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Volume 15
Issue 19
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Remote Sens., Volume 15, Issue 19 (October-1 2023) – 252 articles

Cover Story (view full-size image): This paper compares NASA LVIS (2005) and RIEGL (2021) LiDAR systems in evaluating Tropical Dry Forest (TDF) evolution over 16 years using waveform metrics. Naturally collected LVIS data contrast with RIEGL's point-based data, which is transformed into simulated waveforms. Waveform analysis revealed a 2.8-meter average canopy height increase across successional stages. Metrics like relative height, centroid (Cx, Cy), and radius of gyration displayed consistent shifts, signifying not just height, but structural changes in TDF’s successional classification. This study underscores LiDAR's efficacy in forest assessment, highlighting rapid forest structure shifts. View this paper
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23 pages, 39874 KiB  
Article
Crop Water Productivity from Cloud-Based Landsat Helps Assess California’s Water Savings
by Daniel Foley, Prasad Thenkabail, Adam Oliphant, Itiya Aneece and Pardhasaradhi Teluguntla
Remote Sens. 2023, 15(19), 4894; https://doi.org/10.3390/rs15194894 - 9 Oct 2023
Cited by 1 | Viewed by 2574
Abstract
Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how [...] Read more.
Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how water is used to produce food may help support the increase of Crop Water Productivity (CWP; kg/m3), the ratio of crop output per unit of water input (or crop per drop). Previous large-scale CWP studies have been useful for broad water use modeling at coarser resolutions. However, obtaining more precise CWP, especially for specific crop types in a particular area and growing season as outlined here are important for informing farm-scale water management decision making. Therefore, this study focused on California’s Central Valley utilizing high-spatial resolution satellite imagery of 30 m (0.09 hectares per pixel) to generate more precise CWP for commonly grown and water-intensive irrigated crops. First, two products were modeled and mapped. 1. Landsat based Actual Evapotranspiration (ETa; mm/d) to determine Crop Water Use (CWU; m3/m2), and 2. Crop Productivity (CP; kg/m2) to estimate crop yield per growing season. Then, CWP was calculated by dividing CP by CWU and mapped. The amount of water that can be saved by increasing CWP of each crop was further calculated. For example, in the 434 million m2 study area, a 10% increase in CWP across the 9 crops analyzed had a potential water savings of 31.5 million m3 of water. An increase in CWP is widely considered the best approach for saving maximum quantities of water. This paper proposed, developed, and implemented a workflow of combined methods utilizing cloud computing based remote sensing data. The environmental implications of this work in assessing water savings for food and water security in the 21st century are expected to be significant. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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17 pages, 3848 KiB  
Article
A Multi-Objective Geoacoustic Inversion of Modal-Dispersion and Waveform Envelope Data Based on Wasserstein Metric
by Jiaqi Ding, Xiaofeng Zhao, Pinglv Yang and Yapeng Fu
Remote Sens. 2023, 15(19), 4893; https://doi.org/10.3390/rs15194893 - 9 Oct 2023
Viewed by 1188
Abstract
The inversion of acoustic field data to estimate geoacoustic parameters has been a prominent research focus in the field of underwater acoustics for several decades. Modal-dispersion curves have been used to inverse seabed sound speed and density profiles, but such techniques do not [...] Read more.
The inversion of acoustic field data to estimate geoacoustic parameters has been a prominent research focus in the field of underwater acoustics for several decades. Modal-dispersion curves have been used to inverse seabed sound speed and density profiles, but such techniques do not account for attenuation inversion. In this study, a new approach where modal-dispersion and waveform envelope data are simultaneously inversed under a multi-objective framework is proposed. The inversion is performed using the Multi-Objective Bayesian Optimization (MOBO) method. The posterior probability densities (PPD) of the estimation results are obtained by resampling from the exploited state space using the Gibbs Sampler. In this study, the implemented MOBO approach is compared with individual inversions both from modal-dispersion curves and the waveform data. In addition, the effective use of the Wasserstein metric from optimal transport theory is explored. Then the MOBO performance is tested against two different cost functions based on the L2 norm and the Wasserstein metric, respectively. Numerical experiments are employed to evaluate the effect of different cost functions on inversion performance. It is found that the MOBO approach may have more profound advantages when applied to Wasserstein metrics. Results obtained from our study reveal that the MOBO approach exhibits reduced uncertainty in the inverse results when compared to individual inversion methods, such as modal-dispersion inversion or waveform inversion. However, it is important to note that this enhanced uncertainty reduction comes at the cost of sacrificing accuracy in certain parameters other than the sediment sound speed and attenuation. Full article
(This article belongs to the Special Issue Recent Advances in Underwater and Terrestrial Remote Sensing)
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19 pages, 13458 KiB  
Article
Dim Moving Multi-Target Enhancement with Strong Robustness for False Enhancement
by Yuke Zhang, Xin Chen, Peng Rao and Liangjie Jia
Remote Sens. 2023, 15(19), 4892; https://doi.org/10.3390/rs15194892 - 9 Oct 2023
Cited by 1 | Viewed by 1006
Abstract
In a space-based infrared system, the enhancement of targets plays a crucial role in improving target detection capabilities. However, the existing methods of target enhancement face challenges when dealing with multi-target scenarios and a low signal-to-noise ratio (SNR). Furthermore, false enhancement poses a [...] Read more.
In a space-based infrared system, the enhancement of targets plays a crucial role in improving target detection capabilities. However, the existing methods of target enhancement face challenges when dealing with multi-target scenarios and a low signal-to-noise ratio (SNR). Furthermore, false enhancement poses a serious problem that affects the overall performance. To address these issues, a new enhancement method for a dim moving multi-target with strong robustness for false enhancement is proposed in this paper. Firstly, multi-target localization is applied by spatial–temporal filtering and connected component detection. Then, the motion vectors of each target are obtained using optical flow detection. Finally, the consecutive images are convoluted in 3D based on the convolution kernel guided by the motion vectors of the target. This process allows for the accumulation of the target energy. The experimental results demonstrate that this algorithm can adaptively enhance a multi-target and notably improve the SNR under low SNR conditions. Moreover, it exhibits outstanding performance compared to other algorithms and possesses strong robustness against false enhancement. Full article
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7 pages, 11937 KiB  
Communication
Preliminary Investigation of Sudden Ground Subsidence and Building Tilt in Balitai Town, Tianjin City, on 31 May 2023
by Haonan Jiang, Timo Balz, Jianan Li and Vishal Mishra
Remote Sens. 2023, 15(19), 4891; https://doi.org/10.3390/rs15194891 - 9 Oct 2023
Cited by 5 | Viewed by 1347
Abstract
A short-term rapid subsidence event occurred in the Bi Guiyuan community in Balitai Town, Tianjin City, leading to the tilting of high-rise buildings and the emergency evacuation of over 3000 residents. In response to this incident, InSAR (Interferometric Synthetic Aperture Radar) technology was [...] Read more.
A short-term rapid subsidence event occurred in the Bi Guiyuan community in Balitai Town, Tianjin City, leading to the tilting of high-rise buildings and the emergency evacuation of over 3000 residents. In response to this incident, InSAR (Interferometric Synthetic Aperture Radar) technology was swiftly employed to monitor the subsidence in the area before and after the event. Our observations indicate that the region had maintained stability for 8 months prior to the incident. However, over the course of the 15-day event, the ground experienced more than 10mm of subsidence. By integrating the findings from an InSAR analysis with geological studies, we speculate that the rapid subsidence in the region is related to the extraction of geothermal resources. It is suspected that during drilling operations, the wellbore mistakenly penetrated a massive underground karst cavity. Consequently, this resulted in a sudden rapid leakage of drilling fluid, creating a pressure differential that caused the overlying soil layers to collapse and rapidly sink into the cavity. As a result, short-term rapid subsidence on the ground surface and tilting of high-rise buildings occurred. Full article
(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment II)
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16 pages, 3519 KiB  
Article
Validating and Developing Hyperspectral Indices for Tracing Leaf Chlorophyll Fluorescence Parameters under Varying Light Conditions
by Jie Zhuang, Quan Wang, Guangman Song and Jia Jin
Remote Sens. 2023, 15(19), 4890; https://doi.org/10.3390/rs15194890 - 9 Oct 2023
Cited by 11 | Viewed by 1938
Abstract
Chlorophyll a fluorescence (ChlFa) parameters provide insight into the physiological and biochemical processes of plants and have been widely applied to monitor and evaluate the photochemical process and photosynthetic capacity of plants in a variety of environments. Recent advances in remote sensing provide [...] Read more.
