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Remote Sensing for Geology and Mapping

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (25 May 2024) | Viewed by 45679

Special Issue Editors

Prof. Dr. Chao Chen

E-Mail Website
Guest Editor
School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
Interests: remote sensing; environment
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Chen

E-Mail Website
Guest Editor
School of Geophysics and Spatial Information, China University of Geosciences, Wuhan 430074, China
Interests: intelligent representation and calculation of geological information; geological environment monitoring and evaluation; geospatial information
Special Issues, Collections and Topics in MDPI journals
Dr. Yanni Dong

E-Mail Website
Guest Editor
Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Interests: hyperspectral remote sensing image processing; target detection; dimensionality reduction; classification; metric learning; transfer learning; deep learning; lithologic mapping; geological application of remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing is the acquiring of information from a distance, which plays an important role in geological survey, mapping, and analysis, and can be used to investigate geological characteristics without ground activities. By continually and rapidly obtaining information on resources, environments, and disasters, as well as dynamically monitoring large-scale areas, we are able to accurately assess contamination environment risks, characterize natural and underground resources, predict geological hazards, etc. With the advancing development of AI, big data, and sensor technology, how to accurately perceive the dynamic information of massive remote sensing data is becoming a more challenging but interesting subject for both researchers and engineers.

The rapid progress of geology, mapping, and remote sensing has provided continuous data for atmospheric, ocean, and land studies at spatial and temporal scales. The International Conference on Geology, Mapping and Remote Sensing (ICGMRS) has been held successfully three times. With the support and participation of scholars, experts, institutions, and enterprises in geology, mapping, remote sensing, and marine communication, it has played a positive role in promoting comprehensive improvements, developments, and applications in the scientific community, and has also become a panoramic platform for the current research and application results obtained around the world. This year, the 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023) will be held in Wuhan, Hubei, China, on April 14–16, 2023.

This Special Issue, entitled “Remote Sensing for Geology and Mapping”, aims to select excellent papers both presented at the conference and published outside the conference. We encourage scholars in related fields to share their ideas and insights by submitting their work, and all original research articles and review articles within the scope of this Special Issue are highly welcome. Potential topics include, but are not limited to:

  • Remote sensing and its application:
    • Remote sensing;
    • Planetary remote sensing and mapping;
    • Geographic information science;
    • Remote sensing information engineering;
    • Geographic information system;
    • Global navigation satellite system;
    • Satellite navigation;
    • Earth monitoring and mapping;
    • Classification and data mining techniques;
    • Image processing technology;
    • Hyperspectral image processing;
    • Remote sensing data fusion;
    • Global positioning and navigation system;
    • Remote sensing data quality;
    • Analysis Of remote sensing models;
    • Remote sensing technology application.
  • Surveying and mapping:
    • Surveying and mapping;
    • Marine mapping;
    • General measurement;
    • Photogrammetry;
    • Geodetic survey;
    • Hydrological survey;
    • Mine survey;
    • Engineering survey;
    • Gravity measurement;
    • Aerial photogrammetry;
    • Cartography;
    • City brains, smart oceans, and digital Earth;
    • Sensor technology;
    • Mapping technology;
    • Surveying and mapping instruments;
    • Archeological mapping.
  • Geography and geology:
    • Geological applications of remote sensing;
    • Geological information;
    • Drone systems and geological, mapping, and remote sensing applications;
    • Remote sensing applications in geographical environments, geology, geotechnical engineering, geomechanics, geomorphology, mineral and energy resource exploration, etc.;
    • Remote sensing interpretation of geological structure/tectonic evolution.
  • Related topics:
    • Theories, techniques, and methods related to surveying, mapping, navigation, and oblique photography;
    • Spatial information decision;
    • 3D scene reconstruction;
    • Marine communication;
    • Natural disaster monitoring and emergency management;
    • Sensor system and technology.

Prof. Dr. Chao Chen
Dr. Tao Chen
Dr. Yanni Dong
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

  • remote sensing
  • surveying
  • mapping
  • geographic information system

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
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Related Special Issue

Published Papers (25 papers)

