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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: 15 December 2024 | Viewed by 23105

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Special Issue Editors

Dr. Omid Ghorbanzadeh

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Guest Editor

Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria
Interests: object-based image analysis; machine/deep learning; spatial modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Pedram Ghamisi

E-Mail Website1 Website2
Guest Editor

1. Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology, 09599 Freiberg, Germany
2. Institute of Advanced Research in Artificial Intelligence (IARAI), 1030 Vienna, Austria
Interests: machine (deep) learning; image and signal processing; multisensor data fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Earth observation (EO) data show great potential for providing critical information for monitoring and modeling climate-related and geophysical natural hazards. The recent advances in remote sensing relevant technologies have increased the volume and the variety of EO data obtained at various spatial and temporal resolutions. EO-based mapping plays a vital role in disaster preparedness, early warning, emergency management programs, and humanitarian responses by providing timely and accurate information from the Earth's surface.

Moreover, the advent of the era of fast growth in hardware and high-performance computing technologies has resulted in developing and implementing several state-of-the-art deep/machine learning networks for a wide range of tasks with EO data (e.g., monitoring, modeling, and susceptibility mapping natural hazards).

This Special Issue focuses on both supervised and unsupervised deep/machine learning models for converting EO data into valuable information, meaningful patterns for modeling, and the prediction of upcoming cycles of natural hazards. Therefore, we aim to discover and highlight new deep/machine learning models for EO data analysis to gain a better understanding of natural hazards, their environmental effects, risk assessment (and vulnerability), disaster risk reduction, climate adaptation, disaster resilience, and hazard recovery.

This Special Issue invites submissions that may include, but are not limited to, the following natural hazards:

Landslides
Submarine landslides
Volcanoes
Snow avalanche
Glaciers
Earthquakes
Earthquakes and tsunamis
Storms
Land subsidence
Droughts
Extreme temperatures
Floods
Wildfires/bushfires
Post-fire debris flow
Deforestation
Soil, gully, and piping erosion
Multi-hazards.

Dr. Omid Ghorbanzadeh
Prof. Dr. Pedram Ghamisi
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

artificial intelligence
machine learning
deep learning
optical data
SAR images
pixel-based image analysis
object-based image analysis (OBIA)
time series analysis
spatial modeling
susceptibility mapping
risk assessment
landslides
submarine landslides
volcanoes
snow avalanche
glaciers
earthquakes
earthquakes and tsunamis
storms
land subsidence
droughts
extreme temperatures
floods
wildfires/bushfires
post-fire debris flow
deforestation
soil, gully, and piping erosion
multi-hazards

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Published Papers (9 papers)

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28 pages, 20313 KiB

Open AccessArticle

SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye

by Muzaffer Can Iban and Oktay Aksu

Remote Sens. 2024, 16(15), 2842; https://doi.org/10.3390/rs16152842 - 2 Aug 2024

Viewed by 833

Abstract

Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding in mitigating the risks and potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), to map wildfire susceptibility in Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, and vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used to predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation of the trained ML models showed that the Random Forest (RF) model outperformed XGBoost and LightGBM, achieving the highest test accuracy (95.6%). All of the classifiers demonstrated a strong predictive performance, but RF excelled in sensitivity, specificity, precision, and F-1 score, making it the preferred model for generating a wildfire susceptibility map and conducting a SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this study fills a critical gap by employing a SHAP summary and dependence plots to comprehensively assess each factor’s contribution, enhancing the explainability and reliability of the results. The analysis reveals clear associations between factors such as wind speed, temperature, NDVI, slope, and distance to villages with increased fire susceptibility, while rainfall and distance to streams exhibit nuanced effects. The spatial distribution of the wildfire susceptibility classes highlights critical areas, particularly in flat and coastal regions near settlements and agricultural lands, emphasizing the need for enhanced awareness and preventive measures. These insights inform targeted fire management strategies, highlighting the importance of tailored interventions like firebreaks and vegetation management. However, challenges remain, including ensuring the selected factors’ adequacy across diverse regions, addressing potential biases from resampling spatially varied data, and refining the model for broader applicability. Full article

(This article belongs to the Special Issue Artificial Intelligence for Natural Hazards (AI4NH))