Chlorophyll a fluorescence (ChlFa) parameters provide insight into the physiological and biochemical processes of plants and have been widely applied to monitor and evaluate the photochemical process and photosynthetic capacity of plants in a variety of environments. Recent advances in remote sensing provide new opportunities for the detection of ChlFa at large scales but demand further tremendous efforts. Among such efforts, application of the hyperspectral index is always possible, but the performance of hyperspectral indices in detecting ChlFa parameters under varying light conditions is much less investigated. The objective of this study is to investigate the performance of reported hyperspectral indices for tracking ChlFa parameters under different light conditions and to develop and evaluate novel spectral indices. Therefore, an experiment was conducted to simultaneously measure ChlFa parameters and spectral reflectance of sunlit and shaded leaves under varying light conditions, and 28 reported hyperspectral indices were examined for their performance in tracking the ChlFa parameters. Furthermore, we developed novel hyperspectral indices based on various spectral transformations. The results indicated that the maximum quantum efficiency of photosystem II (PSIImax), the cumulative quantum yield of photochemistry (ΦP), and the fraction of open reaction centers in photosystem II (qL) of sunlit leaves were significantly higher than those of shaded leaves, while the cumulative quantum yield of regulated thermal dissipation (ΦN) and fluorescence (ΦF) of shaded leaves was higher than that of sunlit leaves. Efficient tracing of ChlFa parameters could not be achieved from previously published spectral indices. In comparison, all ChlFa parameters were well quantified in shaded leaves when using novel hyperspectral indices, although the hyperspectral indices for tracing the non-photochemical quenching (NPQ) and ΦF were not stable, especially for sunlit leaves. Our findings justify the use of hyperspectral indices as a practical approach to estimating ChlFa parameters. However, caution should be used when using spectral indices to track ChlFa parameters based on the differences in sunlit and shaded leaves. Full article
(This article belongs to the Special Issue Hyperspectral Remote Sensing of Vegetation Functions: Assessing Vegetation Ecophysiology II)
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23 pages, 6357 KiB  
Article
Performance of the Atmospheric Radiative Transfer Simulator (ARTS) in the 600–1650 cm−1 Region
by Zichun Jin, Zhiyong Long, Shaofei Wang and Yunmeng Liu
Remote Sens. 2023, 15(19), 4889; https://doi.org/10.3390/rs15194889 - 9 Oct 2023
Cited by 2 | Viewed by 1383
Abstract
The Atmospheric Radiative Transfer Simulator (ARTS) has been widely used in the radiation transfer simulation from microwave to terahertz. Due to the same physical principles, ARTS can also be used for simulations of thermal infrared (TIR). However, thorough evaluations of ARTS in the [...] Read more.
The Atmospheric Radiative Transfer Simulator (ARTS) has been widely used in the radiation transfer simulation from microwave to terahertz. Due to the same physical principles, ARTS can also be used for simulations of thermal infrared (TIR). However, thorough evaluations of ARTS in the TIR region are still lacking. Here, we evaluated the performance of ARTS in 600–1650 cm−1 taking the Line-By-Line Radiative Transfer Model (LBLRTM) as a reference model. Additionally, the moderate resolution atmospheric transmission (MODTRAN) band model (BM) and correlated-k (CK) methods were also used for comparison. The comparison results on the 0.001 cm−1 spectral grid showed a high agreement (sub-0.1 K) between ARTS and LBLRTM, while the mean bias difference (MBD) and root mean square difference (RMSD) were less than 0.05 K and 0.3 K, respectively. After convolving with the spectral response functions of the Atmospheric Infra-Red Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), the brightness temperature (BT) differences between ARTS and LBLRTM became smaller with RMSDs of <0.1 K. The comparison results for Jacobians showed that the Jacobians calculated by ARTS and LBLRTM were close for temperature (can be used for Numerical Weather Prediction application) and O3 (excellent Jacobian fit). For the water vapor Jacobian, the Jacobian difference increased with an increasing water vapor content. However, at extremely low water vapor values (0.016 ppmv in this study), LBLRTM exhibited non-physical mutations, while ARTS was smooth. This study aims to help users understand the simulation accuracy of ARTS in the TIR region and the improvement of ARTS via the community. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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19 pages, 8386 KiB  
Article
The Doppler Characteristics of Sea Echoes Acquired by Motion Radar
by Pengbo Du, Yunhua Wang, Xin Li, Jianbo Cui, Yanmin Zhang, Qian Li and Yushi Zhang
Remote Sens. 2023, 15(19), 4888; https://doi.org/10.3390/rs15194888 - 9 Oct 2023
Viewed by 1234
Abstract
The Doppler characteristics of sea surface echoes reflect the time-varying characteristics of the sea surface and can be used to retrieve ocean dynamic parameters and detect targets. On airborne, spaceborne and shipborne radar platforms, radar moves along with the platforms while illuminating the [...] Read more.
The Doppler characteristics of sea surface echoes reflect the time-varying characteristics of the sea surface and can be used to retrieve ocean dynamic parameters and detect targets. On airborne, spaceborne and shipborne radar platforms, radar moves along with the platforms while illuminating the sea surface. In this case, the area of the sea surface illuminated by radar beam changes rapidly with the motion, and the coherence of the backscattered echoes at different times decreases significantly. Therefore, the Doppler characteristics of the echoes would also be affected by the radar motion. At present, the computational requirements needed to simulate the Doppler spectrum of the microwave scattering field from the sea surface based on numerical methods are huge. To overcome this problem, a new method based on the sub-scattering surface elements has been proposed to simulate the Doppler spectrum of sea echoes acquired by a moving microwave radar. A comparison with the results evaluated by the SSA demonstrate the availability and superiority of the new method proposed by us. The influences induced by radar motion, radar beamwidth, incident angle, and thermal noise on the Doppler characteristics are all considered in this new method. The simulated results demonstrate that the spectrum bandwidth of sea surface echoes acquired by radar on the dive staring motion platform becomes somewhat narrower. Full article
(This article belongs to the Section Ocean Remote Sensing)
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23 pages, 2929 KiB  
Article
Misaligned RGB-Infrared Object Detection via Adaptive Dual-Discrepancy Calibration
by Mingzhou He, Qingbo Wu, King Ngi Ngan, Feng Jiang, Fanman Meng and Linfeng Xu
Remote Sens. 2023, 15(19), 4887; https://doi.org/10.3390/rs15194887 - 9 Oct 2023
Cited by 3 | Viewed by 2485
Abstract
Object detection based on RGB and infrared images has emerged as a crucial research area in computer vision, and the synergy of RGB-Infrared ensures the robustness of object-detection algorithms under varying lighting conditions. However, the RGB-IR image pairs captured typically exhibit spatial misalignment [...] Read more.
