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

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 67067

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Special Issue Editors

Dr. Bayes Ahmed

E-Mail Website1 Website2
Guest Editor

Institute for Risk and Disaster Reduction (IRDR), University College London (UCL), London WC1E 6BT, UK
Interests: disaster risk reduction; early warning systems; remote sensing; GIS; vulnerability assessment; risk mapping; landslides

Dr. Akhtar Alam

E-Mail Website
Guest Editor

Department of Geography, University of Kashmir, Srinagar 190006, India
Interests: remote sensing; GIS; GPS; land use and land cover; floods; tectonics; geomorphology; disaster risk

Special Issue Information

Dear Colleagues,

Each year, natural hazards such as earthquakes, tsunamis, cyclones or tornados, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc. result in widespread loss to life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities and changes to natural environment, the frequency, and intensity of extreme natural events and consequent impacts are expected to increase in future.

Technological interventions provide essential provision for the prevention and mitigation of natural hazards. Remote sensing has been one of such technologies that have completely transformed our understanding of natural hazards, including the wide range of processes operating on Earth and other planets.

The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on genesis, spatiotemporal patterns, and forecasting of natural hazards. The collection of data using earth observation (EO) systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.

The application of advanced geospatial technologies is essential in achieving targets set by the UN Sustainable Development Goals and the Sendai Framework for Disaster Risk Reduction. With these in mind, this Special Issue seeks original contributions on the advanced applications of remote sensing, geographic information system (GIS), and other geoinformation-based tools and techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches. The topics may include but are not limited to:

Monitoring and modeling natural hazards;
Landslides and land degradation;
Climate change and cryosphere;
Land use and land cover change;
Time series data and projections;
River hydrology, floods, and floodplains;
Earthquakes, structures, and liquefaction;
Tsunamis, storm surges, and coastal environments;
Hazard and vulnerability assessments;
Risk mapping and quantifications;
Applications of the hyperspectral and LiDAR data;
Developing early warning systems at local to global scale.

Dr. Bayes Ahmed
Dr. Akhtar Alam
Guest Editors

Manuscript Submission Information

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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
disasters, remote sensing
GIS
hazard
risk
vulnerability
early warning system

Published Papers (12 papers)

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37 pages, 22027 KiB

Open AccessArticle

How Does Peri-Urbanization Trigger Climate Change Vulnerabilities? An Investigation of the Dhaka Megacity in Bangladesh

by Md. Golam Mortoja and Tan Yigitcanlar

Remote Sens. 2020, 12(23), 3938; https://doi.org/10.3390/rs12233938 - 1 Dec 2020

Cited by 18 | Viewed by 5802

Abstract

This paper aims to scrutinize in what way peri-urbanization triggers climate change vulnerabilities. By using spatial analysis techniques, the study undertakes the following tasks. First, the study demarcates Dhaka’s—the capital of Bangladesh—peri-urban growth pattern that took place over the last 24-year period (1992–2016). Afterwards, it determines the conformity of ongoing peri-urban practices with Dhaka’s stipulated planning documents. Then, it identifies Dhaka’s specific vulnerabilities to climate change impacts—i.e., flood, and groundwater table depletion. Lastly, it maps out the socioeconomic profile of the climate change victim groups from Dhaka. The findings of the study reveal that: (a) Dhaka lacks adequate development planning, monitoring, and control mechanisms that lead to an increased and uncontrolled peri-urbanization; (b) Dhaka’s explicitly undefined peri-urban growth boundary is the primary factor in misguiding the growth pockets—that are the most vulnerable locations to climate change impacts, and; (c) Dhaka’s most vulnerable group to the increasing climate change impacts are the climate migrants, who have been repeatedly exposed to the climate change-triggered natural hazards. These study findings generate insights into peri-urbanization-triggered climate change vulnerabilities that aid urban policymakers, managers, and planners in their development policy, planning, monitoring and control practices. Full article

