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Remote Sensing
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Remote Sensing for Environment and Disaster
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Remote Sensing for Environment and Disaster

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 13575

Special Issue Editors

Dr. Wataru Takeuchi

E-Mail Website
Guest Editor
Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
Interests: atmosphere and high carbon reservoirs; agriculture; urban environment assessment; natural disaster
Special Issues, Collections and Topics in MDPI journals
Dr. Haemi Park

E-Mail Website
Guest Editor
Earth Observation Research Center(EORC), Japan Aerospace Exploration Agency(JAXA),2-1-1, Sengen, Tsukuba, Ibaraki 305-8505, Japan
Interests: remote sensing of environment; disaster monitoring; carbon and water cycles; climate change effects to terrestrial ecosystems
Dr. Prakhar Misra

E-Mail Website
Guest Editor
Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
Interests: urban remote sensing; land-use and land cover; outdoor and indoor air pollution; socioeconomic development

Special Issue Information

Dear Colleagues,

It is with great pleasure that we invite your submissions to the Special Issue of MDPI Remote Sensing, “Remote Sensing of Environment and Disaster”. This Special Issue focuses on solution-oriented remote sensing research and science to address environmental and disaster-related issues.

Over the past decade, several advances in remote sensing have taken places in terms of sensors, processing algorithms and platforms, availability of open data and analysis of ready data, time-series analysis, and others. Optical and radar imagery is being actively adopted to analyze environment changes and disaster mitigation response. All those contributions of remote sensing have culminated in several environmental decisions among governments and nations. Suggestions for solving issues related to accurate assessment of disasters and appropriate adaptation are particularly crucial for making a sustainable society. To do so, remote sensing imagery and techniques should consider interactions between science and social impacts (e.g., agricultural practice, deforestation, urbanization, heat islands, and so on). The time is ripe for developing methodologies for measuring and evaluating the impact of human activities on environmental changes in urban, agricultural, and forested areas with the aim of addressing United Nations Sustainable Development Goals. These application-based studies are being carried out under international collaborations and operationalized in both developed and developing countries. Thus, we are looking to invite remote sensing application-oriented research in but not limited to the following themes:

  • Sustainable Development Goals
  • Assessment of disaster
  • Environmental vulnerability
  • Land use changes
  • Social impact
  • Urbanization
  • Human health

Dr. Wataru Takeuchi
Dr. Haemi Park
Dr. Prakhar Misra
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

  • Sustainable Development Goals
  • Assessment of disaster
  • Environmental vulnerability
  • Land use changes
  • Social impact
  • Urbanization
  • Human health
  • Open dataset

Published Papers (2 papers)

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Research

20 pages, 11450 KiB  
Article
Impact of COVID-19 Lockdown on the Fisheries Sector: A Case Study from Three Harbors in Western India
by Ram Avtar, Deepak Singh, Deha Agus Umarhadi, Ali P. Yunus, Prakhar Misra, Pranav N. Desai, Asma Kouser, Tonni Agustiono Kurniawan and KBVN Phanindra
Remote Sens. 2021, 13(2), 183; https://doi.org/10.3390/rs13020183 - 07 Jan 2021
Cited by 32 | Viewed by 6504
Abstract
The COVID-19 related lockdowns have brought the planet to a standstill. It has severely shrunk the global economy in the year 2020, including India. The blue economy and especially the small-scale fisheries sector in India have dwindled due to disruptions in the fish [...] Read more.
The COVID-19 related lockdowns have brought the planet to a standstill. It has severely shrunk the global economy in the year 2020, including India. The blue economy and especially the small-scale fisheries sector in India have dwindled due to disruptions in the fish catch, market, and supply chain. This research presents the applicability of satellite data to monitor the impact of COVID-19 related lockdown on the Indian fisheries sector. Three harbors namely Mangrol, Veraval, and Vankbara situated on the north-western coast of India were selected in this study based on characteristics like harbor’s age, administrative control, and availability of cloud-free satellite images. To analyze the impact of COVID in the fisheries sector, we utilized high-resolution PlanetScope data for monitoring and comparison of “area under fishing boats” during the pre-lockdown, lockdown, and post-lockdown phases. A support vector machine (SVM) classification algorithm was used to identify the area under the boats. The classification results were complemented with socio-economic data and ground-level information for understanding the impact of the pandemic on the three sites. During the peak of the lockdown, it was found that the “area under fishing boats” near the docks and those parked on the land area increased by 483%, 189%, and 826% at Mangrol, Veraval, and Vanakbara harbor, respectively. After phase-I of lockdown, the number of parked vessels decreased, yet those already moved out to the land area were not returned until the south-west monsoon was over. A quarter of the annual production is estimated to be lost at the three harbors due to lockdown. Our last observation (September 2020) result shows that regular fishing activity has already been re-established in all three locations. PlanetScope data with daily revisit time has a higher potential to be used in the future and can help policymakers in making informed decisions vis-à-vis the fishing industry during an emergency situation like COVID-19. Full article
(This article belongs to the Special Issue Remote Sensing for Environment and Disaster)
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29 pages, 24498 KiB  
Article
Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment
by Tinka Valentijn, Jacopo Margutti, Marc van den Homberg and Jorma Laaksonen
Remote Sens. 2020, 12(17), 2839; https://doi.org/10.3390/rs12172839 - 01 Sep 2020
Cited by 38 | Viewed by 6122
Abstract
Automated classification of building damage in remote sensing images enables the rapid and spatially extensive assessment of the impact of natural hazards, thus speeding up emergency response efforts. Convolutional neural networks (CNNs) can reach good performance on such a task in experimental settings. [...] Read more.
Automated classification of building damage in remote sensing images enables the rapid and spatially extensive assessment of the impact of natural hazards, thus speeding up emergency response efforts. Convolutional neural networks (CNNs) can reach good performance on such a task in experimental settings. How CNNs perform when applied under operational emergency conditions, with unseen data and time constraints, is not well studied. This study focuses on the applicability of a CNN-based model in such scenarios. We performed experiments on 13 disasters that differ in natural hazard type, geographical location, and image parameters. The types of natural hazards were hurricanes, tornadoes, floods, tsunamis, and volcanic eruptions, which struck across North America, Central America, and Asia. We used 175,289 buildings from the xBD dataset, which contains human-annotated multiclass damage labels on high-resolution satellite imagery with red, green, and blue (RGB) bands. First, our experiments showed that the performance in terms of area under the curve does not correlate with the type of natural hazard, geographical region, and satellite parameters such as the off-nadir angle. Second, while performance differed highly between occurrences of disasters, our model still reached a high level of performance without using any labeled data of the test disaster during training. This provides the first evidence that such a model can be effectively applied under operational conditions, where labeled damage data of the disaster cannot be available timely and thus model (re-)training is not an option. Full article
(This article belongs to the Special Issue Remote Sensing for Environment and Disaster)
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