Remote Sensing

14 pages, 7560 KiB

Open AccessArticle

Evaluation of Tidal Effect in Long-Strip DInSAR Measurements Based on GPS Network and Tidal Models

by Wei Peng, Qijie Wang, Yunmeng Cao, Xuemin Xing and Wenjie Hu

Remote Sens. 2022, 14(12), 2954; https://doi.org/10.3390/rs14122954 - 20 Jun 2022

Cited by 6 | Viewed by 2086

A long-strip differential interferometric synthetic aperture radar (DInSAR) measurement based on multi-frame image mosaicking is currently the realizable approach to measure large-scale ground deformation. As the spatial range of the mosaicked images increases, the nonlinear variation of ground ocean tidal loading (OTL) displacements [...] Read more.

A long-strip differential interferometric synthetic aperture radar (DInSAR) measurement based on multi-frame image mosaicking is currently the realizable approach to measure large-scale ground deformation. As the spatial range of the mosaicked images increases, the nonlinear variation of ground ocean tidal loading (OTL) displacements is more significant, and using plane fitting to remove the large-scale errors will produce large tidal displacement residuals in a region with a complex coastline. To conveniently evaluate the ground tidal effect on mosaic DInSAR interferograms along the west coast of the U.S., a three-dimensional ground OTL displacements grid is generated by integrating tidal constituents’ estimation of the GPS reference station network and global/regional ocean tidal models. Meanwhile, a solid earth tide (SET) model based on IERS conventions is used to estimate the high-precision SET displacements. Experimental results show that the OTL and SET in a long-strip interferogram can reach 77.5 mm, which corresponds to a 19.3% displacement component. Furthermore, the traditional bilinear ramp fitting methods will cause 7.2~20.3 mm residual tidal displacement in the mosaicked interferograms, and the integrated tidal constituents displacements calculation method can accurately eliminate the tendency of tidal displacement in the long-strip interferograms. Full article

(This article belongs to the Special Issue Ground and Structural Deformations Monitoring Systems Integrating Remote Sensing and Ground-Based Data)

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20 pages, 5636 KiB

Open AccessArticle

Classification of Electronic Devices Using a Frequency-Swept Harmonic Radar Approach

by Handan Ilbegi, Halil Ibrahim Turan, Imam Samil Yetik and Harun Taha Hayvaci

Remote Sens. 2022, 14(12), 2953; https://doi.org/10.3390/rs14122953 - 20 Jun 2022

Cited by 5 | Viewed by 2289

Abstract

A new method to classify electronic devices using a Frequency-Swept Harmonic Radar (FSHR) approach is proposed in this paper. The FSHR approach enables us to utilize the frequency diversity of the harmonic responses of the electronic circuits. Unlike previous studies, a frequency-swept signal [...] Read more.

A new method to classify electronic devices using a Frequency-Swept Harmonic Radar (FSHR) approach is proposed in this paper. The FSHR approach enables us to utilize the frequency diversity of the harmonic responses of the electronic circuits. Unlike previous studies, a frequency-swept signal with a constant power is transmitted to Electronic Circuits Under Test (ECUTs). The harmonic response to a frequency-swept transmitted signal is found to be distinguishable for different types of ECUTs. Statistical and Fourier features of the harmonic responses are derived for classification. Later, the harmonic characteristics of the ECUTs are depicted in 3D harmonic and feature spaces for classification. Three-dimensional harmonic and feature spaces are composed of the first three harmonics of the re-radiated signal and the statistical or Fourier features, respectively. We extensively evaluate the performance of our novel method through Monte Carlo simulations in the presence of noise. Full article

(This article belongs to the Special Issue Nonlinear Junction Detection and Harmonic Radar)

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18 pages, 9908 KiB

Open AccessArticle

Spectroscopic and Petrographic Investigations of Lunar Mg-Suite Meteorite Northwest Africa 8687

by Lang Qin, Xing Wu, Liying Huang, Yang Liu and Yongliao Zou

Remote Sens. 2022, 14(12), 2952; https://doi.org/10.3390/rs14122952 - 20 Jun 2022

Cited by 2 | Viewed by 2858

Abstract

Magnesian suite (Mg-suite) rocks represent plutonic materials from the lunar crust, and their global distribution can provide critical information for the early magmatic differentiation and crustal asymmetries of the Moon. Visible and near-infrared (VNIR) spectrometers mounted on orbiters and rovers have been proven [...] Read more.

Magnesian suite (Mg-suite) rocks represent plutonic materials from the lunar crust, and their global distribution can provide critical information for the early magmatic differentiation and crustal asymmetries of the Moon. Visible and near-infrared (VNIR) spectrometers mounted on orbiters and rovers have been proven to be powerful approaches for planetary mineral mapping, which are instrumental in diagnosing Mg-suite rocks. However, due to the scarcity and diversity of Mg-suite samples, laboratory measurements with variable proportions of minerals are imperative for spectral characterization. In this study, spectroscopic investigation and petrographic study were conducted on lunar Mg-suite meteorite Northwest Africa 8687. We classify the sample as a pink spinel-bearing anorthositic norite through spectral and petrographic characteristics. The ground-truth information of the Mg-suite rock is provided for future exploration. Meanwhile, the results imply that the VNIR technique has the potential to identify highland rock types by mineral modal abundance and could further be applied in extraterrestrial samples for primary examination due to its advantage of being fast and non-destructive. Full article

(This article belongs to the Special Issue Planetary Geologic Mapping and Remote Sensing)

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25 pages, 9355 KiB

Open AccessArticle

Turbulence Detection in the Atmospheric Boundary Layer Using Coherent Doppler Wind Lidar and Microwave Radiometer

by Pu Jiang, Jinlong Yuan, Kenan Wu, Lu Wang and Haiyun Xia

Remote Sens. 2022, 14(12), 2951; https://doi.org/10.3390/rs14122951 - 20 Jun 2022

Cited by 14 | Viewed by 3496

Abstract

The refractive index structure constant (

C_{n}^{2}

) is a key parameter used in describing the influence of turbulence on laser transmissions in the atmosphere. Three different methods for estimating

C_{n}^{2}

were analyzed in detail. A new method that [...] Read more.