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

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20 pages, 19588 KiB

Open AccessArticle

Landslide Characterization Applying Sentinel-1 Images and InSAR Technique: The Muyubao Landslide in the Three Gorges Reservoir Area, China

by Chao Zhou, Ying Cao, Kunlong Yin, Yang Wang, Xuguo Shi, Filippo Catani and Bayes Ahmed

Remote Sens. 2020, 12(20), 3385; https://doi.org/10.3390/rs12203385 - 16 Oct 2020

Cited by 66 | Viewed by 4754

Abstract

Landslides are a common natural hazard that causes casualties and unprecedented economic losses every year, especially in vulnerable developing countries. Considering the high cost of in-situ monitoring equipment and the sparse coverage of monitoring points, the Sentinel-1 images and Interferometric Synthetic Aperture Radar (InSAR) technique were used to conduct landslide monitoring and analysis. The Muyubao landslide in the Three Gorges Reservoir area in China was taken as a case study. A total of 37 images from March 2016 to September 2017 were collected, and the displacement time series were extracted using the Stanford Method for Persistent Scatterer (StaMPS) small baselines subset method. The comparison to global positioning system monitoring results indicated that the InSAR processing of the Muyubao landslide was accurate and reliable. Combined with the field investigation, the deformation evolution and its response to triggering factors were analyzed. During this monitoring period, the creeping process of the Muyubao landslide showed obvious spatiotemporal deformation differences. The changes in the reservoir water level were the trigger of the Muyubao landslide, and its deformation mainly occurred during the fluctuation period and high-water level period of the reservoir. Full article

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

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17 pages, 6936 KiB

Open AccessArticle

Assessing Agricultural Vulnerability to Drought in a Heterogeneous Environment: A Remote Sensing-Based Approach

by Mst Ilme Faridatul and Bayes Ahmed

Remote Sens. 2020, 12(20), 3363; https://doi.org/10.3390/rs12203363 - 15 Oct 2020

Cited by 17 | Viewed by 3047

Abstract

Agriculture is one of the fundamental economic activities in most countries; however, this sector suffers from various natural hazards including flood and drought. The determination of drought-prone areas is essential to select drought-tolerant crops in climate sensitive vulnerable areas. This study aims to enhance the detection of agricultural areas with vulnerability to drought conditions in a heterogeneous environment, taking Bangladesh as a case study. The normalized difference vegetation index (NDVI) and land cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images have been incorporated to compute the vegetation index. In this study, a modified vegetation condition index (mVCI) is proposed to enhance the estimation of agricultural drought. The NDVI values ranging between 0.44 to 0.66 for croplands are utilized for the mVCI. The outcomes of the mVCI are compared with the traditional vegetation condition index (VCI). Precipitation and crop yield data are used for the evaluation. The mVCI maps from multiple years (2006–2018) have been produced to compute the drought hazard index (DHI) using a weighted sum overlay method. The results show that the proposed mVCI enhances the detection of agricultural drought compared to the traditional VCI in a heterogeneous environment. The “Aus” rice-growing season (sown in mid-March to mid-April and harvested in mid-July to early August) receives the highest average precipitation (>400 mm), and thereby this season is less vulnerable to drought. A comparison of crop yields reveals the lowest productivity in the drought year (2006) compared to the non-drought year (2018), and the DHI map presents that the north-west region of Bangladesh is highly vulnerable to agricultural drought. This study has undertaken a large-scale analysis that is important to prioritize agricultural zones and initiate development projects based on the associated level of vulnerability. Full article

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

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23 pages, 16175 KiB

Open AccessArticle

Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping

by Mohammed Sarfaraz Gani Adnan, Md Salman Rahman, Nahian Ahmed, Bayes Ahmed, Md. Fazleh Rabbi and Rashedur M. Rahman

Remote Sens. 2020, 12(20), 3347; https://doi.org/10.3390/rs12203347 - 14 Oct 2020