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Research

26 pages, 47344 KiB  
Article
Advancing Skarn Iron Ore Detection through Multispectral Image Fusion and 3D Convolutional Neural Networks (3D-CNNs)
by Jabir Abubakar, Zhaochong Zhang, Zhiguo Cheng, Fojun Yao and Abdoul-Aziz Bio Sidi D. Bouko
Remote Sens. 2024, 16(17), 3250; https://doi.org/10.3390/rs16173250 - 2 Sep 2024
Viewed by 917
Abstract
This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image-processing methodologies. Leveraging the capabilities of ASTER image, band ratio (BR) images, and principal component analysis (PCA) alongside the power of 3D convolutional neural networks (3D-CNNs), the [...] Read more.
This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image-processing methodologies. Leveraging the capabilities of ASTER image, band ratio (BR) images, and principal component analysis (PCA) alongside the power of 3D convolutional neural networks (3D-CNNs), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture accepting input as a 7 × 7 × C image patch, where C represents the combined number of selected ASTER image bands, principal component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image band combinations, including the proposed band combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside ASTER bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected misclassification rates. Surprisingly, utilizing solely ASTER bands as input parameters yielded higher accuracy rates (OA = 93.13%, AA = 91.96%, kappa = 90.91%) compared with scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, kappa = 82.60%) or the integration of BR images to PC bands (OA = 87.78%, AA = 82.39%, kappa = 83.81%). However, the amalgamation of ASTER bands with selected PC bands showed slight improvements in accuracy (OA = 94.65%, AA = 92.93%, kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of ASTER bands, PC bands, and BR images (proposed band combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA = 94.87%, kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the ASTER bands, PC bands, and BR images, with the ASTER bands proving pivotal for optimal skarn classification, the PC bands enhancing intrusions classification accuracy, and the BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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32 pages, 14893 KiB  
Article
Mapping of Clay Montmorillonite Abundance in Agricultural Fields Using Unmixing Methods at Centimeter Scale Hyperspectral Images
by Etienne Ducasse, Karine Adeline, Audrey Hohmann, Véronique Achard, Anne Bourguignon, Gilles Grandjean and Xavier Briottet
Remote Sens. 2024, 16(17), 3211; https://doi.org/10.3390/rs16173211 - 30 Aug 2024
Viewed by 987
Abstract
The composition of clay minerals in soils, and more particularly the presence of montmorillonite (as part of the smectite family), is a key factor in soil swell–shrinking as well as off–road vehicle mobility. Detecting these topsoil clay minerals and quantifying the montmorillonite abundance [...] Read more.
The composition of clay minerals in soils, and more particularly the presence of montmorillonite (as part of the smectite family), is a key factor in soil swell–shrinking as well as off–road vehicle mobility. Detecting these topsoil clay minerals and quantifying the montmorillonite abundance are a challenge since they are usually intimately mixed with other minerals, soil organic carbon and soil moisture content. Imaging spectroscopy coupled with unmixing methods can address these issues, but the quality of the estimation degrades the coarser the spatial resolution is due to pixel heterogeneity. With the advent of UAV-borne and proximal hyperspectral acquisitions, it is now possible to acquire images at a centimeter scale. Thus, the objective of this paper is to evaluate the accuracy and limitations of unmixing methods to retrieve montmorillonite abundance from very-high-resolution hyperspectral images (1.5 cm) acquired from a camera installed on top of a bucket truck over three different agricultural fields, in Loiret department, France. Two automatic endmember detection methods based on the assumption that materials are linearly mixed, namely the Simplex Identification via Split Augmented Lagrangian (SISAL) and the Minimum Volume Constrained Non-negative Matrix Factorization (MVC-NMF), were tested prior to unmixing. Then, two linear unmixing methods, the fully constrained least square method (FCLS) and the multiple endmember spectral mixture analysis (MESMA), and two nonlinear unmixing ones, the generalized bilinear method (GBM) and the multi-linear model (MLM), were performed on the images. In addition, several spectral preprocessings coupled with these unmixing methods were applied in order to improve the performances. Results showed that our selected automatic endmember detection methods were not suitable in this context. However, unmixing methods with endmembers taken from available spectral libraries performed successfully. The nonlinear method, MLM, without prior spectral preprocessing or with the application of the first Savitzky–Golay derivative, gave the best accuracies for montmorillonite abundance estimation using the USGS library (RMSE between 2.2–13.3% and 1.4–19.7%). Furthermore, a significant impact on the abundance estimations at this scale was in majority due to (i) the high variability of the soil composition, (ii) the soil roughness inducing large variations of the illumination conditions and multiple surface scatterings and (iii) multiple volume scatterings coming from the intimate mixture. Finally, these results offer a new opportunity for mapping expansive soils from imaging spectroscopy at very high spatial resolution. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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27 pages, 29974 KiB  
Article
Evidence of Dextral Strike-Slip Movement of the Alakol Lake Fault in the Western Junggar Based on Remote Sensing
by Wenxing Yi, An Li, Liangxin Xu, Zongkai Hu and Xiaolong Li
Remote Sens. 2024, 16(14), 2615; https://doi.org/10.3390/rs16142615 - 17 Jul 2024
Viewed by 716
Abstract
The NW-SE-trending dextral strike-slip faults on the north side of the Tian Shan, e.g., the Karatau fault, Talas–Fergana fault, Dzhalair–Naiman fault, Aktas fault, Dzhungarian fault, and Chingiz fault, play an important role in accommodating crustal shortening. The classic viewpoint is that these strike-slip [...] Read more.
The NW-SE-trending dextral strike-slip faults on the north side of the Tian Shan, e.g., the Karatau fault, Talas–Fergana fault, Dzhalair–Naiman fault, Aktas fault, Dzhungarian fault, and Chingiz fault, play an important role in accommodating crustal shortening. The classic viewpoint is that these strike-slip faults are an adjustment product caused by the difference in the crustal shortening from west to east. Another viewpoint attributes the dextral strike-slip fault to large-scale sinistral shearing. The Alakol Lake fault is a typical dextral strike-slip fault in the north Tian Shan that has not been reported. It is situated along the northern margin of the Dzhungarian gate, stretching for roughly 150 km from Lake Ebinur to Lake Alakol. Our team utilized aerial photographs, satellite stereoimagery, and field observations to map the spatial distribution of the Alakol Lake fault. Our findings provided evidence supporting the assertion that the fault is a dextral strike-slip fault. In reference to its spatial distribution, the Lake Alakol is situated in a pull-apart basin that lies between two major dextral strike-slip fault faults: the Chingiz and Dzhungarian faults. The Alakol Lake fault serves as a connecting structure for these two faults, resulting in the formation of a mega NW-SE dextral strike-slip fault zone. According to our analysis of the dating samples taken from the alluvial fan, as well as our measurement of the displacement of the riser and gully, it appears that the Alakol Lake fault has a dextral strike-slip rate of 0.8–1.2 mm/a (closer to 1.2 mm/a). The strike-slip rate of the Alakol Lake fault is comparatively higher than that of the Chingiz fault in the northern region (~0.7 mm/a) but slower than that of the Dzhungarian fault in the southern region (3.2–5 mm/a). The Chingiz–Alakol–Dzhungarian fault zone shows a gradual decrease in deformation towards the interior of the Kazakhstan platform. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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24 pages, 16296 KiB  
Article
Improving Mineral Classification Using Multimodal Hyperspectral Point Cloud Data and Multi-Stream Neural Network
by Aldino Rizaldy, Ahmed Jamal Afifi, Pedram Ghamisi and Richard Gloaguen
Remote Sens. 2024, 16(13), 2336; https://doi.org/10.3390/rs16132336 - 26 Jun 2024
Viewed by 1900
Abstract
In this paper, we leverage multimodal data to classify minerals using a multi-stream neural network. In a previous study on the Tinto dataset, which consisted of a 3D hyperspectral point cloud from the open-pit mine Corta Atalaya in Spain, we successfully identified mineral [...] Read more.