The refractive index structure constant (

C_{n}^{2}

) is a key parameter used in describing the influence of turbulence on laser transmissions in the atmosphere. Three different methods for estimating

C_{n}^{2}

were analyzed in detail. A new method that uses a combination of these methods for continuous

C_{n}^{2}

profiling with both high temporal and spatial resolution is proposed and demonstrated. Under the assumption of the Kolmogorov “2/3 law”, the

C_{n}^{2}

profile can be calculated by using the wind field and turbulent kinetic energy dissipation rate (TKEDR) measured by coherent Doppler wind lidar (CDWL) and other meteorological parameters derived from a microwave radiometer (MWR). In a horizontal experiment, a comparison between the results from our new method and measurements made by a large aperture scintillometer (LAS) is conducted. The correlation coefficient, mean error, and standard deviation between them in a six-day observation are 0.8073, 8.18 × 10⁻¹⁶ m^−2/3 and 1.27 × 10⁻¹⁵ m^−2/3, respectively. In the vertical direction, the continuous profiling results of

C_{n}^{2}

and other turbulence parameters with high resolution in the atmospheric boundary layer (ABL) are retrieved. In addition, the limitation and uncertainty of this method under different circumstances were analyzed, which shows that the relative error of

C_{n}^{2}

estimation normally does not exceed 30% under the convective boundary layer (CBL). Full article

(This article belongs to the Special Issue Lidar for Advanced Classification and Retrieval of Aerosols)

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21 pages, 19246 KiB

Open AccessArticle

Damage Properties of the Block-Stone Embankment in the Qinghai–Tibet Highway Using Ground-Penetrating Radar Imagery

by Shunshun Qi, Guoyu Li, Dun Chen, Mingtang Chai, Yu Zhou, Qingsong Du, Yapeng Cao, Liyun Tang and Hailiang Jia

Remote Sens. 2022, 14(12), 2950; https://doi.org/10.3390/rs14122950 - 20 Jun 2022

Cited by 9 | Viewed by 4689

Abstract

The block-stone embankment is a special type of embankment widely used to protect the stability of the underlying warm and ice-rich permafrost. Under the influence of multiple factors, certain damages will still occur in the block-stone embankment after a period of operation, which [...] Read more.

The block-stone embankment is a special type of embankment widely used to protect the stability of the underlying warm and ice-rich permafrost. Under the influence of multiple factors, certain damages will still occur in the block-stone embankment after a period of operation, which may weaken or destroy its cooling function, introducing more serious damages to the Qinghai–Tibet Highway (QTH). Ground-penetrating radar (GPR), a nondestructive testing technique, was adopted to investigate the damage properties of the damaged block-stone embankment. GPR imagery, together with the other data and methods (structural characteristics, field survey data, GPR parameters, etc.), indicated four categories of damage: (i) loosening of the upper sand-gravel layer; (ii) loosening of the block-stone layer; (iii) settlement of the block-stone layer; and (iv) dense filling of the block-stones layer. The first two conditions were widely distributed, whereas the settlement and dense filling of the block-stone layer were less so, and the other combined damages also occurred frequently. The close correlation between the different damages indicated a causal relationship. A preliminary discussion of these observations about the influences on the formation of the damage of the block-stone embankment is included. The findings provide some points of reference for the future construction and maintenance of block-stone embankments in permafrost regions. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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20 pages, 7878 KiB

Open AccessArticle

On-Orbit Calibration for Spaceborne Line Array Camera and LiDAR

by Xiangpeng Xu, Sheng Zhuge, Banglei Guan, Bin Lin, Shuwei Gan, Xia Yang and Xiaohu Zhang

Remote Sens. 2022, 14(12), 2949; https://doi.org/10.3390/rs14122949 - 20 Jun 2022

Cited by 5 | Viewed by 2268

Abstract

For a multi-mode Earth observation satellite carrying a line array camera and a multi-beam line array LiDAR, the relative installation attitude of the two sensors is of great significance. In this paper, we propose an on-orbit calibration method for the relative installation attitude [...] Read more.

For a multi-mode Earth observation satellite carrying a line array camera and a multi-beam line array LiDAR, the relative installation attitude of the two sensors is of great significance. In this paper, we propose an on-orbit calibration method for the relative installation attitude of the camera and the LiDAR with no need for the calibration field and additional satellite attitude maneuvers. Firstly, the on-orbit joint calibration model of the relative installation attitude of the two sensors is established. However, there may exist a multi-solution problem in the solving of the above model constrained by non-ground control points. Thus, an alternate iterative method by solving the pseudo-absolute attitude matrix of each sensor in turn is proposed. The numerical validation and simulation experiments results show that the relative positioning error of the line array camera and the LiDAR in the horizontal direction of the ground can be limited to 0.8 m after correction by the method in this paper. Full article

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18 pages, 7402 KiB

Open AccessArticle

Spatial and Temporal Variability of Key Bio-Temperature Indicators and Their Effects on Vegetation Dynamics in the Great Lakes Region of Central Asia

by Xuan Gao and Dongsheng Zhao

Remote Sens. 2022, 14(12), 2948; https://doi.org/10.3390/rs14122948 - 20 Jun 2022

Viewed by 2231

Abstract

Dryland ecosystems are fragile to climate change due to harsh environmental conditions. Climate change affects vegetation growth primarily by altering some key bio-temperature thresholds. Key bio-temperatures are closely related to vegetation growth, and slight changes could produce substantial effects on ecosystem structure and [...] Read more.

Dryland ecosystems are fragile to climate change due to harsh environmental conditions. Climate change affects vegetation growth primarily by altering some key bio-temperature thresholds. Key bio-temperatures are closely related to vegetation growth, and slight changes could produce substantial effects on ecosystem structure and function. Therefore, this study selected the number of days with daily mean temperature above 0 °C (DT₀), 5 °C (DT₅), 10 °C (DT₁₀), 20 °C (DT₂₀), the start of growing season (SGS), the end of growing season (EGS), and the length of growing season (LGS) as bio-temperature indicators to analyze the response of vegetation dynamics to climate change in the Great Lakes Region of Central Asia (GLRCA) for the period 1982–2014. On the regional scale, DT₀, DT₅, DT₁₀, and DT₂₀ exhibited an overall increasing trend. Spatially, most of the study area showed that the negative correlation between DT₀, DT₅, DT₁₀, DT₂₀ with the annual Normalized Difference Vegetation Index (NDVI) increased with increasing bio-temperature thresholds. In particular, more than 88.3% of the study area showed a negative correlation between annual NDVI and DT₂₀, as increased DT₂₀ exacerbated ecosystem drought. Moreover, SGS exhibited a significantly advanced trend at a rate of −0.261 days/year for the regional scale, while EGS experienced a significantly delayed trend at a rate of 0.164 days/year. Because of changes in SGS and EGS, LGS across the GLRCA was extended at a rate of 0.425 days/year, which was mainly attributed to advanced SGS. In addition, our study revealed that about 53.6% of the study area showed a negative correlation between annual NDVI and LGS, especially in the north, indicating a negative effect of climate warming on vegetation growth in the drylands. Overall, the results of this study will help predict the response of vegetation to future climate change in the GLRCA, and support decision-making for implementing effective ecosystem management in arid and semi-arid regions. Full article

(This article belongs to the Topic Climate Change and Environmental Sustainability)

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15 pages, 15759 KiB

Open AccessArticle

Modeling Potential Impacts on Regional Climate Due to Land Surface Changes across Mongolia Plateau

by Guangshuai Li, Lingxue Yu, Tingxiang Liu, Yue Jiao and Jiaxin Yu

Remote Sens. 2022, 14(12), 2947; https://doi.org/10.3390/rs14122947 - 20 Jun 2022

Cited by 8 | Viewed by 2246

Abstract

Although desertification has greatly increased across the Mongolian Plateau during the last decades of the 20th century, recent satellite records documented increasing vegetation growth since the 21st century in some areas of the Mongolian Plateau. Compared to the study of desertification, the opposite [...] Read more.

