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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 13815

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Special Issue Editors

Prof. Dr. Shrinidhi Ambinakudige

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Guest Editor

Department of Geosciences, Mississippi State University, Starkville, MS 39762, USA
Interests: remote sensing and GIS applications in studying cryosphere; land use and land cover; biodiversity; natural disasters; urban geography and vegetation changes

Dr. Padmanava Dash

E-Mail Website
Guest Editor

Department of Geosciences, Mississippi State University, Starkville, MS 39762, USA
Interests: remote sensing; water biogeochemistry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural hazards such as hurricanes, flooding, fires, and extreme weather events can create a devastating loss of human lives and destruction of physical infrastructure and the environment. There is strong coupling the among atmosphere, hydrosphere, biosphere, and lithosphere processes in the Earth’s complex system. The incredibly devastating interaction of humans with nature has increased the intensities and frequencies of natural hazards throughout the globe, causing disasters in vulnerable communities. Many of these natural hazards have been projected to increase in both frequency and intensity in future due to climate change and continued anthropogenic interference. It is important, therefore, to study the impacts of these natural disasters on human society and ultimately mitigate them and reduce anthropogenic interference.

Remote sensing techniques provide an excellent source of information to measure and monitor the impact of natural hazards on the Earth’s surface at a variety of spatial and temporal scales. Understanding the impacts of natural disasters often involves a broad and interdisciplinary research approach. Remote sensing techniques permit quantitative and sustained measurements of events even under challenging situations. Whether the impact is large or small and covers a large or small area, remote sensing can provide timely quality measurements of impacts. Hurricanes, for example, can devastate large areas, and there can be a potential lack of on-the-ground access because of damaged infrastructure, remoteness, or logistics. However, remote sensing, both from spaceborne and airborne platforms, can provide a significant amount of data for both short-term and long-term recovery measures.

This Special Issue provides a platform for researchers studying natural disasters using remote sensing applications and/or interdisciplinary approaches to share their research outcomes. Papers on monitoring and measuring the impacts of natural disasters, remote sensing technologies, and tools for decision support in early warning, prevention, reduction, and mitigation of natural disasters; issues such as the scientific basis of methods; the technology of measurements; modeling and forecasting; machine learning techniques; and sensors onboard satellite and airborne platforms are welcome.

Prof. Dr. Shrinidhi Ambinakudige
Dr. Padmanava Dash
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 disasters
Land cover change
Hazards
Vulnerability
Machine learning
Disaster recovery
Synthetic aperture radar

Published Papers (6 papers)

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Research

37 pages, 20369 KiB

Open AccessArticle

A Remote Sensing Method to Assess the Future Multi-Hazard Exposure of Urban Areas

by Carolina Salvo and Alessandro Vitale

Remote Sens. 2023, 15(17), 4288; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15174288 - 31 Aug 2023

Cited by 1 | Viewed by 1007

Abstract

As more than 75% of the global population is expected to live in urban areas by 2050, there is an urgent need to assess the risk of natural hazards through a future-focused lens so that adequately informed spatial planning decisions can be made to define preventive risk policies in the upcoming decades. The authors propose an innovative methodology to assess the future multi-hazard exposure of urban areas based on remote sensing technologies and statistical and spatial analysis. The authors, specifically, applied remote sensing technologies combined with artificial intelligence to map the built-up area automatically. They assessed and calibrated a transferable Binary Logistic Regression Model (BLRM) to model and predict future urban growth dynamics under different scenarios, such as the business as usual, the slow growth, and the fast growth scenarios. Finally, considering specific socioeconomic exposure indicators, the authors assessed each scenario’s future multi-hazard exposure in urban areas. The proposed methodology is applied to the Municipality of Rende. The results revealed that the multi-hazard exposure significantly changed across the analyzed scenarios and that urban socioeconomic growth is the main driver of risk in urban environments. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Disasters)

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23 pages, 11520 KiB

Open AccessArticle

Black Marble Nighttime Light Data for Disaster Damage Assessment

by Danrong Zhang, Huili Huang, Nimisha Roy, M. Mahdi Roozbahani and J. David Frost

