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Remote Sensing in China University of Geosciences: Celebrating 70th Anniversary

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 (15 September 2023) | Viewed by 25058

Special Issue Editors

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: atmospheric remote sensing; solar radiation; aerosol and cloud radiative effects; atmospheric radiative transfer; air pollution; radiative calibration
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: urban landscapes; thermal environment; human comfort; remote sensing; numerical simulation
Special Issues, Collections and Topics in MDPI journals
School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
Interests: hydrological modelling; water budget assessment; precipitation merging technologies and products based on multiple remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Founded in 1952, the China University of Geosciences (CUG) is one of the leading universities in China that has profound history and tradition. The university is composed of 23 academic faculties offering studies in a wide range of academic disciplines, e.g., geology, mineralogy, tectonics, paleontology, sedimentology, geochemistry, geophysics and environmental sciences. There are currently 86 scientific research institutes and laboratories in the university, including 2 state key laboratories. With 13 academicians of the Chinese Academy of Sciences, 1 academician of the Chinese Academy of Engineering, about 452 professors and 814 associated professors employed, the university has trained nearly 300,000 graduates over the past 70 years. They have made great achievements in their positions and served both society and the people. In the “Double First-Class” list released by the Ministry of Education of China recently, ‘Geology’ and ‘Geological Resources and Geological Engineering’ in the CUG have been once again selected into the first-class disciplines list.

With the development of photogrammetry and remote sensing, various useful tools have been greatly developed and provided in the research of geosciences, ecological and environmental sciences. As an academic unit, the CUG offers remote sensing courses at all university levels (undergraduate, graduate and doctoral studies) in different fields of the university. The technologies enable the CUG scholars to carry out their research in the sky, on the earth, in the ocean and over the polar regions.

This Special Issue will be launched to celebrate the 70th anniversary of the CUG. The Special Issue aims to highlight the role and contribution of remote sensing in the past 70 years and illustrate the progress and achievements in this field from the CUG’s scientific research groups. This Special Issue is soliciting contributions from people currently engaged in scientific research at the CUG, as well as distinguished alumni and anyone participating in collaborations with the CUG. Both original research papers and comprehensive literature reviews with unique scientific insights are welcome.

Prof. Dr. Ming Zhang
Prof. Dr. Qian Cao
Prof. Dr. Zengliang Luo
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

  • geoscience
  • geology
  • geophysics
  • remote sensing monitoring of environments
  • new remote sensing sensors and data
  • image processing
  • urban remote sensing
  • atmospheric radiation
  • climate change
  • hydrological cycle

Published Papers (15 papers)

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Research

18 pages, 5460 KiB  
Article
Assessing the Water Budget Closure Accuracy of Satellite/Reanalysis-Based Hydrological Data Products over Mainland China
by Zengliang Luo, Han Yu, Huan Liu and Jie Chen
Remote Sens. 2023, 15(21), 5230; https://doi.org/10.3390/rs15215230 - 03 Nov 2023
Viewed by 645
Abstract
A good water budget involving four variables, including precipitation (P), evapotranspiration (ET), streamflow (R), and terrestrial water storage change (TWSC), is reflected in two aspects: a high accuracy against observations for each budget component and [...] Read more.
A good water budget involving four variables, including precipitation (P), evapotranspiration (ET), streamflow (R), and terrestrial water storage change (TWSC), is reflected in two aspects: a high accuracy against observations for each budget component and the low water budget closure residual error (ΔRes). Due to the lack of consideration of observations of budget components in existing water budget closure assessment methods (BCMs), when the ΔRes of budget components is low, their error against respective observations may still be high. In this study, we assess the water budget closure accuracy of satellite/reanalysis-based hydrological data products over mainland China based on six popular P products and multiple datasets of additional budget components (ET, R, and TWSC). The results indicated that the ΔRes changes between ±15 mm over mainland China. Satellite P products such as GPM IMERG showed better performance by comparing them with rain gauge-based observations. However, reanalysis P products such as GLDAS and FLDAS showed a better water budget closure since the selected datasets of additional budget components (ET and R) are also derived from reanalysis datasets. This indicates that these same data sources for budget components make it easier to close the water budget. The further development of satellite P products should consider the closure of the water budget with other water cycle variables. Full article
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24 pages, 5828 KiB  
Article
Simulating the Land Use and Carbon Storage for Nature-Based Solutions (NbS) under Multi-Scenarios in the Three Gorges Reservoir Area: Integration of Remote Sensing Data and the RF–Markov–CA–InVEST Model
by Guiyuan Li, Guo Cheng, Guohua Liu, Chi Chen and Yu He
Remote Sens. 2023, 15(21), 5100; https://doi.org/10.3390/rs15215100 - 25 Oct 2023
Cited by 1 | Viewed by 1086
Abstract
Rapid industrialisation and urbanisation have moved contemporary civilization ahead but also deepened clashes with nature. Human society’s long-term evolution faces a number of serious problems, including the climate issue and frequent natural disasters. This research analyses the spatiotemporal evolution features of land use [...] Read more.