Although desertification has greatly increased across the Mongolian Plateau during the last decades of the 20th century, recent satellite records documented increasing vegetation growth since the 21st century in some areas of the Mongolian Plateau. Compared to the study of desertification, the opposite characteristics of land use and vegetation cover changes and their different effects on regional land–atmosphere interaction factors still lack enough attention across this vulnerable region. Using long-term time-series multi-source satellite records and regional climate model, this study investigated the climate feedback to the observed land surface changes from the 1990s to the 2010s in the Mongolia Plateau. Model simulation suggests that vegetation greening induced a local cooling effect, while the warming effect is mainly located in the vegetation degradation area. For the typical vegetation greening area in the southeast of Inner Mongolia, latent heat flux increased over 2 W/m² along with the decrease of sensible heat flux over 2 W/m², resulting in a total evapotranspiration increase by 0.1~0.2 mm/d and soil moisture decreased by 0.01~0.03 mm/d. For the typical vegetation degradation area in the east of Mongolia and mid-east of Inner Mongolia, the latent heat flux decreased over 2 W/m² along with the increase of sensible heat flux over 2 W/m² obviously, while changes in moisture cycling were spatially more associated with variations of precipitation. It means that precipitation still plays an important role in soil moisture for most areas, and some areas would be at potential risk of drought with the asynchronous increase of evapotranspiration and precipitation. Full article

(This article belongs to the Special Issue Remote Sensing for Advancing Nature-Based Climate Solutions)

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22 pages, 7365 KiB

Open AccessArticle

A Dual-Generator Translation Network Fusing Texture and Structure Features for SAR and Optical Image Matching

by Han Nie, Zhitao Fu, Bo-Hui Tang, Ziqian Li, Sijing Chen and Leiguang Wang

Remote Sens. 2022, 14(12), 2946; https://doi.org/10.3390/rs14122946 - 20 Jun 2022

Cited by 6 | Viewed by 3443

Abstract

The matching problem for heterologous remote sensing images can be simplified to the matching problem for pseudo homologous remote sensing images via image translation to improve the matching performance. Among such applications, the translation of synthetic aperture radar (SAR) and optical images is [...] Read more.

The matching problem for heterologous remote sensing images can be simplified to the matching problem for pseudo homologous remote sensing images via image translation to improve the matching performance. Among such applications, the translation of synthetic aperture radar (SAR) and optical images is the current focus of research. However, the existing methods for SAR-to-optical translation have two main drawbacks. First, single generators usually sacrifice either structure or texture features to balance the model performance and complexity, which often results in textural or structural distortion; second, due to large nonlinear radiation distortions (NRDs) in SAR images, there are still visual differences between the pseudo-optical images generated by current generative adversarial networks (GANs) and real optical images. Therefore, we propose a dual-generator translation network for fusing structure and texture features. On the one hand, the proposed network has dual generators, a texture generator, and a structure generator, with good cross-coupling to obtain high-accuracy structure and texture features; on the other hand, frequency-domain and spatial-domain loss functions are introduced to reduce the differences between pseudo-optical images and real optical images. Extensive quantitative and qualitative experiments show that our method achieves state-of-the-art performance on publicly available optical and SAR datasets. Our method improves the peak signal-to-noise ratio (PSNR) by 21.0%, the chromatic feature similarity (FSIMc) by 6.9%, and the structural similarity (SSIM) by 161.7% in terms of the average metric values on all test images compared with the next best results. In addition, we present a before-and-after translation comparison experiment to show that our method improves the average keypoint repeatability by approximately 111.7% and the matching accuracy by approximately 5.25%. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Algorithm for the Analysis of Remote Sensing Images)

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21 pages, 11037 KiB

Open AccessArticle

Positioning of Quadruped Robot Based on Tightly Coupled LiDAR Vision Inertial Odometer

by Fangzheng Gao, Wenjun Tang, Jiacai Huang and Haiyang Chen

Remote Sens. 2022, 14(12), 2945; https://doi.org/10.3390/rs14122945 - 20 Jun 2022

Cited by 8 | Viewed by 3104

Abstract

Quadruped robots, an important class of unmanned aerial vehicles, have broad potential for applications in education, service, industry, military, and other fields. Their independent positioning plays a key role for completing assigned tasks in a complex environment. However, positioning based on global navigation [...] Read more.

Quadruped robots, an important class of unmanned aerial vehicles, have broad potential for applications in education, service, industry, military, and other fields. Their independent positioning plays a key role for completing assigned tasks in a complex environment. However, positioning based on global navigation satellite systems (GNSS) may result in GNSS jamming and quadruped robots not operating properly in environments sheltered by buildings. In this paper, a tightly coupled LiDAR vision inertial odometer (LVIO) is proposed to address the positioning inaccuracy of quadruped robots, which have poor mileage information obtained though legs and feet structures only. With this optimization method, the point cloud data obtained by 3D LiDAR, the image feature information obtained by binocular vision, and the IMU inertial data are combined to improve the precise indoor and outdoor positioning of a quadruped robot. This method reduces the errors caused by the uniform motion model in laser odometer as well as the image blur caused by rapid movements of the robot, which can lead to error-matching in a dynamic scene; at the same time, it alleviates the impact of drift on inertial measurements. Finally, the quadruped robot in the laboratory is used to build a physical platform for verification. The experimental results show that the designed LVIO effectively realizes the positioning of four groups of robots with high precision and strong robustness, both indoors or outdoors, which verify the feasibility and effectiveness of the proposed method. Full article

(This article belongs to the Special Issue UAV Positioning: From Ground to Sky)

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14 pages, 859 KiB

Open AccessTechnical Note

Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm

by Yongkun Zhou, Dan Song, Bowen Ding, Bin Rao, Man Su and Wei Wang

Remote Sens. 2022, 14(12), 2944; https://doi.org/10.3390/rs14122944 - 20 Jun 2022

Cited by 6 | Viewed by 2044

Abstract

The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an [...] Read more.

The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise. Full article

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16 pages, 8460 KiB

Open AccessArticle

Tight Integration of GNSS and Static Level for High Accuracy Dilapidated House Deformation Monitoring

by Jian Yang, Weiming Tang, Wei Xuan and Ruijie Xi

Remote Sens. 2022, 14(12), 2943; https://doi.org/10.3390/rs14122943 - 20 Jun 2022

Cited by 1 | Viewed by 1831

Abstract

Global Navigation Satellite System (GNSS) can provide high-precision three-dimensional real-time or quasi-real-time changes of monitoring points automatically in house monitoring applications. However, due to the signal sheltering problem, large observation noise and multipath effects in urban observing environment with dense buildings, ambiguity resolution [...] Read more.