Cited by 74 | Viewed by 7557

Abstract

Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions. Full article

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

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33 pages, 43292 KiB

Open AccessArticle

On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification

by Kamila Pawluszek-Filipiak and Andrzej Borkowski

Remote Sens. 2020, 12(18), 3054; https://doi.org/10.3390/rs12183054 - 18 Sep 2020

Cited by 27 | Viewed by 7085

Abstract

Many automatic landslide detection algorithms are based on supervised classification of various remote sensing (RS) data, particularly satellite images and digital elevation models (DEMs) delivered by Light Detection and Ranging (LiDAR). Machine learning methods require the collection of both training and testing data to produce and evaluate the classification results. The collection of good quality landslide ground truths to train classifiers and detect landslides in other regions is a challenge, with a significant impact on classification accuracy. Taking this into account, the following research question arises: What is the appropriate training–testing dataset split ratio in supervised classification to effectively detect landslides in a testing area based on DEMs? We investigated this issue for both the pixel-based approach (PBA) and object-based image analysis (OBIA). In both approaches, the random forest (RF) classification was implemented. The experiments were performed in the most landslide-affected area in Poland in the Outer Carpathians-Rożnów Lake vicinity. Based on the accuracy assessment, we found that the training area should be of a similar size to the testing area. We also found that the OBIA approach performs slightly better than PBA when the quantity of training samples is significantly lower than the testing samples. To increase detection performance, the intersection of the OBIA and PBA results together with median filtering and the removal of small elongated objects were performed. This allowed an overall accuracy (OA) = 80% and F1 Score = 0.50 to be achieved. The achieved results are compared and discussed with other landslide detection-related studies. Full article

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

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35 pages, 10221 KiB

Open AccessFeature PaperArticle

Evaluating the Effects of Digital Elevation Models in Landslide Susceptibility Mapping in Rangamati District, Bangladesh

by Yasin Wahid Rabby, Asif Ishtiaque and Md. Shahinoor Rahman

Remote Sens. 2020, 12(17), 2718; https://doi.org/10.3390/rs12172718 - 22 Aug 2020

Cited by 36 | Viewed by 5675

Abstract

Digital elevation models (DEMs) are the most obvious data sources in landslide susceptibility assessment. Many landslide casual factors are often generated from DEMs. Most studies on landslide susceptibility assessments rely on freely available DEMs. However, very little is known about the performance of different DEMs with varying spatial resolutions on the accurate assessment of landslide susceptibility. This study compared the performance of four different DEMs including 30 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), 30–90 m Shuttle Radar Topographic Mission (SRTM), 12.5 m Advanced Land Observation Satellite (ALOS) Phased Array Type L band Synthetic Aperture Radar (PALSAR), and 25 m Survey of Bangladesh (SOB) DEM in landslide susceptibility assessment in the Rangamati district in Bangladesh. This study used three different landslide susceptibility assessment techniques: modified frequency ratio (bivariate model), logistic regression (multivariate model), and random forest (machine-learning model). This study explored two scenarios of landslide susceptibility assessment: using only DEM-derived causal factors and using both DEM-derived factors as well as other common factors. The success and prediction rate curves indicate that the SRTM DEM provides the highest accuracies for the bivariate model in both scenarios. Results also reveal that the ALOS PALSAR DEM shows the best performance in landslide susceptibility mapping using the logistics regression and the random forest models. A relatively finer resolution DEM, the SOB DEM, shows the lowest accuracies compared to other DEMs for all models and scenarios. It can also be noted that the performance of all DEMs except the SOB DEM is close (72%–84%) considering the success and prediction accuracies. Therefore, anyone of the three global DEMs: ASTER, SRTM, and ALOS PALSAR can be used for landslide susceptibility mapping in the study area. Full article

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

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29 pages, 8820 KiB

Open AccessArticle

Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach

by Samy Elmahdy, Tarig Ali and Mohamed Mohamed

Remote Sens. 2020, 12(17), 2695; https://doi.org/10.3390/rs12172695 - 20 Aug 2020