Rapid industrialisation and urbanisation have moved contemporary civilization ahead but also deepened clashes with nature. Human society’s long-term evolution faces a number of serious problems, including the climate issue and frequent natural disasters. This research analyses the spatiotemporal evolution features of land use remote sensing data from 2005, 2010, 2015, and 2020. Under the Nature-based Solutions (NbS) idea, four scenarios are established: Business as Usual (BAU), Woodland Conservation (WLC), Arable Land Conservation (ALC), and Urban Transformation and Development (UTD). The RF–Markov–CA model is used to simulate the spatiotemporal patterns of land use for the years 2025 and 2030. Furthermore, the InVEST model is utilised to assess and forecast the spatiotemporal evolution features of carbon storage. The findings show that (1) the primary land use categories in the Three Gorges Reservoir Area (TGRA) from 2005 to 2020 are arable land and woodland. Arable land has a declining tendency, whereas woodland has an increasing–decreasing trend. (2) The WLC scenario exhibits the greatest growth in woodland and the lowest drop in grassland from 2020 to 2030, indicating a more stable ecosystem. (3) The TGRA demonstrates substantial geographic variation in carbon storage from 2005 to 2030, with a broad distribution pattern of “higher in the north, lower in the south, higher in the east, lower in the west, with the reservoir head > reservoir centre > reservoir tail”. (4) In comparison to the other three scenarios, the WLC scenario sees a slower development of construction and arable land from 2020 to 2030, whereas the ecological land area rises the highest and carbon storage increases. As a result, the WLC scenario is the TGRA’s recommended development choice. The study’s findings have substantial implications for the TGRA’s ecological preservation and management, as well as for the optimisation of ecosystem carbon cycling and the promotion of regional sustainable development. Full article
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21 pages, 14867 KiB  
Article
The Impact of Urbanization on the Supply–Demand Relationship of Ecosystem Services in the Yangtze River Middle Reaches Urban Agglomeration
by Jie Gong, Xin Dai, Lunche Wang, Zigeng Niu, Qian Cao and Chunbo Huang
Remote Sens. 2023, 15(19), 4749; https://doi.org/10.3390/rs15194749 - 28 Sep 2023
Cited by 1 | Viewed by 888
Abstract
The urbanization process can alter the structure of urban land use and result in variations in urban ecosystem services (ESs). Researching the driving mechanism of multi-level indicators of urbanization on the supply and demand of ESs can enhance our understanding of the ecological [...] Read more.
The urbanization process can alter the structure of urban land use and result in variations in urban ecosystem services (ESs). Researching the driving mechanism of multi-level indicators of urbanization on the supply and demand of ESs can enhance our understanding of the ecological and environmental impacts of urbanization. This study investigates the driving mechanisms underlying the relationship between urbanization and the supply–demand dynamics of ecosystem services (ESs) in the Yangtze River Middle Reaches Urban Agglomeration (YRMRUA). First, we assessed the variation in the key ESs (food production, carbon storage, and culture service) from 2000 to 2019 at both city and provincial levels. Second, ES demand and the supply–demand index (SDI) were calculated utilizing socioeconomic indicators. The Geographical Detector model was applied to analyze the individual and combined effects of urbanization on the supply and SDI of ESs. The results showed that an increase in areas of supply and demand was unbalanced in the YRMRUA from 2000 to 2019, with a predominant concentration observed in the provincial capital cities. Scale urbanization exhibits the most substantial influence on the SDI, with a q-value of 0.6, while land urbanization exerts the most pronounced effect on ES supply, with a q-value of 0.7. Furthermore, it is noteworthy that the combined effect of urbanization on ESs surpasses the individual effect, with q-values exceeding 0.5. The interaction between scale urbanization and other indicators has the greatest impact on the SDI of carbon storage. Population and economic urbanization exhibit a more substantial impact on food production and cultural service compared to other primary indicators. Simultaneously, the joint effects of secondary indicators between per capita living area and per capita road area have a greater impact on ES supply than other secondary indicators. These findings illustrate that urbanization indicators are not independent of each other, but have a combined effect. Furthermore, the urbanization process in the YRMRUA has exhibited a gradual deceleration, leading to a diminishing influence on ESs. This study can contribute to the comprehension of urbanization and ESs when dealing with the conflict between urban development and ecological sustainability. Full article
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18 pages, 8095 KiB  
Article
Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
by Che Hai, Lunche Wang, Xinxin Chen, Xuan Gui, Xiaojun Wu and Jia Sun
Remote Sens. 2023, 15(17), 4216; https://doi.org/10.3390/rs15174216 - 28 Aug 2023
Viewed by 957
Abstract
Maize is one of the main food crops and is widely planted in China; however, it is difficult to get timely and precise information on yields. Because of the benefits of remote sensing technology, satellite-based models (e.g., eddy covariance light use efficiency, EC-LUE) [...] Read more.
