Journal Description
Remote Sensing
Remote Sensing
is an international, peer-reviewed, open access journal about the science and application of remote sensing technology, and is published semimonthly online by MDPI. The Remote Sensing Society of Japan (RSSJ) and the Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Remote Sensing, and their members receive a discount on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, PubAg, GeoRef, Astrophysics Data System, Inspec, dblp, and other databases.
- Journal Rank: JCR - Q1 (Geosciences, Multidisciplinary) / CiteScore - Q1 (General Earth and Planetary Sciences)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 23 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: Geomatics
Impact Factor:
5.0 (2022);
5-Year Impact Factor:
5.6 (2022)
Latest Articles
A Multi-Level Robust Positioning Method for Three-Dimensional Ground Penetrating Radar (3D GPR) Road Underground Imaging in Dense Urban Areas
Remote Sens. 2024, 16(9), 1559; https://doi.org/10.3390/rs16091559 (registering DOI) - 27 Apr 2024
Abstract
Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment
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Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment of 3D GPR data. This paper proposed a multi-level robust positioning method to improve the positioning accuracy of 3D GPR in dense urban areas in order to obtain more accurate underground data. In environments with good GNSS signals, fast and high-precision positioning can be achieved based on GNSS data using differential GNSS technology; in scenes with weak GNSS signals, high-precision positioning of subsurface data can be achieved by using GNSS and IMU as well as using GNSS/INS tightly coupled solution technology; in scenes with no GNSS signals, SLAM technology is used for positioning based on INS data and 3D point cloud data. In summary, this method ensures a positioning accuracy of 3D GPR better than 10 cm and high-quality 3D images of underground urban roads in any environment. This provides data support for urban road underground structure surveys and has broad application prospects in underground disease detection and prevention.
Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
Open AccessArticle
Two-Decadal Glacier Changes in the Astak, a Tributary Catchment of the Upper Indus River in Northern Pakistan
by
Muzaffar Ali, Qiao Liu and Wajid Hassan
Remote Sens. 2024, 16(9), 1558; https://doi.org/10.3390/rs16091558 (registering DOI) - 27 Apr 2024
Abstract
Snow and ice melting in the Upper Indus Basin (UIB) is crucial for regional water availability for mountainous communities. We analyzed glacier changes in the Astak catchment, UIB, from 2000 to 2020 using remote sensing techniques based on optical satellite images from Landsat
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Snow and ice melting in the Upper Indus Basin (UIB) is crucial for regional water availability for mountainous communities. We analyzed glacier changes in the Astak catchment, UIB, from 2000 to 2020 using remote sensing techniques based on optical satellite images from Landsat and ASTER digital elevation models. We used a surface feature-tracking technique to estimate glacier velocity. To assess the impact of climate variations, we examined temperature and precipitation anomalies using ERA5 Land climate data. Over the past two decades, the Astak catchment experienced a slight decrease in glacier area (−1.8 km2) and the overall specific mass balance was −0.02 ± 0.1 m w.e. a−1. The most negative mass balance of −0.09 ± 0.06 m w.e. a−1 occurred at elevations between 2810 to 3220 m a.s.l., with a lesser rate of −0.015 ± 0.12 m w.e. a−1 above 5500 m a.s.l. This variation in glacier mass balance can be attributed to temperature and precipitation gradients, as well as debris cover. Recent glacier mass loss can be linked to seasonal temperature anomalies at higher elevations during winter and autumn. Given the reliance of mountain populations on glacier melt, seasonal temperature trends can disturb water security and the well-being of dependent communities.
Full article
(This article belongs to the Special Issue Glacial Lakes and Related Hazards: Mapping, Monitoring, and Risk Assessment)
Open AccessArticle
Data-Driven Assessment of the Impact of Hurricanes Ian and Nicole: Natural and Armored Dunes in the Aftermath of Hurricanes on Florida’s Central East Coast
by
Kelly M. San Antonio, Daniel Burow, Hyun Jung Cho, Matthew J. McCarthy, Stephen C. Medeiros, Yao Zhou and Hannah V. Herrero
Remote Sens. 2024, 16(9), 1557; https://doi.org/10.3390/rs16091557 (registering DOI) - 27 Apr 2024
Abstract
Hurricanes Ian and Nicole caused devastating destruction across Florida in September and November 2022, leaving widespread damage in their wakes. This study focuses on the assessment of barrier islands’ shorelines, encompassing natural sand dunes and dune vegetation as well as armored dunes with
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Hurricanes Ian and Nicole caused devastating destruction across Florida in September and November 2022, leaving widespread damage in their wakes. This study focuses on the assessment of barrier islands’ shorelines, encompassing natural sand dunes and dune vegetation as well as armored dunes with man-made infrastructure such as seawalls. High-resolution satellite imagery from Planet was used to assess the impacts of these hurricanes on the beach shorelines of Volusia, Flagler, and St. Johns Counties on the Florida Central East Coast. Shorefront vegetation was classified into two classes. Normalized Difference Vegetation Index (NDVI) values were calculated before the hurricanes, one month after Hurricane Ian, one month after Hurricane Nicole, and one-year post landfall. LiDAR (Light Detection and Ranging) was incorporated to calculate vertical changes in the shorelines before and after the hurricanes. The results suggest that natural sand dunes were more resilient as they experienced less impact to vegetation and elevation and more substantial recovery than armored dunes. Moreover, the close timeframe of the storm events suggests a compound effect on the weakened dune systems. This study highlights the importance of understanding natural dune resilience to facilitate future adaptive management efforts because armored dunes may have long-term detrimental effects on hurricane-prone barrier islands.