Global Navigation Satellite System (GNSS) can provide high-precision three-dimensional real-time or quasi-real-time changes of monitoring points automatically in house monitoring applications. However, due to the signal sheltering problem, large observation noise and multipath effects in urban observing environment with dense buildings, ambiguity resolution would be hard, and GNSS accuracy cannot always achieve millimeter level to satisfy the requirement of house monitoring. Static level is a precision instrument for measuring elevation difference and its variations, with a precision up to sub-millimeter level. It could be integrated with GNSS to improve the positioning accuracy in height direction. However, the existing integration of GNSS and static level is mostly on a respective results level. In this study, we proposed a method of integrating GNSS and static level observations tightly to enhance the GNSS positioning performance. The hardware design and integration mathematic model in data processing were introduced, and a group of experiments were carried out to verify the performance in positioning with and without the static level observation constraints. It found that the vertical monitoring measurement results of static level can achieve less than 1 mm. The GNSS ambiguity resolution performance can be improved by incorporating the measurement of static level into GNSS positioning equation as external constraints, and the precision of GNSS float solutions was significantly improved. Finally, the static level constraint can further improve the accuracy of the fixed solution from about 2 cm to better than 2 mm in vertical direction, which is even better than the accuracy in horizontal directions with about 3–6 mm with the static level constraint. The tight combination data processing algorithm can significantly improve the working efficiency, accuracy, and reliability of the application of dangerous house monitoring. Full article

(This article belongs to the Section Earth Observation Data)

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29 pages, 31768 KiB

Open AccessArticle

Risk Assessment of Debris Flow in a Mountain-Basin Area, Western China

by Yanyan Zhou, Dongxia Yue, Geng Liang, Shuangying Li, Yan Zhao, Zengzu Chao and Xingmin Meng

Remote Sens. 2022, 14(12), 2942; https://doi.org/10.3390/rs14122942 - 20 Jun 2022

Cited by 17 | Viewed by 3251

Abstract

Debris flow risk comprehensively reflects the natural and social properties of debris flow disasters and is composed of the risk of the disaster-causing body and the vulnerability of the carrier. The Bailong River Basin (BRB) is a typical mountainous environment where regional debris [...] Read more.

Debris flow risk comprehensively reflects the natural and social properties of debris flow disasters and is composed of the risk of the disaster-causing body and the vulnerability of the carrier. The Bailong River Basin (BRB) is a typical mountainous environment where regional debris flow disasters occur frequently, seriously threatening the lives of residents, infrastructure, and regional ecological security. However, there are few studies on the risk assessment of mountainous debris flow disasters in the BRB. By considering a complete catchment, based on remote sensing and GIS methods, we selected 17 influencing factors, such as area, average slope, lithology, NPP, average annual precipitation, landslide density, river density, fault density, etc. and applied a machine learning algorithm to establish a hazard assessment model. The analysis shows that the Extra Trees model is the most effective for debris flow hazard assessments, with an accuracy rate of 88%. Based on socio-economic data and debris flow disaster survey data, we established a vulnerability assessment model by applying the Contributing Weight Superposition method. We used the product of debris flow hazard and vulnerability to construct a debris flow risk assessment model. The catchments at a very high-risk were distributed mainly in the urban area of Wudu District and the northern part of Tanchang County, that is, areas with relatively dense economic activities and a high disaster frequency. These findings indicate that the assessment results provide scientific support for planning measures to prevent or reduce debris flow hazards. The proposed assessment methods can also be used to provide relevant guidance for a regional risk assessment of debris flows in the BRB and other regions. Full article

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14 pages, 5736 KiB

Open AccessTechnical Note

Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices

by Yaron Michael, Gilad Kozokaro, Steve Brenner and Itamar M. Lensky

Remote Sens. 2022, 14(12), 2941; https://doi.org/10.3390/rs14122941 - 20 Jun 2022

Cited by 4 | Viewed by 3265

Abstract

Wildfire simulations depend on fuel representation. Present fuel models are mainly based on the density and properties of different vegetation types. This study aims to improve the accuracy of WRF-Fire wildfire simulations, by using synthetic-aperture radar (SAR) data to estimate the fuel load [...] Read more.

Wildfire simulations depend on fuel representation. Present fuel models are mainly based on the density and properties of different vegetation types. This study aims to improve the accuracy of WRF-Fire wildfire simulations, by using synthetic-aperture radar (SAR) data to estimate the fuel load and the trend of vegetation index to estimate the dryness of woody vegetation. We updated the chaparral and timber standard woody fuel classes in the WRF-Fire fuel settings. We used the ESA global above-ground biomass (AGB) based on SAR data to estimate the fuel load, and the Landsat normalized difference vegetation index (NDVI) trends of woody vegetation to estimate the fuel moisture content. These fuel sub-parameters represent the dynamic changes and spatial variability of woody fuel. We simulated two wildfires in Israel while using three different fuel models: the original 13 Anderson Fire Behavior fuel model, and two modified fuel models introducing AGB alone, and AGB and dryness. The updated fuel model (the basic fuel model plus the AGB and dryness) improved the simulation results significantly, i.e., the Jaccard similarity coefficient increased by 283% on average. Our results demonstrate the potential of combining satellite SAR data and Landsat NDVI trends to improve WRF-Fire wildfire simulations. Full article

(This article belongs to the Topic The Future of Wildfires: Latest Geo-Technologies Applied to Fires and a New Perspective of Forest Resilience)

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21 pages, 1819 KiB

Open AccessReview

Remote Sensing of Surface and Subsurface Soil Organic Carbon in Tidal Wetlands: A Review and Ideas for Future Research

by Rajneesh Sharma, Deepak R. Mishra, Matthew R. Levi and Lori A. Sutter

Remote Sens. 2022, 14(12), 2940; https://doi.org/10.3390/rs14122940 - 20 Jun 2022

Cited by 11 | Viewed by 4972

Abstract

Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland [...] Read more.

Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r² = 0.68 and 0.20, respectively, p < 2.2 × 10⁻¹⁶ for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian’s Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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22 pages, 2760 KiB

Open AccessArticle

A Mask-Guided Transformer Network with Topic Token for Remote Sensing Image Captioning

by Zihao Ren, Shuiping Gou, Zhang Guo, Shasha Mao and Ruimin Li

Remote Sens. 2022, 14(12), 2939; https://doi.org/10.3390/rs14122939 - 20 Jun 2022

Cited by 20 | Viewed by 6097

Abstract

Remote sensing image captioning aims to describe the content of images using natural language. In contrast with natural images, the scale, distribution, and number of objects generally vary in remote sensing images, making it hard to capture global semantic information and the relationships [...] Read more.