Maize is one of the main food crops and is widely planted in China; however, it is difficult to get timely and precise information on yields. Because of the benefits of remote sensing technology, satellite-based models (e.g., eddy covariance light use efficiency, EC-LUE) have a lot of potential for monitoring crop productivity. In this study, the gross primary productivity (GPP) of maize in the NCP was estimated using the EC-LUE model, and the GPP was subsequently transformed into yield using the harvest index. Specifically accounting for the spatiotemporal variation in the harvest index, the statistical yield and estimated GPP from the previous year were used to generate region-specific harvest indexes at the county scale. The model’s performance was assessed using statistical yield data. The results demonstrate that the increase in the total GPP in the summer maize-growing season in the NCP is directly related to the increase in the planting area, and the harvest index has significant heterogeneity in space, and the fluctuation in time is small, and the estimated yield can simulate 64% and 55%, respectively, of the variability in the yield at the county and city scales. The model also accurately captures the inter-annual changes in yield (the average absolute percentage errors are less than 20% for almost all years), but model performance varies by region. It performs better in continuous areas of maize-growing. The results from this study demonstrate that the EC-LUE model can be applied to estimate the yield from a variety of crops (other than winter wheat) and that it can be used in conjunction with a region-specific harvest index to track the production of large-scale crops. Full article
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20 pages, 5619 KiB  
Article
Investigating the Direct and Spillover Effects of Urbanization on Energy-Related Carbon Dioxide Emissions in China Using Nighttime Light Data
by Li Sun, Xianglai Mao, Lan Feng, Ming Zhang, Xuan Gui and Xiaojun Wu
Remote Sens. 2023, 15(16), 4093; https://doi.org/10.3390/rs15164093 - 20 Aug 2023
Viewed by 849
Abstract
Cities are the main emission sources of the CO2 produced by energy use around the globe and have a great impact on the variation of climate. Although the implications of urbanization and socioeconomic elements for carbon emission have been extensively explored, previous [...] Read more.
Cities are the main emission sources of the CO2 produced by energy use around the globe and have a great impact on the variation of climate. Although the implications of urbanization and socioeconomic elements for carbon emission have been extensively explored, previous studies have mostly focused on developed cities, and there is a lack of research into naturally related elements due to the limited data. At present, remote sensing data provide favorable conditions for the study of large-scale and long-time series. Also, the spillover mechanism of urbanization effects on the discharge of carbon has not been fully studied. Therefore, it is necessary to distinguish the types of influence that various urbanization factors have on emissions of CO2. Firstly, this study quantifies the urban CO2 emissions in China by utilizing nighttime lighting images. Then, the spatio-temporal variations and spatial dependence modes of CO2 emissions are explored for 284 cities in China from 2000–2018. Finally, the study further ascertains that multi-dimensional urbanization, socio-economic and climate variables affect the discharge of carbon using spatial regression models. The results indicate that CO2 emissions have a remarkable positive spatial autocorrelation. Urbanization significantly increases CO2 emissions, of which the land urbanization contribution towards CO2 emissions is the most important in terms of spillover effects. Specifically, the data on urbanization’s direct effects reveal that CO2 emissions will increase 0.066%when the urbanization level of a city rises 1%, while the spillover effect indicates that an 0.492% emissions increase is associated with a 1% rise of bordering cities’ average urbanization level. As for the socio-economic factors, population density suppresses CO2 emissions, while technological levels boost CO2 emissions. The natural control factors effect a remarkable impact on CO2 emissions by adjusting energy consumption. This study can provide evidence for regional joint prevention in urban energy conservation, emission reduction, and climate change mitigation. Full article
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24 pages, 32227 KiB  
Article
Improving the Accuracy of Flood Susceptibility Prediction by Combining Machine Learning Models and the Expanded Flood Inventory Data
by Han Yu, Zengliang Luo, Lunche Wang, Xiangyi Ding and Shaoqiang Wang
Remote Sens. 2023, 15(14), 3601; https://doi.org/10.3390/rs15143601 - 19 Jul 2023
Cited by 1 | Viewed by 1308
Abstract
Sufficient historical flood inventory data (FID) are crucial for accurately predicting flood susceptibility using supervised machine learning models. However, historical FID are insufficient in many regions. Remote sensing provides a promising opportunity to expand the FID. However, whether the FID expanded by remote [...] Read more.
