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Remote Sensing
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Remote Sensing in Hazards Monitoring and Risk Assessment
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Remote Sensing in Hazards Monitoring and Risk Assessment

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 July 2025) | Viewed by 8600

Special Issue Editors

Dr. Liangliang Tao

E-Mail Website
Guest Editor
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: microwave and optical remote sensing to retrieve soil moisture and vegetation parameters; agricultural remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dongryeol Ryu

E-Mail Website
Guest Editor
Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC 3010, Austrilia
Interests: microwave remote sensing of soil moisture; hydrological applications of remote sensing; hydrological data assimilation
Special Issues, Collections and Topics in MDPI journals
Dr. Hongquan Sun

E-Mail Website
Guest Editor
National Institute of Natural Hazards, Beijing 100085, China
Interests: drought monitoring; early warning and risk assessment; application of remote sensing flood and drought disasters; informatization of flood and drought disaster prevention
Dr. Hao Sun

E-Mail Website
Guest Editor
College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Interests: optical and thermal remote sensing; remote sensing of soil moisture, agricultural and ecological drought; remote sensing of ecological environment; remote sensing of mining area
Special Issues, Collections and Topics in MDPI journals
Dr. Xi Chen

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: optical remote sensing; acoustical remote sensing of underwater, multi-source remote sensing of flood and emergency rescue

Special Issue Information

Dear Colleagues,

In the last decade, natural hazards such as tsunamis, volcanic eruptions, earthquakes, avalanches, cyclones or tornados, storm surges, flooding, landslides, soil erosion, land subsidence, wildfires, extreme temperatures, drought and so on have resulted in extensive loss to quality of life, critical infrastructure and economy, universally. Hazard or disaster serves as a foundational element across all domains of disaster risk management, with a particular emphasis on hazard understanding. Remote sensing can provide non-destructive and cost-efficient measurements and data to understand and evaluate various natural and human-induced hazards that impact our environment and society. For such disaster monitoring, various kinds of remote sensing observations (e.g., thermal, visual, radar, laser, and/or the fusion of these) can be utilized. In addition, comprehensive situation awareness and decision support for disaster response can be provided by conducting various spatial analysis, including damage estimation, isolation site analysis, and evacuation route analysis, in connection with the recognition of disaster situations from such remote sensing information.

We encourage the submission of novel techniques/approaches for showcasing the latest advancements, innovative methodologies, and practical applications of remote sensing in the field of hazard monitoring, modelling, assessment and mitigation, using any form of remote sensing data (proximal, airborne, and satellite). Original research contributions, exhaustive reviews, remote-sensing methodologies, and relevant applications in disaster monitoring and situational awareness are welcome, as will suggestions for future sensor considerations, algorithm developments, and opportunities for emergency management agency buy-in. In addition to the points above, topics may include but are not limited to:

  • addressed value of remote sensing data in risk/hazard forecasting models;
  • innovative applications of remote sensing data for hazard, vulnerability, and risk mapping;
  • innovative applications in support of disaster reduction strategies (e.g., landscape planning);
  • development of tools and platforms for assessment and validation of hazard/risk models;
  • Application of new sensors/algorithms and in practice monitoring systems;
  • Comparison and evaluation of different remote sensing methods (statistical, physical and hybrid models) in hazard monitoring.

Dr. Liangliang Tao
Prof. Dr. Dongryeol Ryu
Dr. Hongquan Sun
Dr. Hao Sun
Dr. Xi Chen
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

  • remote sensing
  • hazard monitoring
  • risk mapping
  • disaster reduction strategies
  • hazard/risk models