Cited by 30 | Viewed by 5292

Abstract

In an arid region, flash floods (FF), as a response to climate changes, are the most hazardous causing massive destruction and losses to farms, human lives and infrastructure. A first step towards securing lives and infrastructure is the susceptibility mapping and predicting of occurrence sites of FF. Several studies have been applied using an ensemble machine learning model (EMLM) but measuring FF magnitude using a hybrid approach that integrates machine learning (MCL) and geohydrological models have not been widely applied. This study aims to modify a hybrid approach by testing three machine learning models. These are boosted regression tree (BRT), classification and regression trees (CART), and naive Bayes tree (NBT) for FF susceptibility mapping at the northern part of the United Arab Emirates (NUAE). This is followed by applying a group of accuracy metrics (precision, recall and F1 score) and the receiving operating characteristics (ROC) curve. The result demonstrated that the BRT has the highest performance for FF susceptibility mapping followed by the CART and NBT. After that, the produced FF map using the BRT was then modified by dividing it into seven basins, and a set of new FF conditioning parameters namely alluvial plain width, basin gradient and mean slope for each basin was calculated for measuring FF magnitude. The results showed that the mountainous and narrower basins (e.g., RAK, Masafi, Fujairah, and Rol Dadnah) have the highest probability occurrence of FF and FF magnitude, while the wider alluvial plains (e.g., Al Dhaid) have the lowest probability occurrence of FF and FF magnitude. The proposed approach is an effective approach to improve the susceptibility mapping of FF, landslides, land subsidence, and groundwater potentiality obtained using ensemble machine learning, which is used widely in the literature. Full article

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

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24 pages, 5496 KiB

Open AccessArticle

Analysis of Ice Storm Impact on and Post-Disaster Recovery of Typical Subtropical Forests in Southeast China

by Wutao Yao, Yong Ma, Fu Chen, Zhishu Xiao, Zufei Shu, Lijun Chen, Wenhong Xiao, Jianbo Liu, Liyuan Jiang and Shuyan Zhang

Remote Sens. 2020, 12(1), 164; https://doi.org/10.3390/rs12010164 - 2 Jan 2020

Cited by 5 | Viewed by 3014

Abstract

Ice storms greatly affect the structure, dynamics, and functioning of forest ecosystems. Studies on the impact of such disasters, as well as the post-disaster recovery of forests, are important contents in forest biology, ecology, and geography. Remote-sensing technology provides data and methods that can support the study of disasters at the large-to-medium scale and over long time periods. This study took Chebaling National Nature Reserve in Guangdong Province, China, as the study area. First, field-survey data and remote-sensing data were comprehensively analyzed to demonstrate the feasibility of replacing the forest stock volume with the mean annual value of the Enhanced Vegetation Index (EVI), to study forest growth and change. We then used the EVI from 2007 to 2017, together with a variety of other remote-sensing and forest sub-compartment data, to analyze the impact of the 2008 ice storm and the subsequent post-disaster recovery of the forest. Finally, we drew the following conclusions: (1) Topography had a considerable effect on disaster impact and forest recovery in Chebaling. The forest at high altitudes (700–1000 m) and on steep slopes (25–40°) was seriously affected by this disaster but had a stronger post-disaster recovery ability. Meanwhile, the hardest-hit area for coniferous forest was higher and steeper than that for broad-leaved forest. (2) In the same terrain conditions, coniferous forests were less affected by the disaster than broad-leaved forests and showed less variation during the post-disaster recovery process. Nevertheless, broad-leaved forests had faster recovery rates and higher recovery degrees; (3) Under the influence of human activities, the recovery and fluctuation degree for planted forest in the post-disaster recovery process was significantly higher than that for natural forest. The study suggests that forest has high disaster resistance and self-recovery ability after the ice storm, and this ability has a strong correlation with the type of forest and the topographic factors such as elevation and slope. At the same time, human intervention can speed up the recovery of forests after disasters. Full article

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

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28 pages, 6892 KiB

Open AccessArticle

A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India

by Jagabandhu Roy, Sunil Saha, Alireza Arabameri, Thomas Blaschke and Dieu Tien Bui