Sufficient historical flood inventory data (FID) are crucial for accurately predicting flood susceptibility using supervised machine learning models. However, historical FID are insufficient in many regions. Remote sensing provides a promising opportunity to expand the FID. However, whether the FID expanded by remote sensing can improve the accuracy of flood susceptibility modeling needs further study. In this study, a framework was proposed for improving the accuracy of flood susceptibility prediction (FSP) by combining machine learning models and the expanded FID using Sentinel-1A radar images. Five widely used machine learning models were employed to verify the accuracy of the proposed method by taking Wuhan City as a case study, including the random forest (RF), gradient boosting decision tree (GBDT), k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN) models. Sentinel-1A images from time points before, during, and after flood events were used to expand the FID for training the machine learning models. The results showed that the performance of the machine learning models for predicting flood susceptibility was improved greatly by considering the expanded FID, being improved by approximately 1.14–19.74% based on the area under the receiver operating characteristic curve (AUC). Among the used machine learning models, taking into account all the statistical indicators, the ANN showed the best performance, while the SVM showed the best generalization performance in Wuhan City. According to the results of the ANN model, approximately 19% of the area in Wuhan City, mainly distributed near rivers and lakes, is at a high flood susceptibility level. This study provides an essential reference for flood susceptibility analyses in regions with limited flood sampling data. Full article
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20 pages, 20250 KiB  
Article
Vehicle Target Detection Method for Wide-Area SAR Images Based on Coarse-Grained Judgment and Fine-Grained Detection
by Yucheng Song, Shuo Wang, Qing Li, Hongbin Mu, Ruyi Feng, Tian Tian and Jinwen Tian
Remote Sens. 2023, 15(13), 3242; https://doi.org/10.3390/rs15133242 - 23 Jun 2023
Cited by 2 | Viewed by 1338
Abstract
The detection of vehicle targets in wide-area Synthetic Aperture Radar (SAR) images is crucial for real-time reconnaissance tasks and the widespread application of remote sensing technology in military and civilian fields. However, existing detection methods often face difficulties in handling large-scale images and [...] Read more.
The detection of vehicle targets in wide-area Synthetic Aperture Radar (SAR) images is crucial for real-time reconnaissance tasks and the widespread application of remote sensing technology in military and civilian fields. However, existing detection methods often face difficulties in handling large-scale images and achieving high accuracy. In this study, we address the challenges of detecting vehicle targets in wide-area SAR images and propose a novel method that combines coarse-grained judgment with fine-grained detection to overcome these challenges. Our proposed vehicle detection model is based on YOLOv5, featuring a CAM attention module, CAM-FPN network, and decoupled detection head, and it is strengthened with background-assisted supervision and coarse-grained judgment. These various techniques not only improve the accuracy of the detection algorithms, but also enhance SAR image processing speed. We evaluate the performance of our model using the Wide-area SAR Vehicle Detection (WSVD) dataset. The results demonstrate that the proposed method achieves a high level of accuracy in identifying vehicle targets in wide-area SAR images. Our method effectively addresses the challenges of detecting vehicle targets in wide-area SAR images, and has the potential to significantly enhance real-time reconnaissance tasks and promote the widespread application of remote sensing technology in various fields. Full article
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23 pages, 24008 KiB  
Article
PolSAR Image Building Extraction with G0 Statistical Texture Using Convolutional Neural Network and Superpixel
by Mei Li, Qikai Shen, Yun Xiao, Xiuguo Liu and Qihao Chen
Remote Sens. 2023, 15(5), 1451; https://doi.org/10.3390/rs15051451 - 04 Mar 2023
Cited by 2 | Viewed by 1362
Abstract
Polarimetric synthetic aperture radar (PolSAR) has unique advantages in building extraction due to its sensitivity to building structures and all-time/all-weather imaging capabilities. However, the structure of buildings is complex, and buildings are easily confused with other objects in polarimetric SAR images. The speckle [...] Read more.
