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Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology

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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 (10 January 2023) | Viewed by 31892

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Special Issue Editors

Dr. Paraskevas Tsangaratos

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Guest Editor

Department of Geological Studies, School of Mining and Metallurgical Engineering, National University of Athens, 15780 Athens, Greece
Interests: natural hazards; water resources; engineering geology; GIS; machine learning; soft computing; remote sensing
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Dr. Wei Chen

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College of Geology & Environment, Xi’an University of Science and Technology, Xi’an, China
Interests: hydrogeology; environmental impact assessment; natural hazard susceptibility; spatial modeling; machine learning; geology; civil engineering
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Dr. Ioanna Ilia

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Guest Editor

Department of Geological Studies, School of Mining and Metallurgical Engineering, National University of Athens, 15780 Athens, Greece
Interests: natural hazards; environmental geology; engineering geology; GIS; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Haoyuan Hong

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Guest Editor

School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: natural hazards; water resources; machine learning; soft computing; GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As it well established natural hazards in most cases are responsible for severe financial and human losses across the world. Natural hazards, which involve earthquakes, floods, landslides, volcanic eruptions, wildfires, droughts and soil erosion and degradation, are the result of progressive or extreme changes in climatic, tectonic and geo-morphological processes but also the impact of human activities on the geo-environment. Their complex nature, variation in frequency, speed, duration and area affected are some of the characteristics that are responsible for not fully understanding the mechanism behind their evolution and extent of occurrence. The main efforts of scientists from various geophysical disciplines, is to create conceptual models, develop intelligent computing techniques, machine learning (ML) algorithms, apply remote sensing (RS) technology within a geographic information system (GIS) framework that captures their complex nature and provide accurate prediction concerning their spatial and temporal occurrence. ML algorithms provide a “recipe” to computers for how to learn from existing data, produce knowledge and discover hidden and unknown patterns and trends from large databases, whereas GIS appears as a significant technology equipped with tools for data manipulation and advanced modeling. In recent years, ML, which includes algorithms and methods that are based on the concept of fuzzy and neuro-fuzzy logic, decision tree models, artificial neural networks, deep learning and evolutionary algorithms, along with GIS and RS technology, have been proposed as alternative investigation tools for natural risk phenomena, susceptibility and hazardous mapping.

This Special Issue aims to provide an outlet for peer-reviewed publications that implement state-of-the-art methods and techniques incorporating RS technology, ML methods and GIS so as to map, monitor, evaluate and assess natural hazards.

Potential topics of interest include (but are not limited to) regional or global case studies concerning Natural Risk Phenomena Prediction and Assessment, software development and the implementation of machine learning, optimization, deep learning techniques and meta-heuristic algorithms. Specifically, this Special Issue aims to cover, but is not limited to, the following areas:

Monitoring, mapping and assessing earthquakes, landslides, floods, wildfires and soil erosion;
Evaluating the loss and damages after earthquakes, floods, landslides, wildfires and soil erosion.

Dr. Paraskevas Tsangaratos
Dr. Wei Chen
Dr. Ioanna Ilia
Dr. Haoyuan Hong
Guest Editors

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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

earth observation data – remote sensing technology
geographic information systems
machine learning, soft computing
landslide susceptibility, hazardous and risk mapping
flood susceptibility mapping and disaster management
wildfire susceptibility mapping
soil erosion/degradation
earthquakes/tsunamis

Published Papers (10 papers)

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Research

21 pages, 4461 KiB

Open AccessArticle

A Novel Hybrid LMD–ETS–TCN Approach for Predicting Landslide Displacement Based on GPS Time Series Analysis

by Wanqi Luo, Jie Dou, Yonghu Fu, Xiekang Wang, Yujian He, Hao Ma, Rui Wang and Ke Xing

Remote Sens. 2023, 15(1), 229; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15010229 - 31 Dec 2022

Cited by 9 | Viewed by 2821

Abstract

Landslide disasters cause serious property losses and casualties every year. Landslide displacement prediction is fundamental for mitigating landslide disasters. Several approaches have been used to predict landslide displacement, yet a more accurate and reliable displacement prediction still has a poor understanding of landslide early warning systems for landslide mitigation, due to limited data and mutational displacements. To boost the robustness and accuracy of landslide displacement prediction, this paper assembled a new hybrid model containing the local mean decomposition (LMD), innovations state space models for exponential smoothing (ETS), and the temporal convolutional network (TCN). The proposed model, which is based on over 10 years of long-term time series monitoring GPS data, was tested on the selected case—stepwise Baijiabao landslide in the Three Gorges Reservoir area of China (TGRA) was tested by the proposed model. The results presented that the LMD–ETS–TCN model has the best performance in comparison with other benchmark models. Compared with autoregressive integrated moving average (ARIMA), support vector regression (SVR), and long short-term memory neural network (LSTM), the accuracy was noticeably improved by an average of 40.9%, 46.2%, and 22.1%, respectively. The robustness and effectiveness of the presented approach are attested, and it has discernible improvements for landslide displacement prediction. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology)