Remote Sens. 2019, 11(23), 2866; https://doi.org/10.3390/rs11232866 - 2 Dec 2019

Cited by 126 | Viewed by 8055

Abstract

Landslides are among the most harmful natural hazards for human beings. This study aims to delineate landslide hazard zones in the Darjeeling and Kalimpong districts of West Bengal, India using a novel ensemble approach combining the weight-of-evidence (WofE) and support vector machine (SVM) techniques with remote sensing datasets and geographic information systems (GIS). The study area currently faces severe landslide problems, causing fatalities and losses of property. In the present study, the landslide inventory database was prepared using Google Earth imagery, and a field investigation carried out with a global positioning system (GPS). Of the 326 landslides in the inventory, 98 landslides (30%) were used for validation, and 228 landslides (70%) were used for modeling purposes. The landslide conditioning factors of elevation, rainfall, slope, aspect, geomorphology, geology, soil texture, land use/land cover (LULC), normalized differential vegetation index (NDVI), topographic wetness index (TWI), sediment transportation index (STI), stream power index (SPI), and seismic zone maps were used as independent variables in the modeling process. The weight-of-evidence and SVM techniques were ensembled and used to prepare landslide susceptibility maps (LSMs) with the help of remote sensing (RS) data and geographical information systems (GIS). The landslide susceptibility maps (LSMs) were then classified into four classes; namely, low, medium, high, and very high susceptibility to landslide occurrence, using the natural breaks classification methods in the GIS environment. The very high susceptibility zones produced by these ensemble models cover an area of 630 km² (WofE& RBF-SVM), 474 km² (WofE& Linear-SVM), 501km² (WofE& Polynomial-SVM), and 498 km² (WofE& Sigmoid-SVM), respectively, of a total area of 3914 km². The results of our study were validated using the receiver operating characteristic (ROC) curve and quality sum (Qs) methods. The area under the curve (AUC) values of the ensemble WofE& RBF-SVM, WofE & Linear-SVM, WofE & Polynomial-SVM, and WofE & Sigmoid-SVM models are 87%, 90%, 88%, and 85%, respectively, which indicates they are very good models for identifying landslide hazard zones. As per the results of both validation methods, the WofE & Linear-SVM model is more accurate than the other ensemble models. The results obtained from this study using our new ensemble methods can provide proper and significant information to decision-makers and policy planners in the landslide-prone areas of these districts. Full article

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

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19 pages, 7052 KiB

Open AccessArticle

Assessment of the Degree of Building Damage Caused by Disaster Using Convolutional Neural Networks in Combination with Ordinal Regression

by Tianyu Ci, Zhen Liu and Ying Wang

Remote Sens. 2019, 11(23), 2858; https://doi.org/10.3390/rs11232858 - 1 Dec 2019

Cited by 40 | Viewed by 5532

Abstract

We propose a new convolutional neural networks method in combination with ordinal regression aiming at assessing the degree of building damage caused by earthquakes with aerial imagery. The ordinal regression model and a deep learning algorithm are incorporated to make full use of the information to improve the accuracy of the assessment. A new loss function was introduced in this paper to combine convolutional neural networks and ordinal regression. Assessing the level of damage to buildings can be considered as equivalent to predicting the ordered labels of buildings to be assessed. In the existing research, the problem has usually been simplified as a problem of pure classification to be further studied and discussed, which ignores the ordinal relationship between different levels of damage, resulting in a waste of information. Data accumulated throughout history are used to build network models for assessing the level of damage, and models for assessing levels of damage to buildings based on deep learning are described in detail, including model construction, implementation methods, and the selection of hyperparameters, and verification is conducted by experiments. When categorizing the damage to buildings into four types, we apply the method proposed in this paper to aerial images acquired from the 2014 Ludian earthquake and achieve an overall accuracy of 77.39%; when categorizing damage to buildings into two types, the overall accuracy of the model is 93.95%, exceeding such values in similar types of theories and methods. Full article