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33 pages, 11825 KiB

Open AccessArticle

Deep-Learning-Based Automatic Sinkhole Recognition: Application to the Eastern Dead Sea

by Osama Alrabayah, Danu Caus, Robert Alban Watson, Hanna Z. Schulten, Tobias Weigel, Lars Rüpke and Djamil Al-Halbouni

Remote Sens. 2024, 16(13), 2264; https://doi.org/10.3390/rs16132264 - 21 Jun 2024

Cited by 1 | Viewed by 763

Abstract

Sinkholes can cause significant damage to infrastructures, agriculture, and endanger lives in active karst regions like the Dead Sea’s eastern shore at Ghor Al-Haditha. The common sinkhole mapping methods often require costly high-resolution data and manual, time-consuming expert analysis. This study introduces an efficient deep learning model designed to improve sinkhole mapping using accessible satellite imagery, which could enhance management practices related to sinkholes and other geohazards in evaporite karst regions. The developed AI system is centered around the U-Net architecture. The model was initially trained on a high-resolution drone dataset (0.1 m GSD, phase I), covering 250 sinkhole instances. Subsequently, it was additionally fine-tuned on a larger dataset from a Pleiades Neo satellite image (0.3 m GSD, phase II) with 1038 instances. The training process involved an automated image-processing workflow and strategic layer freezing and unfreezing to adapt the model to different input scales and resolutions. We show the usefulness of initial layer features learned on drone data, for the coarser, more readily-available satellite inputs. The validation revealed high detection accuracy for sinkholes, with phase I achieving a recall of 96.79% and an F1 score of 97.08%, and phase II reaching a recall of 92.06% and an F1 score of 91.23%. These results confirm the model’s accuracy and its capability to maintain high performance across varying resolutions. Our findings highlight the potential of using RGB visual bands for sinkhole detection across different karst environments. This approach provides a scalable, cost-effective solution for continuous mapping, monitoring, and risk mitigation related to sinkhole hazards. The developed system is not limited only to sinkholes however, and can be naturally extended to other geohazards as well. Moreover, since it currently uses U-Net as a backbone, the system can be extended to incorporate super-resolution techniques, leveraging U-Net based latent diffusion models to address the smaller-scale, ambiguous geo-structures that are often found in geoscientific data. Full article

(This article belongs to the Special Issue Artificial Intelligence for Natural Hazards (AI4NH))

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20 pages, 2917 KiB

Open AccessArticle

Employing Machine Learning for Seismic Intensity Estimation Using a Single Station for Earthquake Early Warning

by Mohamed S. Abdalzaher, M. Sami Soliman, Moez Krichen, Meznah A. Alamro and Mostafa M. Fouda

Remote Sens. 2024, 16(12), 2159; https://doi.org/10.3390/rs16122159 - 14 Jun 2024

Cited by 2 | Viewed by 775

Abstract

An earthquake early-warning system (EEWS) is an indispensable tool for mitigating loss of life caused by earthquakes. The ability to rapidly assess the severity of an earthquake is crucial for effectively managing earthquake disasters and implementing successful risk-reduction strategies. In this regard, the utilization of an Internet of Things (IoT) network enables the real-time transmission of on-site intensity measurements. This paper introduces a novel approach based on machine-learning (ML) techniques to accurately and promptly determine earthquake intensity by analyzing the seismic activity 2 s after the onset of the p-wave. The proposed model, referred to as 2S1C1S, leverages data from a single station and a single component to evaluate earthquake intensity. The dataset employed in this study, named “INSTANCE,” comprises data from the Italian National Seismic Network (INSN) via hundreds of stations. The model has been trained on a substantial dataset of 50,000 instances, which corresponds to 150,000 seismic windows of 2 s each, encompassing 3C. By effectively capturing key features from the waveform traces, the proposed model provides a reliable estimation of earthquake intensity, achieving an impressive accuracy rate of 99.05% in forecasting based on any single component from the 3C. The 2S1C1S model can be seamlessly integrated into a centralized IoT system, enabling the swift transmission of alerts to the relevant authorities for prompt response and action. Additionally, a comprehensive comparison is conducted between the results obtained from the 2S1C1S method and those derived from the conventional manual solution method, which is considered the benchmark. The experimental results demonstrate that the proposed 2S1C1S model, employing extreme gradient boosting (XGB), surpasses several ML benchmarks in accurately determining earthquake intensity, thus highlighting the effectiveness of this methodology for earthquake early-warning systems (EEWSs). Full article