Object detection based on RGB and infrared images has emerged as a crucial research area in computer vision, and the synergy of RGB-Infrared ensures the robustness of object-detection algorithms under varying lighting conditions. However, the RGB-IR image pairs captured typically exhibit spatial misalignment due to sensor discrepancies, leading to compromised localization performance. Furthermore, since the inconsistent distribution of deep features from the two modalities, directly fusing multi-modal features will weaken the feature difference between the object and the background, therefore interfering with the RGB-Infrared object-detection performance. To address these issues, we propose an adaptive dual-discrepancy calibration network (ADCNet) for misaligned RGB-Infrared object detection, including spatial discrepancy and domain-discrepancy calibration. Specifically, the spatial discrepancy calibration module conducts an adaptive affine transformation to achieve spatial alignment of features. Then, the domain-discrepancy calibration module separately aligns object and background features from different modalities, making the distribution of the object and background of the fusion feature easier to distinguish, therefore enhancing the effectiveness of RGB-Infrared object detection. Our ADCNet outperforms the baseline by 3.3% and 2.5% in mAP50 on the FLIR and misaligned M3FD datasets, respectively. Experimental results demonstrate the superiorities of our proposed method over the state-of-the-art approaches. Full article
(This article belongs to the Special Issue Object Detection and Information Extraction Based on Remote Sensing Imagery)
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21 pages, 11473 KiB  
Article
Retrievals of Chlorophyll-a from GOCI and GOCI-II Data in Optically Complex Lakes
by Yuyu Guo, Xiaoqi Wei, Zehui Huang, Hanhan Li, Ronghua Ma, Zhigang Cao, Ming Shen and Kun Xue
Remote Sens. 2023, 15(19), 4886; https://doi.org/10.3390/rs15194886 - 9 Oct 2023
Cited by 3 | Viewed by 1543
Abstract
The chlorophyll-a (Chla) concentration is a key parameter to evaluate the eutrophication conditions of water, which is very important for monitoring algal blooms. Although Geostationary Ocean Color Imager (GOCI) has been widely used in Chla inversion, the consistency of the [...] Read more.
The chlorophyll-a (Chla) concentration is a key parameter to evaluate the eutrophication conditions of water, which is very important for monitoring algal blooms. Although Geostationary Ocean Color Imager (GOCI) has been widely used in Chla inversion, the consistency of the Rayleigh-corrected reflectance (Rrc) of GOCI and GOCI-II sensors still needs to be further evaluated, and a model suitable for lakes with complex optical properties needs to be constructed. The results show that (1) the derived Chla values of the GOCI and GOCI-II synchronous data were relatively consistent and continuous in three lakes in China. (2) The accuracy of the random forest (RF) model (R2 = 0.84, root mean square error (RMSE) =11.77 μg/L) was higher than that of the empirical model (R2 = 0.79, RMSE = 12.63 μg/L) based on the alternative floating algae index (AFAI). (3) The interannual variation trend fluctuated, with high Chla levels in Lake Chaohu in 2015 and 2019, while those in Lake Hongze were high in 2013, 2015, and 2022, and those in Lake Taihu reached their peak in 2017 and 2019. There were three types of diurnal variation patterns, namely, near-continuous increase (Class 1), near-continuous decrease (Class 2), and first an increase and then a decrease (Class 3), among which Lake Chaohu and Lake Taihu occupied the highest proportion in Class 3. The results analyzed the temporal and spatial variations of Chla in three lakes for 12 years and provided support for the use of GOCI and GOCI-II data and monitoring of Chla in optical complex inland waters. Full article
(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)
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23 pages, 10183 KiB  
Article
A Robust InSAR Phase Unwrapping Method via Improving the pix2pix Network
by Long Zhang, Guoman Huang, Yutong Li, Shucheng Yang, Lijun Lu and Wenhao Huo
Remote Sens. 2023, 15(19), 4885; https://doi.org/10.3390/rs15194885 - 9 Oct 2023
Cited by 1 | Viewed by 1713
Abstract
The main core of InSAR (interferometric synthetic aperture radar) data processing is phase unwrapping, and the output has a direct impact on the quality of the data processing products. Noise introduced from the SAR system and interferometric processing is unavoidable, causing local phase [...] Read more.
The main core of InSAR (interferometric synthetic aperture radar) data processing is phase unwrapping, and the output has a direct impact on the quality of the data processing products. Noise introduced from the SAR system and interferometric processing is unavoidable, causing local phase inaccuracy and limiting the unwrapping results of traditional unwrapping methods. With the successful implementation of deep learning in a variety of industries in recent years, new concepts for phase unwrapping have emerged. This research offers a one-step InSAR phase unwrapping method based on an improved pix2pix network model. We achieved our aim by upgrading the pix2pix network generator model and introducing the concept of quality map guidance. Experiments on InSAR phase unwrapping utilizing simulated and real data with different noise intensities were carried out to compare the method with other unwrapping methods. The experimental results demonstrated that the proposed method is superior to other unwrapping methods and has a good robustness to noise. Full article
(This article belongs to the Section AI Remote Sensing)
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24 pages, 33279 KiB  
Article
Spatiotemporal Analysis of Landscape Ecological Risk and Driving Factors: A Case Study in the Three Gorges Reservoir Area, China
by Zhiyi Yan, Yunqi Wang, Zhen Wang, Churui Zhang, Yujie Wang and Yaoming Li
Remote Sens. 2023, 15(19), 4884; https://doi.org/10.3390/rs15194884 - 9 Oct 2023
Cited by 9 | Viewed by 1683
Abstract
Landscape ecological risk is considered the basis for regional ecosystem management decisions. Thus, it is essential to understand the spatial and temporal evolutionary patterns and drivers of landscape ecological risk. However, existing studies lack exploration of the long-term time series and driving mechanisms [...] Read more.
Landscape ecological risk is considered the basis for regional ecosystem management decisions. Thus, it is essential to understand the spatial and temporal evolutionary patterns and drivers of landscape ecological risk. However, existing studies lack exploration of the long-term time series and driving mechanisms of landscape ecological risk. Based on multi-type remote sensing data, this study assesses landscape pattern changes and ecological risk in the Three Gorges Reservoir Area from 1990 to 2020 and ranks the driving factors using a geographical detector. We then introduce the geographically weighted regression model to explore the local spatial contributions of driving factors. Our results show: (1) From 1990 to 2020, the agricultural land decreased, while forest and construction land expanded in the Three Gorges Reservoir Area. The overall landscape pattern shifted toward aggregation. (2) The landscape ecological risk exhibited a decreasing trend. The areas with relatively high landscape ecological risk were primarily concentrated in the main urban area in the western region of the Three Gorges Reservoir Area and along the Yangtze River, with apparent spatial aggregation. (3) Social and natural factors affected landscape ecological risk. The main driving factors were human interference, annual average temperature, population density, and annual precipitation; interactions occurred between the drivers. (4) The influence of driving factors on landscape ecological risk showed spatial heterogeneity. Spatially, the influence of social factors (human interference and population density) on landscape ecological risk was primarily positively correlated. Meanwhile, the natural factors’ (annual average temperature and annual precipitation) influence on landscape ecological risk varied widely in spatial distribution, and the driving mechanisms were more complex. This study provides a scientific basis and reference for landscape ecological risk management, land use policy formulation, and optimization of ecological security patterns. Full article
(This article belongs to the Topic Aquatic Environment Research for Sustainable Development)
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28 pages, 24166 KiB  
Article
Semi-Supervised Learning Method for the Augmentation of an Incomplete Image-Based Inventory of Earthquake-Induced Soil Liquefaction Surface Effects
by Adel Asadi, Laurie Gaskins Baise, Christina Sanon, Magaly Koch, Snehamoy Chatterjee and Babak Moaveni
Remote Sens. 2023, 15(19), 4883; https://doi.org/10.3390/rs15194883 - 9 Oct 2023
Cited by 5 | Viewed by 2312
Abstract
Soil liquefaction often occurs as a secondary hazard during earthquakes and can lead to significant structural and infrastructure damage. Liquefaction is most often documented through field reconnaissance and recorded as point locations. Complete liquefaction inventories across the impacted area are rare but valuable [...] Read more.