Polarimetric synthetic aperture radar (PolSAR) has unique advantages in building extraction due to its sensitivity to building structures and all-time/all-weather imaging capabilities. However, the structure of buildings is complex, and buildings are easily confused with other objects in polarimetric SAR images. The speckle noise of SAR images will affect the accuracy of building extraction. This paper proposes a novel building extraction approach from PolSAR images with statistical texture and polarization features by using a convolutional neural network and superpixel. A feature space that is sensitive to building, including G0 statistical texture and PualiRGB features, is constructed and used as CNN input. Considering that the building boundary of the CNN classification result is inaccurate due to speckle noise, the simple linear iterative cluster (SLIC) superpixel is utilized to constrain the building extraction result. Finally, the effectiveness of the proposed method has been verified by experimenting with PolSAR images from three different sensors, including ESAR, GF-3, and RADARSAT-2. Experiment results show that compared with the other five PolSAR building extraction methods including threshold, SVM, RVCNN, and PFDCNN, our method without superpixel constraint, the F1-score of this method is the highest, reaching 84.22%, 91.24%, and 87.49%, respectively. The false alarm rate of this method is at least 10% lower and the F1 index is at least 6% higher when the building extraction accuracy is comparable. Further, the discussion and method parameter analysis results show that increasing the use of G0 statistical texture parameters can improve building extraction accuracy and reduce false alarms, and the introduction of superpixel constraints can further reduce false alarms. Full article
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21 pages, 6940 KiB  
Article
Classification of Alteration Zones Based on Drill Core Hyperspectral Data Using Semi-Supervised Adversarial Autoencoder: A Case Study in Pulang Porphyry Copper Deposit, China
by Xu Yang, Jianguo Chen and Zhijun Chen
Remote Sens. 2023, 15(4), 1059; https://doi.org/10.3390/rs15041059 - 15 Feb 2023
Cited by 2 | Viewed by 1325
Abstract
With the development of hyperspectral technology, it has become possible to classify alteration zones using hyperspectral data. Since various altered rocks are comprehensive manifestations of mineral assemblages, their spectra are highly similar, which greatly increases the difficulty of distinguishing among them. In this [...] Read more.
With the development of hyperspectral technology, it has become possible to classify alteration zones using hyperspectral data. Since various altered rocks are comprehensive manifestations of mineral assemblages, their spectra are highly similar, which greatly increases the difficulty of distinguishing among them. In this study, a Semi-Supervised Adversarial Autoencoder (SSAAE) was proposed to classify the alteration zones, using the drill core hyperspectral data collected from the Pulang porphyry copper deposit. The multiscale feature extractor was first integrated into the encoder to fully exploit and mine the latent feature representations of hyperspectral data, which were further transformed into discrete class vectors using a classifier. Second, the decoder reconstructed the original inputs with the latent and class vectors. Third, we imposed a categorical distribution on the discrete class vectors represented in the one-hot form using the adversarial regularization process and incorporated the supervised classification process into the network to better guide the network training using the limited labeled data. The comparison experiments on the synthetic dataset and measured hyperspectral dataset were conducted to quantitatively and qualitatively certify the effect of the proposed method. The results show that the SSAAE outperformed six other methods for classifying alteration zones. Moreover, we further displayed the delineated results of the SSAAE on the cross-section, in which the alteration zones were sensible from a geological point of view and had good spatial consistency with the occurrence of Cu, which further demonstrates that the SSAAE had good applicability for the classification of alteration zones. Full article
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26 pages, 8520 KiB  
Article
A Novel Deep Learning Method for Automatic Recognition of Coseismic Landslides
by Qiyuan Yang, Xianmin Wang, Xinlong Zhang, Jianping Zheng, Yu Ke, Lizhe Wang and Haixiang Guo
Remote Sens. 2023, 15(4), 977; https://doi.org/10.3390/rs15040977 - 10 Feb 2023
Viewed by 2201
Abstract
Massive earthquakes generally trigger thousands of coseismic landslides. The automatic recognition of these numerous landslides has provided crucial support for post-earthquake emergency rescue, landslide risk mitigation, and city reconstruction. The automatic recognition of coseismic landslides has always been a difficult problem due to [...] Read more.