Full article
(This article belongs to the Special Issue Remote Sensing and Ecosystem Modeling for Nature-Based Solutions)
Open AccessArticle
Urban Building Height Extraction from Gaofen-7 Stereo Satellite Images Enhanced by Contour Matching
by
Yunfan Cui, Shuangming Zhao, Wanshou Jiang and Guorong Yu
Remote Sens. 2024, 16(9), 1556; https://doi.org/10.3390/rs16091556 (registering DOI) - 27 Apr 2024
Abstract
The traditional method for extracting the heights of urban buildings involves utilizing dense matching algorithms on stereo images to generate a digital surface model (DSM). However, for urban buildings, the disparity discontinuity issue that troubles the dense matching algorithm makes the elevations of
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The traditional method for extracting the heights of urban buildings involves utilizing dense matching algorithms on stereo images to generate a digital surface model (DSM). However, for urban buildings, the disparity discontinuity issue that troubles the dense matching algorithm makes the elevations of high-rise buildings and the surrounding areas inaccurate. The occlusion caused by trees in greenbelts makes it difficult to accurately extract the ground elevation around the building. To tackle these problems, a method for building height extraction from Gaofen-7 (GF-7) stereo images enhanced by contour matching is presented. Firstly, a contour matching algorithm was proposed to extract accurate building roof elevation from GF-7 images. Secondly, a ground filtering algorithm was employed on the DSM to generate a digital elevation model (DEM), and ground elevation can be extracted from this DEM. The difference between the rooftop elevation and the ground elevation represents the building height. The presented method was verified in Yingde, Guangzhou, Guangdong Province, and Xi’an, Shaanxi Province. The experimental results demonstrate that our proposed method outperforms existing methods in building height extraction concerning accuracy.
Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
Open AccessArticle
A Hidden Eruption: The 21 May 2023 Paroxysm of the Etna Volcano (Italy)
by
Emanuela De Beni, Cristina Proietti, Simona Scollo, Massimo Cantarero, Luigi Mereu, Francesco Romeo, Laura Pioli, Mariangela Sciotto and Salvatore Alparone
Remote Sens. 2024, 16(9), 1555; https://doi.org/10.3390/rs16091555 (registering DOI) - 27 Apr 2024
Abstract
On 21 May 2023, a hidden eruption occurred at the Southeast Crater (SEC) of Etna (Italy); indeed, bad weather prevented its direct and remote observation. Tephra fell toward the southwest, and two lava flows propagated along the SEC’s southern and eastern flanks. The
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On 21 May 2023, a hidden eruption occurred at the Southeast Crater (SEC) of Etna (Italy); indeed, bad weather prevented its direct and remote observation. Tephra fell toward the southwest, and two lava flows propagated along the SEC’s southern and eastern flanks. The monitoring system of the Istituto Nazionale di Geofisica e Vulcanologia testified to its occurrence. We analyzed the seismic and infrasound signals to constrain the temporal evolution of the fountain, which lasted about 5 h. We finally reached Etna’s summit two weeks later and found an unexpected pyroclastic density current (PDC) deposit covering the southern lava flow at its middle portion. We performed unoccupied aerial system and field surveys to reconstruct in 3D the SEC, lava flows, and PDC deposits and to collect some samples. The data allowed for detailed mapping, quantification, and characterization of the products. The resulting lava flows and PDC deposit volumes were (1.54 ± 0.47) × 106 m3 and (1.30 ± 0.26) × 105 m3, respectively. We also analyzed ground-radar and satellite data to evaluate that the plume height ranges between 10 and 15 km. This work is a comprehensive analysis of the fieldwork, UAS, volcanic tremor, infrasound, radar, and satellite data. Our results increase awareness of the volcanic activity and potential dangers for visitors to Etna’s summit area.
Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology to Reduce the Risk of Geological Disaster on Human Life)
Open AccessArticle
Potential of Lightweight Drones and Object-Oriented Image Segmentation in Forest Plantation Assessment
by
Jitendra Dixit, Ashok Kumar Bhardwaj, Saurabh Kumar Gupta, Suraj Kumar Singh, Gowhar Meraj, Pankaj Kumar, Shruti Kanga, Saurabh Singh and Bhartendu Sajan
Remote Sens. 2024, 16(9), 1554; https://doi.org/10.3390/rs16091554 (registering DOI) - 27 Apr 2024
Abstract
Forests play a vital role in maintaining ecological balance and provide numerous benefits. The monitoring and managing of large-scale forest plantations can be challenging and expensive. In recent years, advancements in remote sensing technologies, such as lightweight drones and object-oriented image analysis, have
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Forests play a vital role in maintaining ecological balance and provide numerous benefits. The monitoring and managing of large-scale forest plantations can be challenging and expensive. In recent years, advancements in remote sensing technologies, such as lightweight drones and object-oriented image analysis, have opened up new possibilities for efficient and accurate forest plantation monitoring. This study aimed to explore the utility of lightweight drones as a cost-effective and accurate method for mapping plantation characteristics in two 50 ha forest plots in the Nayla Range, Jaipur. By combining aerial photographs collected by the drone with photogrammetry and limited ground survey data, as well as topography and edaphic variables, this study examined the relative contribution of drone-derived plantation canopy information. The results demonstrate the immense potential of lightweight drones and object-oriented image analysis in providing valuable insights for optimizing silvicultural operations and planting trees in complex forest environments.
Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
Open AccessArticle
Sensitivity of Sentinel-1 Backscatter to Management-Related Disturbances in Temperate Forests
by
Sietse van der Woude, Johannes Reiche, Frank Sterck, Gert-Jan Nabuurs, Marleen Vos and Martin Herold
Remote Sens. 2024, 16(9), 1553; https://doi.org/10.3390/rs16091553 (registering DOI) - 27 Apr 2024
Abstract
The rapid and accurate detection of forest disturbances in temperate forests has become increasingly crucial as policy demands and climate pressure on these forests rise. The cloud-penetrating Sentinel-1 radar constellation provides frequent and high-resolution observations with global coverage, but few studies have assessed
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The rapid and accurate detection of forest disturbances in temperate forests has become increasingly crucial as policy demands and climate pressure on these forests rise. The cloud-penetrating Sentinel-1 radar constellation provides frequent and high-resolution observations with global coverage, but few studies have assessed its potential for mapping disturbances in temperate forests. This study investigated the sensitivity of temporally dense C-band backscatter data from Sentinel-1 to varying management-related disturbance intensities in temperate forests, and the influence of confounding factors such as radar backscatter signal seasonality, shadow, and layover on the radar backscatter signal at a pixel level. A unique network of 14 experimental sites in the Netherlands was used in which trees were removed to simulate different levels of management-related forest disturbances across a range of representative temperate forest species. Results from six years (2016–2022) of Sentinel-1 observations indicated that backscatter seasonality is dependent on species phenology and degree of canopy cover. The backscatter change magnitude was sensitive to medium- and high-severity disturbances, with radar layover having a stronger impact on the backscatter disturbance signal than radar shadow. Combining ascending and descending orbits and complementing polarizations compared to a single orbit or polarization was found to result in a 34% mean increase in disturbance detection sensitivity across all disturbance severities. This study underlines the importance of linking high-quality experimental ground-based data to dense satellite time series to improve future forest disturbance mapping. It suggests a key role for C-band backscatter time series in the rapid and accurate large-area monitoring of temperate forests and, in particular, the disturbances imposed by logging practices or tree mortality driven by climate change factors.
Full article
(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)
Open AccessArticle
Research on Azimuth DBF Method of HRWS SPC MAB SAR Imaging Mode with Non-Ideal Antenna Mode
by
Weihua Zuo, Caipin Li, Sheng Zhang, Dongtao Li, Wencan Peng, Jinwei Li, Dong You and Chongdi Duan
Remote Sens. 2024, 16(9), 1552; https://doi.org/10.3390/rs16091552 (registering DOI) - 26 Apr 2024
Abstract
Single-phase center multiple azimuth beam (SPC MAB) mode is an effective method for high-resolution wide-swath (HRWS) SAR imaging. The traditional azimuth spectrum reconstruction method for SPC MAB mode is based on the combination scheme from which fake targets along the azimuth direction arise
[...] Read more.