Remote Sens. 2023, 15(17), 4257; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15174257 - 30 Aug 2023

Viewed by 1446

Abstract

This research explores the utilization of the Black Marble nighttime light (NTL) product to detect and assess damage caused by hurricanes, tornadoes, and earthquakes. The study first examines average regional NTL trends before and after each disaster, demonstrating that NTL patterns for hurricanes closely align with the features of a resilience curve, unlike those for earthquakes and tornadoes. The relative NTL change ratio is computed using monthly and daily NTL data, effectively reducing variance due to daily fluctuations. Results indicate the robustness of the NTL change ratio in detecting hurricane damage, whereas its performance in earthquake and tornado assessment was inconsistent and inadequate. Furthermore, NTL demonstrates a high performance in identifying hurricane damage in well-lit areas and the potential to detect damage along tornado paths. However, a low correlation between the NTL change ratio and the degree of damage highlights the method’s limitation in quantifying damage. Overall, the study offers a promising, prompt approach for detecting damaged/undamaged areas, with specific relevance to hurricane reconnaissance, and points to avenues for further refinement and investigation. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Disasters)

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18 pages, 6211 KiB

Open AccessArticle

Disaster-Caused Power Outage Detection at Night Using VIIRS DNB Images

by Haodong Cui, Shi Qiu, Yicheng Wang, Yu Zhang, Zhaoyan Liu, Kirsi Karila, Jianxin Jia and Yuwei Chen

Remote Sens. 2023, 15(3), 640; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030640 - 21 Jan 2023

Cited by 2 | Viewed by 1841

Abstract

Rapid disaster assessment is critical for public security and rescue. As a secondary disaster of large-scale meteorological disasters, power outages cause severe outcomes and thus need to be monitored efficiently and without being costly. Power outage detection from space-borne remote sensing imagery offers a broader coverage and is more temporally sensitive than ground-based surveys are. However, it is challenging to determine the affected area accurately and quantitatively evaluate its severity. Therefore, a new method is proposed to solve the above problems by building a power outage detection model (PODM) and drawing a power outage spatial distribution map (POSDM). This paper takes the winter storm Uri, of 2021, as the meteorological disaster background and Harris County, Texas, which was seriously affected, as the research object. The proposed method utilises the cloud-free VIIRS DNB nadir and close nadir images (<60 degrees) collected during the 3 months before and 15 days after Uri. The core idea beneath the proposed method is to compare the radiance difference in the affected area before and after the disaster, and a large difference in radiance indicates the happening of power outages. The raw radiance of night light measurement is first corrected to remove lunar and atmospheric effects to improve accuracy. Then, the maximum and minimum pixels in the target area of the image are considered outliers and iteratively eliminated until the standard deviation change before and after elimination is less than 1% to finalize the outlier removals. The case study results in Harris show that the PODM detects 28% of outages (including traffic area) compared to 17% of outages (living area only) reported by ground truth data, indicating general agreement with the proposed method. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Disasters)

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23 pages, 14195 KiB

Open AccessArticle

Damaged Building Extraction Using Modified Mask R-CNN Model Using Post-Event Aerial Images of the 2016 Kumamoto Earthquake

by Yihao Zhan, Wen Liu and Yoshihisa Maruyama

Remote Sens. 2022, 14(4), 1002; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14041002 - 18 Feb 2022