In this paper, we leverage multimodal data to classify minerals using a multi-stream neural network. In a previous study on the Tinto dataset, which consisted of a 3D hyperspectral point cloud from the open-pit mine Corta Atalaya in Spain, we successfully identified mineral classes by employing various deep learning models. However, this prior work solely relied on hyperspectral data as input for the deep learning models. In this study, we aim to enhance accuracy by incorporating multimodal data, which includes hyperspectral images, RGB images, and a 3D point cloud. To achieve this, we have adopted a graph-based neural network, known for its efficiency in aggregating local information, based on our past observations where it consistently performed well across different hyperspectral sensors. Subsequently, we constructed a multi-stream neural network tailored to handle multimodality. Additionally, we employed a channel attention module on the hyperspectral stream to fully exploit the spectral information within the hyperspectral data. Through the integration of multimodal data and a multi-stream neural network, we achieved a notable improvement in mineral classification accuracy: 19.2%, 4.4%, and 5.6% on the LWIR, SWIR, and VNIR datasets, respectively. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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22 pages, 33755 KiB  
Article
Uncovering a Seismogenic Fault in Southern Iran through Co-Seismic Deformation of the Mw 6.1 Doublet Earthquake of 14 November 2021
by Peyman Namdarsehat, Wojciech Milczarek, Natalia Bugajska-Jędraszek, Seyed-Hani Motavalli-Anbaran and Matin Khaledzadeh
Remote Sens. 2024, 16(13), 2318; https://doi.org/10.3390/rs16132318 - 25 Jun 2024
Viewed by 1424
Abstract
On 14 November 2021, a doublet earthquake, each event of which had an Mw of 6.1, struck near Fin in the Simply Folded Belt (SFB) in southern Iran. The first quake occurred at 12:07:04 UTC, followed by a second one just a minute [...] Read more.
On 14 November 2021, a doublet earthquake, each event of which had an Mw of 6.1, struck near Fin in the Simply Folded Belt (SFB) in southern Iran. The first quake occurred at 12:07:04 UTC, followed by a second one just a minute and a half later. The SFB is known for its blind thrust faults, typically not associated with surface ruptures. These earthquakes are usually linked to the middle and lower layers of the sedimentary cover. Identifying the faults that trigger earthquakes in the region remains a significant challenge and is subject to high uncertainty. This study aims to identify and determine the fault(s) that may have caused the doublet earthquake. To achieve this goal, we utilized the DInSAR method using Sentinel-1 to detect deformation, followed by finite-fault inversion and magnetic interpretation to determine the location, geometry, and slip distribution of the fault(s). Bayesian probabilistic joint inversion was used to model the earthquake sources and derive the geometric parameters of potential fault planes. The study presents two potential fault solutions—one dipping to the north and the other to the south. Both solutions showed no significant difference in strike and fault location, suggesting a single fault. Based on the results of the seismic inversion, it appears that a north-dipping fault with a strike, dip, and rake of 257°, 74°, and 77°, respectively, is more consistent with the geological setting of the area. The fault plane has a width of roughly 3.6 km, a length of 13.4 km, and a depth of 5.6 km. Our results revealed maximum displacements along the radar line of sight reaching values of up to −360 mm in the ascending orbit, indicating an unknown fault with horizontal displacements at the surface ranging from −144 to 170 mm and maximum vertical displacements between −204 and 415 mm. Aeromagnetic data for Iran were utilized with an average flight-line spacing of 7.5 km. The middle of the data observation period was considered to apply the RTP filter, and the DRTP method was used. We calculated the gradient of the residual anomaly in the N-S direction due to the direction of the existing faults and folds. The gradient map identified the fault and potential extension of the observed anomalies related to a fault with an ENE-WSW strike, which could extend to the ~ E-W. We suggest that earthquakes occur in the sedimentary cover of the SFB where subsurface faulting is involved, with Hormuz salt acting as an important barrier to rupture. The multidisciplinary approach used in this study, including InSAR and magnetic data, underscores the importance of accurate fault characterization. These findings provide valuable insights into the seismic hazard of the area. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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15 pages, 6663 KiB  
Article
Aquaculture Ponds Identification Based on Multi-Feature Combination Strategy and Machine Learning from Landsat-5/8 in a Typical Inland Lake of China
by Gang Xie, Xiaohui Bai, Yanbo Peng, Yi Li, Chuanxing Zhang, Yang Liu, Jinhui Liang, Lei Fang, Jinyue Chen, Jilin Men, Xinfeng Wang, Guoqiang Wang, Qiao Wang and Shilong Ren
Remote Sens. 2024, 16(12), 2168; https://doi.org/10.3390/rs16122168 - 15 Jun 2024
Cited by 2 | Viewed by 1014
Abstract
Inland aquaculture ponds, as an important land use type, have brought great economic benefits to local people but at the same time have caused many environmental problems threatening regional ecology security. Therefore, understanding the spatiotemporal pattern of aquaculture ponds and its potential influence [...] Read more.
Inland aquaculture ponds, as an important land use type, have brought great economic benefits to local people but at the same time have caused many environmental problems threatening regional ecology security. Therefore, understanding the spatiotemporal pattern of aquaculture ponds and its potential influence on water quality is vital for the sustainable development of inland lakes. In this study, based on Landsat5/8 images, three types of land features, namely spectral features, index features, and texture features, and five machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), artificial neural network (ANN), k-nearest neighbor (KNN), and Gaussian naive Bayes (GNB), were combined to identify aquaculture ponds and some other primary land use types around a typical inland lake of China. The results demonstrated that the XGBoost algorithm that integrated the three features performed the best among all groups of the five machine learning algorithms and the three features, with an overall accuracy of up to 96.15%. In particular, the texture features provided additional useful information besides the spectral features to allow more accurately separation of aquaculture ponds from other land use types and thus improve the land use mapping ability in complex inland lakes. Next, this study examined the tendency of aquaculture ponds and found a segmented increase mode, namely sharp increase during 1984–2003 and then slow elevation since 2003. Further positive correlation detected between the area of aquaculture ponds and the phytoplankton population dynamics suggest a likely influence of aquaculture activity on the lake water quality. This study provides an important scientific basis for the sustainable management and ecological protection of inland lakes. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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21 pages, 23185 KiB  
Article
InSAR-DEM Block Adjustment Model for Upcoming BIOMASS Mission: Considering Atmospheric Effects
by Kefu Wu, Haiqiang Fu, Jianjun Zhu, Huacan Hu, Yi Li, Zhiwei Liu, Afang Wan and Feng Wang
Remote Sens. 2024, 16(10), 1764; https://doi.org/10.3390/rs16101764 - 16 May 2024
Viewed by 953
Abstract
The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy [...] Read more.
The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy topography, it is crucial to calibrate systematic errors of different strips through interferometric SAR (InSAR) DEM (digital elevation model) block adjustment. Furthermore, the BIOMASS mission will operate in repeat-pass interferometric mode, facing the atmospheric delay errors introduced by changes in atmospheric conditions. However, the existing block adjustment methods aim to calibrate systematic errors in bistatic mode, which can avoid possible errors from atmospheric effects through interferometry. Therefore, there is still a lack of systematic error calibration methods under the interference of atmospheric effects. To address this issue, we propose a block adjustment model considering atmospheric effects. Our model begins by employing the sub-aperture decomposition technique to form forward-looking and backward-looking interferograms, then multi-resolution weighted correlation analysis based on sub-aperture interferograms (SA-MRWCA) is utilized to detect atmospheric delay errors. Subsequently, the block adjustment model considering atmospheric effects can be established based on the SA-MRWCA. Finally, we use robust Helmert variance component estimation (RHVCE) to build the posterior stochastic model to improve parameter estimation accuracy. Due to the lack of spaceborne P-band data, this paper utilized L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR data, which is also long-wavelength, to emulate systematic error calibration of the BIOMASS mission. We chose climatically diverse inland regions of Asia and the coastal regions of South America to assess the model’s effectiveness. The results show that the proposed block adjustment model considering atmospheric effects improved accuracy by 72.2% in the inland test site, with root mean square error (RMSE) decreasing from 10.85 m to 3.02 m. Moreover, the accuracy in the coastal test site improved by 80.2%, with RMSE decreasing from 16.19 m to 3.22 m. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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17 pages, 11829 KiB  