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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29 pages, 9465 KB  
Article
Modeling Seasonal Fire Probability in Thailand: A Machine Learning Approach Using Multiyear Remote Sensing Data
by Enikoe Bihari, Karen Dyson, Kayla Johnston, Daniel Marc G. dela Torre, Akkarapon Chaiyana, Karis Tenneson, Wasana Sittirin, Ate Poortinga, Veerachai Tanpipat, Kobsak Wanthongchai, Thannarot Kunlamai, Elijah Dalton, Chanarun Saisaward, Marina Tornorsam, David Ganz and David Saah
Remote Sens. 2025, 17(19), 3378; https://doi.org/10.3390/rs17193378 - 7 Oct 2025
Viewed by 912
Abstract
Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, [...] Read more.
Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, replicable, and operationally viable seasonal fire probability mapping methodology using a Random Forest (RF) machine learning model in the Google Earth Engine (GEE) platform. We trained the model on historical fire occurrence and fire predictor layers from 2016–2023 and applied it to 2024 conditions to generate a probabilistic fire prediction. Our novel approach improves upon existing operational methods and scientific literature in several ways. It uses a more representative sample design which is agnostic to the burn history of fire presences and absences, pairs fire and fire predictor data from each year to account for interannual variation in conditions, empirically refines the most influential fire predictors from a comprehensive set of predictors, and provides a reproducible and accessible framework using GEE. Predictor variables include both socioeconomic and environmental drivers of fire, such as topography, fuels, potential fire behavior, forest type, vegetation characteristics, climate, water availability, crop type, recent burn history, and human influence and accessibility. The model achieves an Area Under the Curve (AUC) of 0.841 when applied to 2016–2023 data and 0.848 when applied to 2024 data, indicating strong discriminatory power despite the additional spatial and temporal variability introduced by our sample design. The highest fire probabilities emerge in forested and agricultural areas at mid elevations and near human settlements and roads, which aligns well with the known anthropogenic drivers of fire in Thailand. Distinct areas of model uncertainty are also apparent in cropland and forests which are only burned intermittently, highlighting the importance of accounting for localized burning cycles. Variable importance analysis using the Gini Impurity Index identifies both natural and anthropogenic predictors as key and nearly equally important predictors of fire, including certain forest and crop types, vegetation characteristics, topography, climate, human influence and accessibility, water availability, and recent burn history. Our findings demonstrate the heavy influence of data preprocessing and model design choices on model results. The model outputs are provided as interpretable probability maps and the methods can be adapted to future years or augmented with local datasets. Our methodology presents a scalable advancement in wildfire probability mapping with machine learning and open-source tools, particularly for data-constrained landscapes. It will support Thailand’s fire managers in proactive fire response and planning and also inform broader regional fire risk assessment efforts. Full article
(This article belongs to the Special Issue Remote Sensing in Hazards Monitoring and Risk Assessment)
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17 pages, 50284 KB  
Article
Synergistic Impacts of Land Deformation and Rapid Socio-Ecological Changes on Disaster Risk in Indonesian Alluvial Plains Using Multiple Satellite Datasets
by Satomi Kimijima, Masahiko Nagai, Zahid Mushtaq Wani and Dianto Bachriadi
Remote Sens. 2025, 17(9), 1514; https://doi.org/10.3390/rs17091514 - 24 Apr 2025
Cited by 2 | Viewed by 645
Abstract
Unique, small-scale tectonic and geological systems are occasionally vulnerable to natural hazards. Although the combination of such systems with rapid socio-ecological change can enhance the risk of disasters, such synergistic impacts have not been well studied. The primary goal of this study was [...] Read more.
Unique, small-scale tectonic and geological systems are occasionally vulnerable to natural hazards. Although the combination of such systems with rapid socio-ecological change can enhance the risk of disasters, such synergistic impacts have not been well studied. The primary goal of this study was to investigate the potential synergistic impact of land deformation and rapid socio-ecological changes on disaster risk in lowland alluvial regions of a collision zone in the Gorontalo Regency of Gorontalo Province, Indonesia. In this region, socio-ecological changes such as urbanization and rapid lake shrinkage are significant. Frequent occurrence of flood hazards threatens local livelihood. Differential interferometric synthetic aperture radar analysis of Sentinel-1 C-band data from April 2020 to April 2023 was applied to assess land deformation. Thereafter, supervised classification of moderate and high spatiotemporal resolution optical satellite time series was used to assess the relationship between land deformation and built-up area. The findings revealed both significant land deformation and rapid socio-ecological changes. Vertical deformation rates were as high as ~6 cm/year and were primarily attributable to tectonic activity; they were particularly apparent in rapidly developing and highly populated residential areas. Rapid shrinkage of a lake resulted from the local geological system and socioeconomic changes in the region, which together possibly exacerbated the hazard risk because of their effects on land deformation. These results indicate the potential danger to both infrastructure and human inhabitants at a regional level due to the synergistic effects of natural processes and socio-ecological changes. The study design and data that were used facilitated a comprehensive assessment of the potential impacts on disaster risk. These findings are expected to be integrated into locally specific hazard (e.g., flood inundation and ground fissuring) risk mitigation and management strategies. Full article
(This article belongs to the Special Issue Remote Sensing in Hazards Monitoring and Risk Assessment)
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20 pages, 6556 KB  
Article
Critical Threshold-Based Heat Damage Evolution Monitoring to Tea Plants with Remotely Sensed LST over Mainland China
by Peijuan Wang, Xin Li, Junxian Tang, Dingrong Wu, Lifeng Pang and Yuanda Zhang
Remote Sens. 2024, 16(10), 1784; https://doi.org/10.3390/rs16101784 - 17 May 2024
Viewed by 1420
Abstract
Tea plants (Camellia sinensis (L.) Kuntze) are a cash crop that thrive under warm and moist conditions. However, tea plants are becoming increasingly vulnerable to heat damage (HD) during summer growing seasons due to global climate warming. Because China ranks first in [...] Read more.
Tea plants (Camellia sinensis (L.) Kuntze) are a cash crop that thrive under warm and moist conditions. However, tea plants are becoming increasingly vulnerable to heat damage (HD) during summer growing seasons due to global climate warming. Because China ranks first in the world in both harvested tea area and total tea production, monitoring and tracking HD to tea plants in a timely manner has become a significant and urgent task for scientists and tea producers in China. In this study, the spatiotemporal characteristics of HD evolution were analyzed, and a tracking method using HD LST-weighted geographical centroids was constructed based on HD pixels identified by the critical LST threshold and daytime MYD11A1 products over the major tea planting regions of mainland China from two typical HD years (2013 and 2022). Results showed that the average number of HD days in 2022 was five more than in 2013. Daily HD extent increased at a rate of 0.66% per day in 2022, which was faster than that in 2013 with a rate of 0.21% per day. In two typical HD years, the tea regions with the greatest HD extent were concentrated south of the Yangtze River (SYR), with average HD pixel ratios of greater than 50%, then north of the Yangtze River (NYR) and southwest China (SWC), with average HD pixel ratios of around 40%. The regions with the least HD extent were in South China (SC), where the HD ratios were less than 40%. The HD LST-weighted geographical centroid trajectories showed that HD to tea plants in 2013 initially moved from southwest to northeast, and then moved west. In 2022, HD moved from northeast to west and south. Daily HD centroids were mainly concentrated at the conjunction of SYR, SWC, and SC in 2013, and in northern SWC in 2022, where they were near to the centroid of the tea planting gardens. The findings in this study confirmed that monitoring HD evolution of tea plants over a large spatial extent based on reconstructed remotely sensed LST values and critical threshold was an effective method benefiting from available MODIS LST products. Moreover, this method can identify and track the spatial distribution characteristics of HD to tea plants in a timely manner, and it will therefore be helpful for taking effective preventative measures to mitigate economic losses resulting from HD. Full article
(This article belongs to the Special Issue Remote Sensing in Hazards Monitoring and Risk Assessment)
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Review