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20 pages, 13558 KiB

Open AccessArticle

Mapping Burned Areas with Multitemporal–Multispectral Data and Probabilistic Unsupervised Learning

by Rogério G. Negri, Andréa E. O. Luz, Alejandro C. Frery and Wallace Casaca

Remote Sens. 2022, 14(21), 5413; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215413 - 28 Oct 2022

Cited by 3 | Viewed by 1623

Abstract

The occurrence of forest fires has increased significantly in recent years across the planet. Events of this nature have resulted in the leveraging of new automated methodologies to identify and map burned areas. In this paper, we introduce a unified data-driven framework capable of mapping areas damaged by fire by integrating time series of remotely sensed multispectral images, statistical modeling, and unsupervised classification. We collect and analyze multiple remote-sensing images acquired by the Landsat-8, Sentinel-2, and Terra satellites between August–October 2020, validating our proposal with three case studies in Brazil and Bolivia whose affected regions have suffered from recurrent forest fires. Besides providing less noisy mappings, our methodology outperforms other evaluated methods in terms of average scores of 90%, 0.71, and 0.65 for overall accuracy, F1-score, and kappa coefficient, respectively. The proposed method provides spatial-adherence mappings of the burned areas whose segments match the estimates reported by the MODIS Burn Area product. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology)

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19 pages, 6127 KiB

Open AccessArticle

Landslide Susceptibility Modeling Using Remote Sensing Data and Random SubSpace-Based Functional Tree Classifier

by Tao Peng, Yunzhi Chen and Wei Chen

Remote Sens. 2022, 14(19), 4803; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194803 - 26 Sep 2022

Cited by 9 | Viewed by 1803

Abstract

In this study, a random subspace-based function tree (RSFT) was developed for landslide susceptibility modeling, and by comparing with a bagging-based function tree (BFT), classification regression tree (CART), and Naïve-Bayes tree (NBTree) Classifier, to judge the performance difference between the hybrid model and the single models. In the first step, according to the characteristics of the geological environment and previous literature, 12 landslide conditioning factors were selected, including aspect, slope, profile curvature, plan curvature, elevation, topographic wetness index (TWI), lithology, and normalized difference vegetation index (NDVI), land use, soil, distance to river and distance to the road. Secondly, 328 historical landslides were randomly divided into a training group and a validation group in a ratio of 70/30, and the important analysis of landslide points and conditional factors was carried out using the functional tree (FT) model. In the third step, all data are loaded into FT, RSFT, BFT, CART, and NBTree models for the generation of landslide susceptibility maps (LSM). Comparisons were made by the area under the receiver operating characteristic curve (AUC) to determine efficiency and effectiveness. According to the verification results, the five models selected this time all perform reasonably, but the RSFT model has the highest prediction rate (AUC = 0.838), which is better than the other three single machine learning models. The results of this study also demonstrated that the hybrid model generally improves the predictive power of the benchmark landslide susceptibility models. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology)

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16 pages, 14941 KiB

Open AccessArticle

A Forest Fire Susceptibility Modeling Approach Based on Light Gradient Boosting Machine Algorithm

by Yanyan Sun, Fuquan Zhang, Haifeng Lin and Shuwen Xu

Remote Sens. 2022, 14(17), 4362; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174362 - 02 Sep 2022

Cited by 14 | Viewed by 2380

Abstract

A forest fire susceptibility map generated with the fire susceptibility model is the basis of fire prevention resource allocation. A more reliable susceptibility map helps improve the effectiveness of resource allocation. Thus, further improving the prediction accuracy is always the goal of fire susceptibility modeling. This paper developed a forest fire susceptibility model based on an ensemble learning method, namely light gradient boosting machine (LightGBM), to produce an accurate fire susceptibility map. In the modeling, a subtropical national forest park in the Jiangsu province of China was used as the case study area. We collected and selected eight variables from the fire occurrence driving factors for modeling based on correlation analysis. These variables are from topographic factors, climatic factors, human activity factors, and vegetation factors. For comparative analysis, another two popular modeling methods, namely logistic regression (LR) and random forest (RF) were also applied to construct the fire susceptibility models. The results show that temperature was the main driving factor of fire in the area. In the produced fire susceptibility map, the extremely high and high susceptibility areas that were classified by LR, RF, and LightGBM were 5.82%, 18.61%, and 19%, respectively. The F1-score of the LightGBM model is higher than the LR and RF models. The accuracy of the model of LightGBM, RF, and LR is 88.8%, 84.8%, and 82.6%, respectively. The area under the curve (AUC) of them is 0.935, 0.918, and 0.868, respectively. The introduced ensemble learning method shows better ability on performance evaluation metrics. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology)

