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Sustainability
Special Issues
Empowering Disaster Management with Remote Sensing and Social Sensing Advances
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Empowering Disaster Management with Remote Sensing and Social Sensing Advances

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4667

Special Issue Editors

Dr. Zhihua Wang

E-Mail Website
Guest Editor
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing; GIS; land use and land cover; coastal environment; disaster resilience; coupled human–natural system; sustainability
Dr. Lei Zou

E-Mail Website
Guest Editor
Department of Geography, Texas A&M University, College Station, TX 3147, USA
Interests: geographic information science (GIScience); spatial big data analytics; social sensing; remote sensing; human–environment interactions; disaster resilience; sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Yi Qiang

E-Mail Website
Guest Editor
School of Geosciences, University of South Florida, Tampa, FL 33620, USA
Interests: GIScience; spatial data science; disaster resilience; visual analytics; geocomputation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Disasters such as landslides, floods, fires, droughts, earthquakes, pests, and COVID-19 can suddenly change our environments and behaviors, cause destruction of our properties, and even take our lives at local, regional, and global scales, thus significantly impeding us in reaching our sustainable development goals. Although governments and scientists have expended tremendous efforts to cope with these disasters, many challenges still remain in rapidly and accurately acquiring spatial–temporal disaster information, management for hindering and mitigating disaster, and facilitating early warning, assessment, and recovery, especially in developing countries/areas.

Remote Sensing is progressively supporting disaster mapping and monitoring because it can allow quick and accurate physical observation of the earth’ surface before, during, and after disasters at multiple spatial–temporal scales, especially the macro spatial scale. Concurrently, Social Sensing, such as data collected from social media, cellphone apps, vehicles, sensor networks, and crowdsourcing, is widely used in human settlements to monitor or analyze human reactions to disasters. Both approaches have been integrated with Geographical Information Systems (GIS) to investigate the spatial-temporal patterns of disaster cycles, societal impacts, and human responses, offering a powerful system for disaster management.

This Special Issue seeks solutions integrating advanced Remote Sensing, Social Sensing and GIS in disaster management for sustainability considering spatial-temporal information aspects. We invite contributions that share novel remote sensing, social sensing, and GIS methods or applications across the entire cycle of disaster managements, i.e., preparedness, response, recovery, and mitigation.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Reviews or discussions of remote sensing, social sensing, or GIS applications in disaster management;
  • Innovative remote sensing techniques for disaster management, e.g., target extracting, land use/land cover classification, surface parameters quantitative retrieval, etc.;
  • Revealing human behaviors through social sensing and geospatial big data;
  • GIS based evaluation of disaster vulnerability, resilience, and risk;
  • Frameworks, systems, or cyberinfrastructures integrating Remote Sensing, Social Sensing and GIS for disaster management;
  • Case studies of disaster management, such as spatial planning, monitoring, modeling, evaluating, predication, etc.

We look forward to receiving your contributions.

Dr. Zhihua Wang
Dr. Lei Zou
Dr. Yi Qiang
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. Sustainability 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 2400 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

  • remote sensing
  • social sensing
  • GIS
  • disaster
  • sustainability

Published Papers (2 papers)

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Research

22 pages, 11626 KiB  
Article
A Deep Learning Semantic Segmentation Method for Landslide Scene Based on Transformer Architecture
by Zhaoqiu Wang, Tao Sun, Kun Hu, Yueting Zhang, Xiaqiong Yu and Ying Li
Sustainability 2022, 14(23), 16311; https://0-doi-org.brum.beds.ac.uk/10.3390/su142316311 - 6 Dec 2022
Cited by 4 | Viewed by 2138
Abstract
Semantic segmentation technology based on deep learning has developed rapidly. It is widely used in remote sensing image recognition, but is rarely used in natural disaster scenes, especially in landslide disasters. After a landslide disaster occurs, it is necessary to quickly carry out [...] Read more.
Semantic segmentation technology based on deep learning has developed rapidly. It is widely used in remote sensing image recognition, but is rarely used in natural disaster scenes, especially in landslide disasters. After a landslide disaster occurs, it is necessary to quickly carry out rescue and ecological restoration work, using satellite data or aerial photography data to quickly analyze the landslide area. However, the precise location and area estimation of the landslide area is still a difficult problem. Therefore, we propose a deep learning semantic segmentation method based on Encoder-Decoder architecture for landslide recognition, called the Separable Channel Attention Network (SCANet). The SCANet consists of a Poolformer encoder and a Separable Channel Attention Feature Pyramid Network (SCA-FPN) decoder. Firstly, the Poolformer can extract global semantic information at different levels with the help of transformer architecture, and it greatly reduces computational complexity of the network by using pooling operations instead of a self-attention mechanism. Secondly, the SCA-FPN we designed can fuse multi-scale semantic information and complete pixel-level prediction of remote sensing images. Without bells and whistles, our proposed SCANet outperformed the mainstream semantic segmentation networks with fewer model parameters on our self-built landslide dataset. The mIoU scores of SCANet are 1.95% higher than ResNet50-Unet, especially. Full article
(This article belongs to the Special Issue Empowering Disaster Management with Remote Sensing and Social Sensing Advances)
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23 pages, 9959 KiB  
Article
Remote Sensing Land Use Evolution in Earthquake-Stricken Regions of Wenchuan County, China
by Junmei Kang, Zhihua Wang, Hongbin Cheng, Jun Wang and Xiaoliang Liu
Sustainability 2022, 14(15), 9721; https://0-doi-org.brum.beds.ac.uk/10.3390/su14159721 - 7 Aug 2022
Cited by 8 | Viewed by 1784
Abstract
Earthquakes and their secondary geological disasters have a certain impact on the land cover, which leads to the degradation of the ecological environment and the stability of the ecosystem. At present, there are few studies on the spatial–temporal evolution characteristics of land-use change [...] Read more.
Earthquakes and their secondary geological disasters have a certain impact on the land cover, which leads to the degradation of the ecological environment and the stability of the ecosystem. At present, there are few studies on the spatial–temporal evolution characteristics of land-use change in earthquake-stricken regions, especially the lack of quantitative evaluation of the impact of earthquakes on land use at the micro-scale. The “5·12” Wenchuan ms8.0 earthquake caused serious damage to the surface resources in the disaster area. The study on the spatial–temporal evolution characteristics of land-use change in the disaster area can provide a reference for the remote sensing dynamic monitoring of the ecological environment. Therefore, based on geographical big data, this paper used a land-use comprehensive degree index, land-use transfer matrix, and landscape ecological index to explore and analyze the spatial–temporal evolution characteristics of land use in Wenchuan County before and after the earthquake. The results showed that the types of cropland, forest, built-up, and bare land changed greatly before and after the earthquake. During the earthquake recovery period, the comprehensive index of land use in the study area basically showed an increasing trend. Under the effect of artificial measures and natural restoration, land use was continuously improved, and vegetation was restored well. After 2008, the Patch Density (PD) and Landscape Shape Index (LSI) values of most landscape types decreased, and the Aggregation Index (AI) values increased, indicating that the ecological environment of the whole region showed a benign development in the post-earthquake period. The results not only contribute to the establishment of scientific ecological environment management in earthquake-stricken regions but also contribute to the formulation of long-term ecological environment monitoring and ecological restoration planning according to the law of land-use change. Full article
(This article belongs to the Special Issue Empowering Disaster Management with Remote Sensing and Social Sensing Advances)
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