Massive earthquakes generally trigger thousands of coseismic landslides. The automatic recognition of these numerous landslides has provided crucial support for post-earthquake emergency rescue, landslide risk mitigation, and city reconstruction. The automatic recognition of coseismic landslides has always been a difficult problem due to the relatively small size of a landslide and various complicated environmental backgrounds. This work proposes a novel semantic segmentation network, EGCN, to improve the landslide identification accuracy. EGCN conducts coseismic landslide recognition by a recognition index set as the input data, CGBlock as the basic module, and U-Net as the baseline. The CGBlock module can extract the relatively stable global context-dependent features (global context features) and the unstable local features by the GNN Branch and CNN Branch (GNN Branch contains the proposed EISGNN) and integrates them via adaptive weights. This method has four advantages. (1) The recognition indices are established according to the causal mechanism of coseismic landslides. The rationality of the indices guarantees the accuracy of landslide recognition. (2) The module of EISGNN is suggested based on the entropy importance coefficient and GATv2. Owing to the feature aggregation among nodes with high entropy importance, global and useful context dependency can be synthesized and the false alarm of landslide recognition can be reduced. (3) CGBlock automatically integrates context features and local spatial features, and has strong adaptability for the recognition of coseismic landslides located in different environments. (4) Owing to CGBlock being the basic module and U-Net being the baseline, EGCN can integrate the context features and local spatial characteristics at both high and low levels. Thus, the accuracy of landslide recognition can be improved. The meizoseismal region of the Ms 7.0 Jiuzhaigou earthquake is selected as an example to conduct coseismic landslide recognition. The values of the precision indices of Overall Accuracy, mIoU, Kappa, F1-score, Precision, and Recall reached 0.99854, 0.99709, 0.97321, 0.97396, 0.97344, and 0.97422, respectively. The proposed method outperforms the current major deep learning methods. Full article
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24 pages, 9210 KiB  
Article
Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet
by Yufeng Fu, Qiuming Cheng, Linhai Jing, Bei Ye and Hanze Fu
Remote Sens. 2023, 15(2), 439; https://doi.org/10.3390/rs15020439 - 11 Jan 2023
Cited by 6 | Viewed by 2663
Abstract
Several large-scale porphyry copper deposits (PCDs) with high economic value have been excavated in the Duolong ore district, Tibet, China. However, the high altitudes and harsh conditions in this area make traditional exploration difficult. Hydrothermal alteration minerals related to PCDs with diagnostic spectral [...] Read more.
Several large-scale porphyry copper deposits (PCDs) with high economic value have been excavated in the Duolong ore district, Tibet, China. However, the high altitudes and harsh conditions in this area make traditional exploration difficult. Hydrothermal alteration minerals related to PCDs with diagnostic spectral absorption features in the visible–near-infrared–shortwave-infrared ranges can be effectively identified by remote sensing imagery. Mainly based on hyperspectral imagery supplemented by multispectral imagery and geochemical element data, the Duolong ore district was selected to conduct data-driven PCD prospectivity modelling. A total of 11 known deposits and 17 evidential layers of multisource geoscience information related to Cu mineralization constitute the input datasets of the predictive models. A deep learning convolutional neural network (CNN) model was applied to mineral prospectivity mapping, and its applicability was tested by comparison to conventional machine learning models, such as support vector machine and random forest. CNN achieves the greatest classification performance with an accuracy of 0.956. This is the first trial in Duolong to conduct mineral prospectivity mapping combined with remote imagery and geochemistry based on deep learning methods. Four metallogenic prospective sites were delineated and verified through field reconnaissance, indicating that the application of deep learning-based methods in PCD prospecting proposed in this paper is feasible by utilizing geoscience big data such as remote sensing datasets and geochemical elements. Full article
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21 pages, 14144 KiB  
Article
Complementarity Characteristics of Actual and Potential Evapotranspiration and Spatiotemporal Changes in Evapotranspiration Drought Index over Ningxia in the Upper Reaches of the Yellow River in China
by Huihui Liu, Dongdong Song, Jinling Kong, Zengguang Mu, Xixuan Wang, Yizhu Jiang and Jingya Zhang
Remote Sens. 2022, 14(23), 5953; https://doi.org/10.3390/rs14235953 - 24 Nov 2022
Cited by 3 | Viewed by 1329
Abstract
Based on energy balance theory, using Theil–Sen median trend analysis and the Mann–Kendall test, this research studied the applicability of the complementary theory of evapotranspiration (ET) over Ningxia in the Upper Reaches of the Yellow River with MOD16 ET product and the measured [...] Read more.