(This article belongs to the Special Issue Artificial Intelligence for Natural Hazards (AI4NH))

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36 pages, 57413 KiB

Open AccessArticle

BD-SKUNet: Selective-Kernel UNets for Building Damage Assessment in High-Resolution Satellite Images

by Seyed Ali Ahmadi, Ali Mohammadzadeh, Naoto Yokoya and Arsalan Ghorbanian

Remote Sens. 2024, 16(1), 182; https://doi.org/10.3390/rs16010182 - 31 Dec 2023

Cited by 5 | Viewed by 2057

Abstract

When natural disasters occur, timely and accurate building damage assessment maps are vital for disaster management responders to organize their resources efficiently. Pairs of pre- and post-disaster remote sensing imagery have been recognized as invaluable data sources that provide useful information for building damage identification. Recently, deep learning-based semantic segmentation models have been widely and successfully applied to remote sensing imagery for building damage assessment tasks. In this study, a two-stage, dual-branch, UNet architecture, with shared weights between two branches, is proposed to address the inaccuracies in building footprint localization and per-building damage level classification. A newly introduced selective kernel module improves the performance of the model by enhancing the extracted features and applying adaptive receptive field variations. The xBD dataset is used to train, validate, and test the proposed model based on widely used evaluation metrics such as F1-score and Intersection over Union (IoU). Overall, the experiments and comparisons demonstrate the superior performance of the proposed model. In addition, the results are further confirmed by evaluating the geographical transferability of the proposed model on a completely unseen dataset from a new region (Bam city earthquake in 2003). Full article

(This article belongs to the Special Issue Artificial Intelligence for Natural Hazards (AI4NH))

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18 pages, 7275 KiB

Open AccessArticle

Landslide Susceptibility Mapping in a Mountainous Area Using Machine Learning Algorithms

by Himan Shahabi, Reza Ahmadi, Mohsen Alizadeh, Mazlan Hashim, Nadhir Al-Ansari, Ataollah Shirzadi, Isabelle D. Wolf and Effi Helmy Ariffin

Remote Sens. 2023, 15(12), 3112; https://doi.org/10.3390/rs15123112 - 14 Jun 2023

Cited by 10 | Viewed by 2456

Abstract

Landslides are a dangerous natural hazard that can critically harm road infrastructure in mountainous places, resulting in significant damage and fatalities. The primary purpose of this study was to assess the efficacy of three machine learning algorithms (MLAs) for landslide susceptibility mapping including random forest (RF), decision tree (DT), and support vector machine (SVM). We selected a case study region that is frequently affected by landslides, the important Kamyaran–Sarvabad road in the Kurdistan province of Iran. Altogether, 14 landslide evaluation factors were input into the MLAs including slope, aspect, elevation, river density, distance to river, distance to fault, fault density, distance to road, road density, land use, slope curvature, lithology, stream power index (SPI), and topographic wetness index (TWI). We identified 64 locations of landslides by field survey of which 70% were randomly employed for building and training the three MLAs while the remaining locations were used for validation. The area under the receiver operating characteristics (AUC) reached a value of 0.94 for the decision tree compared to 0.82 for the random forest, and 0.75 for support vector machines model. Thus, the decision tree model was most accurate in identifying the areas at risk for future landslides. The obtained results may inform geoscientists and those in decision-making roles for landslide management. Full article

(This article belongs to the Special Issue Artificial Intelligence for Natural Hazards (AI4NH))

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40 pages, 21368 KiB

Open AccessArticle

Assessment of Wildfire Susceptibility and Wildfire Threats to Ecological Environment and Urban Development Based on GIS and Multi-Source Data: A Case Study of Guilin, China

by Weiting Yue, Chao Ren, Yueji Liang, Jieyu Liang, Xiaoqi Lin, Anchao Yin and Zhenkui Wei

Remote Sens. 2023, 15(10), 2659; https://doi.org/10.3390/rs15102659 - 19 May 2023