Cited by 13 | Viewed by 2942

Abstract

Remote sensing is an effective method of evaluating building damage after a large-scale natural disaster, such as an earthquake or a typhoon. In recent years, with the development of computer vision technology, deep learning algorithms have been used for damage assessment from aerial images. In April 2016, a series of earthquakes hit the Kyushu region, Japan, and caused severe damage in the Kumamoto and Oita Prefectures. Numerous buildings collapsed because of the strong and continuous shaking. In this study, a deep learning model called Mask R-CNN was modified to extract residential buildings and estimate their damage levels from post-event aerial images. Our Mask R-CNN model employs an improved feature pyramid network and online hard example mining. Furthermore, a non-maximum suppression algorithm across multiple classes was also applied to improve prediction. The aerial images captured on 29 April 2016 (two weeks after the main shock) in Mashiki Town, Kumamoto Prefecture, were used as the training and test sets. Compared with the field survey results, our model achieved approximately 95% accuracy for building extraction and over 92% accuracy for the detection of severely damaged buildings. The overall classification accuracy for the four damage classes was approximately 88%, demonstrating acceptable performance. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Disasters)

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15 pages, 3648 KiB

Open AccessArticle

Coordination Conflicts between Urban Resilience and Urban Land Evolution in Chinese Hilly City of Mianyang

by Qi Cao, Yudie Huang, Baisong Ran, Gang Zeng, Anton Van Rompaey and Manjiang Shi

Remote Sens. 2021, 13(23), 4887; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234887 - 01 Dec 2021

Cited by 5 | Viewed by 1999

Abstract

Urban resilience, the combinational characteristic of nature and society, that reflects the dynamic accumulation process that is multi-level and multi-dimensional. Particularly, the rational spatial distribution structure of land mixture and compactness is an effective way to improve urban resilience because the evolution of morphology and density of the urban land blocks in the process of land spatial conversion reflect the performance characteristics of complexity, diversity, stability, compactness, and connectivity. Therefore, we evaluated the relationship between urban resilience and land use and land cover (LULC) change, to find the keys to resilient urban development for urban land and space planning. In this study, taking the Chinese hilly city of Mianyang as an example, the results show: (1) the complexity of homogeneous patch shape and heterogeneous patch combination leads to the decrease of urban morphology resilience. (2) the development trend of LULC spatial layout and structure ratio were more rational with the increased of land mixing degree. (3) the speed and intensity of urban expansion were basically coordinated with the development of urban resilience. The research provides the new ideas, approaches, and toolkits for solving the intractable problems of urban spatial planning based on coordinating conflicts between urban resilience and urban land evolution. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Disasters)

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16 pages, 7585 KiB

Open AccessArticle

Identifying Damaged Buildings in Aerial Images Using the Object Detection Method

by Lingfei Shi, Feng Zhang, Junshi Xia, Jibo Xie, Zhe Zhang, Zhenhong Du and Renyi Liu

Remote Sens. 2021, 13(21), 4213; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214213 - 21 Oct 2021

Cited by 17 | Viewed by 3251

Abstract

The collapse of buildings caused by the earthquake seriously threatened human lives and safety. So, the quick detection of collapsed buildings from post-earthquake images is essential for disaster relief and disaster damage assessment. Compared with the traditional building extraction methods, the methods based on convolutional neural networks perform better because it can automatically extract high-dimensional abstract features from images. However, there are still many problems with deep learning in the extraction of collapsed buildings. For example, due to the complex scenes after the earthquake, the collapsed buildings are easily confused with the background, so it is difficult to fully use the multiple features extracted by collapsed buildings, which leads to time consumption and low accuracy of collapsed buildings extraction when training the model. In addition, model training is prone to overfitting, which reduces the performance of model migration. This paper proposes to use the improved classic version of the you only look once model (YOLOv4) to detect collapsed buildings from the post-earthquake aerial images. Specifically, the k-means algorithm is used to optimally select the number and size of anchors from the image. We replace the Resblock in CSPDarkNet53 in YOLOv4 with the ResNext block to improve the backbone’s ability and the performance of classification. Furthermore, to replace the loss function of YOLOv4 with the Focal-EOIU loss function. The result shows that compared with the original YOLOv4 model, our proposed method can extract collapsed buildings more accurately. The AP (average precision) increased from 88.23% to 93.76%. The detection speed reached 32.7 f/s. Our method not only improves the accuracy but also enhances the detection speed of the collapsed buildings. Moreover, providing a basis for the detection of large-scale collapsed buildings in the future. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Disasters)

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