Single-phase center multiple azimuth beam (SPC MAB) mode is an effective method for high-resolution wide-swath (HRWS) SAR imaging. The traditional azimuth spectrum reconstruction method for SPC MAB mode is based on the combination scheme from which fake targets along the azimuth direction arise because the inter-beam interference is not considered. When the real antenna mode is inconsistent with the ideal one, the disadvantages of the combination scheme become more serious. In this paper, based on the basic theory of the low-pass, band-limited, multiple-channel under-sampling and reconstruction, a novel digital beam-forming method is proposed for the SPC MAB imaging mode with ideal antenna mode first. The method analyzes the system functions of the sub-beams, based on which digital beam-forming filters are designed for all the sub-beams. The designed filters can reconstruct the correct wide-bandwidth azimuth spectrum and suppress the inter-beam interference simultaneously. Furthermore, the proposed method is extended to SPC MAB mode with the non-ideal antenna mode. The simulation experiments prove the validities of the proposed method both for azimuth spectral reconstruction and the inter-beams interfering suppressing, no matter that the SPC MAB’s antenna mode is ideal or non-ideal.
Full article
Open AccessArticle
Polarized Bidirectional Reflectance Distribution Function Matrix Derived from Two-Scale Roughness Theory and Its Applications in Active Remote Sensing
by
Lingli He, Fuzhong Weng, Jinghan Wen and Tong Jia
Remote Sens. 2024, 16(9), 1551; https://doi.org/10.3390/rs16091551 (registering DOI) - 26 Apr 2024
Abstract
A polarized bidirectional reflectance distribution function (pBRDF) matrix was developed based on the two-scale roughness theory to provide consistent simulations of fully polarized microwave emission and scattering, required for the ocean–atmosphere-coupled radiative transfer model. In this study, the potential of the two-scale pBRDF
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A polarized bidirectional reflectance distribution function (pBRDF) matrix was developed based on the two-scale roughness theory to provide consistent simulations of fully polarized microwave emission and scattering, required for the ocean–atmosphere-coupled radiative transfer model. In this study, the potential of the two-scale pBRDF matrix was explored for simulating ocean full-polarization backscattering and bistatic-scattering normalized radar cross sections (NRCSs). Comprehensive numerical simulations of the two-scale pBRDF matrix across the L-, C-, X-, and Ku-bands were carried out, and the simulations were compared with experimental data, classical electromagnetic, and GMFs. The results show that the two-scale pBRDF matrix demonstrates reasonable dependencies on ocean surface wind speeds, relative wind direction (RWD), geometries, and frequencies and has a reliable accuracy in general. In addition, the two-scale pBRDF matrix simulations were compared with the observations from the advanced scatterometer (ASCAT) onboard MetOP-C satellites, with a correlation coefficient of 0.9634 and a root mean square error (RMSE) of 2.5083 dB. In the bistatic case, the two-scale pBRDF matrix simulations were compared with Cyclone Global Navigation Satellite System (CYGNSS) observations, demonstrating a good correlation coefficient of 0.8480 and an RMSE of 1.2859 dB. In both cases, the two-scale pBRDF matrix produced fairly good simulations at medium-to-high wind speeds. The relatively large differences at low wind speeds (<5 m/s) were due probably to the swell effects. This study proves that the two-scale pBRDF matrix is suitable for the applications of multiple types of active instruments and can consistently simulate the ocean surface passive and active signals.
Full article
Open AccessArticle
Maritime Moving Target Reconstruction via MBLCFD in Staggered SAR System
by
Xin Qi, Yun Zhang, Yicheng Jiang, Zitao Liu, Xinyue Ma and Xuan Liu
Remote Sens. 2024, 16(9), 1550; https://doi.org/10.3390/rs16091550 - 26 Apr 2024
Abstract
Imaging maritime targets requires a high resolution and wide swath (HWRS) in a synthetic aperture radar (SAR). When operated with a variable pulse repetition interval (PRI), a staggered SAR can realize HRWS imaging, which needs to be reconstructed due to echo pulse loss
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Imaging maritime targets requires a high resolution and wide swath (HWRS) in a synthetic aperture radar (SAR). When operated with a variable pulse repetition interval (PRI), a staggered SAR can realize HRWS imaging, which needs to be reconstructed due to echo pulse loss and a nonuniformly sampled signal along the azimuth. The existing reconstruction algorithms are designed for stationary scenes in a staggered SAR mode, and thus, produce evident image defocusing caused by complex target motion for moving targets. Typically, the nonuniform sampling and complex motion of maritime targets aggravate the spectrum aliasing in a staggered SAR mode, causing inevitable ambiguity and degradation in its reconstruction performance. To this end, this study analyzed the spectrum of maritime targets in a staggered SAR system through theoretical derivation. After this, a reconstruction method named MBLCFD (Modified Best Linear Unbaised and Complex-Lag Time-Frequency Distribution) is proposed to refocus the blurred maritime target. First, the signal model of the maritime target with 3D rotation accompanying roll–pitch–yaw movement was established under the curved orbit of the satellite. The best linear unbiased (BLU) method was modified to alleviate the coupling of nonuniform sampling and target motion. A precise SAR algorithm was performed based on the method of inverse reversion to counteract the effect of a curved orbit and wide swath. Based on the hybrid SAR/ISAR technique, the complex-lag time-frequency distribution was exploited to refocus the maritime target images. Simulations and experiments were carried out to verify the effectiveness of the proposed method, providing precise refocusing performance in staggered mode.
Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Signal and Image Processing)
Open AccessArticle
Carrier-Free Ultra-Wideband Sensor Target Recognition in the Jungle Environment
by
Jianchao Li, Shuning Zhang, Lingzhi Zhu, Si Chen, Linsheng Hou, Xiaoxiong Li and Kuiyu Chen
Remote Sens. 2024, 16(9), 1549; https://doi.org/10.3390/rs16091549 - 26 Apr 2024
Abstract
Carrier-free ultra-wideband sensors have high penetrability anti-jamming solid ability, which is not easily affected by the external environment, such as weather. Also, it has good performance in the complex jungle environment. In this paper, we propose a jungle vehicle identification system based on
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Carrier-free ultra-wideband sensors have high penetrability anti-jamming solid ability, which is not easily affected by the external environment, such as weather. Also, it has good performance in the complex jungle environment. In this paper, we propose a jungle vehicle identification system based on a carrier-free ultra-wideband sensor. Firstly, a composite jungle environment with the target vehicle is modeled. From this model, the simulation obtains time-domain echoes under the excitation of carrier-free ultra-wideband sensor signals in different orientations. Secondly, the time-domain signals are transformed into MTF images through the Markov transfer field to show the statistical characteristics of the time-domain echoes. At the same time, we propose an improved RepVGG network. The structure of the RepVGG network contains five stages, which consist of several RepVGG Blocks. Each RepVGG Block is created by combining convolutional kernels of different sizes using a weighted sum. We add the self-attention module to the output of stage 0 to improve the ability to extract the features of the MTF map and better capture the complex relationship between characteristics during training. In addition, a self-attention module is added before the linear layer classification output in stage 4 to improve the classification accuracy of the network. Moreover, a combined cross-entropy loss and sparsity penalty loss function helps enhance the performance and accuracy of the network. The experimental results show that the system can recognize jungle vehicle targets well.
Full article
(This article belongs to the Topic Radar Signal and Data Processing with Applications)
Open AccessFeature PaperArticle
Overview and Analysis of Ground Subsidence along China’s Urban Subway Network Based on Synthetic Aperture Radar Interferometry
by
Shunyao Wang, Zhenwei Chen, Guo Zhang, Zixing Xu, Yutao Liu and Yuan Yuan
Remote Sens. 2024, 16(9), 1548; https://doi.org/10.3390/rs16091548 - 26 Apr 2024
Abstract
Deformation along a subway rail network is related to the safe operation of the subway and the stability of construction facilities on the surface, making long-term deformation monitoring imperative. Long-term monitoring of surface deformation along the subway network and statistical analysis of the
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Deformation along a subway rail network is related to the safe operation of the subway and the stability of construction facilities on the surface, making long-term deformation monitoring imperative. Long-term monitoring of surface deformation along the subway network and statistical analysis of the overall deformation situation are lacking in China. Therefore, targeting 35 Chinese cities whose subway mileage exceeds 50 km, we extracted their surface deformation along subway networks between 2018 and 2022, using spaceborne synthetic aperture radar (SAR) interferometry (InSAR) technology and Sentinel-1 satellite data. We verified the results with the continuous global navigation satellite system (GNSS) stations’ data and found that the root mean square error (RMSE) of the InSAR results was 3.75 mm/year. Statistical analysis showed that ground subsidence along the subways was more prominent in Beijing, Tianjin, and other areas in the North China Plain, namely Kunming (which is dominated by karst landforms), as well as Shanghai, Guangzhou, Qingdao, and other coastal cities. In addition, an analysis revealed that the severity of surface subsidence correlated positively with a city’s gross domestic product (GDP) with a Pearson correlation of 0.787, since the higher the GDP, the more frequent the construction and maintenance of subway, and the more commuters there are, which in turn exacerbates the disturbance to the surface. Additionally, the type of land cover also affects the ground deformation. Our findings provide a reference for constructing, operating, and maintaining the urban subway systems in China.
Full article
(This article belongs to the Special Issue Remote Sensing in Urban Infrastructure and Building Monitoring)
Open AccessArticle
Analysis of the Low-Frequency Debris Flow Disaster Induced by a Local Rainstorm on 12 July 2022, in Pingwu County, China
by
Mei Liu, Mingfeng Deng, Ningsheng Chen, Shufeng Tian and Tao Wang
Remote Sens. 2024, 16(9), 1547; https://doi.org/10.3390/rs16091547 - 26 Apr 2024
Abstract
Low-frequency debris flows often lead to severe disasters due to large energy releases and strong concealment. However, the understanding of formation conditions, movement processes, and disaster-causing mechanisms of low-frequency debris flow is still limited, especially regarding occurrences within the large catchment (>50 km
[...] Read more.
