Remote Sensing

29 pages, 20072 KiB

Open AccessArticle

Spatiotemporal Patterns and Regional Differences in Soil Thermal Conductivity on the Qinghai–Tibet Plateau

by Wenhao Liu, Ren Li, Tonghua Wu, Xiaoqian Shi, Lin Zhao, Xiaodong Wu, Guojie Hu, Jimin Yao, Dong Wang, Yao Xiao, Junjie Ma, Yongliang Jiao, Shenning Wang, Defu Zou, Xiaofan Zhu, Jie Chen, Jianzong Shi and Yongping Qiao

Remote Sens. 2023, 15(4), 1168; https://doi.org/10.3390/rs15041168 - 20 Feb 2023

Cited by 4 | Viewed by 1848

Abstract

The Qinghai–Tibet Plateau is an area known to be sensitive to global climate change, and the problems caused by permafrost degradation in the context of climate warming potentially have far-reaching effects on regional hydrogeological processes, ecosystem functions, and engineering safety. Soil thermal conductivity [...] Read more.

The Qinghai–Tibet Plateau is an area known to be sensitive to global climate change, and the problems caused by permafrost degradation in the context of climate warming potentially have far-reaching effects on regional hydrogeological processes, ecosystem functions, and engineering safety. Soil thermal conductivity (STC) is a key input parameter for temperature and surface energy simulations of the permafrost active layer. Therefore, understanding the spatial distribution patterns and variation characteristics of STC is important for accurate simulation and future predictions of permafrost on the Qinghai–Tibet Plateau. However, no systematic research has been conducted on this topic. In this study, based on a dataset of 2972 STC measurements, we simulated the spatial distribution patterns and spatiotemporal variation of STC in the shallow layer (5 cm) of the Qinghai–Tibet Plateau and the permafrost area using a machine learning model. The monthly analysis results showed that the STC was high from May to August and low from January to April and from September to December. In addition, the mean STC in the permafrost region of the Qinghai–Tibet Plateau was higher during the thawing period than during the freezing period, while the STC in the eastern and southeastern regions is generally higher than that in the western and northwestern regions. From 2005 to 2018, the difference between the STC in the permafrost region during the thawing and freezing periods gradually decreased, with a slight difference in the western hinterland region and a large difference in the eastern region. In areas with specific landforms such as basins and mountainous areas, the changes in the STC during the thawing and freezing periods were different or even opposite. The STC of alpine meadow was found to be most sensitive to the changes during the thawing and freezing periods within the permafrost zone, while the STC for bare land, alpine desert, and alpine swamp meadow decreased overall between 2005 and 2018. The results of this study provide important baseline data for the subsequent analysis and simulation of the permafrost on the Qinghai–Tibet Plateau. Full article

(This article belongs to the Special Issue Remote Sensing and Land Surface Process Models for Permafrost Studies)

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26 pages, 10296 KiB

Open AccessArticle

A Spatial–Temporal Joint Radar-Communication Waveform Design Method with Low Sidelobe Level of Beampattern

by Liu Liu, Xingdong Liang, Yanlei Li, Yunlong Liu, Xiangxi Bu and Mingming Wang

Remote Sens. 2023, 15(4), 1167; https://doi.org/10.3390/rs15041167 - 20 Feb 2023

Cited by 2 | Viewed by 1699

Abstract

A joint radar-communication (JRC) system utilizes the integrated transmit waveform and a single platform to perform radar and communication functions simultaneously. Admittedly, the multibeam waveform design approach could transmit the assigned waveforms in different beams with the aid of spatial and temporal degrees [...] Read more.

A joint radar-communication (JRC) system utilizes the integrated transmit waveform and a single platform to perform radar and communication functions simultaneously. Admittedly, the multibeam waveform design approach could transmit the assigned waveforms in different beams with the aid of spatial and temporal degrees of freedom. However, a high sidelobe level (SLL) in the beampattern reduces energy efficiency and expands exposure probability. In this study, we propose a novel spatial–temporal joint waveform design method based on the beamforming algorithm to form a low SLL beampattern. Waveform synthesis constraints are considered to synthesize desired radar and communication waveforms at designated directions. Furthermore, we impose the constant modulus constraint to lessen the impact of the high peak-to-average ratio (PAPR). The optimization process of the whole model can be summarized as two stages. First, the covariance matrix is created by convex optimization with respect to the minimum SLL. Second, the integrated transmit waveform is tuned through an alternating projection algorithm. Based on the simulation findings, we demonstrate that the proposed method outperforms the traditional methods in terms of low SLL and waveform synthesis. Meanwhile, we validate the effectiveness of the proposed method using semi-physical experiment results. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

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18 pages, 12031 KiB

Open AccessArticle

An Integrated GNSS/MEMS Accelerometer System for Dynamic Structural Response Monitoring under Thunder Loading

by Jian Wang, Xu Liu, Fei Liu, Cai Chen and Yuyang Tang

Remote Sens. 2023, 15(4), 1166; https://doi.org/10.3390/rs15041166 - 20 Feb 2023

Cited by 2 | Viewed by 1613

Abstract

Dynamic response monitoring is of great significance for large engineering structural anomaly diagnosis and early warning. Although the global navigation satellite system (GNSS) has been widely used to measure the dynamic structural response, it has the limitation of a relatively low sampling rate. [...] Read more.

Dynamic response monitoring is of great significance for large engineering structural anomaly diagnosis and early warning. Although the global navigation satellite system (GNSS) has been widely used to measure the dynamic structural response, it has the limitation of a relatively low sampling rate. The micro-electro-mechanical system (MEMS) accelerometer has a high sampling frequency, but it belongs to the approaches of acceleration measurements as the absolute position is unavailable. Hence, in this paper, an integrated vibration monitoring system that includes a GNSS receiver and 3-axis MEMS accelerometers was developed to obtain the dynamic responses under the thunder loading. First, a new denoising algorithm for thunderstorm-induced vibration data was proposed based on variational mode decomposition (VMD) and the characteristics of white noise, and the low-frequency disturbance was separated from the GNSS displacement time series. Then, a power spectral density (PSD) analysis using data collected by the integrated system was carried out to extract low/high natural frequencies. Finally, field monitoring data collected at Huanghuacheng, Hefangkou, and Qilianguan in Beijing’s Huairou District were used to validate the effectiveness of the integrated system and processing scheme. According to the results, the proposed integrated GNSS/MEMS accelerometer system can not only be used to detect thunder loading events, but also completely extract the natural frequency based on PSD analysis. The high natural frequencies detected from the accelerometer data of the four Great Wall monitoring stations excited by the thunderstorms are 42.12 Hz, 12.94 Hz, 12.58 Hz, and 5.95 Hz, respectively, while the low natural frequencies detected from the GNSS are 0.02 Hz, 0.019 Hz, 0.016 Hz, and 0.014 Hz, respectively. Moreover, thunderstorms can cause the Great Wall to vibrate with a maximum displacement of 14.3 cm. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Cultural Heritage Research)

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15 pages, 4662 KiB

Open AccessArticle

Post-Hurricane Damage Severity Classification at the Individual Tree Level Using Terrestrial Laser Scanning and Deep Learning

by Carine Klauberg, Jason Vogel, Ricardo Dalagnol, Matheus Pinheiro Ferreira, Caio Hamamura, Eben Broadbent and Carlos Alberto Silva

Remote Sens. 2023, 15(4), 1165; https://doi.org/10.3390/rs15041165 - 20 Feb 2023

Cited by 1 | Viewed by 2521

Abstract

Natural disturbances like hurricanes can cause extensive disorder in forest structure, composition, and succession. Consequently, ecological, social, and economic alterations may occur. Terrestrial laser scanning (TLS) and deep learning have been used for estimating forest attributes with high accuracy, but to date, no [...] Read more.