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29 pages, 6873 KB  
Review
Digital Twin Technology for Urban Flood Risk Management: A Systematic Review of Remote Sensing Applications and Early Warning Systems
by Mohammed Hlal, Jean-Claude Baraka Munyaka, Jérôme Chenal, Rida Azmi, El Bachir Diop, Mariem Bounabi, Seyid Abdellahi Ebnou Abdem, Mohamed Adou Sidi Almouctar and Meriem Adraoui
Remote Sens. 2025, 17(17), 3104; https://doi.org/10.3390/rs17173104 - 5 Sep 2025
Cited by 2 | Viewed by 3973
Abstract
Digital Twin (DT) technology has emerged as a transformative tool in urban flood risk management (UFRM), enabling real-time data integration, predictive modeling, and decision support. This systematic review synthesizes existing literature to evaluate the scientific impact, technological advancements, and practical applications of DTs [...] Read more.
Digital Twin (DT) technology has emerged as a transformative tool in urban flood risk management (UFRM), enabling real-time data integration, predictive modeling, and decision support. This systematic review synthesizes existing literature to evaluate the scientific impact, technological advancements, and practical applications of DTs in UFRM. Using the PRISMA 2020 framework, we retrieved 1085 records (Scopus = 85; Web of Science = 1000), merged and deduplicated them using DOI and fuzzy-matched titles, screened titles/abstracts, and assessed full texts. This process yielded 85 unique peer-reviewed studies published between 2018 and 2025. Key findings highlight the role of remote sensing (e.g., satellite imagery, IoT sensors) in enhancing DT accuracy, the integration of machine learning for predictive analytics, and case studies demonstrating reduced flood response times by up to 40%. Challenges such as data interoperability and computational demands are discussed, alongside future directions for scalable, AI-driven DT frameworks. This review identifies key technical and governance challenges while recommending the development of modular, AI-driven DT frameworks, particularly tailored for resource-constrained regions. Full article
(This article belongs to the Special Issue Remote Sensing in Hazards Monitoring and Risk Assessment)
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