Remote sensing image captioning aims to describe the content of images using natural language. In contrast with natural images, the scale, distribution, and number of objects generally vary in remote sensing images, making it hard to capture global semantic information and the relationships between objects at different scales. In this paper, in order to improve the accuracy and diversity of captioning, a mask-guided Transformer network with a topic token is proposed. Multi-head attention is introduced to extract features and capture the relationships between objects. On this basis, a topic token is added into the encoder, which represents the scene topic and serves as a prior in the decoder to help us focus better on global semantic information. Moreover, a new Mask-Cross-Entropy strategy is designed in order to improve the diversity of the generated captions, which randomly replaces some input words with a special word (named [Mask]) in the training stage, with the aim of enhancing the model’s learning ability and forcing exploration of uncommon word relations. Experiments on three data sets show that the proposed method can generate captions with high accuracy and diversity, and the experimental results illustrate that the proposed method can outperform state-of-the-art models. Furthermore, the CIDEr score on the RSICD data set increased from 275.49 to 298.39. Full article

(This article belongs to the Topic Big Data and Artificial Intelligence)

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18 pages, 3758 KiB

Open AccessArticle

Atmospheric Effects and Precursors of Rainfall over the Swiss Plateau

by Wenyue Wang and Klemens Hocke

Remote Sens. 2022, 14(12), 2938; https://doi.org/10.3390/rs14122938 - 20 Jun 2022

Cited by 4 | Viewed by 2151

Abstract

In this study, we investigate the characteristics of atmospheric parameters before, during, and after rain events in Bern, Switzerland. Ground-based microwave radiometer data of the TROpospheric WAter RAdiometer (TROWARA) with a time resolution of 7 s, observations of a weather station, and the [...] Read more.

In this study, we investigate the characteristics of atmospheric parameters before, during, and after rain events in Bern, Switzerland. Ground-based microwave radiometer data of the TROpospheric WAter RAdiometer (TROWARA) with a time resolution of 7 s, observations of a weather station, and the composite analysis method are used to derive the temporal evolution of rain events and to identify possible rainfall precursors during a 10-year period (1199 available rain events). A rainfall climatology is developed using parameters integrated water vapor (IWV), integrated liquid water (ILW), rain rate, infrared brightness temperature (T

_{IR}

), temperature, pressure, relative humidity, wind speed, and air density. It was found that the IWV is reduced by about 2.2 mm at the end of rain compared to the beginning. IWV and T

_{IR}

rapidly increase to a peak at the onset of the rainfall. Precursors of rainfall are that the temperature reaches its maximum around 30 to 60 min before rain, while the pressure and relative humidity are minimal. IWV fluctuates the most before rain (obtained with a 10 min bandpass). In 60% of rain events, the air density decreases 2 to 6 h before the onset of rain. The seasonality and the duration of rain events as well as the diurnal cycle of atmospheric parameters are also considered. Thus, a prediction of rainfall is possible with a true detection rate of 60% by using the air density as a precursor. Further improvements in the nowcasting of rainfall are possible by using a combination of various atmospheric parameters which are monitored by a weather station and a ground-based microwave radiometer. Full article

(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management)

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11 pages, 4116 KiB

Open AccessArticle

A Martian Analogues Library (MAL) Applicable for Tianwen-1 MarSCoDe-LIBS Data Interpretation

by Changqing Liu, Zhongchen Wu, Xiaohui Fu, Ping Liu, Yanqing Xin, Ayang Xiao, Hongchun Bai, Shangke Tian, Sheng Wan, Yiheng Liu, Enming Ju, Guobin Jin, Xuejin Lu, Xiaobin Qi and Zongcheng Ling

Remote Sens. 2022, 14(12), 2937; https://doi.org/10.3390/rs14122937 - 20 Jun 2022

Cited by 4 | Viewed by 2580

Abstract

China’s first Mars exploration mission, named Tianwen-1, landed on Mars on 15 May 2021. The Mars Surface Composition Detector (MarSCoDe) payload onboard the Zhurong rover applied the laser-induced breakdown spectroscopy (LIBS) technique to acquire chemical compositions of Martian rocks and soils. The quantitative [...] Read more.

China’s first Mars exploration mission, named Tianwen-1, landed on Mars on 15 May 2021. The Mars Surface Composition Detector (MarSCoDe) payload onboard the Zhurong rover applied the laser-induced breakdown spectroscopy (LIBS) technique to acquire chemical compositions of Martian rocks and soils. The quantitative interpretation of MarSCoDe-LIBS spectra needs to establish a LIBS spectral database that requires plenty of terrestrial geological standards. In this work, we selected 316 terrestrial standards including igneous rocks, sedimentary rocks, metamorphic rocks, and ores, whose chemical compositions, rock types, and chemical weathering characteristics were comparable to those of Martian materials from previous orbital and in situ detections. These rocks were crushed, ground, and sieved into powders less than <38 μm and pressed into pellets to minimize heterogeneity at the scale of laser spot. The chemical compositions of these standards were independently measured by X-ray fluorescence (XRF). Subsequently, the LIBS spectra of MAL standards were acquired using an established LIBS system at Shandong University (SDU-LIBS). In order to evaluate the performance of these standards in LIBS spectral interpretation, we established multivariate models using partial least squares (PLS) and least absolute shrinkage and selection (LASSO) algorithms to predict the abundance of major elements based on SDU-LIBS spectra. The root mean squared error (RMSE) values of these models are comparable to those of the published models for MarSCoDe, ChemCam, and SuperCam, suggesting these PLS and LASSO models work well. From our research, we can conclude that these 316 MAL targets are good candidates to acquire geochemistry information based on the LIBS technique. These targets could be regarded as geological standards to build a LIBS database using a prototype of MarSCoDe in the near future, which is critical to obtain accurate chemical compositions of Martian rocks and soils based on MarSCoDe-LIBS spectral data. Full article

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30 pages, 5790 KiB

Open AccessArticle

Evaluation and Hydrological Application of Four Gridded Precipitation Datasets over a Large Southeastern Tibetan Plateau Basin

by Yueguan Zhang, Qin Ju, Leilei Zhang, Chong-Yu Xu and Xide Lai

Remote Sens. 2022, 14(12), 2936; https://doi.org/10.3390/rs14122936 - 19 Jun 2022

Cited by 12 | Viewed by 2437

Abstract

Reliable precipitation is crucial for hydrological studies over Tibetan Plateau (TP) basins with sparsely distributed rainfall gauges. In this study, four widely used precipitation products, including the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of the water resources (APHRODITE), the High [...] Read more.