Natural disturbances like hurricanes can cause extensive disorder in forest structure, composition, and succession. Consequently, ecological, social, and economic alterations may occur. Terrestrial laser scanning (TLS) and deep learning have been used for estimating forest attributes with high accuracy, but to date, no study has combined both TLS and deep learning for assessing the impact of hurricane disturbance at the individual tree level. Here, we aim to assess the capability of TLS and convolutional neural networks (CNNs) combined for classifying post-Hurricane Michael damage severity at the individual tree level in a pine-dominated forest ecosystem in the Florida Panhandle, Southern U.S. We assessed the combined impact of using either binary-color or multicolored-by-height TLS-derived 2D images along with six CNN architectures (Densenet201, EfficientNet_b7, Inception_v3, Res-net152v2, VGG16, and a simple CNN). The confusion matrices used for assessing the overall accuracy were symmetric in all six CNNs and 2D image variants tested with overall accuracy ranging from 73% to 92%. We found higher F-1 scores when classifying trees with damage severity varying from extremely leaning, trunk snapped, stem breakage, and uprooted compared to trees that were undamaged or slightly leaning (<45°). Moreover, we found higher accuracies when using VGG16 combined with multicolored-by-height TLS-derived 2D images compared with other methods. Our findings demonstrate the high capability of combining TLS with CNNs for classifying post-hurricane damage severity at the individual tree level in pine forest ecosystems. As part of this work, we developed a new open-source R package (rTLsDeep) and implemented all methods tested herein. We hope that the promising results and the rTLsDeep R package developed in this study for classifying post-hurricane damage severity at the individual tree level will stimulate further research and applications not just in pine forests but in other forest types in hurricane-prone regions. Full article

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19 pages, 1644 KiB

Open AccessArticle

Fast Resolution Enhancement for Real Beam Mapping Using the Parallel Iterative Deconvolution Method

by Ping Zhang, Yongchao Zhang, Deqing Mao, Jianan Yan and Shuaidi Liu

Remote Sens. 2023, 15(4), 1164; https://doi.org/10.3390/rs15041164 - 20 Feb 2023

Viewed by 1179

Abstract

Super-resolution methods for real beam mapping (RBM) imagery play a significant role in many microwave remote sensing applications. However, the existing super-resolution methods require high-dimensional matrix operations in the case of wide-field imaging, which makes it difficult to satisfy the requirements of real-time [...] Read more.

Super-resolution methods for real beam mapping (RBM) imagery play a significant role in many microwave remote sensing applications. However, the existing super-resolution methods require high-dimensional matrix operations in the case of wide-field imaging, which makes it difficult to satisfy the requirements of real-time signal processing. To solve this problem, this paper introduces an improved Poisson distribution-based maximum likelihood (IPML) method by adding an adaptive iterative acceleration factor to effectively improve the algorithm convergence speed without introducing high-dimensional matrix operations. Furthermore, a GPU-based parallel processing architecture is proposed through the multithreading characteristics of the computing platform, and a cooperative CPU–GPU working model is constructed. This can realize the parallel optimization of the echo reception, preprocessing, and super-resolution processing. We verify that the proposed parallel super-resolution method can significantly improve the computational efficiency without sacrificing performance, using a real dataset. Full article

(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)

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22 pages, 6986 KiB

Open AccessArticle

Sea Surface Temperature Gradients Estimation Using Top-of-Atmosphere Observations from the ESA Earth Explorer 10 Harmony Mission: Preliminary Studies

by Daniele Ciani, Mattia Sabatini, Bruno Buongiorno Nardelli, Paco Lopez Dekker, Björn Rommen, David S. Wethey, Chunxue Yang and Gian Luigi Liberti

Remote Sens. 2023, 15(4), 1163; https://doi.org/10.3390/rs15041163 - 20 Feb 2023

Cited by 2 | Viewed by 2190

Abstract

The Harmony satellite mission was recently approved as the next European Space Agency (ESA) Earth Explorer 10. The mission science objectives cover several applications related to solid earth, the cryosphere, upper-ocean dynamics and air–sea interactions. The mission consists of a constellation of two [...] Read more.

The Harmony satellite mission was recently approved as the next European Space Agency (ESA) Earth Explorer 10. The mission science objectives cover several applications related to solid earth, the cryosphere, upper-ocean dynamics and air–sea interactions. The mission consists of a constellation of two satellites, flying with the Copernicus Sentinel 1 (C or D) spacecraft, each hosting a C-band receive-only radar and a thermal infrared (TIR) payload. From an ocean dynamics/air–sea interaction perspective, the mission will provide the unique opportunity to observe simultaneously the signature of submesoscale upper-ocean processes via synthetic aperture radar and TIR imagery. The TIR imager is based on microbolometer technology and its acquisitions will rely on four channels: three narrow-band channels yielding observations at a ≃1 km spatial sampling distance (SSD) and a panchromatic (PAN, 8–12

μ

m) channel characterized by a ≃300 m SSD. Our study investigates the potential of Harmony in retrieving spatial features related to sea surface temperature (SST) gradients from the high-resolution PAN channel, relying on top-of-atmosphere (TOA) observations. Compared to a standard SST gradient retrieval, our approach does not require atmospheric correction, thus avoiding uncertainties due to inter-channel co-registration and radiometric consistency, with the possibility of exploiting the higher resolution of the PAN channel. The investigations were carried out simulating the future Harmony TOA radiances (TARs), as well as relying on existing state-of-the-art level 1 satellite products. Our approach enables the correct description of SST features at the sea surface avoiding the generation of spurious features due to atmospheric correction and/or instrumental issues. In addition, analyses based on existing satellite products suggest that the clear-sky TOA observations, in a typical mid-latitude scene, allow the reconstruction of up to 85% of the gradient magnitudes found at the sea-surface level. The methodology is less efficient in tropical areas, suffering from smoothing effects due to the high concentrations of water vapor. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature II)

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17 pages, 8772 KiB

Open AccessArticle

Future Scenarios of Land Use/Land Cover (LULC) Based on a CA-Markov Simulation Model: Case of a Mediterranean Watershed in Morocco

by Mohamed Beroho, Hamza Briak, El Khalil Cherif, Imane Boulahfa, Abdessalam Ouallali, Rachid Mrabet, Fassil Kebede, Alexandre Bernardino and Khadija Aboumaria

Remote Sens. 2023, 15(4), 1162; https://doi.org/10.3390/rs15041162 - 20 Feb 2023

Cited by 25 | Viewed by 3893

Abstract

Modeling of land use and land cover (LULC) is a very important tool, particularly in the agricultural field: it allows us to know the potential changes in land area in the future and to consider developments in order to prevent probable risks. The [...] Read more.