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22 pages, 3060 KiB

Open AccessArticle

A Fully Unsupervised Machine Learning Framework for Algal Bloom Forecasting in Inland Waters Using MODIS Time Series and Climatic Products

by Pedro Henrique M. Ananias, Rogério G. Negri, Maurício A. Dias, Erivaldo A. Silva and Wallace Casaca

Remote Sens. 2022, 14(17), 4283; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174283 - 30 Aug 2022

Cited by 8 | Viewed by 2150

Abstract

Progressively monitoring water quality is crucial, as aquatic contaminants can pose risks to human health and other organisms. Machine learning can support the development of new effective tools for water monitoring, including the detection of algal blooms from remotely sensed image series. Therefore, in this paper, we introduce the Algal Bloom Forecast (ABF) framework, a fully automated framework for algal bloom prediction in inland water bodies. Our approach combines machine learning, time series of remotely sensed products (i.e., Moderate-Resolution Imaging Spectroradiometer (MODIS) images), environmental data and spectral indices to build anomaly detection models that can predict the occurrence of algal bloom events in the posterior period. Our assessments focused on the application of the ABF framework equipped with the support vector machine (SVM), random forest (RF), and long short-term memory (LSTM) methods, the outcomes of which were compared through different evaluation metrics such as global accuracy, the kappa coefficient, F1-Score and R²-Score. Case studies covering the Erie (USA), Chilika (India) and Taihu (China) lakes are presented to demonstrate the effectiveness and flexibility of our learning approach. Based on comprehensive experimental tests, we found that the best algal bloom predictions were achieved by bringing together the ABF design with the RF model. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology)

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14 pages, 30495 KiB

Open AccessArticle

Study on the Uncertainty of Machine Learning Model for Earthquake-Induced Landslide Susceptibility Assessment

by Haixia Feng, Zelang Miao and Qingwu Hu

Remote Sens. 2022, 14(13), 2968; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14132968 - 21 Jun 2022

Cited by 13 | Viewed by 1853

Abstract

The landslide susceptibility assessment based on machine learning can accurately predict the probability of landslides happening in the region. However, there are uncertainties in machine learning applications. In this paper, Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) are used to assess the landslide susceptibility in order to discuss the model uncertainty. The model uncertainty is explained in three ways: landslide susceptibility zoning result, risk area (high and extremely high) statistics, and the area under Receiver Operating Characteristic Curve (ROC). The findings indicate that: (1) Landslides are restricted by influence factors and have the distribution law of relatively concentrated and strip-shaped distribution in space. (2) The percentage of real landslide in risk area is 86%, 87%, 82%, and 61% in SVM, RF, LR, and ANN, respectively. The area under ROC of RF, SVM, LR, and ANN, respectively, is 90.92%, 80.45%, 73.75%, and 71.95%. (3) Compared with the prediction accuracy of the training set and test set from the same earthquake, the accuracy of landslide prediction in the different earthquakes is reduced. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology)

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17 pages, 11005 KiB

Open AccessEditor’s ChoiceArticle

Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events

by Saed Asaly, Lee-Ad Gottlieb, Nimrod Inbar and Yuval Reuveni

Remote Sens. 2022, 14(12), 2822; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122822 - 12 Jun 2022

Cited by 21 | Viewed by 10894

Abstract

There are significant controversies surrounding the detection of precursors that may precede earthquakes. Natural hazard signatures associated with strong earthquakes can appear in the lithosphere, troposphere, and ionosphere, where current remote sensing technologies have become valuable tools for detecting and measuring early warning signals of stress build-up deep in the Earth’s crust (presumably associated with earthquake events). Here, we propose implementing a machine learning support vector machine (SVM) technique, applied with GPS ionospheric total electron content (TEC) pre-processed time series estimations, to evaluate potential precursors caused by earthquakes and manifested as disturbances in the TEC data. After filtering and screening our data for solar or geomagnetic influences at different time scales, our results indicate that for large earthquakes (>Mw 6), true negative predictions can be achieved with 85.7% accuracy, and true positive predictions with an accuracy of 80%. We tested our method with different skill scores, such as accuracy (0.83), precision (0.85), recall (0.8), the Heidke skill score (0.66), and true skill statistics (0.66). Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology)

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24 pages, 15372 KiB

Open AccessArticle

Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification

by Ding Xia, Huiming Tang, Sixuan Sun, Chunyan Tang and Bocheng Zhang

Remote Sens. 2022, 14(11), 2707; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112707 - 04 Jun 2022