Article
Deep Learning-Based Landslide Recognition Incorporating Deformation Characteristics
by Zhihai Li, Anchi Shi, Xinran Li, Jie Dou, Sijia Li, Tingxuan Chen and Tao Chen
Remote Sens. 2024, 16(6), 992; https://doi.org/10.3390/rs16060992 - 12 Mar 2024
Cited by 4 | Viewed by 1768
Abstract
Landslide disasters pose a significant threat, with their highly destructive nature underscoring the critical importance of timely and accurate recognition for effective early warning systems and emergency response efforts. In recent years, substantial advancements have been made in the realm of landslide recognition [...] Read more.
Landslide disasters pose a significant threat, with their highly destructive nature underscoring the critical importance of timely and accurate recognition for effective early warning systems and emergency response efforts. In recent years, substantial advancements have been made in the realm of landslide recognition (LR) based on remote sensing data, leveraging deep learning techniques. However, the intricate and varied environments in which landslides occur often present challenges in detecting subtle changes, especially when relying solely on optical remote sensing images. InSAR (Interferometric Synthetic Aperture Radar) technology emerges as a valuable tool for LR, providing more detailed ground deformation data and enhancing the theoretical foundation. To harness the slow deformation characteristics of landslides, we developed the FCADenseNet model. This model is designed to learn features and patterns within ground deformation data, with a specific focus on improving LR. A noteworthy aspect of our model is the integration of an attention mechanism, which considers various monitoring factors. This holistic approach enables the comprehensive detection of landslide disasters across entire watersheds, providing valuable information on landslide hazards. Our experimental results demonstrate the effectiveness of the FCADenseNet model, with an F1-score of 0.7611, which is 9.53% higher than that of FC_DenseNet. This study substantiates the feasibility and efficacy of combining InSAR with deep learning methods for LR. The insights gained from this research contribute to the advancement of regional landslide geological hazard monitoring, identification, and prevention strategies. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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25 pages, 8604 KiB  
Article
Improving Seismic Fault Recognition with Self-Supervised Pre-Training: A Study of 3D Transformer-Based with Multi-Scale Decoding and Fusion
by Zeren Zhang, Ran Chen and Jinwen Ma
Remote Sens. 2024, 16(5), 922; https://doi.org/10.3390/rs16050922 - 6 Mar 2024
Cited by 3 | Viewed by 1800
Abstract
Seismic fault interpretation holds great significance in the fields of geophysics and geology. However, conventional methods of seismic fault recognition encounter various issues. For example, models trained on synthetic data often exhibit inadequate generalization when applied to field seismic data, and supervised learning [...] Read more.
Seismic fault interpretation holds great significance in the fields of geophysics and geology. However, conventional methods of seismic fault recognition encounter various issues. For example, models trained on synthetic data often exhibit inadequate generalization when applied to field seismic data, and supervised learning is heavily dependent on the quantity and quality of annotated data, being susceptible to the subjectivity of interpreters. To address these challenges, we propose applying self-supervised pre-training methods to seismic fault recognition, exploring the transfer of 3D Transformer-based backbone networks and different pre-training methods on fault recognition tasks, thereby enabling the model to learn more powerful feature representations from extensive unlabeled datasets. Additionally, we propose an innovative pre-training strategy for the entire segmentation network based on the characteristics of seismic data and introduce a multi-scale decoding and fusion module that significantly improves recognition accuracy. Specifically, during the pre-training stage, we compare various self-supervision methods, like MAE, SimMIM, SimCLR, and a joint self-supervised learning approach. We adopt multi-scale decoding step-by-step fitting expansion targets during the fine-tuning stage. Ultimately merging features to refine fault edges, the model displays superior adaptability when handling narrow, elongated, and unevenly distributed fault annotations. Experiments demonstrate that our proposed method achieves state-of-the-art performance on Thebe, the currently largest publicly annotated dataset in this field. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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26 pages, 7023 KiB  
Article
Tracking the 2D/3D Morphological Changes of Tidal Flats Using Time Series Remote Sensing Data in Northern China
by Zhiquan Gan, Shurong Guo, Chunpeng Chen, Hanjie Zheng, Yuekai Hu, Hua Su and Wenting Wu
Remote Sens. 2024, 16(5), 886; https://doi.org/10.3390/rs16050886 - 2 Mar 2024
Cited by 1 | Viewed by 1225
Abstract
Tidal flats in northern China are essential parts of the East Asian-Australasian Flyway, the densest pathway for migratory waterbirds, and are of great ecological and economic importance. They are threatened by human activities and climate change, raising the urgency surrounding tracking the spatiotemporal [...] Read more.
Tidal flats in northern China are essential parts of the East Asian-Australasian Flyway, the densest pathway for migratory waterbirds, and are of great ecological and economic importance. They are threatened by human activities and climate change, raising the urgency surrounding tracking the spatiotemporal dynamics of tidal flats. However, there is no cost-effective way to map morphological changes on a large spatial scale due to the inaccessibility of the mudflats. In this study, we proposed a pixel-based multi-indices tidal flat mapping algorithm that precisely characterizes 2D/3D morphological changes in tidal flats in northern China using time-series remote sensing data. An overall accuracy of 0.95 in delineating tidal flats to a 2D extent was achieved, with 11,716 verification points. Our results demonstrate that the reduction in sediment discharge from rivers along the coastlines of the Yellow and Bohai Seas has resulted in an overall decline in the area of tidal flats, from 4856.40 km2 to 4778.32 km2. Specifically, 3D analysis showed that significant losses were observed in the mid-to-high-tidal flat zones, while low-elevation tidal flats experienced an increase in area due to the transformations in mid-to-high-tidal flats. Our results indicate that the sediment inputs from rivers and the succession of native vegetation are the primary drivers leading to 2D/3D morphological changes of tidal flats following the cessation of extensive land reclamation in northern China. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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19 pages, 17245 KiB  
Article
Accurate and Rapid Extraction of Aquatic Vegetation in the China Side of the Amur River Basin Based on Landsat Imagery
by Mengna Chen, Rong Zhang, Mingming Jia, Lina Cheng, Chuanpeng Zhao, Huiying Li and Zongming Wang
Remote Sens. 2024, 16(4), 654; https://doi.org/10.3390/rs16040654 - 9 Feb 2024
Cited by 1 | Viewed by 2261
Abstract
Since the early 1950s, the development of human settlements and over-exploitation of agriculture in the China side of the Amur River Basin (CARB) have had a major impact on the water environment of the surrounding lakes, resulting in a decrease of aquatic vegetation. [...] Read more.