Soil liquefaction often occurs as a secondary hazard during earthquakes and can lead to significant structural and infrastructure damage. Liquefaction is most often documented through field reconnaissance and recorded as point locations. Complete liquefaction inventories across the impacted area are rare but valuable for developing empirical liquefaction prediction models. Remote sensing analysis can be used to rapidly produce the full spatial extent of liquefaction ejecta after an event to inform and supplement field investigations. Visually labeling liquefaction ejecta from remotely sensed imagery is time-consuming and prone to human error and inconsistency. This study uses a partially labeled liquefaction inventory created from visual annotations by experts and proposes a pixel-based approach to detecting unlabeled liquefaction using advanced machine learning and image processing techniques, and to generating an augmented inventory of liquefaction ejecta with high spatial completeness. The proposed methodology is applied to aerial imagery taken from the 2011 Christchurch earthquake and considers the available partial liquefaction labels as high-certainty liquefaction features. This study consists of two specific comparative analyses. (1) To tackle the limited availability of labeled data and their spatial incompleteness, a semi-supervised self-training classification via Linear Discriminant Analysis is presented, and the performance of the semi-supervised learning approach is compared with supervised learning classification. (2) A post-event aerial image with RGB (red-green-blue) channels is used to extract color transformation bands, statistical indices, texture components, and dimensionality reduction outputs, and performances of the classification model with different combinations of selected features from these four groups are compared. Building footprints are also used as the only non-imagery geospatial information to improve classification accuracy by masking out building roofs from the classification process. To prepare the multi-class labeled data, regions of interest (ROIs) were drawn to collect samples of seven land cover and land use classes. The labeled samples of liquefaction were also clustered into two groups (dark and light) using the Fuzzy C-Means clustering algorithm to split the liquefaction pixels into two classes. A comparison of the generated maps with fully and manually labeled liquefaction data showed that the proposed semi-supervised method performs best when selected high-ranked features of the two groups of statistical indices (gradient weight and sum of the band squares) and dimensionality reduction outputs (first and second principal components) are used. It also outperforms supervised learning and can better augment the liquefaction labels across the image in terms of spatial completeness. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide II)
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17 pages, 5526 KiB  
Article
Record Low Arctic Stratospheric Ozone in Spring 2020: Measurements of Ground-Based Differential Optical Absorption Spectroscopy in Ny-Ålesund during 2017–2021
by Qidi Li, Yuhan Luo, Yuanyuan Qian, Ke Dou, Fuqi Si and Wenqing Liu
Remote Sens. 2023, 15(19), 4882; https://doi.org/10.3390/rs15194882 - 9 Oct 2023
Cited by 1 | Viewed by 1045
Abstract
The Arctic stratospheric ozone depletion event in spring 2020 was the most severe compared with previous years. We retrieved the critical indicator ozone vertical column density (VCD) using zenith scattered light differential optical absorption spectroscopy (ZSL-DOAS) from March 2017 to September 2021 in [...] Read more.
The Arctic stratospheric ozone depletion event in spring 2020 was the most severe compared with previous years. We retrieved the critical indicator ozone vertical column density (VCD) using zenith scattered light differential optical absorption spectroscopy (ZSL-DOAS) from March 2017 to September 2021 in Ny-Ålesund, Svalbard, Norway. The average ozone VCD over Ny-Ålesund between 18 March and 18 April 2020 was approximately 274.8 Dobson units (DU), which was only 64.7 ± 0.1% of that recorded in other years (2017, 2018, 2019, and 2021). The daily peak difference was 195.7 DU during this period. The retrieved daily averages of ozone VCDs were compared with satellite observations from the Global Ozone Monitoring Experiment-2 (GOME-2), a Brewer spectrophotometer, and a Système d’Analyze par Observation Zénithale (SAOZ) spectrometer at Ny-Ålesund. As determined using the empirical cumulative density function, ozone VCDs from the ZSL-DOAS dataset were strongly correlated with data from the GOME-2 and SAOZ at lower and higher values, and ozone VCDs from the Brewer instrument were overestimated. The resulting Pearson correlation coefficients were relatively high at 0.97, 0.87, and 0.91, respectively. In addition, the relative deviations were 2.3%, 3.1%, and 3.5%, respectively. Sounding and ERA5 data indicated that severe ozone depletion occurred between mid-March and mid-April 2020 in the 16–20 km altitude range over Ny-Ålesund, which was strongly associated with the overall persistently low temperatures in the winter of 2019/2020. Using ZSL-DOAS observations, we obtained ozone VCDs and provided evidence for the unprecedented ozone depletion during the Arctic spring of 2020. This is essential for the study of polar ozone changes and their effect on climate change and ecological conditions. Full article
(This article belongs to the Special Issue Multi-Scale Variability of Stratospheric and Tropospheric Ozone and Related Processes)
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20 pages, 4578 KiB  
Article
Novel Compact Polarized Martian Wind Imaging Interferometer
by Chunmin Zhang, Yanqiang Wang, Biyun Zhang, Tingyu Yan, Zeyu Chen and Zhengyi Chen
Remote Sens. 2023, 15(19), 4881; https://doi.org/10.3390/rs15194881 - 9 Oct 2023
Cited by 1 | Viewed by 1367
Abstract
The Mars Atmospheric Wind Imaging Interferometer offers several advantages, notably its high throughput, enabling the acquisition of precise and high vertical resolution data on the temperature and wind fields in the Martian atmosphere. Considering the current absence of such an Interferometer, this paper [...] Read more.
The Mars Atmospheric Wind Imaging Interferometer offers several advantages, notably its high throughput, enabling the acquisition of precise and high vertical resolution data on the temperature and wind fields in the Martian atmosphere. Considering the current absence of such an Interferometer, this paper introduces a novel Mars wind field imaging interferometer. In analyzing the photochemical model of O2 (a1Δg) 1.27 μm molecular airglow radiation in the Martian atmosphere and considering the impact of instrument signal-to-noise ratio (SNR), we have chosen an optical path difference (OPD) of 8.6 cm for the interferometer. The all-solid-state polarized wind imaging interferometer is miniaturized by incorporating two arm glasses as the compensation medium in its construction, achieving the effects of field-widening and temperature compensation. Additionally, an F-P Etalon is designed to selectively filter the desired three spectral lines of O2 dayglow, and its effect is evaluated through simulations. The accuracy of the proposed compact Mars polarized wind imaging interferometer for detecting Mars’ wind field and temperature field has been validated through rigorous theoretical derivation and comprehensive computer simulations. The interferometer boasts several advantages, including its compact and small size, static stability, minimal stray light, and absence of moving parts. It establishes the theoretical, technological, and instrumental engineering foundations for future simultaneous static measurement of Martian global atmospheric wind fields, temperature fields, and ozone concentrations from spacecraft, thereby significantly contributing to the dataset for investigating Martian atmospheric dynamics. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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22 pages, 8432 KiB  
Article
Drone Photogrammetry for Accurate and Efficient Rock Joint Roughness Assessment on Steep and Inaccessible Slopes
by Jiamin Song, Shigui Du, Rui Yong, Changshuo Wang and Pengju An
Remote Sens. 2023, 15(19), 4880; https://doi.org/10.3390/rs15194880 - 9 Oct 2023
Cited by 4 | Viewed by 1860
Abstract
The roughness of rock joints exerts a substantial influence on the mechanical behavior of rock masses. In order to identify potential failure mechanisms and to design effective protection measures, the accurate measurement of joint roughness is essential. Traditional methods, such as contact profilometry, [...] Read more.
The roughness of rock joints exerts a substantial influence on the mechanical behavior of rock masses. In order to identify potential failure mechanisms and to design effective protection measures, the accurate measurement of joint roughness is essential. Traditional methods, such as contact profilometry, laser scanning, and close-range photogrammetry, encounter difficulties when assessing steep and inaccessible slopes, thus hindering the safety and precision of data collection. This study aims to assess the feasibility of utilizing drone photogrammetry to quantify the roughness of rock joints on steep and inaccessible slopes. Field experiments were conducted, and the results were compared to those of 3D laser scanning in order to validate the approach’s procedural details, applicability, and measurement accuracy. Under a 3 m image capture distance using drone photogrammetry, the root mean square error of the multiscale model-to-model cloud comparison (M3C2) distance and the average roughness measurement error were less than 0.5 mm and 10%, respectively. The results demonstrate the feasibility and potential of drone photogrammetry for joint roughness measurement challenges, providing a useful tool for practitioners and researchers pursuing innovative solutions for assessing rock joint roughness on precipitous and hazardous slopes. Full article
(This article belongs to the Special Issue Rockfall Hazard Analysis Using Remote Sensing Techniques)
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18 pages, 14118 KiB  
Article
Analysis of Ionospheric Anomalies before the Tonga Volcanic Eruption on 15 January 2022
by Jiandi Feng, Yunbin Yuan, Ting Zhang, Zhihao Zhang and Di Meng
Remote Sens. 2023, 15(19), 4879; https://doi.org/10.3390/rs15194879 - 9 Oct 2023
Cited by 11 | Viewed by 2270
Abstract
In this paper, GNSS stations’ observational data, global ionospheric maps (GIM) and the electron density of FORMOSAT-7/COSMIC-2 occultation are used to study ionospheric anomalies before the submarine volcanic eruption of Hunga Tonga–Hunga Ha’apai on 15 January 2022. (i) We detect the negative total [...] Read more.