Modeling of land use and land cover (LULC) is a very important tool, particularly in the agricultural field: it allows us to know the potential changes in land area in the future and to consider developments in order to prevent probable risks. The idea is to give a representation of probable future situations based on certain assumptions. The objective of this study is to make future predictions in land use and land cover in the watershed “9 April 1947”, and in the years 2028, 2038 and 2050. Then, the maps obtained with the climate predictions will be integrated into an agro-hydrological model to know the water yield, the sediment yield and the water balance of the studied area by 2050.The future land use and land cover (LULC) scenarios were created using a CA-Markov forecasting model. The results of the simulation of the LULC changes were considered satisfactory, as shown by the values obtained from the kappa indices for agreement (κstandard) = 0.73, kappa for lack of information (κno) = 0.76, and kappa for location at grid cell level (κlocation) = 0.80. Future scenarios modeled in LULC indicate a decrease in agricultural areas and wetlands, both of which can be seen as a warning of crop loss. There is, on the other hand, an increase in forest areas that could be an advantage for the biodiversity of the fauna and flora in the “9 April 1947” watershed. Full article

(This article belongs to the Special Issue Image Analysis for Forest Environmental Monitoring)

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22 pages, 65930 KiB

Open AccessArticle

A Multi-Resolution Approach to Point Cloud Registration without Control Points

by Eleanor A. Bash, Lakin Wecker, Mir Mustafizur Rahman, Christine F. Dow, Greg McDermid, Faramarz F. Samavati, Ken Whitehead, Brian J. Moorman, Dorota Medrzycka and Luke Copland

Remote Sens. 2023, 15(4), 1161; https://doi.org/10.3390/rs15041161 - 20 Feb 2023

Cited by 3 | Viewed by 2517

Abstract

Terrestrial photographic imagery combined with structure-from-motion (SfM) provides a relatively easy-to-implement method for monitoring environmental systems, even in remote and rough terrain. However, the collection of in-situ positioning data and the identification of control points required for georeferencing in SfM processing is the [...] Read more.

Terrestrial photographic imagery combined with structure-from-motion (SfM) provides a relatively easy-to-implement method for monitoring environmental systems, even in remote and rough terrain. However, the collection of in-situ positioning data and the identification of control points required for georeferencing in SfM processing is the primary roadblock to using SfM in difficult-to-access locations; it is also the primary bottleneck for using SfM in a time series. We describe a novel, computationally efficient, and semi-automated approach for georeferencing unreferenced point clouds (UPC) derived from terrestrial overlapping photos to a reference dataset (e.g., DEM or aerial point cloud; hereafter RPC) in order to address this problem. The approach utilizes a Discrete Global Grid System (DGGS), which allows us to capitalize on easily collected rough information about camera deployment to coarsely register the UPC using the RPC. The DGGS also provides a hierarchical set of grids which supports a hierarchical modified iterative closest point algorithm with natural correspondence between the UPC and RPC. The approach requires minimal interaction in a user-friendly interface, while allowing for user adjustment of parameters and inspection of results. We illustrate the approach with two case studies: a close-range (<1 km) vertical glacier calving front reconstructed from two cameras at Fountain Glacier, Nunavut and a long-range (>3 km) scene of relatively flat glacier ice reconstructed from four cameras overlooking Nàłùdäy (Lowell Glacier), Yukon, Canada. We assessed the accuracy of the georeferencing by comparing the UPC to the RPC, as well as surveyed control points; the consistency of the registration was assessed using the difference between successive registered surfaces in the time series. The accuracy of the registration is roughly equal to the ground sampling distance and is consistent across time steps. These results demonstrate the promise of the approach for easy-to-implement georeferencing of point clouds from terrestrial imagery with acceptable accuracy, opening the door for new possibilities in remote monitoring for change-detection, such as monitoring calving rates, glacier surges, or other seasonal changes at remote field locations. Full article

(This article belongs to the Special Issue Remote Sensing of the Cryosphere)

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19 pages, 4881 KiB

Open AccessArticle

Multi-Instrumental Observations of Midlatitude Plasma Irregularities over Eastern Asia during a Moderate Magnetic Storm on 16 July 2003

by Hailun Ye, Wen Yi, Baozhu Zhou, Jianfei Wu, Bingkun Yu, Penghao Tian, Jianyuan Wang, Chi Long, Maolin Lu, Xianghui Xue, Tingdi Chen and Xiangkang Dou

Remote Sens. 2023, 15(4), 1160; https://doi.org/10.3390/rs15041160 - 20 Feb 2023

Cited by 4 | Viewed by 1549

Abstract

This study presents the observations of midlatitude plasma irregularities over Eastern Asia during a moderate magnetic storm on 16 July 2003. Multi-instrumental observations, including the ground-based ionosondes, the GNSS networks, and the CHAMP and ROCSAT-1 satellites, were utilized to investigate the occurrence and [...] Read more.

This study presents the observations of midlatitude plasma irregularities over Eastern Asia during a moderate magnetic storm on 16 July 2003. Multi-instrumental observations, including the ground-based ionosondes, the GNSS networks, and the CHAMP and ROCSAT-1 satellites, were utilized to investigate the occurrence and characteristics of midlatitude plasma irregularities. The midlatitude strong spread F (SSF) mainly occurred in the midnight–morning sector as observed by ionosondes over Japan during this storm. SSF was related to plasma depletions, which is also recorded by GNSS network in the form of the enhancement of the rate of total electron content (TEC) change index (ROTI). The possible mechanism for the generation of SSF is that the enhanced eastward electric fields, associated with the prompt penetration electric fields and disturbance dynamo electric fields, cause the uplift and latitudinal extension of equatorial plasma bubbles (EPBs) to generate the observed midlatitude SSF further. Meanwhile, plasma density increased significantly under the influence of this storm. In addition, other common type of spread F, frequency spread F (FSF), was observed over Japan on the non-storm day and/or at high latitude station WK545, which seems to be closely related to the coupling of medium-scale traveling ionospheric disturbances (MSTIDs) and sporadic E (Es) layer. The above results indicate that various types of midlatitude spread F can be produced by different physical mechanisms. It is found that SSF can significantly affect the performance of radio wave propagation compared with FSF. Our results show that space weather events have a significant influence on the day-to-day variability of the occurrence and characteristics of ionospheric F-region irregularities at midlatitudes. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Monitoring Ionospheric Physics and Ionospheric Weather Forecasting)

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21 pages, 5940 KiB

Open AccessArticle

Data-Independent Phase-Only Beamforming of FDA-MIMO Radar for Swarm Interference Suppression

by Geng Chen, Chunyang Wang, Jian Gong, Ming Tan and Yibin Liu

Remote Sens. 2023, 15(4), 1159; https://doi.org/10.3390/rs15041159 - 20 Feb 2023

Cited by 2 | Viewed by 1272

Abstract

This paper proposes two data-independent phase-only beamforming algorithms for frequency diverse array multiple-input multiple-output radar against swarm interference. The proposed strategy can form a deep null at the interference area to achieve swarm interference suppression by tuning the phase of the weight vector, [...] Read more.