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

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17 pages, 10605 KiB

Open AccessArticle

Sequential InSAR Time Series Deformation Monitoring of Land Subsidence and Rebound in Xi’an, China

by Baohang Wang, Chaoying Zhao, Qin Zhang and Mimi Peng

Remote Sens. 2019, 11(23), 2854; https://doi.org/10.3390/rs11232854 - 1 Dec 2019

Cited by 23 | Viewed by 4597

Abstract

Interferometric synthetic aperture radar (InSAR) time series deformation monitoring plays an important role in revealing historical displacement of the Earth’s surface. Xi’an, China, has suffered from severe land subsidence along with ground fissure development since the 1960s, which has threatened and will continue to threaten the stability of urban artificial constructions. In addition, some local areas in Xi’an suffered from uplifting for some specific period. Time series deformation derived from multi-temporal InSAR techniques makes it possible to obtain the temporal evolution of land subsidence and rebound in Xi’an. In this paper, we used the sequential InSAR time series estimation method to map the ground subsidence and rebound in Xi’an with Sentinel-1A data during 2015 to 2019, allowing estimation of surface deformation dynamically and quickly. From 20 June 2015 to 17 July 2019, two areas subsided continuously (Sanyaocun-Fengqiyuan and Qujiang New District), while Xi’an City Wall area uplifted with a maximum deformation rate of 12 mm/year. Furthermore, Yuhuazhai subsided from 20 June 2015 to 14 October 2018, and rebound occurred from 14 October 2018 to 17 July 2019, which can be explained as the response to artificial water injection. In the process of artificial water injection, the rebound pattern can be further divided into immediate elastic recovery deformation and time-dependent visco-elastic recovery deformation. Full article

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

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14 pages, 10525 KiB

Open AccessLetter

Mapping, Monitoring, and Prediction of Floods Due to Ice Jam and Snowmelt with Operational Weather Satellites

by Mitchell D. Goldberg, Sanmei Li, Daniel T. Lindsey, William Sjoberg, Lihang Zhou and Donglian Sun

Remote Sens. 2020, 12(11), 1865; https://doi.org/10.3390/rs12111865 - 9 Jun 2020

Cited by 9 | Viewed by 3444

Abstract

Among all the natural hazards throughout the world, floods occur most frequently. They occur in high latitude regions, such as: 82% of the area of North America; most of Russia; Norway, Finland, and Sweden in North Europe; China and Japan in Asia. River flooding due to ice jams may happen during the spring breakup season. The Northeast and North Central region, and some areas of the western United States, are especially harmed by floods due to ice jams and snowmelt. In this study, observations from operational satellites are used to map and monitor floods due to ice jams and snowmelt. For a coarse-to-moderate resolution sensor on board the operational satellites, like the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the National Polar-orbiting Partnership (NPP) and the Joint Polar Satellite System (JPSS) series, and the Advanced Baseline Imager (ABI) on board the GOES-R series, a pixel is usually composed of a mix of water and land. Water fraction can provide more information and can be estimated through mixed-pixel decomposition. The flood map can be derived from the water fraction difference after and before flooding. In high latitude areas, while conventional observations are usually sparse, multiple observations can be available from polar-orbiting satellites during a single day, and river forecasters can observe ice movement, snowmelt status and flood water evolution from satellite-based flood maps, which is very helpful in ice jam determination and flood prediction. The high temporal resolution of geostationary satellite imagery, like that of the ABI, can provide the greatest extent of flood signals, and multi-day composite flood products from higher spatial resolution imagery, such as VIIRS, can pinpoint areas of interest to uncover more details. One unique feature of our JPSS and GOES-R flood products is that they include not only normal flood type, but also a special flood type as the supra-snow/ice flood, and moreover, snow and ice masks. Following the demonstrations in this study, it is expected that the JPSS and GOES-R flood products, with ice and snow information, can allow dynamic monitoring and prediction of floods due to ice jams and snowmelt for wide-end users. Full article

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

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