Low-frequency debris flows often lead to severe disasters due to large energy releases and strong concealment. However, the understanding of formation conditions, movement processes, and disaster-causing mechanisms of low-frequency debris flow is still limited, especially regarding occurrences within the large catchment (>50 km2). This study presents a typical case of large-scale, low-frequency debris flow occurring in the Heishui catchment (102.65 km2), Pingwu County, China. The movement process, disaster characteristics, and causes of the Heishui debris flow were analyzed in detail through field investigations and remote sensing interpretation. The results indicated that the Heishui debris flow is a large-scale, low-frequency, dilute debris flow with a recurrence period of over 100 years. The debris flow was primarily initiated from the right branch gully, Longchi gully, triggered by a local rainstorm with a maximum hourly rainfall return period of over 20 years. The main cause of casualties and building damage is attributed to large energy releases from boulder blockages and outbursts that occurred in the middle part of the main channel. This led to a sudden increase in peak discharge to 1287 m3/s, with a volume of 3.5 × 105 m3 of solid materials being transported to the outlet of the gully. It is essential to enhance the identification of debris flows by comprehensively considering tributary gullies’ susceptibility and strengthening joint meteorological and hydrological monitoring networks in the middle and upper reaches within large catchments. This preliminary work contributes towards improving prevention and mitigation strategies for low-frequency debris flows occurring within large catchments.
Full article
(This article belongs to the Special Issue Anticipation of Flash Floods and Rainfall-Induced Hydro-Geomorphic Hazards)
Open AccessReview
Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms
by
Haotian Liu, Lu Ma, Zhaohui Wang and Gang Qiao
Remote Sens. 2024, 16(9), 1546; https://doi.org/10.3390/rs16091546 - 26 Apr 2024
Abstract
Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a
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Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms under a unified system framework, providing researchers with a comprehensive and objective understanding of UWA channel prediction. Finally, it discusses the directions and challenges for future research. The survey finds that linear prediction algorithms are the most widely applied, and deep learning, as the most advanced type of algorithm, has moved this field into a new stage. The experimental results show that the linear algorithms have the lowest computational complexity, and when the training samples are sufficient, deep learning algorithms have the best prediction performance.
Full article
(This article belongs to the Special Issue Space-Air-Ground-Ocean Integrated Sensing and Information Transmission)
Open AccessArticle
Deep Learning Test Platform for Maritime Applications: Development of the eM/S Salama Unmanned Surface Vessel and Its Remote Operations Center for Sensor Data Collection and Algorithm Development
by
Juha Kalliovaara, Tero Jokela, Mehdi Asadi, Amin Majd, Juhani Hallio, Jani Auranen, Mika Seppänen, Ari Putkonen, Juho Koskinen, Tommi Tuomola, Reza Mohammadi Moghaddam and Jarkko Paavola
Remote Sens. 2024, 16(9), 1545; https://doi.org/10.3390/rs16091545 - 26 Apr 2024
Abstract
In response to the global megatrends of digitalization and transportation automation, Turku University of Applied Sciences has developed a test platform to advance autonomous maritime operations. This platform includes the unmanned surface vessel eM/S Salama and a remote operations center, both of which
[...] Read more.
In response to the global megatrends of digitalization and transportation automation, Turku University of Applied Sciences has developed a test platform to advance autonomous maritime operations. This platform includes the unmanned surface vessel eM/S Salama and a remote operations center, both of which are detailed in this article. The article highlights the importance of collecting and annotating multi-modal sensor data from the vessel. These data are vital for developing deep learning algorithms that enhance situational awareness and guide autonomous navigation. By securing relevant data from maritime environments, we aim to enhance the autonomous features of unmanned surface vessels using deep learning techniques. The annotated sensor data will be made available for further research through open access. An image dataset, which includes synthetically generated weather conditions, is published alongside this article. While existing maritime datasets predominantly rely on RGB cameras, our work underscores the need for multi-modal data to advance autonomous capabilities in maritime applications.
