Komang Iwan Suniada
Remote Sens. 2022, 14(17), 4409; https://doi.org/10.3390/rs14174409 - 5 Sep 2022
Cited by 14 | Viewed by 7211
Abstract
Coastal zones are considered to be highly vulnerable to the effects of climate change, such as erosion, flooding, and storms, including sea level rise (SLR). The effects of rising sea levels endanger several nations, including Indonesia, and it potentially affects the coastal population [...] Read more.
Coastal zones are considered to be highly vulnerable to the effects of climate change, such as erosion, flooding, and storms, including sea level rise (SLR). The effects of rising sea levels endanger several nations, including Indonesia, and it potentially affects the coastal population and natural environment. Quantification is needed to determine the degree of vulnerability experienced by a coast since measuring vulnerability is a fundamental phase towards effective risk reduction. Therefore, the main objective of this research is to identify how vulnerable the coastal zone of Bali Province by develop a Coastal Vulnerability Index (CVI) of areas exposed to the sea-level rise on regional scales using remote sensing and Geographic Information System (GIS) approaches. This study was conducted in Bali Province, Indonesia, which has a beach length of ~640 km, and six parameters were considered in the creation to measure the degree of coastal vulnerability by CVI: geomorphology, shoreline change rate, coastal elevation, sea-level change rate, tidal range, and significant wave height. The different vulnerability parameters were assigned ranks ranging from 1 to 5, with 1 indicating the lowest and 5 indicating the highest vulnerabilities. The study revealed that about 138 km (22%) of the mapped shoreline is classified as being at very high vulnerability and 164 km (26%) of shoreline is at high vulnerability. Of remaining shoreline, 168 km (26%) and 169 km (26%) are at moderate and low risk of coastal vulnerability, respectively. This study outcomes can provide an updated vulnerability map and valuable information for the Bali Province coast, aimed at increasing awareness among decision-makers and related stakeholders for development in mitigation and adaptation strategies. Additionally, the result may be utilized as basic data to build and implement appropriate coastal zone management. Full article
(This article belongs to the Special Issue Inauguration of Earth Observation for Emergency Management Section)
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15 pages, 2635 KiB  
Article
Assessing the Accuracy of MODIS MCD64A1 C6 and FireCCI51 Burned Area Products in Mediterranean Ecosystems
by Thomas Katagis and Ioannis Z. Gitas
Remote Sens. 2022, 14(3), 602; https://doi.org/10.3390/rs14030602 - 27 Jan 2022
Cited by 23 | Viewed by 5002
Abstract
The catastrophic impact of wildfires on the economy and ecosystems of Mediterranean countries in recent years, along with insufficient policies that favor disproportionally high funding for fire suppression, demand a more comprehensive understanding of fire regimes. Satellite remote sensing products support the generation [...] Read more.
The catastrophic impact of wildfires on the economy and ecosystems of Mediterranean countries in recent years, along with insufficient policies that favor disproportionally high funding for fire suppression, demand a more comprehensive understanding of fire regimes. Satellite remote sensing products support the generation of relevant burned-area (BA) information, since they provide the means for the systematic monitoring of large areas worldwide at low cost. This research study assesses the accuracy of the two publicly available MODIS BA products, MCD64A1 C6 and FireCCI51, at a national scale in a Mediterranean country. The research period covered four fire seasons, and a comparison was conducted against a higher-resolution Sentinel-2 dataset. The specific objectives were to assess their performance in detecting fire events occurring primarily in forest and semi-natural lands and to investigate their spatial and temporal uncertainties. Monthly fire observations were processed and analyzed to derive a comprehensive set of accuracy metrics. We found that fire size has an impact on their detection accuracy, with higher detection occurring in fires larger than 100 ha. Detection of smaller (<100 ha) fires was favored by the 250 m FireCCI51 product, but not from MCD64A1 C6, which exhibited less than 50% detection probability in the same range. Their spatial estimates of burned area exhibited a fairly satisfactory agreement with the reference data, reaching an average of 78% in detection rate. MCD64A1 C6 exhibited a more consistent spatial performance overall and better temporal accuracy, whereas FireCCI51 did not substantially outperform the former despite its finer resolution. Additional research is required for a more rigorous assessment of the variability of these burned area products, yet this research provides further insight and has implications for their use in fire-related applications at the local to the national scale. Full article
(This article belongs to the Special Issue Inauguration of Earth Observation for Emergency Management Section)
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Graphical abstract

23 pages, 19277 KiB  
Article
Microwave Brightness Temperature (MBT) Background in Bayan Har Block, Qinghai-Tibet Plateau and Its Importance in Searching for Seismic MBT Anomalies
by Yuan Qi, Lixin Wu, Yifan Ding, Yingjia Liu, Xiao Wang and Wenfei Mao
Remote Sens. 2022, 14(3), 534; https://doi.org/10.3390/rs14030534 - 23 Jan 2022
Cited by 2 | Viewed by 3121
Abstract
The abnormal behaviors of microwave brightness temperature (MBT) before and after some strong inland earthquakes have been studied for more than 15 years, but the normal features of MBT background in the investigated regions still lack essential attention. This study focused on the [...] Read more.
The abnormal behaviors of microwave brightness temperature (MBT) before and after some strong inland earthquakes have been studied for more than 15 years, but the normal features of MBT background in the investigated regions still lack essential attention. This study focused on the extremely seismically active Bayan Har block on the Qinghai-Tibet Plateau in China, and revealed the spatiotemporal variations of monthly mean background and monthly standard deviation (STD) of MBT by using data of 10.65 and 89 GHz from AMSR-2 instrument. In terms of space, the results revealed that the MBT backgrounds at the two frequencies both basically exhibited a negative correlation with regional altitude but were more pronounce at high frequency. They also showed different response characteristics to the properties of soil and vegetation. In terms of time, the low-frequency background exhibited a complex month-to-month variation, with auxiliary data suggesting a joint contribution of surface soil moisture (SSM) and seasonal temperature; while the high-frequency background presented good agreement only with the variation in surface temperature. Meanwhile, the monthly STD of MBT was discovered being affected by SSM at the low-frequency and by snowfall events at the high-frequency. By employing MBT data of 10.65 GHz from AMSR-E and AMSR-2 sensors, the spatiotemporal evolutions of MBT anomalies before, during and after the Ms 7.1 Yushu earthquake on 13 April 2010 and the Ms 7.4 Maduo earthquake on 21 May 2021 were obtained referring to dynamic monthly mean background. A typical strip-shaped positive MBT anomaly just covering the Bayan Har block was found occurring prior to the two earthquakes, and the time series of average MBT anomaly inside the block was analyzed by using multiple datasets. The typical abnormal MBT strip was discriminated being independent of non-seismic factors and regarded as a possible precursor for both earthquakes. This research uncovered the normal features of MBT background and demonstrated the common characteristics of MBT anomalies preceding two strike-slip earthquakes inside the Bayan Har block. It has instructive significance for studying, understanding and searching for seismic MBT anomalies on Qinghai-Tibet Plateau. Full article
(This article belongs to the Special Issue Inauguration of Earth Observation for Emergency Management Section)
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