Since the early 1950s, the development of human settlements and over-exploitation of agriculture in the China side of the Amur River Basin (CARB) have had a major impact on the water environment of the surrounding lakes, resulting in a decrease of aquatic vegetation. According to the United Nations Sustainable Development Goals, a comprehensive understanding of the extent and variability of aquatic vegetation is crucial for preserving the structure and functionality of stable aquatic ecosystems. Currently, there is a deficiency in the CARB long-sequence dataset of aquatic vegetation distribution in China. This shortage hampers effective support for actual management. Therefore, the development of a fast, robust, and automatic method for accurate extraction of aquatic vegetation becomes crucial for large-scale applications. Our objective is to gather information on the spatial and temporal distribution as well as changes in aquatic vegetation within the CARB. Utilizing a hybrid approach that combines the maximum spectral index composite and Otsu algorithm, along with the integration of convolutional neural networks (CNN) and random forest, we applied this methodology to obtain an annual dataset of aquatic vegetation spanning from 1985 to 2020 using Landsat series imagery. The accuracy of this method was validated through both field investigations and Google Images. Upon assessing the confusion matrix spanning from 1985 to 2020, the producer accuracy for aquatic vegetation classification consistently exceeded 87%. Further quantitative analysis unveiled a discernible decreasing trend in both the water and vegetation areas of lakes larger than 20 km2 within the CARB over the past 36 years. Specifically, the total water area decreased from 3575 km2 to 3412 km2, while the vegetation area decreased from 745 km2 to 687 km2. These changes may be attributed to a combination of climate change and human activities. These quantitative data hold significant practical implications for establishing a scientific restoration path for lake aquatic vegetation. They are particularly valuable for constructing the historical background and reference indices of aquatic vegetation. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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23 pages, 5199 KiB  
Article
A High-Precision Target Geolocation Algorithm for a Spaceborne Bistatic Interferometric Synthetic Aperture Radar System Based on an Improved Range–Doppler Model
by Chao Xing, Zhenfang Li, Fanyi Tang, Feng Tian and Zhiyong Suo
Remote Sens. 2024, 16(3), 532; https://doi.org/10.3390/rs16030532 - 30 Jan 2024
Cited by 1 | Viewed by 997
Abstract
A trend in the development of spaceborne Synthetic Aperture Radar (SAR) technology is the shift from a single-satellite repeated observation mode to a multi-satellite collaborative observation mode. However, current multi-satellite collaborative geolocation algorithms face challenges, such as geometric model mismatch and poor baseline [...] Read more.
A trend in the development of spaceborne Synthetic Aperture Radar (SAR) technology is the shift from a single-satellite repeated observation mode to a multi-satellite collaborative observation mode. However, current multi-satellite collaborative geolocation algorithms face challenges, such as geometric model mismatch and poor baseline estimation accuracy, arising from highly dynamic changes among multi-satellites. This paper introduces a high-precision and efficient geolocation algorithm for a spaceborne bistatic interferometric SAR (BiInSAR) system based on an improved range–Doppler (IRD) model. The proposed algorithm encompasses three key contributions. Firstly, a comprehensive description of the spatial baseline geometric model unique to the bistatic configuration is provided, with a specific focus on deriving the perpendicular baseline expression. Secondly, IRD geolocation functions are established to meet the specific requirements of the bistatic configuration. Then, a novel BiInSAR geolocation algorithm based on the IRD’s functions is proposed, which can significantly improve the target geolocation accuracy by modifying the range–Doppler equation to suit the bistatic configuration. Meanwhile, a low-coupling parallel calculation method is proposed, which can improve the calculation speed by two to three times. Finally, the accuracy and efficiency of the algorithm are demonstrated using experimental data acquired by the TH-2 satellite, which is China’s first spaceborne BiInSAR system. The experimental results prove that the IRD algorithm exhibits geolocation accuracy with an average error of less than 1 m and a standard deviation of less than 2.5 m while maintaining computational efficiency at a calculation speed of 1,429,678 pixels per second. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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29 pages, 21933 KiB  
Article
Enhancing Hyperspectral Anomaly Detection with a Novel Differential Network Approach for Precision and Robust Background Suppression
by Jiajia Zhang, Pei Xiang, Xiang Teng, Dong Zhao, Huan Li, Jiangluqi Song, Huixin Zhou and Wei Tan
Remote Sens. 2024, 16(3), 434; https://doi.org/10.3390/rs16030434 - 23 Jan 2024
Cited by 2 | Viewed by 1564
Abstract
The existing deep-learning-based hyperspectral anomaly detection methods detect anomalies by reconstructing a clean background. However, these methods model the background of the hyperspectral image (HSI) through global features, neglecting local features. In complex background scenarios, these methods struggle to obtain accurate background priors [...] Read more.
The existing deep-learning-based hyperspectral anomaly detection methods detect anomalies by reconstructing a clean background. However, these methods model the background of the hyperspectral image (HSI) through global features, neglecting local features. In complex background scenarios, these methods struggle to obtain accurate background priors for training constraints, thereby limiting the anomaly detection performance. To enhance the capability of the network in extracting local features and improve anomaly detection performance, a hyperspectral anomaly detection method based on differential network is proposed. First, we posit that anomalous pixels are challenging to be reconstructed through the features of surrounding pixels. A differential convolution method is introduced to extract local punctured neighborhood features in the HSI. The differential convolution contains two types of kernels with different receptive fields. These kernels are adopted to obtain the outer window features and inner window features. Second, to improve the feature extraction capability of the network, a local detail attention and a local Transformer attention are proposed. These attention modules enhance the inner window features. Third, the obtained inner window features are subtracted from the outer window features to derive differential features, which encapsulate local punctured neighborhood characteristics. The obtained differential features are employed to reconstruct the background of the HSI. Finally, the anomaly detection results are extracted from the difference between the input HSI and the reconstructed background of the HSI. In the proposed method, for each receptive field kernel, the optimization objective is to reconstruct the input HSI rather than the background HSI. This way circumvents problems where the background constraint biases might affect detection performance. The proposed method offers researchers a new and effective approach for applying deep learning in a local area to the field of hyperspectral anomaly detection. The experiments are conducted with multiple metrics on five real-world datasets. The proposed method outperforms eight state-of-the-art methods in both subjective and objective evaluations. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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24 pages, 18613 KiB  
Article
Optimizing Terrain Classification Methods for the Determination of Bedrock Depth and the Average Shear Wave Velocity of Soil
by Inhyeok Choi and Dongyoup Kwak
Remote Sens. 2024, 16(2), 233; https://doi.org/10.3390/rs16020233 - 6 Jan 2024
Cited by 1 | Viewed by 1484
Abstract
The advancement of remote sensing has enabled the creation of high-resolution Digital Elevation Models (DEMs). Topographic features such as slope gradient (SG), local convexity (LC), and surface texture (ST), derived from DEMs, are related to subsurface geological conditions. In South Korea, bedrock depth [...] Read more.
The advancement of remote sensing has enabled the creation of high-resolution Digital Elevation Models (DEMs). Topographic features such as slope gradient (SG), local convexity (LC), and surface texture (ST), derived from DEMs, are related to subsurface geological conditions. In South Korea, bedrock depth (Dbedrock) and the average shear wave velocity of soil (VSsoil) serve as metrics for determining the site class, which represents the degree of site amplification in seismic design criteria. These metrics, typically measured through geotechnical and geophysical investigations, require predictive methods for preliminary estimation over large areas. Previous studies developed an automatic terrain classification (AC) scheme using SG, LC, and ST, and subsequent research revealed that terrain classification effectively represents subsurface conditions such as Dbedrcok and average shear wave velocity down to 30 m depth. However, AC intrinsically depends on the regional features of DEMs, dividing regions based on nested means of topographic features (SG, LC, and ST). In this study, we developed two terrain classification methods to determine the thresholds of class divisions, aiming to optimize Dbedrock and VSsoil predictions: Sequentially Optimized Classification (SOC) and Non-Sequentially Optimized Classification (NOC). Through the study of the sensitivity of terrain classification methods, smoothing levels, and threshold levels for terrain class generation, we identified the best classification method by comparing it with the geological and mountainous region distribution. Subsequently, we developed DEM-dependent regression models for each class to enhance the accuracy of predicting Dbedrock and VSsoil. The main findings of this study are: (1) the terrain class map suggested in this study represents the distribution of alluvial plane and mountainous regions well, and (2) the DEM calibration for each class provides increased accuracy of Dbedrock and VSsoil predictions in South Korea. We anticipate that the terrain class map, along with Dbedrock and VSsoil maps, will be effectively utilized in geological interpretations and land-use planning for seismic design. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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28 pages, 12139 KiB  