In this paper, GNSS stations’ observational data, global ionospheric maps (GIM) and the electron density of FORMOSAT-7/COSMIC-2 occultation are used to study ionospheric anomalies before the submarine volcanic eruption of Hunga Tonga–Hunga Ha’apai on 15 January 2022. (i) We detect the negative total electron content (TEC) anomalies by three GNSS stations on 5 January before the volcanic eruption after excluding the influence of solar and geomagnetic disturbances and lower atmospheric forcing. The GIMs also detect the negative anomaly in the global ionospheric TEC only near the epicenter of the eruption on 5 January, with a maximum outlier exceeding 6 TECU. (ii) From 1 to 3 January (local time), the equatorial ionization anomaly (EIA) peak shifts significantly towards the Antarctic from afternoon to night. The equatorial ionization anomaly double peak decreases from 4 January, and the EIA double peak disappears and merges into a single peak on 7 January. Meanwhile, the diurnal maxima of TEC at TONG station decrease by nearly 10 TECU and only one diurnal maximum occurred on 4 January (i.e., 5 January of UT), but the significant ionospheric diurnal double-maxima (DDM) are observed on other dates. (iii) We find a maximum value exceeding NmF2 at an altitude of 100~130 km above the volcanic eruption on 5 January (i.e., a sporadic E layer), with an electron density of 7.5 × 105 el/cm3. Full article
(This article belongs to the Special Issue Ionosphere Monitoring with Remote Sensing II)
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20 pages, 6432 KiB  
Article
A Multi-Domain Joint Novel Method for ISAR Imaging of Multi-Ship Targets
by Yangyang Zhang, Ning Xu, Ning Li and Zhengwei Guo
Remote Sens. 2023, 15(19), 4878; https://doi.org/10.3390/rs15194878 - 8 Oct 2023
Cited by 4 | Viewed by 1323
Abstract
As a key object on the ocean, regulating civilian and military ship targets more effectively is a very important part of maintaining maritime security. One of the ways to obtain high-resolution images of ship targets is the inverse synthetic aperture radar (ISAR) imaging [...] Read more.
As a key object on the ocean, regulating civilian and military ship targets more effectively is a very important part of maintaining maritime security. One of the ways to obtain high-resolution images of ship targets is the inverse synthetic aperture radar (ISAR) imaging technique. However, in the actual ISAR imaging process, ship targets in a formation often lead to complicated motion conditions. Due to the close distance between the ship targets, the rough imaging results of the targets cannot be completely separated in the image domain, and the small differences in motion parameters lead to overlapping phenomena in the Doppler history. Therefore, for situations in which ship formation targets with little difference in motion parameters are included in the same radar beam, this paper proposes a multi-domain joint ISAR separation imaging method for multi-ship targets. First, the method performs echo separation using the Hough transform (HT) with the minimum entropy autofocus method in the image domain. Secondly, the time–frequency curve is extracted in the time–frequency domain using the short-time Fourier transform (STFT) for time–frequency analysis, which solves the problem of the ship formation targets being aliased on both echo and Doppler history after range compression and achieves the purpose of separating the echo signals of the sub-ship targets with high accuracy. Eventually, better-focused images of each target are obtained via further motion compensation and precise imaging. Finally, the effectiveness of the proposed method is verified using a simulation and measured data. Full article
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications II)
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26 pages, 8949 KiB  
Article
Numerical Modeling of Land Surface Temperature over Complex Geologic Terrains: A Remote Sensing Approach
by Saeid Asadzadeh and Carlos Roberto Souza Filho
Remote Sens. 2023, 15(19), 4877; https://doi.org/10.3390/rs15194877 - 8 Oct 2023
Cited by 3 | Viewed by 1942
Abstract
A physically-based image processing approach, based on a single-source surface energy balance framework, is developed here to model the land surface temperature (LST) over complex/rugged geologic terrains at medium to high spatial resolution (<102 m). This approach combines atmospheric parameters with a [...] Read more.
A physically-based image processing approach, based on a single-source surface energy balance framework, is developed here to model the land surface temperature (LST) over complex/rugged geologic terrains at medium to high spatial resolution (<102 m). This approach combines atmospheric parameters with a bulk-layer soil model and remote-sensing-based parameterization schemes to simulate surface temperature over bare surfaces. The model’s inputs comprise a digital elevation model, surface temperature data, and a set of land surface parameters including albedo, emissivity, roughness length, thermal conductivity, soil porosity, and soil moisture content, which are adjusted for elevation, solar time, and moisture contents when necessary. High-quality weather data were acquired from a nearby weather station. By solving the energy balance, heat, and water flow equations per pixel and subsequently integrating the surface and subsurface energy fluxes over time, a model-simulated temperature map/dataset is generated. The resulting map can then be contrasted with concurrent remote sensing LST (typically nighttime) data aiming to remove the diurnal effects and constrain the contribution of the subsurface heating component. The model’s performance and sensitivity were assessed across two distinct test sites in China and Iran, using point-scale observational data and regional-scale ASTER imagery, respectively. The model, known as the Surface Kinetic Temperature Simulator (SkinTES), has direct applications in resource exploration and geological studies in arid to semi-arid regions of the world. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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26 pages, 2173 KiB  
Article
Uvsq-Sat NG, a New CubeSat Pathfinder for Monitoring Earth Outgoing Energy and Greenhouse Gases
by Mustapha Meftah, Cannelle Clavier, Alain Sarkissian, Alain Hauchecorne, Slimane Bekki, Franck Lefèvre, Patrick Galopeau, Pierre-Richard Dahoo, Andrea Pazmino, André-Jean Vieau, Christophe Dufour, Pierre Maso, Nicolas Caignard, Frédéric Ferreira, Pierre Gilbert, Odile Hembise Fanton d’Andon, Sandrine Mathieu, Antoine Mangin, Catherine Billard and Philippe Keckhut
Remote Sens. 2023, 15(19), 4876; https://doi.org/10.3390/rs15194876 - 8 Oct 2023
Cited by 6 | Viewed by 2670
Abstract
Climate change is undeniably one of the most pressing and critical challenges facing humanity in the 21st century. In this context, monitoring the Earth’s Energy Imbalance (EEI) is fundamental in conjunction with greenhouse gases (GHGs) in order to comprehensively understand and address climate [...] Read more.