Cited by 15 | Viewed by 2756

Abstract

The frequent occurrence and spread of wildfires pose a serious threat to the ecological environment and urban development. Therefore, assessing regional wildfire susceptibility is crucial for the early prevention of wildfires and formulation of disaster management decisions. However, current research on wildfire susceptibility primarily focuses on improving the accuracy of models, while lacking in-depth study of the causes and mechanisms of wildfires, as well as the impact and losses they cause to the ecological environment and urban development. This situation not only increases the uncertainty of model predictions but also greatly reduces the specificity and practical significance of the models. We propose a comprehensive evaluation framework to analyze the spatial distribution of wildfire susceptibility and the effects of influencing factors, while assessing the risks of wildfire damage to the local ecological environment and urban development. In this study, we used wildfire information from the period 2013–2022 and data from 17 susceptibility factors in the city of Guilin as the basis, and utilized eight machine learning algorithms, namely logistic regression (LR), artificial neural network (ANN), K-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM), and eXtreme gradient boosting (XGBoost), to assess wildfire susceptibility. By evaluating multiple indicators, we obtained the optimal model and used the Shapley Additive Explanations (SHAP) method to explain the effects of the factors and the decision-making mechanism of the model. In addition, we collected and calculated corresponding indicators, with the Remote Sensing Ecological Index (RSEI) representing ecological vulnerability and the Night-Time Lights Index (NTLI) representing urban development vulnerability. The coupling results of the two represent the comprehensive vulnerability of the ecology and city. Finally, by integrating wildfire susceptibility and vulnerability information, we assessed the risk of wildfire disasters in Guilin to reveal the overall distribution characteristics of wildfire disaster risk in Guilin. The results show that the AUC values of the eight models range from 0.809 to 0.927, with accuracy values ranging from 0.735 to 0.863 and RMSE values ranging from 0.327 to 0.423. Taking into account all the performance indicators, the XGBoost model provides the best results, with AUC, accuracy, and RMSE values of 0.927, 0.863, and 0.327, respectively. This indicates that the XGBoost model has the best predictive performance. The high-susceptibility areas are located in the central, northeast, south, and southwest regions of the study area. The factors of temperature, soil type, land use, distance to roads, and slope have the most significant impact on wildfire susceptibility. Based on the results of the ecological vulnerability and urban development vulnerability assessments, potential wildfire risk areas can be identified and assessed comprehensively and reasonably. The research results of this article not only can improve the specificity and practical significance of wildfire prediction models but also provide important reference for the prevention and response of wildfires. Full article

(This article belongs to the Special Issue Artificial Intelligence for Natural Hazards (AI4NH))

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44 pages, 25511 KiB

Open AccessArticle

Nemo: An Open-Source Transformer-Supercharged Benchmark for Fine-Grained Wildfire Smoke Detection

by Amirhessam Yazdi, Heyang Qin, Connor B. Jordan, Lei Yang and Feng Yan

Remote Sens. 2022, 14(16), 3979; https://doi.org/10.3390/rs14163979 - 16 Aug 2022

Cited by 9 | Viewed by 4573

Abstract

Deep-learning (DL)-based object detection algorithms can greatly benefit the community at large in fighting fires, advancing climate intelligence, and reducing health complications caused by hazardous smoke particles. Existing DL-based techniques, which are mostly based on convolutional networks, have proven to be effective in wildfire detection. However, there is still room for improvement. First, existing methods tend to have some commercial aspects, with limited publicly available data and models. In addition, studies aiming at the detection of wildfires at the incipient stage are rare. Smoke columns at this stage tend to be small, shallow, and often far from view, with low visibility. This makes finding and labeling enough data to train an efficient deep learning model very challenging. Finally, the inherent locality of convolution operators limits their ability to model long-range correlations between objects in an image. Recently, encoder–decoder transformers have emerged as interesting solutions beyond natural language processing to help capture global dependencies via self- and inter-attention mechanisms. We propose Nemo: a set of evolving, free, and open-source datasets, processed in standard COCO format, and wildfire smoke and fine-grained smoke density detectors, for use by the research community. We adapt Facebook’s DEtection TRansformer (DETR) to wildfire detection, which results in a much simpler technique, where the detection does not rely on convolution filters and anchors. Nemo is the first open-source benchmark for wildfire smoke density detection and Transformer-based wildfire smoke detection tailored to the early incipient stage. Two popular object detection algorithms (Faster R-CNN and RetinaNet) are used as alternatives and baselines for extensive evaluation. Our results confirm the superior performance of the transformer-based method in wildfire smoke detection across different object sizes. Moreover, we tested our model with 95 video sequences of wildfire starts from the public HPWREN database. Our model detected 97.9% of the fires in the incipient stage and 80% within 5 min from the start. On average, our model detected wildfire smoke within 3.6 min from the start, outperforming the baselines. Full article