Reliable precipitation is crucial for hydrological studies over Tibetan Plateau (TP) basins with sparsely distributed rainfall gauges. In this study, four widely used precipitation products, including the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of the water resources (APHRODITE), the High Asia Reanalysis (HAR), and the satellite-based precipitation estimates from Global Precipitation Measurement (GPM) and Tropical Rainfall Measurement Mission (TRMM), were comprehensively evaluated by combining statistical analysis and hydrological simulation over the Upper Brahmaputra (UB) River Basin of TP during 2001–2013. In respect to the statistical assessment, the overall performances of GPM and HAR are comparable to each other, and both are superior to the other two datasets. For hydrological assessment, both daily and monthly GPM-based streamflow simulations perform the best not only at the UB outlet with very good results, but they also illustrate satisfactory results at Yangcun and Lhasa hydrological stations within the UB. Runoff simulation using HAR only performs well at the UB outlet, whereas it shows poor results at both Yangcun and Lhasa stations. The simulated results based on APHRODITE and TRMM show poor performances at UB. Generally, the GPM shows an encouraging potential for hydro-meteorological investigation over UB, although with some bias in flood simulation. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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16 pages, 3684 KiB

Open AccessArticle

Space-Time Adaptive Processing Clutter-Suppression Algorithm Based on Beam Reshaping for High-Frequency Surface Wave Radar

by Jiaming Li, Qiang Yang, Xin Zhang, Xiaowei Ji and Dezhu Xiao

Remote Sens. 2022, 14(12), 2935; https://doi.org/10.3390/rs14122935 - 19 Jun 2022

Cited by 3 | Viewed by 2571

Abstract

In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced [...] Read more.

In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced HFSWR systems. However, in STAP, the pattern is distorted if a clutter component is contained in the main lobe, which leads to errors in estimating the target angle and Doppler frequency. To solve the main-lobe distortion problem, this study developed a clutter-suppression method based on beam reshaping (BR). In this method, clutter components were estimated and maximally suppressed in the side lobe while ensuring that the main lobe remained intact. The results of the proposed algorithm were evaluated by comparison with those of standard STAP and sparse-representation STAP (SR-STAP). Among the tested algorithms, the proposed BR algorithm had the best suppression performance and the most accurate main-lobe peak response, thereby preserving the target angle and Doppler frequency information. The BR algorithm can assist with target detection and tracking despite a background with ionospheric clutter. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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13 pages, 6727 KiB

Open AccessTechnical Note

Eye-Safe Aerosol and Cloud Lidar Based on Free-Space Intracavity Upconversion Detection

by Wenjie Yue, Tao Chen, Wei Kong, Xin Chen, Genghua Huang and Rong Shu

Remote Sens. 2022, 14(12), 2934; https://doi.org/10.3390/rs14122934 - 19 Jun 2022

Cited by 6 | Viewed by 2563

Abstract

We report an eye-safe aerosol and cloud lidar with an Erbium-doped fiber laser (EDFL) and a free-space intracavity upconversion detector as the transmitter and receiver, respectively. The EDFL was home-made, which could produce linearly-polarized pulses at a repetition rate of 15 kHz with [...] Read more.

We report an eye-safe aerosol and cloud lidar with an Erbium-doped fiber laser (EDFL) and a free-space intracavity upconversion detector as the transmitter and receiver, respectively. The EDFL was home-made, which could produce linearly-polarized pulses at a repetition rate of 15 kHz with pulse energies of ~70 μJ and pulse durations of ~7 ns centered at 1550 nm. The echo photons were upconverted to ~631 nm via the sum frequency generation process in a bow-tie cavity, where a Nd:YVO₄ and a PPLN crystal served as the pump and nonlinear frequency conversion devices, respectively. The upconverted visible photons were recorded by a photomultiplier tube and their timestamps were registered by a customized time-to-digital converter for distance-resolved measurement. Reflected signals peaked at ~6.8 km from a hard target were measured with a distance resolution of 0.6 m for an integral duration of 10 s. Atmospheric backscattered signals, with a range of ~6 km, were also detectable for longer integral durations. The evolution of aerosols and clouds were recorded by this lidar in a preliminary experiment with a continuous measuring time of over 18 h. Clear boundary and fine structures of clouds were identified with a spatial resolution of 9.6 m during the measurement, showing its great potential for practical aerosol and cloud monitoring. Full article

(This article belongs to the Special Issue Lidar for Advanced Classification and Retrieval of Aerosols)

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18 pages, 5600 KiB

Open AccessArticle

Influence of Spatial Resolution on Satellite-Based PM_2.5 Estimation: Implications for Health Assessment

by Heming Bai, Yuli Shi, Myeongsu Seong, Wenkang Gao and Yuanhui Li

Remote Sens. 2022, 14(12), 2933; https://doi.org/10.3390/rs14122933 - 19 Jun 2022

Cited by 8 | Viewed by 2595

Abstract

Satellite-based PM_2.5 estimation has been widely used to assess health impact associated with PM_2.5 exposure and might be affected by spatial resolutions of satellite input data, e.g., aerosol optical depth (AOD). Here, based on Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD [...] Read more.

Satellite-based PM_2.5 estimation has been widely used to assess health impact associated with PM_2.5 exposure and might be affected by spatial resolutions of satellite input data, e.g., aerosol optical depth (AOD). Here, based on Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD in 2020 over the Yangtze River Delta (YRD) and three PM_2.5 retrieval models, i.e., the mixed effects model (ME), the land-use regression model (LUR) and the Random Forest model (RF), we compare these model performances at different spatial resolutions (1, 3, 5 and 10 km). The PM_2.5 estimations are further used to investigate the impact of spatial resolution on health assessment. Our cross-validated results show that the model performance is not sensitive to spatial resolution change for the ME and LUR models. By contrast, the RF model can create a more accurate PM_2.5 prediction with a finer AOD spatial resolution. Additionally, we find that annual population-weighted mean (PWM) PM_2.5 concentration and attributable mortality strongly depend on spatial resolution, with larger values estimated from coarser resolution. Specifically, compared to PWM PM_2.5 at 1 km resolution, the estimation at 10 km resolution increases by 7.8%, 22.9%, and 9.7% for ME, LUR, and RF models, respectively. The corresponding increases in mortality are 7.3%, 18.3%, and 8.4%. Our results also show that PWM PM_2.5 at 10 km resolution from the three models fails to meet the national air quality standard, whereas the estimations at 1, 3 and 5 km resolutions generally meet the standard. These findings suggest that satellite-based health assessment should consider the spatial resolution effect. Full article

(This article belongs to the Special Issue Observation and Impact Evaluation of Atmospheric Pollution by Remote Sensing)

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20 pages, 5789 KiB

Open AccessArticle

Variability in the Spatiotemporal Distribution Patterns of Greater Amberjack in Response to Environmental Factors in the Taiwan Strait Using Remote Sensing Data

by Mubarak Mammel, Muhamad Naimullah, Ali Haghi Vayghan, Jhen Hsu, Ming-An Lee, Jun-Hong Wu, Yi-Chen Wang and Kuo-Wei Lan

Remote Sens. 2022, 14(12), 2932; https://doi.org/10.3390/rs14122932 - 19 Jun 2022

Cited by 11 | Viewed by 2758

Abstract

The environmental characteristics of the Taiwan Strait (TS) have been linked to variations in the abundance and distribution of greater amberjack (Seriola dumerili) populations. Greater amberjack is a commercially and ecologically valuable species in ecosystems, and its spatial distribution patterns are [...] Read more.