Full article
(This article belongs to the Special Issue Deep Learning and Computer Vision in Remote Sensing-III)
Open AccessArticle
An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation
by
Dahu Wang, Liu Chang and Chao Wang
Remote Sens. 2024, 16(9), 1544; https://doi.org/10.3390/rs16091544 - 26 Apr 2024
Abstract
In cluttered electromagnetic environments, radar is often disturbed by varied clutter, making target detection challenging. Therefore, achieving effective clutter suppression is crucial for radar target detection. However, traditional clutter suppression methods face three key challenges: (1) significant degradation in target signal detection performance
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In cluttered electromagnetic environments, radar is often disturbed by varied clutter, making target detection challenging. Therefore, achieving effective clutter suppression is crucial for radar target detection. However, traditional clutter suppression methods face three key challenges: (1) significant degradation in target signal detection performance when the clutter’s Doppler spectrum completely masks the target signal; (2) heavy reliance on prior knowledge for optimal performance; and (3) inherent signal energy loss during clutter suppression. To address these challenges, we propose a clutter suppression scheme based on blind source separation (BSS). Initially, the scheme utilizes parallel principal skewness analysis (PPSA) to process the echo signals in the range domain, which helps in identifying the position of moving targets. Subsequently, PPSA is applied once more to process the moving targets in the Doppler domain, allowing for the precise determination of their relative velocities. Subsequently, we evaluate the scheme’s performance with simulated and real data, comparing it with traditional clutter suppression methods and other BSS techniques. The results confirm the effectiveness of the scheme in clutter suppression.
Full article
Open AccessTechnical Note
Correcting the Location Error of Persistent Scatterers in an Urban Area Based on Adaptive Building Contours Matching: A Case Study of Changsha
by
Miaowen Hu, Bing Xu, Jia Wei, Bangwei Zuo, Yunce Su and Yirui Zeng
Remote Sens. 2024, 16(9), 1543; https://doi.org/10.3390/rs16091543 - 26 Apr 2024
Abstract
Persistent Scatterer InSAR (PS-InSAR) technology enables the monitoring of displacement in millimeters. However, without the use of external parameter correction, radar scatterers exhibit poor geopositioning precision in meters, limiting the correlation between observed deformation and the actual structure. The integration of PS-InSAR datasets
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Persistent Scatterer InSAR (PS-InSAR) technology enables the monitoring of displacement in millimeters. However, without the use of external parameter correction, radar scatterers exhibit poor geopositioning precision in meters, limiting the correlation between observed deformation and the actual structure. The integration of PS-InSAR datasets and building databases is often overlooked in deformation research. This paper presents a novel strategy for matching between PS points and building contours based on spatial distribution characteristics. A convex hull is employed to simplify the building outline. Considering the influence of building height and incident angle on geometric distortion, an adaptive buffer zone is established. The PS points on a building are further identified through the nearest neighbor method. In this study, both ascending and descending TerraSAR-X orbit datasets acquired between 2016 and 2019 were utilized for PS-InSAR monitoring. The efficacy of the proposed method was evaluated by comparing the PS-InSAR results obtained from different orbits. Through a process of comparison and verification, it was demonstrated that the matching effect between PS points and building contours was significantly enhanced, resulting in an increase of 29.2% in the number of matching PS points. The results indicate that this novel strategy can be employed to associate PS points with building outlines without the need for complex calculations, thereby providing a robust foundation for subsequent building risk assessment.
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(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring II)
Open AccessArticle
A Bayesian Approach for Forecasting the Probability of Large Earthquakes Using Thermal Anomalies from Satellite Observations
by
Zhonghu Jiao and Xinjian Shan
Remote Sens. 2024, 16(9), 1542; https://doi.org/10.3390/rs16091542 - 26 Apr 2024
Abstract
Studies have demonstrated the potential of satellite thermal infrared observations to detect anomalous signals preceding large earthquakes. However, the lack of well-defined precursory characteristics and inherent complexity and stochasticity of the seismicity continue to impede robust earthquake forecasts. This study investigates the potential
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Studies have demonstrated the potential of satellite thermal infrared observations to detect anomalous signals preceding large earthquakes. However, the lack of well-defined precursory characteristics and inherent complexity and stochasticity of the seismicity continue to impede robust earthquake forecasts. This study investigates the potential of pre-seismic thermal anomalies, derived from five satellite-based geophysical parameters, i.e., skin temperature, air temperature, total integrated column water vapor burden, outgoing longwave radiation (OLR), and clear-sky OLR, as valuable indicators for global earthquake forecasts. We employed a spatially self-adaptive multiparametric anomaly identification scheme to refine these anomalies, and then estimated the posterior probability of an earthquake occurrence given observed anomalies within a Bayesian framework. Our findings reveal a promising link between thermal signatures and global seismicity, with elevated forecast probabilities exceeding 0.1 and significant probability gains in some strong earthquake-prone regions. A time series analysis indicates probability stabilization after approximately six years. While no single parameter consistently dominates, each contributes precursory information, suggesting a promising avenue for a multi-parametric approach. Furthermore, novel anomaly indices incorporating probabilistic information significantly reduce false alarms and improve anomaly recognition. Despite remaining challenges in developing dynamic short-term probabilities, rigorously testing detection algorithms, and improving ensemble forecast strategies, this study provides compelling evidence for the potential of thermal anomalies to play a key role in global earthquake forecasts. The ability to reliably estimate earthquake forecast probabilities, given the ever-present threat of destructive earthquakes, holds considerable societal and ecological importance for mitigating earthquake risk and improving preparedness strategies.