Climate change is undeniably one of the most pressing and critical challenges facing humanity in the 21st century. In this context, monitoring the Earth’s Energy Imbalance (EEI) is fundamental in conjunction with greenhouse gases (GHGs) in order to comprehensively understand and address climate change. The French Uvsq-Sat NG pathfinder mission addresses this issue through the implementation of a Six-Unit CubeSat, which has dimensions of 111.3 × 36.6 × 38.8 cm in its unstowed configuration. Uvsq-Sat NG is a satellite mission spearheaded by the Laboratoire Atmosphères, Observations Spatiales (LATMOS), and supported by the International Satellite Program in Research and Education (INSPIRE). The launch of this mission is planned for 2025. One of the Uvsq-Sat NG objectives is to ensure the smooth continuity of the Earth Radiation Budget (ERB) initiated via the Uvsq-Sat and Inspire-Sat satellites. Uvsq-Sat NG seeks to achieve broadband ERB measurements using state-of-the-art yet straightforward technologies. Another goal of the Uvsq-Sat NG mission is to conduct precise and comprehensive monitoring of atmospheric gas concentrations (CO2 and CH4) on a global scale and to investigate its correlation with Earth’s Outgoing Longwave Radiation (OLR). Uvsq-Sat NG carries several payloads, including Earth Radiative Sensors (ERSs) for monitoring incoming solar radiation and outgoing terrestrial radiation. A Near-Infrared (NIR) Spectrometer is onboard to assess GHGs’ atmospheric concentrations through observations in the wavelength range of 1200 to 2000 nm. Uvsq-Sat NG also includes a high-definition camera (NanoCam) designed to capture images of the Earth in the visible range. The NanoCam will facilitate data post-processing acquired via the spectrometer by ensuring accurate geolocation of the observed scenes. It will also offer the capability of observing the Earth’s limb, thus providing the opportunity to roughly estimate the vertical temperature profile of the atmosphere. We present here the scientific objectives of the Uvsq-Sat NG mission, along with a comprehensive overview of the CubeSat platform’s concepts and payload properties as well as the mission’s current status. Furthermore, we also describe a method for the retrieval of atmospheric gas columns (CO2, CH4, O2, H2O) from the Uvsq-Sat NG NIR Spectrometer data. The retrieval is based on spectra simulated for a range of environmental conditions (surface pressure, surface reflectance, vertical temperature profile, mixing ratios of primary gases, water vapor, other trace gases, cloud and aerosol optical depth distributions) as well as spectrometer characteristics (Signal-to-Noise Ratio (SNR) and spectral resolution from 1 to 6 nm). Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere II)
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19 pages, 38475 KiB  
Article
Segmentation and Connectivity Reconstruction of Urban Rivers from Sentinel-2 Multi-Spectral Imagery by the WaterSCNet Deep Learning Model
by Zixuan Dui, Yongjian Huang, Mingquan Wang, Jiuping Jin and Qianrong Gu
Remote Sens. 2023, 15(19), 4875; https://doi.org/10.3390/rs15194875 - 8 Oct 2023
Cited by 1 | Viewed by 1482
Abstract
Quick and automatic detection of the distribution and connectivity of urban rivers and their changes from satellite imagery is of great importance for urban flood control, river management, and ecological conservation. By improving the E-UNet model, this study proposed a cascaded river segmentation [...] Read more.
Quick and automatic detection of the distribution and connectivity of urban rivers and their changes from satellite imagery is of great importance for urban flood control, river management, and ecological conservation. By improving the E-UNet model, this study proposed a cascaded river segmentation and connectivity reconstruction deep learning network model (WaterSCNet) to segment urban rivers from Sentinel-2 multi-spectral imagery and simultaneously reconstruct their connectivity obscured by road and bridge crossings from the segmentation results. The experimental results indicated that the WaterSCNet model could achieve better river segmentation and connectivity reconstruction results compared to the E-UNet, U-Net, SegNet, and HRNet models. Compared with the classic U-Net model, the MCC, F1, Kappa, and Recall evaluation metrics of the river segmentation results of the WaterSCNet model were improved by 3.24%, 3.10%, 3.36%, and 3.93%, respectively, and the evaluation metrics of the connectivity reconstruction results were improved by 4.25%, 4.11%, 4.37%, and 4.83%, respectively. The variance of the evaluation metrics of the five independent experiments indicated that the WaterSCNet model also had the best robustness compared to the other four models. Full article
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24 pages, 27592 KiB  
Article
The Frinco Castle: From an Integrated Survey to 3D Modelling and a Stratigraphic Analysis for Helping Knowledge and Reconstruction
by Filippo Diara and Marco Roggero
Remote Sens. 2023, 15(19), 4874; https://doi.org/10.3390/rs15194874 - 8 Oct 2023
Cited by 2 | Viewed by 2028
Abstract
The Frinco Castle (AT-Italy) was the focus of a critical requalification and restoration project and historical knowledge. The initial medieval nucleus was modified and enriched by other architectural parts giving the current shape over the centuries. These additions gave the castle its actual [...] Read more.
The Frinco Castle (AT-Italy) was the focus of a critical requalification and restoration project and historical knowledge. The initial medieval nucleus was modified and enriched by other architectural parts giving the current shape over the centuries. These additions gave the castle its actual internal and external complexity and an extreme structural fragility: in 2014, a significant portion collapsed. The main objective of this work was to obtain 3D metric documentation and a historical interpretation of the castle for reconstruction and fruition purposes. The local administration has planned knowledge processes from 2021: an integrated 3D geodetic survey of the entire castle and stratigraphic investigations of masonries. Both surveys were essential for understanding the architectural composition as well as the historical evolution of the court. NURBS modelling and a stratigraphic analysis of masonries allowed for the implementation of 3D immersion related to the historical interpretation. Furthermore, this modelling choice was essential for virtually reconstructing the collapsed area and helping the restoration phase. Full article
(This article belongs to the Special Issue Applications of Laser Scanning and Photogrammetry in Civil Engineering and Architecture: Beyond 3D Modeling II)
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16 pages, 3096 KiB  
Technical Note
Revealing the Potential of Deep Learning for Detecting Submarine Pipelines in Side-Scan Sonar Images: An Investigation of Pre-Training Datasets
by Xing Du, Yongfu Sun, Yupeng Song, Lifeng Dong and Xiaolong Zhao
Remote Sens. 2023, 15(19), 4873; https://doi.org/10.3390/rs15194873 - 8 Oct 2023
Cited by 2 | Viewed by 1674
Abstract
This study introduces a novel approach to the critical task of submarine pipeline or cable (POC) detection by employing GoogleNet for the automatic recognition of side-scan sonar (SSS) images. The traditional interpretation methods, heavily reliant on human interpretation, are replaced with a more [...] Read more.
This study introduces a novel approach to the critical task of submarine pipeline or cable (POC) detection by employing GoogleNet for the automatic recognition of side-scan sonar (SSS) images. The traditional interpretation methods, heavily reliant on human interpretation, are replaced with a more reliable deep-learning-based methodology. We explored the enhancement of model accuracy via transfer learning and scrutinized the influence of three distinct pre-training datasets on the model’s performance. The results indicate that GoogleNet facilitated effective identification, with accuracy and precision rates exceeding 90%. Furthermore, pre-training with the ImageNet dataset increased prediction accuracy by about 10% compared to the model without pre-training. The model’s prediction ability was best promoted by pre-training datasets in the following order: Marine-PULSE ≥ ImageNet > SeabedObjects-KLSG. Our study shows that pre-training dataset categories, dataset volume, and data consistency with predicted data are crucial factors affecting pre-training outcomes. These findings set the stage for future research on automatic pipeline detection using deep learning techniques and emphasize the significance of suitable pre-training dataset selection for CNN models. Full article
(This article belongs to the Special Issue Deep Transfer Learning for Remote Sensing II)
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21 pages, 11996 KiB  
Article
Construction and Analysis of the EMC Evaluation Model for Vehicular Communication Systems Based on Digital Maps
by Guangshuo Zhang, Hongmin Lu, Shiwei Zhang, Fulin Wu, Yangzhen Qin and Bo Jiang
Remote Sens. 2023, 15(19), 4872; https://doi.org/10.3390/rs15194872 - 8 Oct 2023
Viewed by 1138
Abstract
With the development of vehicular communication technology, the electromagnetic compatibility requirements of vehicular communication systems are becoming more demanding. The traditional four-level electromagnetic compatibility evaluation model is widely applied in many scenarios. However, this model neglects the mutual interference of electronic devices inside [...] Read more.