Article
Spatiotemporal Analysis and Prediction of Carbon Emissions from Energy Consumption in China through Nighttime Light Remote Sensing
by Zhaoxu Zhang, Shihong Fu, Jiayi Li, Yuchen Qiu, Zhenwei Shi and Yuanheng Sun
Remote Sens. 2024, 16(1), 23; https://doi.org/10.3390/rs16010023 - 20 Dec 2023
Cited by 4 | Viewed by 1883
Abstract
With burgeoning economic development, a surging influx of greenhouse gases, notably carbon dioxide (CO2), has precipitated global warming, thus accentuating the critical imperatives of monitoring and predicting carbon emissions. Conventional approaches employed in the examination of carbon emissions predominantly rely on [...] Read more.
With burgeoning economic development, a surging influx of greenhouse gases, notably carbon dioxide (CO2), has precipitated global warming, thus accentuating the critical imperatives of monitoring and predicting carbon emissions. Conventional approaches employed in the examination of carbon emissions predominantly rely on energy statistics procured from the National Bureau of Statistics and local statistical bureaus. However, these conventional data sources, often encapsulated in statistical yearbooks, exclusively furnish insights into energy consumption at the national and provincial levels, so the assessment at a more granular scale, such as the municipal and county levels, poses a formidable challenge. This study, using nighttime light data and statistics records spanning from 2000 to 2019, undertook a comparative analysis, scrutinizing various modeling methodologies, encompassing linear, exponential, and logarithmic models, with the aim of assessing carbon emissions across diverse spatial scales. A multifaceted analysis unfolded, delving into the key attributes of China’s carbon emissions, spanning total carbon emissions, per capita carbon emissions, and carbon emission intensity. Spatial considerations were also paramount, encompassing an examination of carbon emissions across provincial, municipal, and county scales, as well as an intricate exploration of spatial patterns, including the displacement of the center of gravity and the application of trend analyses. These multifaceted analyses collectively contributed to the endeavor of predicting China’s future carbon emission trajectory. The findings of the study revealed that at the national scale, total carbon emissions exhibited an annual increment throughout the period spanning 2000 to 2019. Secondly, upon an in-depth evaluation of model fitting, it was evident that the logarithmic model emerged as the most adept in terms of fitting, presenting a mean R2 value of 0.83. Thirdly, the gravity center of carbon emissions in China was situated within Henan Province, and there was a discernible overall shift towards the southwest. In 2025 and 2030, it is anticipated that the average quantum of China’s carbon emissions will reach 7.82 × 102 million and 25.61 × 102 million metric tons, with Shandong Province emerging as the foremost contributor. In summary, this research serves as a robust factual underpinning and an indispensable reference point for advancing the scientific underpinnings of China’s transition to a low-carbon economy and the judicious formulation of policies governing carbon emissions. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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17 pages, 11350 KiB  
Article
High-Resolution Mapping of Mangrove Species Height in Fujian Zhangjiangkou National Mangrove Nature Reserve Combined GF-2, GF-3, and UAV-LiDAR
by Ran Chen, Rong Zhang, Chuanpeng Zhao, Zongming Wang and Mingming Jia
Remote Sens. 2023, 15(24), 5645; https://doi.org/10.3390/rs15245645 - 6 Dec 2023
Cited by 3 | Viewed by 2365
Abstract
Mangroves as an important blue carbon ecosystem have a unique ability to sequester and store large amounts of carbon. The height of mangrove forest is considered to be a critical factor in evaluating carbon sink capacity. However, considering the highly complicated nature of [...] Read more.
Mangroves as an important blue carbon ecosystem have a unique ability to sequester and store large amounts of carbon. The height of mangrove forest is considered to be a critical factor in evaluating carbon sink capacity. However, considering the highly complicated nature of the mangrove system, accurate estimation of mangrove species height is challenging. Gaofen-2 (GF-2) panchromatic and multispectral sensor (PMS), Gaofen-3 (GF-3) SAR images, and unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have the capability to capture detailed information about both the horizontal and vertical structures of mangrove forests, which offer a cost-effective and reliable approach to predict mangrove species height. To accurately estimate mangrove species height, this study obtained a variety of characteristic parameters from GF-2 PMS and GF-3 SAR data and utilized the canopy height model (CHM) derived from UAV-LiDAR data as the observed data of mangrove forest height. Based on these parameters and the random forest (RF) regression algorithm, the mangrove species height result had a root-mean-square error (RMSE) of 0.91 m and an R2 of 0.71. The Kandelia obovate (KO) exhibited the tallest tree height, reaching a maximum of 9.6 m. The polarization features, HH, VV, and texture feature, mean_1 (calculated based on the mean value of blue band in GF-2 image), had a reasonable correlation with canopy height. Among them, the most significant factor in determining the height of mangrove forest was HH. In areas where it is difficult to conduct field surveys, the results provided an opportunity to update access to acquire forest structural attributes. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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26 pages, 19821 KiB  
Article
Multi-Scale Remote Sensing Assessment of Ecological Environment Quality and Its Driving Factors in Watersheds: A Case Study of Huashan Creek Watershed in China
by Yajing Liao, Guirong Wu and Zhenyu Zhang
Remote Sens. 2023, 15(24), 5633; https://doi.org/10.3390/rs15245633 - 5 Dec 2023
Cited by 7 | Viewed by 1621
Abstract
The Huashan Creek watershed is the largest water source and the main production area of honeydew in Pinghe County, whose extensive cultivation of honeydew has exacerbated soil and water pollution. However, the spatial application of remote sensing ecological index (RSEI) in this watershed [...] Read more.
The Huashan Creek watershed is the largest water source and the main production area of honeydew in Pinghe County, whose extensive cultivation of honeydew has exacerbated soil and water pollution. However, the spatial application of remote sensing ecological index (RSEI) in this watershed and key driving factors are not clear considering the applicability of data quality and the diversity of methodological scales. To explore the RSEI and driving factors at distinct scales in Huashan Creek watershed, this study constructed the RSEI based on the environmental balance matrix at seven scales in 2020, revealed its spatial response characteristics at different scales, and analyzed the key drivers. The results show that the 240 m grid as well as rural and watershed scale convergence analyses satisfy the assessment of RSEI, whose Moran indexes are 0.558, 0.595, and 0.146, respectively. The RSEIs at different scales have significant spatial aggregation characteristics, but the overall status is moderate. The central town–riparian area with poor RSEI contrasts with the western mountainous area, which has comparatively better quality. Population has a major influence on RSEI at multiple scales (0.8), with elevation and patch index acting significantly at the village and grid scales, respectively. These findings help to identify the spatial distribution of quality and control mechanisms of RSEI in the Huashan Creek watershed and provide new insights into key scales and drivers of ecological restoration practices in the watershed. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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19 pages, 11698 KiB  
Article
A Comparative Study of Landslide Susceptibility Mapping Using Bagging PU Learning in Class-Prior Probability Shift Datasets
by Lingran Zhao, Hangling Ma, Jiahui Dong, Xueling Wu, Hang Xu and Ruiqing Niu
Remote Sens. 2023, 15(23), 5547; https://doi.org/10.3390/rs15235547 - 28 Nov 2023
Cited by 2 | Viewed by 1272
Abstract