This paper proposes two data-independent phase-only beamforming algorithms for frequency diverse array multiple-input multiple-output radar against swarm interference. The proposed strategy can form a deep null at the interference area to achieve swarm interference suppression by tuning the phase of the weight vector, which can effectively reduce the hardware cost of the receiver. Specifically, the first algorithm imposes constant modulus constraint and sidelobe level constraint, and the phase-only weight vector is solved. The second algorithm performs a constant modulus decomposition of the weight vector to obtain two phase-only weight vectors, and uses two parallel phase shifters to synthesize one beamforming weight. Both methods can obtain the phase-only weight to realize suppression for swarm interference. Simulation results demonstrate that our strategy shows superiority in beam shape, output signal-to-interference-noise ratio, and phase shifter quantization performance, and has the potential for use in many applications, such as radar countermeasures and electronic defense. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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16 pages, 17740 KiB

Open AccessTechnical Note

Multiscale Analysis of Reflected Radiation on Lunar Surface Region Based on MRRT Model

by Yunfei Liu, Qiang Guo and Guifu Wang

Remote Sens. 2023, 15(4), 1158; https://doi.org/10.3390/rs15041158 - 20 Feb 2023

Viewed by 1140

Abstract

The moon has stable luminosity. Radiometric calibration on the lunar region is a good step in the right direction with the expansion of instrument observation capabilities. The uneven composition and terrain types of the lunar surface make it possible for inaccuracies in albedo [...] Read more.

The moon has stable luminosity. Radiometric calibration on the lunar region is a good step in the right direction with the expansion of instrument observation capabilities. The uneven composition and terrain types of the lunar surface make it possible for inaccuracies in albedo calculation from coarse-scale data if the within-pixel topology is overlooked. The expression between the region’s bidirectional reflectance factor (BRF) and the actual microtopography reflectance was established by the multiple reflections of radiation between terrains (MRRT) model. This research studied the radiation properties on the lunar surface region at various spatial resolutions (scales) based on the MRRT model. To determine the ideal scale of microtopography to be built, the scale-effect evaluation factor of albedo is established, and the scale-effect function is fitted. Experiments demonstrate that a microtopography with a spatial resolution of 60 m to 120 m, with 80 m being the most suitable scale, can be constructed for an area having (6000 × 6000) m². This research adds to the MRRT model’s applicability analysis in multiscale DEM modeling, helps choose and build a radiation calibration field on the lunar surface, and lays the groundwork for employing the area of the lunar surface for radiation calibration. Full article

(This article belongs to the Topic Techniques and Science Exploitations for Earth Observation and Planetary Exploration)

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24 pages, 16752 KiB

Open AccessArticle

Exploiting the Sensitivity of Dual-Frequency Smartphones and GNSS Geodetic Receivers for Jammer Localization

by Polona Pavlovčič-Prešeren, Franc Dimc and Matej Bažec

Remote Sens. 2023, 15(4), 1157; https://doi.org/10.3390/rs15041157 - 20 Feb 2023

Cited by 2 | Viewed by 1688

Abstract

Smartphones now dominate the Global Navigation Satellite System (GNSS) devices capable of collecting raw data. However, they also offer valuable research opportunities in intentional jamming, which has become a serious threat to the GNSS. Smartphones have the potential to locate jammers, but their [...] Read more.

Smartphones now dominate the Global Navigation Satellite System (GNSS) devices capable of collecting raw data. However, they also offer valuable research opportunities in intentional jamming, which has become a serious threat to the GNSS. Smartphones have the potential to locate jammers, but their robustness and sensitivity range need to be investigated first. In this study, the response of smartphones with dual-frequency, multi-constellation reception capability, namely, a Xiaomi Mi8, a Xiaomi 11T, a Samsung Galaxy S20, and a Huawei P40, to various single- and multi-frequency jammers is investigated. The two-day jamming experiments were conducted in a remote area with minimal impact on users, using these smartphones and two Leica GS18 and two Leica GS15 geodetic receivers, which were placed statically at the side of a road and in a line, approximately 10 m apart. A vehicle with jammers installed passed them several times at a constant speed. In one scenario, a person carrying the jammer was constantly tracked using a tacheometer to determine the exact distance to the receivers for each time stamp. The aim was, first, to determine the effects of the various jammers on the smartphones’ positioning capabilities and to compare their response in terms of the speed and quality of repositioning with professional geodetic receivers. Second, a method was developed to determine the position of the interference source by varying the signal loss threshold and the recovery time on the smartphone and the decaying carrier-to-noise ratio (CNR). The results indicate that GNSS observations from smartphones have an advantage over geodetic receivers in terms of localizing jammers because they do not lose the signal near the source of the jamming, but they are characterized by sudden drops in the CNR. Full article

(This article belongs to the Special Issue Smartphone-Derived GNSS Measurements Characterization for Precise Positioning and Navigation Applications)

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41 pages, 7840 KiB

Open AccessReview

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

by Shuran Zheng, Jinling Wang, Chris Rizos, Weidong Ding and Ahmed El-Mowafy

Remote Sens. 2023, 15(4), 1156; https://doi.org/10.3390/rs15041156 - 20 Feb 2023

Cited by 17 | Viewed by 10463

Abstract

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since [...] Read more.

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system. 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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32 pages, 15277 KiB

Open AccessArticle

Spatiotemporal Dynamics of Water Quality Indicators in Koka Reservoir, Ethiopia

by Endaweke Assegide, Hailu Shiferaw, Degefie Tibebe, Maria V. Peppa, Claire L. Walsh, Tena Alamirew and Gete Zeleke

Remote Sens. 2023, 15(4), 1155; https://doi.org/10.3390/rs15041155 - 20 Feb 2023

Cited by 2 | Viewed by 2087

Abstract

The science and application of the Earth observation system are receiving growing traction and wider application, and the scope is becoming wider and better owing to the availability of the higher resolution of satellite remote sensing products. A water quality monitoring model was [...] Read more.

The science and application of the Earth observation system are receiving growing traction and wider application, and the scope is becoming wider and better owing to the availability of the higher resolution of satellite remote sensing products. A water quality monitoring model was developed using Sentinel-2 satellite remote sensing data set to investigate the spatiotemporal dynamics of water quality indicators at Koka Reservoir. L1C images were processed with an Atmospheric correction processor ACOLITE. The months from June 2021 to May 2022 and the years 2017 to 2022 were used for the temporal analyses. Algorithms were developed by using regression analysis and developing empirical models by correlating satellite reflectance data with in situ Chlorophyll-a (Chl-a), turbidity (TU), and Total suspended matter (TSS) measurements. All of the analyzed parameters have determination coefficients (R²) greater than 0.67, indicating that they can be turned into predictive models. R² for the developed algorithms were 0.91, 0.92, and 0.67, indicating that good correlations have been found between field-based and estimated Chl-a, TU, and TSS, respectively. Accordingly, the mean monthly Chl-a, TU, and TSS levels have ranged from (59.69 to 144.25 g/L), (79.67 to 115.39 NTU), and (38.46 to 368.97 mg/L), respectively. The annual mean Chl-a, TU, and TSS vary from (52.86–96.19 µg/L), (71.04–83 NTU), and (36.58–159.26 mg/L), respectively, showing that the reservoir has been continuously polluted over the last seven years. The spatial study found that the distributions of Chl-a, TU, and TSS were heterogeneous, with Chl-a being greater in the south and southwest, and TU and TSS being higher on the western shore of the reservoir. In conclusion, these results show that there are spatial as well as temporal variations on water quality parameters. The proposed algorithms are capable of detecting optically active water quality indicators and can be applied in similar environmental situations. Full article

(This article belongs to the Section Ocean Remote Sensing)

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22 pages, 9084 KiB

Open AccessArticle

SAM-HIT: A Simulated Annealing Multispectral to Hyperspectral Imagery Data Transformation

by Ali Mohamed, Ashraf Emam and Basem Zoheir

Remote Sens. 2023, 15(4), 1154; https://doi.org/10.3390/rs15041154 - 20 Feb 2023

Cited by 2 | Viewed by 1402

Abstract

Space-borne hyperspectral imagery data are known for their high spectral resolution in a number of narrow wavelength intervals, which makes these data useful for mineral mapping. However, the available free-of-charge hyperspectral scenes cover only narrow and scattered geographic areas. In contrast, multispectral imagery [...] Read more.