Based on energy balance theory, using Theil–Sen median trend analysis and the Mann–Kendall test, this research studied the applicability of the complementary theory of evapotranspiration (ET) over Ningxia in the Upper Reaches of the Yellow River with MOD16 ET product and the measured data of meteorological stations, based on which ET drought index (EDI) was proposed for the first time. Moreover, the usability of EDI was also verified and its influencing factors were analyzed. The results revealed that there was a complementary relationship between AET and PET in 91.1% of the area in Ningxia, including strictly complementary and asymmetrically complementary relationships in 69.2% and 21.9% of the total area, respectively. EDI ranged from 0 to 1 and was useful to accurately reflect the degree of drought of the study area on the annual and monthly scales. From 2001 to 2020, the average annual EDI was 0.66, and the smallest monthly EDI was in January and the largest was in May. EDI of different time scales had different influencing factors. Precipitation was the most influencing factor of annual EDI, but the influencing factors of monthly EDI was different over time. However, surface non-precipitation water replenishment, such as irrigation, had great impact both on annual EDI and monthly EDI. The application scope of the theory of ET complementarity was extended to the study area for the first time, and EDI was proposed and applied, which will provide a theoretical basis and empirical reference for drought research based on ET data in arid and semi-arid areas. Full article
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19 pages, 6200 KiB  
Article
Inter-Comparison of Diverse Heatwave Definitions in the Analysis of Spatiotemporally Contiguous Heatwave Events over China
by Heyang Song, Dongdong Kong, Li Xiong, Xihui Gu and Jianyu Liu
Remote Sens. 2022, 14(16), 4082; https://doi.org/10.3390/rs14164082 - 20 Aug 2022
Cited by 2 | Viewed by 1764
Abstract
A heatwave (HW) is a spatiotemporally contiguous event that is spatially widespread and lasts many days. HWs impose severe impacts on many aspects of society and terrestrial ecosystems. Here, we systematically investigate the influence of the selected threshold method (the absolute threshold method [...] Read more.
A heatwave (HW) is a spatiotemporally contiguous event that is spatially widespread and lasts many days. HWs impose severe impacts on many aspects of society and terrestrial ecosystems. Here, we systematically investigate the influence of the selected threshold method (the absolute threshold method (ABS), quantile-based method (QTL), and moving quantile-based method (QTLmov)) and selected variables (heat index (HI), air temperature (Tair)) on the change patterns of spatiotemporally contiguous heatwave (STHW) characteristics over China from 1961–2017. Moreover, we discuss the different STHW change patterns among different HW severities (mild, moderate, and severe) and types (daytime and nighttime). The results show that (1) all threshold methods show a consistent phenomenon in most regions of China: STHWs have become longer-lasting (6.42%, 66.25%, and 148.58% HW days (HWD) increases were found from 1991–2017 compared to 1961–1990 corresponding to ABS, QTL, and QTLmov, respectively, as below), more severe (14.83%, 89.17%, and 158.92% increases in HW severity (HWS) increases), and more spatially widespread (14.92%, 134%, and 245.83% increases in the summed HW area (HWAsum)). However, the HW frequency (HWF) of moderate STHWs in some regions decreased as mild and moderate STHWs became extreme; (2) for threshold methods that do not consider seasonal variations (i.e., ABS and QTL), the spatial HI exceedance continuity was relatively weak, thus resulting in underestimated STHW characteristics increase rates; (3) for different variables defining STHWs, the relative changing ratio of the HI-based STHW was approximately 20% higher than that of the Tair-based STHW for all STHW characteristics, under the QTLmov threshold; (4) for different STHW types, the nighttime STHW was approximately 60% faster than the daytime STHW increase considering the QTL threshold and approximately 120% faster for the QTLmov method. This study provides a systematic investigation of different STHW definition methods and will benefit future STHW research. Full article
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22 pages, 33931 KiB  
Article
PS-InSAR Based Monitoring of Land Subsidence by Groundwater Extraction for Lahore Metropolitan City, Pakistan
by Muhammad Afaq Hussain, Zhanlong Chen, Ying Zheng, Muhammad Shoaib, Junwei Ma, Ijaz Ahmad, Aamir Asghar and Junaid Khan
Remote Sens. 2022, 14(16), 3950; https://doi.org/10.3390/rs14163950 - 14 Aug 2022
Cited by 13 | Viewed by 3363
Abstract
Groundwater dynamics caused by extraction and recharge are one of the primary causes of subsidence in the urban environment. Lahore is the second largest metropolitan city in Pakistan. The rapid expansion of this urban area due to high population density has increased the [...] Read more.