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(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
Open AccessArticle
Understanding the Spatiotemporal Dynamics and Influencing Factors of the Rice–Crayfish Field in Jianghan Plain, China
by
Fang Luo, Yiqing Zhang and Xiang Zhao
Remote Sens. 2024, 16(9), 1541; https://doi.org/10.3390/rs16091541 - 26 Apr 2024
Abstract
The rice–crayfish co-culture system, a representative of Agri-aqua food systems, has emerged worldwide as an effective strategy for enhancing agricultural land use efficiency and boosting sustainability, particularly in China and Southeast Asia. Despite its widespread adoption in China’s Jianghan Plain, the exact spatiotemporal
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The rice–crayfish co-culture system, a representative of Agri-aqua food systems, has emerged worldwide as an effective strategy for enhancing agricultural land use efficiency and boosting sustainability, particularly in China and Southeast Asia. Despite its widespread adoption in China’s Jianghan Plain, the exact spatiotemporal dynamics and factors influencing this practice in this region are yet to be clarified. Therefore, understanding the spatiotemporal dynamics and influencing factors of the rice–crayfish fields (RCFs) is crucial for promoting the rice–crayfish co-culture system, and optimizing land use policies. In this study, we identified the spatial distribution of RCF using Sentinel-2 images and land use spatiotemporal data to analyze its spatiotemporal dynamics during the period of 2016–2020. Additionally, we used the Multiscale Geographically Weighted Regression model to explore the key factors influencing RCF’s spatiotemporal changes. Our findings reveal that (1). the RCF area in Jianghan Plain expanded from 1216.04 km2 to 2429.76 km2 between 2016 and 2020, marking a 99.81% increase. (2). RCF in Jianghan Plain evolved toward a more contiguous and clustered spatial pattern, suggesting a clear industrial agglomeration in this area. (3). The expansion of the RCFs was majorly influenced by its landscape and local agricultural conditions. Significantly, the Aggregation and Landscape Shape Indexes positively impacted this expansion, whereas proximity to rural areas and towns had a negative impact. This study provides a solid foundation for promoting the rice–crayfish co-culture system and sustainably developing related industries. To ensure the sustainable development of rice–crayfish co-culture industries in Jianghan Plain, we recommend that local governments optimize the spatial layout of rural settlements, improve transportation infrastructure, and enhance regional agricultural water sources and irrigation system construction, all in line with the national strategy of rural revitalization and village planning. Additionally, promoting the concentration and contiguity of RCF through land consolidation can achieve efficient development of these industries.
Full article
(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment Volume II)
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Open AccessArticle
Quantitative Research on the Morphological Characteristics of Lunar Impact Craters of Different Stratigraphic Ages since the Imbrian Period
by
Ke Zhang, Jianzhong Liu, Li Zhang, Yaya Gu, Zongyu Yue, Sheng Zhang, Jingyi Zhang, Huibin Qin and Jingwen Liu
Remote Sens. 2024, 16(9), 1540; https://doi.org/10.3390/rs16091540 - 26 Apr 2024
Abstract
Impact craters serve as recorders of lunar evolutionary history, and determining the stratigraphic ages of craters is crucial. However, the age of many craters on the Moon remains undetermined. The morphology of craters is closely related to their stratigraphic ages. In the study,
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Impact craters serve as recorders of lunar evolutionary history, and determining the stratigraphic ages of craters is crucial. However, the age of many craters on the Moon remains undetermined. The morphology of craters is closely related to their stratigraphic ages. In the study, we systematically and quantitatively analyzed seven morphological parameters of 432 impact craters with known stratigraphic ages (Copernican, Eratosthenian, Imbrian), including crater depth, wall width, wall height, rim height, irregularity, volume, and roughness, as well as rock abundance. The study provided a range of morphological parameters for craters from the Copernican, Eratosthenian, and Imbrian. Additionally, we derived power law relationships between five morphological parameters and crater diameter, excluding irregularity and roughness. Furthermore, the transitional crater diameters from simple to complex crater morphology were determined for the Copernican and Eratosthenian, approximately 13 km and 15 km, respectively. These results suggest systematic differences in the lunar regolith in different stratigraphic ages. For impact craters of the same diameter, as crater age increases, irregularity tends to be greater, while crater depth, wall width, wall height, rim height, volume, roughness, and rock abundance tend to be smaller. Therefore, in cases where the diameter is determined, the actual values of morphological parameters and rock abundance can be used to constrain the stratigraphic age information of craters of an unknown age.
Full article
(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)
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