Space-borne hyperspectral imagery data are known for their high spectral resolution in a number of narrow wavelength intervals, which makes these data useful for mineral mapping. However, the available free-of-charge hyperspectral scenes cover only narrow and scattered geographic areas. In contrast, multispectral imagery scenes have a nearly complete spatial coverage and wider wavelength intervals. The low spectral resolution of the multispectral data, however, limits their efficiency in the mineral mapping of small geological massifs or hydrothermal alteration halos. The present contribution presents a new transformation tool (SAM-HIT) to simulate the hyperspectral sensor responses in unscanned areas based on partially overlapping hyperspectral and multispectral scenes. Simulation or prediction of the pseudohyperspectral data is here accomplished by using the simulated annealing linear optimization algorithm, which allows the lowest possible mismatch between the original and predicted data. The high visual and numerical correlation of the resultant data confirms the reliability of the newly adopted transformation. Further, the application of the SAM-HIT to a well-exposed part of the Egyptian basement complex with available hyperspectral data showed high concordance and nearly identical band signatures, opening a new outlook for mineral exploration in vast areas by a nearly automated cost-free means. Full article

(This article belongs to the Special Issue Integration of Remote Sensing and Airborne Geophysical Methods in Geological Studies)

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12 pages, 2974 KiB

Open AccessTechnical Note

Highly Accurate Radar Cross-Section and Transfer Function Measurement of a Digital Calibration Transponder without Known Reference—Part I: Measurement and Results

by Jens Reimann, Anna Maria Büchner, Sebastian Raab, Klaus Weidenhaupt, Matthias Jirousek and Marco Schwerdt

Remote Sens. 2023, 15(4), 1153; https://doi.org/10.3390/rs15041153 - 20 Feb 2023

Cited by 2 | Viewed by 1063

Abstract

Active Radar Calibrators (ARC), also called calibration transponders, are often used as reference targets for absolute radiometric calibration of radar systems due to their large achievable Radar Cross-Section (RCS). However, before using a transponder as a reference target, the hardware itself has to [...] Read more.

Active Radar Calibrators (ARC), also called calibration transponders, are often used as reference targets for absolute radiometric calibration of radar systems due to their large achievable Radar Cross-Section (RCS). However, before using a transponder as a reference target, the hardware itself has to be calibrated. A novel method, called the three-transponder method, was proposed some years ago and allows for RCS calibration of digital transponders without using any RCS targets as reference. In this paper, this technique is further refined and applied to a setup utilizing only one digital transponder. The accurate measurement design is described and a novel, elaborated data processing scheme is developed to minimize remaining noise and clutter effects in the data. A comprehensive error analysis is presented in the second part of this paper. Full article

(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)

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21 pages, 6380 KiB

Open AccessArticle

Evolution of Small and Micro Wetlands and Their Driving Factors in the Yangtze River Delta—A Case Study of Wuxi Area

by Jiamin Zhang, Lei Chu, Zengxin Zhang, Bin Zhu, Xiaoyan Liu and Qiang Yang

Remote Sens. 2023, 15(4), 1152; https://doi.org/10.3390/rs15041152 - 20 Feb 2023

Cited by 4 | Viewed by 2125

Abstract

Understanding the long-term dynamics and driving factors behind small and micro wetlands is critical for their management and future sustainability. This study explored the impacts of natural and anthropogenic factors on the spatiotemporal evolution of these areas in Wuxi area using the support [...] Read more.

Understanding the long-term dynamics and driving factors behind small and micro wetlands is critical for their management and future sustainability. This study explored the impacts of natural and anthropogenic factors on the spatiotemporal evolution of these areas in Wuxi area using the support vector machine (SVM) classification method and the geographic detector model based on Landsat satellite image data from 1985 to 2020. The results revealed that: (1) Natural wetlands were prominent in Wuxi area, with an average proportion of 70%, and although they exhibited a downward trend over the last ten years, the scale of natural small and micro wetlands increased 1.5-fold—from 4349.59 hm² in 1985 to 10,841.59 hm² in 2020. (2) The small and micro wetlands in Wuxi area had obvious seasonal variations, with most being 0.1–1 hm² and 1–3 hm², respectively. From the perspective of spatial distribution, they were primarily distributed in Yixing district, which accounts for 34% of Wuxi area. (3) The distribution of small and micro wetlands was systematically affected by natural and human activities. The main factors that affected the distribution of small and micro wetlands were the average annual temperature and GDP, with the interactions between all factors being nonlinear and bi-linear. The influences of natural factors on small and micro wetlands were weakened, with human activities steadily emerging as the dominant factor that affected their distribution. The results of this study can provide supportive data and a scientific basis for the ecological restoration and protection of wetlands. Full article

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21 pages, 12641 KiB

Open AccessArticle

CTFuseNet: A Multi-Scale CNN-Transformer Feature Fused Network for Crop Type Segmentation on UAV Remote Sensing Imagery

by Jianjian Xiang, Jia Liu, Du Chen, Qi Xiong and Chongjiu Deng

Remote Sens. 2023, 15(4), 1151; https://doi.org/10.3390/rs15041151 - 20 Feb 2023

Cited by 4 | Viewed by 2423

Abstract

Timely and accurate acquisition of crop type information is significant for irrigation scheduling, yield estimation, harvesting arrangement, etc. The unmanned aerial vehicle (UAV) has emerged as an effective way to obtain high resolution remote sensing images for crop type mapping. Convolutional neural network [...] Read more.