(This article belongs to the Special Issue Artificial Intelligence for Natural Hazards (AI4NH))

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19 pages, 3793 KiB

Open AccessArticle

Landslide Detection Based on ResU-Net with Transformer and CBAM Embedded: Two Examples with Geologically Different Environments

by Zhiqiang Yang, Chong Xu and Lei Li

Remote Sens. 2022, 14(12), 2885; https://doi.org/10.3390/rs14122885 - 16 Jun 2022

Cited by 38 | Viewed by 4099

Abstract

An efficient method of landslide detection can provide basic scientific data for emergency command and landslide susceptibility mapping. Compared to a traditional landslide detection approach, convolutional neural networks (CNN) have been proven to have powerful capabilities in reducing the time consumed for selecting the appropriate features for landslides. Currently, the success of transformers in natural language processing (NLP) demonstrates the strength of self-attention in global semantic information acquisition. How to effectively integrate transformers into CNN, alleviate the limitation of the receptive field, and improve the model generation are hot topics in remote sensing image processing based on deep learning (DL). Inspired by the vision transformer (ViT), this paper first attempts to integrate a transformer into ResU-Net for landslide detection tasks with small datasets, aiming to enhance the network ability in modelling the global context of feature maps and drive the model to recognize landslides with a small dataset. Besides, a spatial and channel attention module was introduced into the decoder to effectually suppress the noise in the feature maps from the convolution and transformer. By selecting two landslide datasets with different geological characteristics, the feasibility of the proposed model was validated. Finally, the standard ResU-Net was chosen as the benchmark to evaluate the proposed model rationality. The results indicated that the proposed model obtained the highest mIoU and F1-score in both datasets, demonstrating that the ResU-Net with a transformer embedded can be used as a robust landslide detection method and thus realize the generation of accurate regional landslide inventory and emergency rescue. Full article

(This article belongs to the Special Issue Artificial Intelligence for Natural Hazards (AI4NH))

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20 pages, 7554 KiB

Open AccessTechnical Note

Coupling Progressive Deep Learning with the AdaBoost Framework for Landslide Displacement Rate Prediction in the Baihetan Dam Reservoir, China

by Weida Ni, Liuyuan Zhao, Lele Zhang, Ke Xing and Jie Dou

Remote Sens. 2023, 15(9), 2296; https://doi.org/10.3390/rs15092296 - 27 Apr 2023

Cited by 5 | Viewed by 2483

Abstract

Disasters caused by landslides pose a considerable threat to people’s lives and property, resulting in substantial losses each year. Landslide displacement rate prediction (LDRP) provides a useful fundamental tool for mitigating landslide disasters. However, more accurately predicting LDRP remains a challenge in the study of landslides. Lately, ensemble deep learning algorithms have shown promise in delivering a more precise and effective spatial modeling solution. The core aims of this research are to explore and evaluate the prediction capability of three progressive evolutionary deep learning (DL) techniques, i.e., a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU) ensemble AdaBoost algorithm for modeling rainfall-induced and reservoir-induced landslides in the Baihetan reservoir area in China. The outcomes show that the ensemble DL model could predict the Wangjiashan landslide in the Baihetan reservoir area with improved accuracy. The highest accuracy was achieved in the testing set when the window length equaled 30. However, assembling two predictors outperformed the accuracy of assembling three predictors, with the mean absolute error and root mean square error reaching 1.019 and 1.300, respectively. These findings suggest that the combination of strong learners and DL can yield satisfactory prediction results. Full article

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Attention-based Wildland Fire Spread Modeling Using Fire-tracking Satellite Observations
Author: Zou
Highlights: 1. The fire-tracking satellite observational dataset enables the characterization of complex fire behavior across diversified fire-prone regions at high spatiotemporal resolutions 2. The new attention-based fire spread modeling architecture significantly improves the modeling performance of recursive fire prediction 3. The well-trained fire spread models show great potential for practical applications such as short-term and long-term fire risk assessment and management

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