The environmental characteristics of the Taiwan Strait (TS) have been linked to variations in the abundance and distribution of greater amberjack (Seriola dumerili) populations. Greater amberjack is a commercially and ecologically valuable species in ecosystems, and its spatial distribution patterns are pivotal to fisheries management and conservation. However, the relationship between the catch rates of S. dumerili and the environmental changes and their impact on fish communities remains undetermined in the TS. The goal of this study was to determine the spatiotemporal distribution pattern of S. dumerili with environmental characteristics in the TS from south to north (20°N–29°N and 115°E–127°E), applying generalized additive models (GAMs) and spatiotemporal fisheries data from logbooks and voyage data recorders from Taiwanese fishing vessels (2014–2017) as well as satellite-derived remote sensing environmental data. We used the generalized linear model (GLM) and GAM to analyze the effect of environmental factors and catch rates. The predictive performance of the two statistical models was quantitatively assessed by using the root mean square difference. Results reveal that the GAM outperforms the GLM model in terms of the functional relationship of the GAM for generating a reliable predictive tool. The model selection process was based on the significance of model terms, increase in deviance explained, decrease in residual factor, and reduction in Akaike’s information criterion. We then developed a species distribution model based on the best GAMs. The deviance explained indicated that sea surface temperature, linked to high catch rates, was the key factor influencing S. dumerili distributions, whereas mixed layer depth was the least relevant factor. The model predicted a relatively high S. dumerili catch rate in the northwestern region of the TS in summer, with the area extending to the East China Sea. The target species is strongly influenced by biophysical environmental conditions, and potential fishing areas are located throughout the waters of the TS. The findings of this study showed how S. dumerili populations respond to environmental variables and predict species distributions. Data on the habitat preferences and distribution patterns of S. dumerili are essential for understanding the environmental conditions of the TS, which can inform future priorities for conservation planning and management. Full article

(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade Ⅱ)

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17 pages, 28369 KiB

Open AccessArticle

Wide and Deep Fourier Neural Network for Hyperspectral Remote Sensing Image Classification

by Jiangbo Xi, Okan K. Ersoy, Ming Cong, Chaoying Zhao, Wei Qu and Tianjun Wu

Remote Sens. 2022, 14(12), 2931; https://doi.org/10.3390/rs14122931 - 19 Jun 2022

Cited by 18 | Viewed by 3029

Abstract

Hyperspectral remote sensing image (HSI) classification is very useful in different applications, and recently, deep learning has been applied for HSI classification successfully. However, the number of training samples is usually limited, causing difficulty in use of very deep learning models. We propose [...] Read more.

Hyperspectral remote sensing image (HSI) classification is very useful in different applications, and recently, deep learning has been applied for HSI classification successfully. However, the number of training samples is usually limited, causing difficulty in use of very deep learning models. We propose a wide and deep Fourier network to learn features efficiently by using pruned features extracted in the frequency domain. It is composed of multiple wide Fourier layers to extract hierarchical features layer-by-layer efficiently. Each wide Fourier layer includes a large number of Fourier transforms to extract features in the frequency domain from a local spatial area using sliding windows with given strides.These extracted features are pruned to retain important features and reduce computations. The weights in the final fully connected layers are computed using least squares. The transform amplitudes are used for nonlinear processing with pruned features. The proposed method was evaluated with HSI datasets including Pavia University, KSC, and Salinas datasets. The overall accuracies (OAs) of the proposed method can reach 99.77%, 99.97%, and 99.95%, respectively. The average accuracies (AAs) can achieve 99.55%, 99.95%, and 99.95%, respectively. The Kappa coefficients are as high as 99.69%, 99.96%, and 99.94%, respectively. The experimental results show that the proposed method achieved excellent performance among other compared methods. The proposed method can be used for applications including classification, and image segmentation tasks, and has the ability to be implemented with lightweight embedded computing platforms. The future work is to improve the method to make it available for use in applications including object detection, time serial data prediction, and fast implementation. Full article

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19 pages, 21421 KiB

Open AccessArticle

Land Subsidence Monitoring and Dynamic Prediction of Reclaimed Islands with Multi-Temporal InSAR Techniques in Xiamen and Zhangzhou Cities, China

by Guangrong Li, Chaoying Zhao, Baohang Wang, Xiaojie Liu and Hengyi Chen

Remote Sens. 2022, 14(12), 2930; https://doi.org/10.3390/rs14122930 - 19 Jun 2022

Cited by 13 | Viewed by 2639

Abstract

Artificial islands and land reclamation are one of the most important ways to expand urban space in coastal cities. Long-term consolidation of reclaimed material and compaction of marine sediments can cause ground subsidence, which may threaten the buildings and infrastructure on the reclaimed [...] Read more.

Artificial islands and land reclamation are one of the most important ways to expand urban space in coastal cities. Long-term consolidation of reclaimed material and compaction of marine sediments can cause ground subsidence, which may threaten the buildings and infrastructure on the reclaimed lands. Therefore, it is crucial to monitor the land subsidence and predict the future deformation trend to mitigate the damage and take measures for the land reclamation and any infrastructure. In this paper, a total of 125 SAR images acquired by the C-band Sentinel-1A satellite between June 2017 and September 2021 are collected. The small baseline subsets (SBAS) SAR interferometry (InSAR) method is first conducted to detect the land deformation in Xiamen and Zhangzhou cities of Fujian Province, China, and the distributed scatterers (DS)-InSAR method is used to recover the complete deformation history of some typical areas including Xiamen Airport in Dadeng Island and Shuangyu Island. Then, the sequential estimation and the geotechnical model are jointly applied to demonstrate the current and future evolution of land subsidence of the constructed roads on Shuangyu Island. The results show that the maximum cumulative deformation reaches 425 mm of Xiamen Xiang’an Airport and 626 mm of Shuangyu Island, and the maximum deformation is predicted to be as large as 1.1 m by 2026 of Shuangyu Island. This research will provide important guidelines for the design and construction of Xiamen Xiang’an Airport and Shuangyu Island to prevent and control land subsidence. Full article

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23 pages, 16367 KiB

Open AccessArticle

The Different Impacts of Climate Variability and Human Activities on NPP in the Guangdong–Hong Kong–Macao Greater Bay Area

by Yanyan Wu, Zhaohui Luo and Zhifeng Wu

Remote Sens. 2022, 14(12), 2929; https://doi.org/10.3390/rs14122929 - 19 Jun 2022

Cited by 11 | Viewed by 2767

Abstract

As two main drivers of vegetation dynamics, climate variability and human activities greatly influence net primary productivity (NPP) variability by altering the hydrothermal conditions and biogeochemical cycles. Therefore, studying NPP variability and its drivers is crucial to understanding the patterns and mechanisms that [...] Read more.