Timely and accurate acquisition of crop type information is significant for irrigation scheduling, yield estimation, harvesting arrangement, etc. The unmanned aerial vehicle (UAV) has emerged as an effective way to obtain high resolution remote sensing images for crop type mapping. Convolutional neural network (CNN)-based methods have been widely used to predict crop types according to UAV remote sensing imagery, which has excellent local feature extraction capabilities. However, its receptive field limits the capture of global contextual information. To solve this issue, this study introduced the self-attention-based transformer that obtained long-term feature dependencies of remote sensing imagery as supplementary to local details for accurate crop-type segmentation in UAV remote sensing imagery and proposed an end-to-end CNN–transformer feature-fused network (CTFuseNet). The proposed CTFuseNet first provided a parallel structure of CNN and transformer branches in the encoder to extract both local and global semantic features from the imagery. A new feature-fusion module was designed to flexibly aggregate the multi-scale global and local features from the two branches. Finally, the FPNHead of feature pyramid network served as the decoder for the improved adaptation to the multi-scale fused features and output the crop-type segmentation results. Our comprehensive experiments indicated that the proposed CTFuseNet achieved a higher crop-type-segmentation accuracy, with a mean intersection over union of 85.33% and a pixel accuracy of 92.46% on the benchmark remote sensing dataset and outperformed the state-of-the-art networks, including U-Net, PSPNet, DeepLabV3+, DANet, OCRNet, SETR, and SegFormer. Therefore, the proposed CTFuseNet was beneficial for crop-type segmentation, revealing the advantage of fusing the features found by the CNN and the transformer. Further work is needed to promote accuracy and efficiency of this approach, as well as to assess the model transferability. Full article

(This article belongs to the Special Issue Synergy of UAV Imagery and Artificial Intelligence for Agriculture)

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18 pages, 21033 KiB

Open AccessArticle

Spatiotemporal Patterns of Evapotranspiration in Central Asia from 2000 to 2020

by Xingming Hao, Xue Fan, Zhuoyi Zhao and Jingjing Zhang

Remote Sens. 2023, 15(4), 1150; https://doi.org/10.3390/rs15041150 - 20 Feb 2023

Cited by 3 | Viewed by 1621

Abstract

Evapotranspiration (ET) affects the dry and wet conditions of a region, particularly in arid Central Asia, where changes in evapotranspiration profoundly influence society, the economy, and ecosystems. However, the changing trends in and driving factors of evapotranspiration in Central Asia remain [...] Read more.

Evapotranspiration (ET) affects the dry and wet conditions of a region, particularly in arid Central Asia, where changes in evapotranspiration profoundly influence society, the economy, and ecosystems. However, the changing trends in and driving factors of evapotranspiration in Central Asia remain unclear. Therefore, we used estimated ET and reanalysis data to answer research questions. Our results showed that (1) potential evapotranspiration (PET) and ET showed a generally downward trend, in which PET and ET decreased in 37.93% and 17.42% of the total area, respectively. However, PET and ET showed opposite trends in 59.41% of the study area, mainly showing a decrease in PET and an increase in ET. (2) The absolute contribution rates of vegetation–human activity coupling factor (VH), PET, and precipitation (P) to ET were 43.19%, 40.02%, and 16.79%, respectively, and the VH was the main determiner of ET. (3) Transpiration (ET_c) dominated the change in ET in 56.4% of the region, whereas soil evaporation (ET_s) dominated the change in ET in the rest of the region. The coverage threshold that determines the dominant contributions of ET_c and ETs to ET was approximately 18–19%. Below this coverage threshold, the contribution rate of ET_s to ET exceeded that of ET_c and vice versa. In the context of global climate change, this study can provide scientific support for the restoration of water resources and sustainability evaluation of water resources. Full article

(This article belongs to the Special Issue Remote Sensing of Interaction between Human and Natural Ecosystem)

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15 pages, 5533 KiB

Open AccessArticle

Greenland-Ice-Sheet Surface Temperature and Melt Extent from 2000 to 2020 and Implications for Mass Balance

by Zhenxiang Fang, Ninglian Wang, Yuwei Wu and Yujie Zhang

Remote Sens. 2023, 15(4), 1149; https://doi.org/10.3390/rs15041149 - 20 Feb 2023

Cited by 2 | Viewed by 3267

Abstract

Accurate monitoring of surface temperature and melting on the Greenland Ice Sheet (GrIS) is important for tracking the ice sheet’s mass balance as well as global and Arctic climate change. Using a moderate-resolution-imaging-spectroradiometer (MODIS)-derived land-surface-temperature (LST) data product with a resolution of 1 [...] Read more.

Accurate monitoring of surface temperature and melting on the Greenland Ice Sheet (GrIS) is important for tracking the ice sheet’s mass balance as well as global and Arctic climate change. Using a moderate-resolution-imaging-spectroradiometer (MODIS)-derived land-surface-temperature (LST) data product with a resolution of 1 km from 2000 to 2020, the temporal and spatial variations of annual and seasonal ‘clear-sky’ surface temperature were evaluated. We also monitored summer surface melting and studied the relationship between the mass balance of the ice sheet and changes in surface temperature and melting. The results show that the mean annual LST during the study period is −24.86 ± 5.46 °C, with the highest of −22.48 ± 5.61 °C in 2010 and the lowest temperature of −26.49 ± 5.30 °C in 2015. With the change of season, the spatial variation of the ice-sheet surface temperature changes greatly. 2012 and 2019 experienced the warmest summers (−5.92 ± 4.01 °C and −6.51 ± 3.93 °C), with extreme cumulative melting detected on the ice-sheet surface (89.9% and 89.7%, respectively), and 2002 also experienced a greater extent of melting. But short period of melt in 2002 and 2019 (30.6% and 31.4%, respectively), accounted for a larger proportion, with neither the duration nor intensity of the melt reaching that of 2012. There is a strong correlation between the GrIS surface temperature and its mass balance. By fitting the relationship between surface temperature and mass balance, it was found that 93.83% (6.17%) of the ice-sheet response to surface-temperature change was via surface-mass balance (discharge and basal-mass balance). Full article

(This article belongs to the Section Ocean Remote Sensing)

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24 pages, 9358 KiB

Open AccessArticle

Prediction of Future Land Use/Land Cover Changes Using a Coupled CA-ANN Model in the Upper Omo–Gibe River Basin, Ethiopia

by Paulos Lukas, Assefa M. Melesse and Tadesse Tujuba Kenea

Remote Sens. 2023, 15(4), 1148; https://doi.org/10.3390/rs15041148 - 20 Feb 2023

Cited by 17 | Viewed by 7607

Abstract

Land use/land cover change evaluation and prediction using spatiotemporal data are crucial for environmental monitoring and better planning and management of land use. The main objective of this study is to evaluate land use/land cover changes for the time period of 1991–2022 and [...] Read more.

Land use/land cover change evaluation and prediction using spatiotemporal data are crucial for environmental monitoring and better planning and management of land use. The main objective of this study is to evaluate land use/land cover changes for the time period of 1991–2022 and predict future changes using the CA-ANN model in the Upper Omo–Gibe River basin. Landsat-5 TM for 1991, 1997, and 2004, Landsat-7 ETM+ for 2010, and Landsat-8 (OLI) for 2016 and 2022 were downloaded from the USGS Earth Explorer Data Center. A random forest machine learning algorithm was employed for LULC classification. The LULC classification result was evaluated using an accuracy assessment technique to assure the correctness of the classification method employing the kappa coefficient. Kappa coefficient values of the classification indicate that there was strong agreement between the classified and reference data. Using the MOLUSCE plugin of QGIS and the CA-ANN model, future LULC changes were predicted. Artificial neural network (ANN) and cellular automata (CA) machine learning methods were made available for LULC change modeling and prediction via the QGIS MOLUSCE plugin. Transition potential modeling was computed, and future LULC changes were predicted using the CA-ANN model. An overall accuracy of 86.53% and an overall kappa value of 0.82 were obtained by comparing the actual data of 2022 with the simulated LULC data from the same year. The study findings revealed that between 2022 and 2037, agricultural land (63.09%) and shrubland (5.74%) showed significant increases, and forest (−48.10%) and grassland (−0.31%) decreased. From 2037 to 2052, the built-up area (2.99%) showed a significant increase, and forest and agricultural land (−2.55%) showed a significant decrease. From 2052 to 2067, the projected LULC simulation result showed that agricultural land (3.15%) and built-up area (0.32%) increased, and forest (−1.59%) and shrubland (−0.56%) showed significant decreases. According to the study’s findings, the main drivers of LULC changes are the expansion of built-up areas and agricultural land, which calls for a thorough investigation using additional data and models to give planners and policymakers clear information on LULC changes and their environmental effects. Full article

