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Remote Sensing
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Remote Sensing Applications in Natural Hazards and Sustainable Development
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Remote Sensing Applications in Natural Hazards and Sustainable Development

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 988

Special Issue Editors

Dr. Yaohui Liu

E-Mail Website
Guest Editor
1. School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
2. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Interests: remote sensing; artificial intelligence; natural hazards; ecological environment
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Yu

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: landslide extraction; remote sensing image processing; machine learning; deep learning
Dr. Fangbin Liu

E-Mail Website
Guest Editor
School of Geography and Tourism, Qilu Normal University, Ji’nan 250200, China
Interests: machine learning; earthquake seismology; structural geology; landscape evolution

Special Issue Information

Dear Colleagues,

As the frequency and intensity of natural disasters increase due to climate change and urbanization, the need for effective monitoring, assessment, and management strategies becomes paramount. Remote sensing offers invaluable insights often unattainable through ground-based observations alone, enabling researchers and practitioners to gather critical data over large areas and in real time, which is essential for informed decision-making.

This Special Issue focuses on the transformative role of remote sensing technologies in addressing the challenges posed by natural hazards while promoting sustainable development. We invite contributions that explore various applications, including the use of satellite imagery for disaster risk assessment, the monitoring of land use changes in vulnerable regions, and the evaluation of the effectiveness of disaster response strategies. We encourage interdisciplinary research that integrates remote sensing with geographic information systems (GISs), machine learning, and other analytical tools to enhance our understanding of natural hazards and their impacts on communities and ecosystems, which could help with achieving the Sustainable Development Goals set by the United Nations. By showcasing innovative methodologies and best practices, we hope to inspire further research and collaboration in this field.

We invite researchers, practitioners, and experts to submit original research articles, reviews, and case studies that highlight innovative applications of remote sensing in the context of natural hazard assessment and sustainable development. This Special Issue’s insights will not only advance scientific understanding but also provide practical solutions to enhance resilience and sustainability in the face of geohazards and natural hazards.

Dr. Yaohui Liu
Dr. Bo Yu
Dr. Fangbin Liu
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

  • natural hazards
  • risk
  • sustainable development goals
  • resilience
  • flood
  • earthquake
  • geology
  • landslide
  • ecology
  • fire
  • sustainably

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

Published Papers (2 papers)

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Research

21 pages, 33456 KiB  
Article
Evolution of Rockfall Based on Structure from Motion Reconstruction of Street View Imagery and Unmanned Aerial Vehicle Data: Case Study from Koto Panjang, Indonesia
by Tiggi Choanji, Michel Jaboyedoff, Yuniarti Yuskar, Anindita Samsu, Li Fei and Marc-Henri Derron
Remote Sens. 2025, 17(11), 1888; https://doi.org/10.3390/rs17111888 - 29 May 2025
Viewed by 274
Abstract
This study explores the growing application of 3D remote sensing in geohazard studies, particularly for rock slope monitoring. It highlights the use of cost-effective Street View Imagery (SVI) and Unmanned Aerial Vehicles (UAV) through Structure-from-Motion (SfM) photogrammetry as tools for 3D rockfall monitoring. [...] Read more.
This study explores the growing application of 3D remote sensing in geohazard studies, particularly for rock slope monitoring. It highlights the use of cost-effective Street View Imagery (SVI) and Unmanned Aerial Vehicles (UAV) through Structure-from-Motion (SfM) photogrammetry as tools for 3D rockfall monitoring. Using multi-temporal SVI and UAV Imagery from the Koto Panjang cliff in Indonesia, we quantify rockfall volume changes over seven years and assess associated geohazards. The results reveal a total rockfall retreat of 5270 m3, with an average annual rate of 7.53 m3/year. Structural analysis identified six major discontinuity sets and confirmed inherent instability within the rock mass. Kinematic simulations using SVI and UAV-derived data further assessed rockfall trajectories and potential impact zones. Results indicate that 40% of simulated rockfall deposits accumulated near existing roads, with significant differences in distribution based on scree slope angles. This emphasizes the role of scree slope in influencing rockfall propagation. In conclusion, SVI and UAV imagery presents a valuable tool for 3D point cloud reconstruction and rockfall hazard assessment, particularly in areas lacking historical data. The study showcases the effectiveness of using SVI and UAV imagery in quantifying historical past rockfall volume and identifies critical areas for mitigation strategies, highlighting the importance of scree slope angle in managing rockfall hazard. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Natural Hazards and Sustainable Development)
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16 pages, 3009 KiB  
Article
Advancing Corn Yield Mapping in Kenya Through Transfer Learning
by Ahaan Bohra, Sophie Nottmeyer, Chenchen Ren, Shuo Chen and Yuchi Ma
Remote Sens. 2025, 17(10), 1717; https://doi.org/10.3390/rs17101717 - 14 May 2025
Viewed by 402
Abstract
Crop yield mapping is essential for food security and policy making. Recent machine learning (ML) and deep learning (DL) methods have achieved impressive accuracy in crop yield estimation. However, these models require numerous training samples that are scarce in regions with underdeveloped infrastructure. [...] Read more.
Crop yield mapping is essential for food security and policy making. Recent machine learning (ML) and deep learning (DL) methods have achieved impressive accuracy in crop yield estimation. However, these models require numerous training samples that are scarce in regions with underdeveloped infrastructure. Furthermore, domain shifts between different spatial regions prevent DL models trained in one region from being directly applied to another without domain adaptation. This effect is particularly pronounced between regions with significant climate and environmental variations such as the U.S. and Kenya. To address this issue, we propose using fine-tuning-based transfer learning, which learns general associations between predictors and response variables from the data-abundant source domain and then fine-tunes the model on the data-scarce target domain. We assess the model’s performance on estimating corn yields using Kenya (target domain) and the U.S. (source domain). Feature variables, including time-series vegetation indices (VIs) and sequential meteorological variables from both domains, are used to pre-train and fine-tune the deep neural network model. The model is fine-tuned using data from 5 years (2019–2023) and tested using leave-one-year-out cross validation. The fine-tuned DNN achieves an overall R2 of 0.632—higher than both the U.S.-only and Kenya-only baselines—but paired significance tests show no aggregate difference, though a statistically significant gain does occur in 2023 under anomalous heat conditions. These results demonstrate that fine-tuning can reliably transfer learned representations across continents and, under certain climatic scenarios, yield meaningful improvements. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Natural Hazards and Sustainable Development)
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