Groundwater dynamics caused by extraction and recharge are one of the primary causes of subsidence in the urban environment. Lahore is the second largest metropolitan city in Pakistan. The rapid expansion of this urban area due to high population density has increased the demand for groundwater to meet commercial and household needs. Land subsidence due to inadequate groundwater extraction has long been a concern in Lahore. This paper aims to present the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technique for monitoring the recent land subsidence in Lahore, based on the Sentinel-1 data obtained from January 2020 to December 2021. PS-InSAR techniques are very efficient and cost-effective, determining land subsidence and providing useful results. Areas of high groundwater discharge are prone to high subsidence of −110 mm, while the surroundings show an uplifting of +21 mm during the study period. The PS-InSAR study exposes the subsidence area in detail, particularly when the subsoil is characterized by alluvial and clay deposits and large building structures. This type of observation is quite satisfactory and similar to ground-based surface deformation pertinent to a high subsidence rate. Results will enable more effective urban planning, land infrastructure building, and risk assessment related to subsidence. Full article
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18 pages, 4811 KiB  
Article
Influence of Terrestrial Water Storage on Flood Potential Index in the Yangtze River Basin, China
by Peng Yang, Wenyu Wang, Xiaoyan Zhai, Jun Xia, Yulong Zhong, Xiangang Luo, Shengqing Zhang and Nengcheng Chen
Remote Sens. 2022, 14(13), 3082; https://doi.org/10.3390/rs14133082 - 27 Jun 2022
Cited by 4 | Viewed by 1703
Abstract
In a changing environment, changes in terrestrial water storage (TWS) in basins have a significant impact on potential floods and affect flood risk assessment. Therefore, we aimed to study the impact of TWS on potential floods. In this study, we reconstructed the TWS [...] Read more.
In a changing environment, changes in terrestrial water storage (TWS) in basins have a significant impact on potential floods and affect flood risk assessment. Therefore, we aimed to study the impact of TWS on potential floods. In this study, we reconstructed the TWS based on precipitation and temperature, evaluated the reconstructed TWS data based on Gravity Recovery and Climate Experiment (GRACE)-TWS data, and analyzed and calculated the flood potential index (FPI) in the Yangtze River Basin (YRB). The related influencing factors were analyzed based on the Global Land Data Assimilation System (GLDAS) data and Granger’s causality test. The main conclusions are as follows: (1) although the GRACE-TWS anomaly (GRACE-TWSA) in the YRB showed an increasing trend for the averaged TWSA over all grids in the whole basin (i.e., 0.31 cm/a, p < 0.05), the variable infiltration capacity-soil moisture anomalies (VIC-SMA) showed a decreasing trend (i.e., −0.048 cm/a, p > 0.05) during April 2002–December 2019; (2) a larger relative contribution of detrended precipitation to FPI was found in the Jialingjiang River Basin (JRB), Wujiang River Basin (WRB), Dongting Lake Rivers Basin (DLRB), YinBin-Yichang reaches (YB-YC), and Yichang-Hukou reaches (YC-HK), while the contribution of detrended TWS to FPI in the Poyang Lake Rivers Basin (PLRB) was larger than that in other basins; and (3) the original and detrended soil moisture (SM) and TWS in the YRB showed a significant positive correlation (p < 0.05), while the significant effect of SM on TWS caused a change in FPI in the YRB and its sub-basins. This study is of great significance for the correct understanding of the FPI and the accurate assessment of flood risk. Full article
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