(This article belongs to the Special Issue Advances of Remote Sensing in Land Cover and Land Use Mapping)

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21 pages, 6342 KiB

Open AccessArticle

Hyperspectral Image Classification via Information Theoretic Dimension Reduction

by Md Rashedul Islam, Ayasha Siddiqa, Masud Ibn Afjal, Md Palash Uddin and Anwaar Ulhaq

Remote Sens. 2023, 15(4), 1147; https://doi.org/10.3390/rs15041147 - 20 Feb 2023

Cited by 5 | Viewed by 1376

Abstract

Hyperspectral images (HSIs) are one of the most successfully used tools for precisely and potentially detecting key ground surfaces, vegetation, and minerals. HSIs contain a large amount of information about the ground scene; therefore, object classification becomes the most difficult task for such [...] Read more.

Hyperspectral images (HSIs) are one of the most successfully used tools for precisely and potentially detecting key ground surfaces, vegetation, and minerals. HSIs contain a large amount of information about the ground scene; therefore, object classification becomes the most difficult task for such a high-dimensional HSI data cube. Additionally, the HSI’s spectral bands exhibit a high correlation, and a large amount of spectral data creates high dimensionality issues as well. Dimensionality reduction is, therefore, a crucial step in the HSI classification pipeline. In order to identify a pertinent subset of features for effective HSI classification, this study proposes a dimension reduction method that combines feature extraction and feature selection. In particular, we exploited the widely used denoising method minimum noise fraction (MNF) for feature extraction and an information theoretic-based strategy, cross-cumulative residual entropy (CCRE), for feature selection. Using the normalized CCRE, minimum redundancy maximum relevance (mRMR)-driven feature selection criteria were used to enhance the quality of the selected feature. To assess the effectiveness of the extracted features’ subsets, the kernel support vector machine (KSVM) classifier was applied to three publicly available HSIs. The experimental findings manifest a discernible improvement in classification accuracy and the qualities of the selected features. Specifically, the proposed method outperforms the traditional methods investigated, with overall classification accuracies on Indian Pines, Washington DC Mall, and Pavia University HSIs of 97.44%, 99.71%, and 98.35%, respectively. Full article

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11 pages, 7868 KiB

Open AccessCommunication

Non-Destructive Diagnosis on the Masaccio Frescoes at the Brancacci Chapel, Church of Santa Maria del Carmine (Florence)

by Giovanni Leucci, Lara De Giorgi, Ivan Ferrari, Francesco Giuri, Lucrezia Longhitano, Alberto Felici and Cristiano Riminesi

Remote Sens. 2023, 15(4), 1146; https://doi.org/10.3390/rs15041146 - 20 Feb 2023

Cited by 2 | Viewed by 1063

Abstract

The Basilica of Santa Maria del Carmine in Florence, in the Oltrarno area, was built in 1268 (pre-Renaissance low medieval context) and consecrated in 1422. Following a devastating fire in the interior of the original church, in 1771, very little remained. Among the [...] Read more.

The Basilica of Santa Maria del Carmine in Florence, in the Oltrarno area, was built in 1268 (pre-Renaissance low medieval context) and consecrated in 1422. Following a devastating fire in the interior of the original church, in 1771, very little remained. Among the parts that were saved were the Corsini and Brancacci chapels. The architect Giuseppe Ruggeri was responsible for the reconstruction of the church, which was completed in 1782 (with the exception of the gabled façade which remained unfinished, as can still be seen today). Geophysical investigations were undertaken into the Brancacci chapel in order to have information on the structure of the wall that contains wall paintings by Masaccio, Masolino, and Filippino Lippi, to understand the stratigraphy of the mortars, and to formulate some hypotheses on the causes of their detachment. The results are interesting. Full article

(This article belongs to the Special Issue Multiscale and Multitemporal High Resolution Remote Sensing for Archaeology and Heritage: From Research to Preservation)

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18 pages, 4797 KiB

Open AccessArticle

Mapping Cropland Abandonment in Mountainous Areas in China Using the Google Earth Engine Platform

by Suchen Xu, Wu Xiao, Chen Yu, Hang Chen and Yongzhong Tan

Remote Sens. 2023, 15(4), 1145; https://doi.org/10.3390/rs15041145 - 20 Feb 2023

Cited by 5 | Viewed by 2532

Abstract

Knowledge about the spatial-temporal pattern of cropland abandonment is the premise for the management of abandoned croplands. Traditional mapping approaches of abandoned croplands usually utilize a multi-date classification-based land cover change trajectory. It requires quality training samples for land cover classification at each [...] Read more.

Knowledge about the spatial-temporal pattern of cropland abandonment is the premise for the management of abandoned croplands. Traditional mapping approaches of abandoned croplands usually utilize a multi-date classification-based land cover change trajectory. It requires quality training samples for land cover classification at each epoch, which is challenging in regions of smallholder agriculture in the absence of high-resolution imagery. Facing these challenges, a theoretical model is proposed to recognize abandoned croplands based on post-abandonment secondary succession. It applies the continuous change detection and classification (CCDC) temporal segmentation algorithm to Landsat time series (1986~2021) to obtain disjoint segments, representing croplands’ status. The post-abandonment secondary succession showing a greening trend is recognized using NDVI-based harmonic analysis, so as to capture its preceding abandonment. This algorithm is applied to a mountainous area in southwest China, where cropland abandonments are widespread. Validation based on stratified random samples referenced by a vegetation index time series and satellite images shows that the detected abandoned croplands have user accuracy, producer accuracy and an F1 score ranging from 43% to 71%, with variation among abandonment year. The study area has a potential cropland extent of 22,294 km², within which 9252 km² of the cropland was abandoned. The three peak years of abandonment were 1994, 2000, and 2011. The algorithm is suitable to be applied to large-scale mapping due to its automatic manner. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)

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18 pages, 5493 KiB

Open AccessArticle

Detection and Attribution of Alpine Inland Lake Changes by Using Random Forest Algorithm

by Wei Guo, Xiangnan Ni, Yi Mu, Tong Liu and Junzhe Zhang

Remote Sens. 2023, 15(4), 1144; https://doi.org/10.3390/rs15041144 - 20 Feb 2023

Cited by 1 | Viewed by 1519

Abstract

The alpine inland lake dynamics have been good indicators of changes in terrestrial hydrological cycles under global climate change. However, the relationship between alpine inland lake and climatic factors remained largely uncertain. This study examines the spatial-temporal change of the fluctuation of the [...] Read more.