With the development of vehicular communication technology, the electromagnetic compatibility requirements of vehicular communication systems are becoming more demanding. The traditional four-level electromagnetic compatibility evaluation model is widely applied in many scenarios. However, this model neglects the mutual interference of electronic devices inside a vehicle, and it cannot evaluate whether reduced radio receiver sensitivity, antenna isolation, and communication distance satisfy the system requirements for vehicular communication, thus making it unsuitable for digital communication systems. With the development of remote sensing technology, high-precision digital maps are easy to acquire and thus widely used. In this work, a modified five-level evaluation model based on digital maps is proposed, where digital maps are employed to support receiver sensitivity, antenna isolation, and communication performance evaluation. Through remote sensing technology and digital maps, a terrain profile is obtained, and a more accurate vehicle communication propagation model is established. In the experiment, an actual armored vehicular communication system example is applied to verify the performance of the proposed five-level evaluation model. Compared with the free-space propagation model, the error of the actual power received by the receiver is reduced by 0.97%, and the error of the communication distance where the sensitivity of the receiver is reduced by more than the system EMC threshold is reduced by 16.78%. The calculated antenna isolation degree is basically consistent with the actual measurement data. The model is able to evaluate the electromagnetic compatibility of an armored vehicular communication system more quickly, accurately, and comprehensively compared to previous evaluation models. Full article
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21 pages, 4281 KiB  
Article
Insighting Drivers of Population Exposure to Ambient Ozone (O3) Concentrations across China Using a Spatiotemporal Causal Inference Method
by Junming Li, Jing Xue, Jing Wei, Zhoupeng Ren, Yiming Yu, Huize An, Xingyan Yang and Yixue Yang
Remote Sens. 2023, 15(19), 4871; https://doi.org/10.3390/rs15194871 - 8 Oct 2023
Viewed by 1361
Abstract
Ground-level ozone (O3) is a well-known atmospheric pollutant aside from particulate matter. China as a global populous country is facing serious surface O3 pollution. To detect the complex spatiotemporal transformation of the population exposure to ambient O3 pollution [...] Read more.
Ground-level ozone (O3) is a well-known atmospheric pollutant aside from particulate matter. China as a global populous country is facing serious surface O3 pollution. To detect the complex spatiotemporal transformation of the population exposure to ambient O3 pollution in China from 2005 to 2019, the Bayesian multi-stage spatiotemporal evolution hierarchy model was employed. To insight the drivers of the population exposure to ambient O3 pollution in China, a Bayesian spatiotemporal LASSO regression model (BST-LASSO-RM) and a spatiotemporal propensity score matching (STPSM) were firstly applied; then, a spatiotemporal causal inference method integrating the BST-LASSO-RM and STPSM was presented. The results show that the spatial pattern of the annual population-weighted ground-level O3 (PWGLO3) concentrations, representing population exposure to ambient O3, in China has transformed since 2014. Most regions (72.2%) experienced a decreasing trend in PWGLO3 pollution in the early stage, but in the late stage, most areas (79.3%) underwent an increasing trend. Some drivers on PWGLO3 concentrations have partial spatial spillover effects. The PWGLO3 concentrations in a region can be driven by this region’s surrounding areas’ economic factors, wind speed, and PWGLO3 concentrations. The major drivers with six local factors in 2005–2014 changed to five local factors and one spatial adjacent factor in 2015–2019. The driving of the traffic and green factors have no spatial spillover effects. Three traffic factors showed a negative driving effect in the early stage, but only one, bus ridership per capita (BRPC), retains the negative driving effect in the late stage. The factor with the maximum driving contribution is BRPC in the early stage, but PM2.5 pollution in the late stage, and the corresponding driving contribution is 17.57%. Green area per capita and urban green coverage rates have positive driving effects. The driving effects of the climate factors intensified from the early to the later stage. Full article
(This article belongs to the Special Issue Remote Sensing of Aerosols, Planetary Boundary Layer, and Clouds)
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23 pages, 20473 KiB  
Article
Application of Sparse Regularization in Spherical Radial Basis Functions-Based Regional Geoid Modeling in Colorado
by Haipeng Yu, Guobin Chang, Shubi Zhang, Yuhua Zhu and Yajie Yu
Remote Sens. 2023, 15(19), 4870; https://doi.org/10.3390/rs15194870 - 8 Oct 2023
Cited by 4 | Viewed by 1336
Abstract
Spherical radial basis function (SRBF) is an effective method for calculating regional gravity field models. Calculating gravity field models with high accuracy and resolution requires dense basis functions, resulting in complex models. This study investigated the application of sparse regularization in SRBFs-based regional [...] Read more.
Spherical radial basis function (SRBF) is an effective method for calculating regional gravity field models. Calculating gravity field models with high accuracy and resolution requires dense basis functions, resulting in complex models. This study investigated the application of sparse regularization in SRBFs-based regional gravity field modeling. L1-norm regularization, also known as the least absolute shrinkage selection operator (LASSO), was employed in the parameter estimation procedure. LASSO differs from L2-norm regularization in that the solution obtained by LASSO is sparse, specifically with a portion of the parameters being zero. A sparse model would be advantageous for improving the numerical efficiency by reducing the number of SRBFs. The optimization problem of the LASSO was solved using the fast iterative shrinkage threshold algorithm, which is known for its high efficiency. The regularization parameter was selected using the Akaike information criterion. It was specifically tailored to the L1-norm regularization problem. An approximate covariance matrix of the estimated parameters in the sparse solution was analytically constructed from a Bayesian viewpoint. Based on the remove–compute–restore technique, a regional geoid model of Colorado (USA) was calculated. The numerical results suggest that the LASSO adopted in this study provided competitive results compared to Tikhonov regularization; however, the number of basis functions in the final model was less than 25% of the Tikhonov regularization. Without significantly reducing model accuracy, the LASSO solution provides a very simple model. This is the first study to apply the LASSO to SRBFs-based modeling of the regional gravity field in real gravity observation data. Full article
(This article belongs to the Section Earth Observation Data)
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24 pages, 13285 KiB  
Article
Study on the Regeneration Probability of Understory Coniferous Saplings in the Liangshui Nature Reserve Based on Four Modeling Techniques
by Haiping Zhao, Yuman Sun, Weiwei Jia, Fan Wang, Zipeng Zhao and Simin Wu
Remote Sens. 2023, 15(19), 4869; https://doi.org/10.3390/rs15194869 - 8 Oct 2023
Viewed by 1417
Abstract
Forests are one of the most important natural resources for humans, and understanding the regeneration probability of undergrowth in forests is very important for future forest spatial structure and forest management. In addition, the regeneration of understory saplings is a key process in [...] Read more.
Forests are one of the most important natural resources for humans, and understanding the regeneration probability of undergrowth in forests is very important for future forest spatial structure and forest management. In addition, the regeneration of understory saplings is a key process in the restoration of forest ecosystems. By studying the probability of sapling regeneration in forests, we can understand the impact of different stand factors and environmental factors on sapling regeneration. This could help provide a scientific basis for the restoration and protection of forest ecosystems. The Liangshui Nature Reserve of Yichun City, Heilongjiang Province, is a coniferous and broadleaved mixed forest. In this study, we assess the regeneration probability of coniferous saplings (CRP) in natural forests in 665 temporary plots in the Liangshui Nature Reserve. Using Sentinel-1 and Sentinel-2 images provided by the European Space Agency, as well as digital elevation model (DEM) data, we calculated the vegetation index, microwave vegetation index (RVI S1), VV, VH, texture features, slope, and DEM and combined them with field survey data to construct a logistic regression (LR) model, geographically weighted logistic regression (GWLR) model, random forest (RF) model, and multilayer perceptron (MLP) model to predict and analyze the CRP value of each pixel in the study area. The accuracy of the models was evaluated with the average values of the area under the ROC curve (AUC), kappa coefficient (KAPPA), root mean square error (RMSE), and mean absolute error (MAE) verified by five-fold cross-validation. The results showed that the RF model had the highest accuracy. The variable factor with the greatest impact on CRP was the DEM. The construction of the GWLR model considered more spatial factors and had a lower residual Moran index value. The four models had higher CRP prediction results in the low-latitude and low-longitude regions of the study area, and in the high-latitude and high-longitude regions of the study area, most pixels had a CRP value of 0 (i.e., no coniferous sapling regeneration occurred). Full article
(This article belongs to the Special Issue Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling)
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16 pages, 10728 KiB  
Technical Note
Revealing the Kinematic Characteristics and Tectonic Implications of a Buried Fault through the Joint Inversion of GPS and Strong-Motion Data: The Case of the 2022 Mw7.0 Taiwan Earthquake
by Chuanchao Huang, Chaodi Xie, Guohong Zhang, Wan Wang, Min-Chien Tsai and Jyr-Ching Hu
Remote Sens. 2023, 15(19), 4868; https://doi.org/10.3390/rs15194868 - 8 Oct 2023
Viewed by 1452
Abstract
Understanding the kinematic characteristics of the Longitudinal Valley Fault Zone (LVFZ) can help us to better understand the evolution of orogens. The 2022 Mw7.0 Taitung earthquake that occurred in Taiwan provides us with a good opportunity to understand the motion characteristics of the [...] Read more.