Cited by 22 | Viewed by 2239

Abstract

A landslide susceptibility model based on a metaheuristic optimization algorithm (germinal center optimization (GCO)) and support vector classification (SVC) is proposed and applied to landslide susceptibility mapping in the Three Gorges Reservoir area in this paper. The proposed GCO-SVC model was constructed via the following steps: First, data on 11 influencing factors and 292 landslide polygons were collected to establish the spatial database. Then, after the influencing factors were subjected to multicollinearity analysis, the data were randomly divided into training and testing sets at a ratio of 7:3. Next, the SVC model with 5-fold cross-validation was optimized by hyperparameter space search using GCO to obtain the optimal hyperparameters, and then the best model was constructed based on the optimal hyperparameters and training set. Finally, the best model acquired by GCO-SVC was applied for landslide susceptibility mapping (LSM), and its performance was compared with that of 6 popular models. The proposed GCO-SVC model achieved better performance (0.9425) than the genetic algorithm support vector classification (GA-SVC; 0.9371), grid search optimized support vector classification (GRID-SVC; 0.9198), random forest (RF; 0.9085), artificial neural network (ANN; 0.9075), K-nearest neighbor (KNN; 0.8976), and decision tree (DT; 0.8914) models in terms of the area under the receiver operating characteristic curve (AUC), and the trends of the other metrics were consistent with that of the AUC. Therefore, the proposed GCO-SVC model has some advantages in LSM and may be worth promoting for wide use. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology)

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19 pages, 9697 KiB

Open AccessArticle

Choosing the Right Horizontal Resolution for Gully Erosion Susceptibility Mapping Using Machine Learning Algorithms: A Case in Highly Complex Terrain

by Annan Yang, Chunmei Wang, Qinke Yang, Guowei Pang, Yongqing Long, Lei Wang, Lijuan Yang and Richard M. Cruse

Remote Sens. 2022, 14(11), 2580; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112580 - 27 May 2022

Cited by 5 | Viewed by 1940

Abstract

Gully erosion susceptibility (GES) maps are essential for managing land resources and erosion control. Choosing the optimal horizontal resolution in GES mapping is a challenge. In this study, the optimal resolution for GES mapping in a complex loess hilly area on the Chinese Loess Plateau was tested using two machine learning algorithms. Unmanned aerial vehicle (UAV) images with a 9 cm resolution and GNSS RTK field-measured data were employed as base datasets, and 11 factors were used in the machine learning models. A series of horizontal resolutions, from 0.5–30 m, was used to determine which was the optimal level and how the resolution influenced the GES mapping. The results showed that the optimal resolution for GES mapping was 2.5–5 m in the loess hilly area, for both the random forest (RF) and extreme gradient-boosting (XGBoost) machine learning algorithms employed in this study. High resolutions overestimated the probability of gully erosion in stable regions, and it became difficult to identify gully and non-gully regions at too-coarse resolutions. The variable importance for GES mapping changed with the resolution and varied among variables. Slope gradient, land use, and contributing area were, in general, the three most critical factors. Land use remained an important factor at all the tested resolution levels. The importance of the slope gradient was underestimated at coarse resolutions (10–30 m), and the importance of the contributing area was underestimated at resolutions that were comparatively fine (0.5–1 m). This study provides an essential reference for selecting the optimal resolution for gully mapping, and thus, offers support for approaches attempting to map gullies using UAV. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology)

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17 pages, 7683 KiB

Open AccessArticle

Mapping Fire Susceptibility in the Brazilian Amazon Forests Using Multitemporal Remote Sensing and Time-Varying Unsupervised Anomaly Detection

by Andréa Eliza O. Luz, Rogério G. Negri, Klécia G. Massi, Marilaine Colnago, Erivaldo A. Silva and Wallace Casaca

Remote Sens. 2022, 14(10), 2429; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102429 - 18 May 2022

Cited by 7 | Viewed by 2633

Abstract

The economic and environmental impacts of wildfires have leveraged the development of new technologies to prevent and reduce the occurrence of these devastating events. Indeed, identifying and mapping fire-susceptible areas arise as critical tasks, not only to pave the way for rapid responses to attenuate the fire spreading, but also to support emergency evacuation plans for the families affected by fire-related tragedies. Aiming at simultaneously mapping and measuring the risk of fires in the forest areas of Brazil’s Amazon, in this paper we combine multitemporal remote sensing, derivative spectral indices, and anomaly detection into a fully unsupervised methodology. We focus our analysis on recent forest fire events that occurred in the Brazilian Amazon by exploring multitemporal images acquired by both Landsat-8 Operational Land Imager and Modis sensors. We experimentally confirm that the current methodology is capable of predicting fire outbreaks immediately at posterior instants, which attests to the operational performance and applicability of our approach to preventing and mitigating the impact of fires in Brazilian forest regions. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology)

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