Landslide susceptibility mapping is typically based on binary prediction probabilities. However, non-landslide samples in modeling datasets are often unlabeled data, and the phenomenon of class-priori shift, that is, the proportion of landslide samples frequently deviates from real-world scenarios and is spatially heterogeneous. By [...] Read more.
Landslide susceptibility mapping is typically based on binary prediction probabilities. However, non-landslide samples in modeling datasets are often unlabeled data, and the phenomenon of class-priori shift, that is, the proportion of landslide samples frequently deviates from real-world scenarios and is spatially heterogeneous. By comparing the classification performance and predicted probability distributions across multiple unbalanced datasets with known and unknown sample proportions, this study assesses the landslide susceptibility model’s generalization ability in the context of class-prior shifts. The study investigates the potential of Bagging PU Learning, a semi-supervised learning approach, in improving the generalization performance of landslide susceptibility models and proposes the Bagging PU-GDBT algorithm. Our findings highlight the effectiveness of Bagging PU Learning in enhancing the recall of landslides and the generalization capabilities of models on unbalanced datasets. This method reduces prediction uncertainties, especially in high and very high susceptibility zones. Furthermore, results emphasize the superiority of models trained on balanced datasets with 1:1 sample ratio for landslide susceptibility mapping over those trained on unbalanced datasets. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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21 pages, 21373 KiB  
Article
Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series
by Haohai Jin, Shiyu Fang and Chao Chen
Remote Sens. 2023, 15(20), 4986; https://doi.org/10.3390/rs15204986 - 16 Oct 2023
Cited by 7 | Viewed by 2358
Abstract
Surface water is an important parameter for water resource management and terrestrial water circulation research that is closely related to human production and livelihood. With the rapid development of remote sensing technology and cloud computing platforms, the use of remote sensing technology for [...] Read more.
Surface water is an important parameter for water resource management and terrestrial water circulation research that is closely related to human production and livelihood. With the rapid development of remote sensing technology and cloud computing platforms, the use of remote sensing technology for large-scale and long-term surface water monitoring and investigation has become a research trend. Based on the Google Earth Engine (GEE) cloud platform and Landsat series satellite data, in this study, the Emergency Geomatics Service (EGS) operational surface water mapping algorithm and water index masking were utilized to extract the spatial scope of the water body. The validated models of the Secchi disk depth (SDD), chlorophyll-a (Chl-a) and suspended solids (SS) concentration were applied to water quality parameter inversion and water quality evaluation. Surface water extent extraction and water quality maps were created to analyze the spatial distribution of the water body and the spatial–temporal evolution characteristics of the water quality parameters. A verification experiment was carried out with the surface water in Zhejiang Province as the research object. The results show that the surface water in the study area from 1990 to 2022 could be accurately extracted. The kappa coefficients were all greater than 0.90, and the overall accuracies of the extractions were greater than 95.31%. From 1990 to 2022, the total surface water area in Zhejiang Province initially decreased and then increased. The minimum water area of 2027.49 km2 occurred in 2005, and the maximum water area of 2614.96 km2 occurred in 2020, with an annual average variation of 193.92 km2. Since 2015, the proportion of high SS and Chl-a concentrations, and low SDD water bodies in Zhejiang Province have decreased, and the proportion with better water quality has increased significantly. The spatial distribution map of the surface water and the inversion results of the water quality parameters obtained in this study provide a valuable reference and guidance for regional water resource management, disaster monitoring and early warning, environmental protection, and aquaculture. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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17 pages, 15078 KiB  
Article
A Quick-Look Software for In Situ Magnetic Field Modeling from Onboard Unmanned Aircraft Vehicles (UAVs) Measurements
by Erwan Thebault and Lydie-Sarah Gailler
Remote Sens. 2023, 15(18), 4549; https://doi.org/10.3390/rs15184549 - 15 Sep 2023
Viewed by 1219
Abstract
UAVs represent a tremendous opportunity to perform geophysical and repeated experiments, particularly in volcanic contexts. Their ability to be deployed rapidly and fly at various altitudes and the fact that they are easy to operate despite complex field conditions make them attractive for [...] Read more.
UAVs represent a tremendous opportunity to perform geophysical and repeated experiments, particularly in volcanic contexts. Their ability to be deployed rapidly and fly at various altitudes and the fact that they are easy to operate despite complex field conditions make them attractive for magnetic surveys. Detailed maps of the magnetic field in turn bring key constraints on the rocks’ composition, thermal anomalies, intrusive systems, and crustal contrast evolution. Yet, raw magnetic field measurements require careful processing to minimize directional, positional, and crossover errors. Moreover, stitching together adjacent or overlapping surveys acquired at different times and altitudes is not a trivial task. Therefore, it is challenging in remote areas to directly evaluate the consistency of a survey and to ascertain the success of the field mission. In this paper, we present a fast algorithm allowing for a quick-look modeling of scalar magnetic intensity measurements. The approach relies on rectangular harmonic analysis (RHA). The field measurements are automatically corrected for a global main field. Then, they are projected along this main field and modeled in terms of RHA functions. The software can exploit the quality indices provided with data and a procedure is applied to mitigate the effect of outliers. Maps for the scalar and the vector anomaly fields are readily built on an interpolated regular grid leveled at a constant altitude. In order to assess the modeling and the inversion procedures, analyses are carried out with synthetic measurements derived from a high-resolution global lithospheric magnetic field model estimated on the French aeromagnetic grid and at UAV locations with some added nonrandom noise. These analyses indicate that RHA is efficient for first-order and direct mapping of the crustal magnetic field structures measured by UAVs but that it could be applied on airborne and marine magnetic intensity data covering dense and large geographical extensions. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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20 pages, 4607 KiB  
Article
Study on Road Network Vulnerability Considering the Risk of Landslide Geological Disasters in China’s Tibet
by Yunchang Yao, Liang Cheng, Song Chen, Hui Chen, Mingfei Chen, Ning Li, Zeming Li, Shengkun Dongye, Yifan Gu and Junfan Yi
Remote Sens. 2023, 15(17), 4221; https://doi.org/10.3390/rs15174221 - 28 Aug 2023
Cited by 4 | Viewed by 2428
Abstract
Road traffic is occasionally blocked by landslide geological disasters in remote mountainous areas, causing obstruction to economic society and national defense construction. It is vital to conduct landslide geological disaster risk assessment and vulnerability research on the road network. Based on landslide geological [...] Read more.
Road traffic is occasionally blocked by landslide geological disasters in remote mountainous areas, causing obstruction to economic society and national defense construction. It is vital to conduct landslide geological disaster risk assessment and vulnerability research on the road network. Based on landslide geological disaster risk on the road network, this study analyzed the potential effects of the main environmental elements. Due to the lack of previous research works, this study proposed an effective, rational, and understandable multicriteria heuristic analytical hierarchy process model, fuzzy comprehensive evaluation, and frequency ratio-interactive fuzzy stack analysis for vulnerability assessment of road networks in large and complex networks. Based on the comprehensive use of geographic information technology, the road network vulnerability of Tibet in China was evaluated by introducing slope, topographic relief, normalized difference vegetation index (NDVI), annual mean precipitation, distance from river drainage, glaciers and snow, habitation, seismic center and geological fault zone, and soil erosion intensity. According to the findings of the study, the three-stage framework proposed in this study can provide correct inferences and explanations for the potential phenomena of landslide geological disasters; the geological disaster risk are unevenly distributed in the study area; the distribution of the road network vulnerability in China’s Tibet significantly differs among different cities; the high-vulnerability section presents significant regional characteristics, which overlap with the area with a high risk of landslide geological disasters, and its distribution is mostly located in traffic arteries, link aggregations, and relatively frequent human activity. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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20 pages, 11447 KiB  