Understanding the kinematic characteristics of the Longitudinal Valley Fault Zone (LVFZ) can help us to better understand the evolution of orogens. The 2022 Mw7.0 Taitung earthquake that occurred in Taiwan provides us with a good opportunity to understand the motion characteristics of the Central Range Fault (CRF) and the strain partitioning pattern within the Longitudinal Valley Fault (LVF). We obtained the coseismic displacement and slip distribution of the 2022 Taiwan earthquake based on the strong-motion and GPS data available. The causative fault of this earthquake is the west-dipping Central Range Fault, which is buried beneath the western boundary of the LVF. The coseismic displacement field exhibits a quadrant distribution pattern, indicating a left-lateral strike-slip mechanism with a maximum displacement exceeding 1.25 m. The joint inversion results show that the size of the main asperity is 40 km × 20 km, and the maximum slip amount of 2.6 m is located at a depth of 10 km, equivalent to an earthquake of Mw7.04. The LVFZ is composed of LVF and CRF, which accommodates nearly half of the oblique convergence rate between the Philippine Sea Plate and the Eurasian Plate. There is a phenomenon of strain partitioning in the southern segment of the Longitudinal Valley Fault Zone. The Central Mountain Range Fault is primarily responsible for accommodating strike-slip motion, while the Longitudinal Valley Fault is mainly responsible for accommodating thrust motion. Full article
(This article belongs to the Special Issue Monitoring Subtle Ground Deformation of Geohazards from Space)
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15 pages, 5099 KiB  
Article
The Latest Desertification Process and Its Driving Force in Alxa League from 2000 to 2020
by Jiali Xie, Zhixiang Lu, Shengchun Xiao and Changzhen Yan
Remote Sens. 2023, 15(19), 4867; https://doi.org/10.3390/rs15194867 - 8 Oct 2023
Cited by 6 | Viewed by 1474
Abstract
Alxa League of Inner Mongolia Autonomous Region is a concentrated desert distribution area in China, and the latest desertification process and its driving mechanism under the comprehensive influence of the extreme dry climate and intense human activities has attracted much attention. Landsat data, [...] Read more.
Alxa League of Inner Mongolia Autonomous Region is a concentrated desert distribution area in China, and the latest desertification process and its driving mechanism under the comprehensive influence of the extreme dry climate and intense human activities has attracted much attention. Landsat data, including ETM+ images obtained in 2000, TM images obtained in 2010, and OLI images obtained in 2020, were used to extract three periods of desertification land information using the classification and regression tree (CART) decision tree classification method in Alxa League. The spatio-temporal variation characteristics of desertification land were analyzed by combining the transfer matrix and barycenter migration model; the effects of climate change and human activities on regional desertification evolution were separated and recombined using the multiple regression residual analysis method and by considering the influence of non-zonal factors. The results showed that from 2000 to 2020, the overall area of desertification land in Alxa League was reduced, the desertification degree was alleviated, the desertification trend was reversed, and the desertification degree in the northern part of the region was more serious than in the southern part. The barycenter of the slight, moderate, and severe desertification land migrated to the southeast, whereas the serious desertification land’s barycenter migrated to the northwest in the period of 2000–2010; however, all of them hardly moved from 2010 to 2020. The degree of desertification reversal in the south was more significant than in the north. Regional desertification reversal was mainly influenced by the combination of human activities and climate change, and the area accounted for 61.5%; meanwhile, the localized desertification development was mainly affected by human activities and accounted for 76.8%. Full article
(This article belongs to the Special Issue Remote Sensing for Land Degradation and Drought Monitoring II)
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33 pages, 5284 KiB  
Review
Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management: A Review
by Michael Gbenga Ogungbuyi, Caroline Mohammed, Iffat Ara, Andrew M. Fischer and Matthew Tom Harrison
Remote Sens. 2023, 15(19), 4866; https://doi.org/10.3390/rs15194866 - 8 Oct 2023
Cited by 6 | Viewed by 2641
Abstract
The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the quantification of remote methods, there has been [...] Read more.
The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the quantification of remote methods, there has been growing interest in using satellite imagery and machine learning to quantify pastures at the field scale. Here, we systematically reviewed 214 journal articles published between 1991 to 2021 to determine how vegetation indices derived from satellite imagery impacted the type and quantification of pasture indicators. We reveal that previous studies have been limited by highly spatiotemporal satellite imagery and prognostic analytics. While the number of studies on pasture classification, degradation, productivity, and management has increased exponentially over the last five years, the majority of vegetation parameters have been derived from satellite imagery using simple linear regression approaches, which, as a corollary, often result in site-specific parameterization that become spurious when extrapolated to new sites or production systems. Few studies have successfully invoked machine learning as retrievals to understand the relationship between image patterns and accurately quantify the biophysical variables, although many studies have purported to do so. Satellite imagery has contributed to the ability to quantify pasture indicators but has faced the barrier of monitoring at the paddock/field scale (20 hectares or less) due to (1) low sensor (coarse pixel) resolution, (2) infrequent satellite passes, with visibility in many locations often constrained by cloud cover, and (3) the prohibitive cost of accessing fine-resolution imagery. These issues are perhaps a reflection of historical efforts, which have been directed at the continental or global scales, rather than at the field level. Indeed, we found less than 20 studies that quantified pasture biomass at pixel resolutions of less than 50 hectares. As such, the use of remote sensing technologies by agricultural practitioners has been relatively low compared with the adoption of physical agronomic interventions (such as ‘no-till’ practices). We contend that (1) considerable opportunity for advancement may lie in fusing optical and radar imagery or hybrid imagery through the combination of optical sensors, (2) there is a greater accessibility of satellite imagery for research, teaching, and education, and (3) developers who understand the value proposition of satellite imagery to end users will collectively fast track the advancement and uptake of remote sensing applications in agriculture. Full article
(This article belongs to the Special Issue Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)
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29 pages, 8828 KiB  
Review
An Overview of Coastline Extraction from Remote Sensing Data
by Xixuan Zhou, Jinyu Wang, Fengjie Zheng, Haoyu Wang and Haitao Yang
Remote Sens. 2023, 15(19), 4865; https://doi.org/10.3390/rs15194865 - 8 Oct 2023
Cited by 11 | Viewed by 4799
Abstract
The coastal zone represents a unique interface between land and sea, and addressing the ecological crisis it faces is of global significance. One of the most fundamental and effective measures is to extract the coastline’s location on a large scale, dynamically, and accurately. [...] Read more.
The coastal zone represents a unique interface between land and sea, and addressing the ecological crisis it faces is of global significance. One of the most fundamental and effective measures is to extract the coastline’s location on a large scale, dynamically, and accurately. Remote sensing technology has been widely employed in coastline extraction due to its temporal, spatial, and sensor diversity advantages. Substantial progress has been made in coastline extraction with diversifying data types and information extraction methods. This paper focuses on discussing the research progress related to data sources and extraction methods for remote sensing-based coastline extraction. We summarize the suitability of data and some extraction algorithms for several specific coastline types, including rocky coastlines, sandy coastlines, muddy coastlines, biological coastlines, and artificial coastlines. We also discuss the significant challenges and prospects of coastline dataset construction, remotely sensed data selection, and the applicability of the extraction method. In particular, we propose the idea of extracting coastlines based on the coastline scene knowledge map (CSKG) semantic segmentation method. This review serves as a comprehensive reference for future development and research pertaining to coastal exploitation and management. Full article
(This article belongs to the Section Ocean Remote Sensing)
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