As two main drivers of vegetation dynamics, climate variability and human activities greatly influence net primary productivity (NPP) variability by altering the hydrothermal conditions and biogeochemical cycles. Therefore, studying NPP variability and its drivers is crucial to understanding the patterns and mechanisms that sustain regional ecosystem structures and functions under ongoing climate variability and human activities. In this study, three indexes, namely the potential NPP (NPP_p), actual NPP (NPP_a), and human-induced NPP (NPP_h), and their variability from 2000 to 2020 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) were estimated and analyzed. Six main scenarios were generated based on change trends in the three indexes over the past 21 years, and the different relative impacts of climate variability and human activities on NPP_a variability were quantitatively analyzed and identified. The results showed that the NPP_p, NPP_a, and NPP_h had heterogeneous spatial distributions, and the average NPP_p and NPP_a values over the whole study area increased at rates of 3.63 and 6.94 gC·m⁻²·yr⁻¹ from 2000 to 2020, respectively, while the NPP_h decreased at a rate of −4.43 gC·m⁻²·yr⁻¹. Climate variability and the combined effects of climate variability and human activities were the major driving factors of the NPP_a increases, accounting for more than 72% of the total pixels, while the combined effects of the two factors caused the NPP_a values to increase by 32–54% of the area in all cities expect Macao and across all vegetation ecosystems. Human activities often led to decreases in NPP_a over more than 16% of the total pixels, and were mainly concentrated in the central cities of the GBA. The results can provide a reference for understanding NPP changes and can offer a theoretical basis for implementing ecosystem restoration, ecological construction, and conservation practices in the GBA. Full article

(This article belongs to the Special Issue Recent Advances in Terrestrial Vegetation Productivity with Remote Sensing Techniques)

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20 pages, 24276 KiB

Open AccessArticle

Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars

by Farid Atmani, Bodo Bookhagen and Taylor Smith

Remote Sens. 2022, 14(12), 2928; https://doi.org/10.3390/rs14122928 - 19 Jun 2022

Cited by 5 | Viewed by 3006

Abstract

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground [...] Read more.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes. Full article

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24 pages, 4691 KiB

Open AccessArticle

UAV Remote Sensing for High-Throughput Phenotyping and for Yield Prediction of Miscanthus by Machine Learning Techniques

by Giorgio Impollonia, Michele Croci, Andrea Ferrarini, Jason Brook, Enrico Martani, Henri Blandinières, Andrea Marcone, Danny Awty-Carroll, Chris Ashman, Jason Kam, Andreas Kiesel, Luisa M. Trindade, Mirco Boschetti, John Clifton-Brown and Stefano Amaducci

Remote Sens. 2022, 14(12), 2927; https://doi.org/10.3390/rs14122927 - 19 Jun 2022

Cited by 17 | Viewed by 5012

Abstract

Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired [...] Read more.

Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. The random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass, and standing biomass) using 15 VIs time series, and predicted yield using peak descriptors derived from these VIs time series with root mean square error of 2.3 Mg DM ha⁻¹. The study demonstrates the potential of UAVs’ multispectral images in HTP applications and in yield prediction, providing important information needed to increase sustainable biomass production. Full article

(This article belongs to the Special Issue Deep and Machine Learning Applications in Remote Sensing Data to Monitor and Manage Crops Using Precision Agriculture Systems)

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17 pages, 8705 KiB

Open AccessArticle

Smartphone-Based Unconstrained Step Detection Fusing a Variable Sliding Window and an Adaptive Threshold

by Ying Xu, Guofeng Li, Zeyu Li, Hao Yu, Jianhui Cui, Jin Wang and Yu Chen

Remote Sens. 2022, 14(12), 2926; https://doi.org/10.3390/rs14122926 - 19 Jun 2022

Cited by 4 | Viewed by 2856

Abstract

Step detection for smartphones plays an important role in the pedestrian dead reckoning (PDR) for indoor positioning. Aiming at the problem of low step detection accuracy of smartphones in complex unconstrained states in PDR, smartphone-based unconstrained step detection method fusing a variable sliding [...] Read more.

Step detection for smartphones plays an important role in the pedestrian dead reckoning (PDR) for indoor positioning. Aiming at the problem of low step detection accuracy of smartphones in complex unconstrained states in PDR, smartphone-based unconstrained step detection method fusing a variable sliding window and an adaptive threshold is proposed. In this method, the dynamic updating algorithm of a peak threshold is developed, and the minimum peak value filtered after a sliding window filter is used as the adaptive peak threshold, which solves the problem that the peak threshold of different motion states is difficult to update adaptively. Then, a variable sliding window collaborative time threshold method is proposed, which solves the problem that the adjacent windows cannot be contacted, and the initial peak and the end peak are difficult to accurately identify. To evaluate the performance of the proposed unconstrained step detection algorithm, 50 experiments in constrained and unconstrained states are conducted by 25 volunteers holding 21 different types of smartphones. Experimental results show: The average step counting accuracy of the proposed unconstrained step detection algorithm is over 98%. Compared with the open source program Stepcount, the average step counting accuracy of the proposed algorithm is improved by 10.0%. The smartphone-based unconstrained step detection fusing a variable sliding window and an adaptive threshold has a strong ability to adapt to complex unconstrained states, and the average step counting accuracy rate is only 0.6% lower than that of constrained states. This algorithm has a wide audience and is friendly for different genders and smartphones with different prices. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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19 pages, 4850 KiB

Open AccessArticle

Attention-Unet-Based Near-Real-Time Precipitation Estimation from Fengyun-4A Satellite Imageries

by Yanbo Gao, Jiping Guan, Fuhan Zhang, Xiaodong Wang and Zhiyong Long

Remote Sens. 2022, 14(12), 2925; https://doi.org/10.3390/rs14122925 - 18 Jun 2022

Cited by 24 | Viewed by 3121

Abstract

Reliable near-real-time precipitation estimation is crucial for scientific research and resistance to natural disasters such as floods. Compared with ground-based precipitation measurements, satellite-based precipitation measurements have great advantages, but precipitation estimation based on satellite is still a challenging issue. In this paper, we [...] Read more.

Reliable near-real-time precipitation estimation is crucial for scientific research and resistance to natural disasters such as floods. Compared with ground-based precipitation measurements, satellite-based precipitation measurements have great advantages, but precipitation estimation based on satellite is still a challenging issue. In this paper, we propose a deep learning model named Attention-Unet for precipitation estimation. The model utilizes the high temporal, spatial and spectral resolution data of the FY4A satellite to improve the accuracy of precipitation estimation. To evaluate the effectiveness of the proposed model, we compare it with operational near-real-time satellite-based precipitation products and deep learning models which proved to be effective in precipitation estimation. We use classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR), Critical success index (CSI), and regression metrics including Root Mean Square Error (RMSE) and Pearson correlation coefficient (CC) to evaluate the performance of precipitation identification and precipitation amounts estimation, respectively. Furthermore, we select an extreme precipitation event to validate the generalization ability of our proposed model. Statistics and visualizations of the experimental results show the proposed model has better performance than operational precipitation products and baseline deep learning models in both precipitation identification and precipitation amounts estimation. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product. This study suggests that applying an appropriate deep learning algorithm may provide an opportunity to improve the quality of satellite-based precipitation products. Full article

(This article belongs to the Topic Advanced Research in Precipitation Measurements)

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Remote Sens., Volume 14, Issue 12 (June-2 2022) – 229 articles

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