Article
A Spatial Data-Driven Approach for Mineral Prospectivity Mapping
by Indishe P. Senanayake, Anthony S. Kiem, Gregory R. Hancock, Václav Metelka, Chris B. Folkes, Phillip L. Blevin and Anthony R. Budd
Remote Sens. 2023, 15(16), 4074; https://doi.org/10.3390/rs15164074 - 18 Aug 2023
Cited by 5 | Viewed by 4384
Abstract
Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposits. However, detailed knowledge-based geological exploration and interpretations generally involve significant costs, time, and human resources. In this study, an ensemble machine learning approach was tested using geoscience datasets to map [...] Read more.
Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposits. However, detailed knowledge-based geological exploration and interpretations generally involve significant costs, time, and human resources. In this study, an ensemble machine learning approach was tested using geoscience datasets to map Cu-Au and Pb-Zn mineral prospectivity in the Cobar Basin, NSW, Australia. The input datasets (magnetic, gravity, faults, electromagnetic, and magnetotelluric data layers) were chosen by considering their association with Cu-Au and Pb-Zn mineralization patterns. Three machine learning algorithms, namely random forest (RF), support vector machine (SVM), and maximum-likelihood (MaxL) classification, were applied to the input data. The results of the three algorithms were ensembled to produce Cu-Au and Pb-Zn prospectivity maps over the Cobar Basin with improved classification accuracy. The findings demonstrate good agreement with known mineral occurrence points and existing mineral prospectivity maps developed using the weights-of-evidence (WofE) method. The ability to capture training points accurately and the simplicity of the proposed approach make it advantageous over complex mineral prospectivity mapping methods, to serve as a preliminary evaluation technique. The methodology can be modified with different datasets and algorithms, facilitating the investigations of mineral prospectivity in other regions and providing guidance for more detailed, high-resolution geological investigations. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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18 pages, 26215 KiB  
Article
An Approach for Monitoring Shallow Surface Outcrop Mining Activities Based on Multisource Satellite Remote Sensing Data
by Shiyao Li, Run Wang, Lei Wang, Shaoyu Liu, Jiang Ye, Hang Xu and Ruiqing Niu
Remote Sens. 2023, 15(16), 4062; https://doi.org/10.3390/rs15164062 - 17 Aug 2023
Cited by 2 | Viewed by 1769
Abstract
Monitoring mine activities can help management track the status of mineral resource exploration and mine rehabilitation. It is crucial to the sustainable development of the mining industry and the protection of the geological environment in mining areas. To monitor the mining activities of [...] Read more.
Monitoring mine activities can help management track the status of mineral resource exploration and mine rehabilitation. It is crucial to the sustainable development of the mining industry and the protection of the geological environment in mining areas. To monitor the mining activities of shallow surface outcrops in the arid and semi-arid regions of northwest China, this paper proposes a remote sensing monitoring approach of mining activities based on deep learning and integrated interferometric synthetic aperture radar technique. This approach uses the DeepLabV3-ResNet model to identify and extract the spatial location of the mine patches and then uses object-oriented analysis and spatial analysis methods to optimize the mine patch boundaries. SBAS-InSAR technique is used to obtain the time-series deformation information of the mine patches and is combined with the multi-temporal optical imagery to analyze the mining activities in the study area. The proposed approach has a recognition accuracy of 95.80% for the identification and extraction of mine patches, with an F1-score of 0.727 at the pixel level, and the average area similarity for all patches is 0.78 at the object-oriented level. The proposed approach possesses the capability to analyze mining activities, indicating promising prospects for engineering applications. It provides a reference for monitoring mining activities using multisource satellite remote sensing. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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19 pages, 4728 KiB  
Article
Mapping Alteration Minerals Using ZY-1 02D Hyperspectral Remote Sensing Data in Coalbed Methane Enrichment Areas
by Li Chen, Xinxin Sui, Rongyuan Liu, Hong Chen, Yu Li, Xian Zhang and Haomin Chen
Remote Sens. 2023, 15(14), 3590; https://doi.org/10.3390/rs15143590 - 18 Jul 2023
Cited by 7 | Viewed by 1822
Abstract
As a clean energy resource, coalbed methane (CBM) is an important industry in China’s dual-carbon strategic planning. Despite the immense potential of CBM resources in China, the current exploration level remains low due to outdated survey technology, impeding large-scale exploration and development. This [...] Read more.
As a clean energy resource, coalbed methane (CBM) is an important industry in China’s dual-carbon strategic planning. Despite the immense potential of CBM resources in China, the current exploration level remains low due to outdated survey technology, impeding large-scale exploration and development. This study investigates the application of hyperspectral data in CBM enrichment areas, specifically focusing on the extraction of alteration minerals in the Hudi coal mine area of the Qinshui Basin using ZY-1 02D and Hyperion hyperspectral data. The hyperspectral alteration mineral identification methods are summarized and analyzed. A method that combines spectral feature matching and diagnostic characteristic parameters is proposed for mineral extraction based on the spectral characteristics of different minerals. The extraction results are verified through field samples using X-ray diffraction analysis. Results show that (1) both ZY-1 02D and Hyperion hyperspectral data yield favorable extraction results for clay and carbonate minerals; (2) the overall accuracy of clay and carbonate minerals extraction is higher using ZY-1 02D data compared with Hyperion data, with accuracies of 81.67% and 79.03%, respectively; (3) the proposed method effectively extracts alteration minerals in CBM enrichment areas using hyperspectral data, thereby providing valuable technical support for the application of hyperspectral data. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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18 pages, 6908 KiB  
Article
Research on Long-Term Tidal-Height-Prediction-Based Decomposition Algorithms and Machine Learning Models
by Wenchao Ban, Liangduo Shen, Fan Lu, Xuanru Liu and Yun Pan
Remote Sens. 2023, 15(12), 3045; https://doi.org/10.3390/rs15123045 - 10 Jun 2023
Cited by 3 | Viewed by 2235
Abstract
Tidal-level prediction is crucial for ensuring the safety and efficiency of offshore marine activities, port and channel management, water transportation resource development, and life-saving operations. Although tidal harmonic analysis is among the most prevalent methods for predicting tidal water level fluctuations, it relies [...] Read more.
Tidal-level prediction is crucial for ensuring the safety and efficiency of offshore marine activities, port and channel management, water transportation resource development, and life-saving operations. Although tidal harmonic analysis is among the most prevalent methods for predicting tidal water level fluctuations, it relies on extensive data, and its long-term prediction accuracy can be limited. To enhance prediction performance, this paper proposes a model that combines the variational mode decomposition (VMD) algorithm with the long short-term memory (LSTM) neural network. The initial step involves decomposing the original data using the VMD algorithm, followed by applying the LSTM to each decomposition component. Finally, all prediction results are superimposed and summed. The model is tested using the 2018 tidal time series data from the Lvsi station in Zhoushan City and the 2020 tidal time series data from the Ganpu station. The results are compared with those from the classical harmonic analysis model, the traditional machine learning model, and the decomposition-based machine learning method. The experimental outcomes demonstrate the superior predictive capabilities of the proposed model. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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