The alpine inland lake dynamics have been good indicators of changes in terrestrial hydrological cycles under global climate change. However, the relationship between alpine inland lake and climatic factors remained largely uncertain. This study examines the spatial-temporal change of the fluctuation of the lake by using dense time series Landsat TM/ETM/OLI images to delineate water boundary information based on the Random Forest algorithm and using ICESat (Ice, Cloud and land Elevation Satellite) dataset to monitor changes in variations of water level. Variations of Qinghai Lake (QHL) were analyzed from 1987 to 2020 and the mechanism of these changes was discussed with meteorological data. The results indicated that the QHL fluctuated strongly showing a pattern of shrinkage–expansion over the last three decades. The lake storage significantly decreased by −2.58 × 10⁸ m³·yr⁻¹ (R² = 0.86, p < 0.01) from 1989 to 2004 and sharply increased (6.92 × 10⁸ m³·yr⁻¹, R² = 0.92, p < 0.01) after 2004. The relationship between the lake and climate over the last 30 years implies that the decreasing evaporation and increasing precipitation were the major factors affecting the fluctuation of lake storage. Meanwhile, the temporal heterogeneity of the driving mechanism of climate change led to the phased characteristics of lake storage change. In detail, obvious warming led to the shrinkage of the QHL before 2004 through increasing evaporation, while humidifying and accelerating wind stilling dominated the expansion of the QHL after 2004 by increasing precipitation and decreasing evaporation. This paper indicated that the frameworks of multi-source remote sensing and accurate detection of water bodies were required to protect the high-altitude lakes from further climate changes based on the findings of this paper of the QHL recently. The framework presented herein can provide accurate detection and monitoring of water bodies in different locations in the Qinghai-Tibet Plateau, and provide a necessary basis for future political activities and decisions in terms of sustainable water resource management. Full article

(This article belongs to the Special Issue Remote Sensing of Land Water Bodies)

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17 pages, 4965 KiB

Open AccessArticle

Aboveground Forest Biomass Estimation by the Integration of TLS and ALOS PALSAR Data Using Machine Learning

by Arunima Singh, Sunni Kanta Prasad Kushwaha, Subrata Nandy, Hitendra Padalia, Surajit Ghosh, Ankur Srivastava and Nikul Kumari

Remote Sens. 2023, 15(4), 1143; https://doi.org/10.3390/rs15041143 - 20 Feb 2023

Cited by 4 | Viewed by 2409

Abstract

Forest inventory parameters play an important role in understanding various biophysical processes of forest ecosystems. The present study aims at integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data to assess Aboveground Biomass (AGB) in the Barkot Forest [...] Read more.

Forest inventory parameters play an important role in understanding various biophysical processes of forest ecosystems. The present study aims at integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data to assess Aboveground Biomass (AGB) in the Barkot Forest Range, Uttarakhand, India. The integration was performed to overcome the AGB saturation issue in ALOS PALSAR L-band SAR data for the high biomass density forest of the study area using 13 plots. Various parameters, namely, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, Yamaguchi decomposition components, polarimetric parameters, and backscatter values of HH and HV band intensity, were derived from the ALOS SAR data. However, TLS was used to obtain the diameter at breast height (dbh) and tree height for the sample plots. A total of 23 parameters was retrieved using TLS and SAR data for integration with the LiDAR footprint. The integration was performed using Random Forest (RF) and Artificial Neural Network (ANN). The statistical measures for RF were found to be promising compared with ANN for AGB estimation. The R² value obtained for the RF was 0.94, with an RMSE of 59.72 ton ha⁻¹ for the predicted biomass value. The RMSE% was 15.92, while the RMSE_CV was 0.15. The R² value for ANN was 0.77, with an RMSE of 98.46 ton ha⁻¹. The RMSE% was 26.0, while the RMSE_CV was 0.26. RF performed better in estimating the biomass, which ranged from 122.46 to 581.89 ton ha⁻¹, while uncertainty ranged from 15.75 to 85.14 ton ha⁻¹. The integration of SAR and LiDAR data using machine learning shows great potential in overcoming AGB saturation of SAR data. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry)

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16 pages, 4631 KiB

Open AccessArticle

MMCAN: Multi-Modal Cross-Attention Network for Free-Space Detection with Uncalibrated Hyperspectral Sensors

by Feiyi Fang, Tao Zhou, Zhenbo Song and Jianfeng Lu

Remote Sens. 2023, 15(4), 1142; https://doi.org/10.3390/rs15041142 - 20 Feb 2023

Cited by 2 | Viewed by 2245

Abstract

Free-space detection plays a pivotal role in autonomous vehicle applications, and its state-of-the-art algorithms are typically based on semantic segmentation of road areas. Recently, hyperspectral images have proven useful supplementary information in multi-modal segmentation for providing more texture details to the RGB representations, [...] Read more.

Free-space detection plays a pivotal role in autonomous vehicle applications, and its state-of-the-art algorithms are typically based on semantic segmentation of road areas. Recently, hyperspectral images have proven useful supplementary information in multi-modal segmentation for providing more texture details to the RGB representations, thus performing well in road segmentation tasks. Existing multi-modal segmentation methods assume that all the inputs are well-aligned, and then the problem is converted to fuse feature maps from different modalities. However, there exist cases where sensors cannot be well-calibrated. In this paper, we propose a novel network named multi-modal cross-attention network (MMCAN) for multi-modal free-space detection with uncalibrated hyperspectral sensors. We first introduce a cross-modality transformer using hyperspectral data to enhance RGB features, then aggregate these representations alternatively via multiple stages. This transformer promotes the spread and fusion of information between modalities that cannot be aligned at the pixel level. Furthermore, we propose a triplet gate fusion strategy, which can increase the proportion of RGB in the multiple spectral fusion processes while maintaining the specificity of each modality. The experimental results on a multi-spectral dataset demonstrate that our MMCAN model has achieved state-of-the-art performance. The method can be directly used on the pictures taken in the field without complex preprocessing. Our future goal is to adapt the algorithm to multi-object segmentation and generalize it to other multi-modal combinations. Full article

(This article belongs to the Special Issue Hyperspectral Object Tracking)

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17 pages, 5778 KiB

Open AccessArticle

Developing a near Real-Time Cloud Cover Retrieval Algorithm Using Geostationary Satellite Observations for Photovoltaic Plants

by Pan Xia, Min Min, Yu Yu, Yun Wang and Lu Zhang

Remote Sens. 2023, 15(4), 1141; https://doi.org/10.3390/rs15041141 - 20 Feb 2023

Cited by 2 | Viewed by 1609

Abstract

Clouds can block solar radiation from reaching the surface, so timely and effective cloud cover test and forecasting is critical to the operation and economic efficiency of photovoltaic (PV) plants. Traditional cloud cover algorithms based on meteorological satellite observation require many auxiliary data [...] Read more.

Clouds can block solar radiation from reaching the surface, so timely and effective cloud cover test and forecasting is critical to the operation and economic efficiency of photovoltaic (PV) plants. Traditional cloud cover algorithms based on meteorological satellite observation require many auxiliary data and computing resources, which are hard to implement or transplant for applications at PV plants. In this study, a portable and fast cloud mask algorithm (FCMA) is developed to provide near real-time (NRT) spatial-temporally matched cloud cover products for PV plants. The geostationary satellite imager data from the Advanced Himawari Imager aboard Himawari-8 and the related operational cloud mask algorithm (OCMA) are employed as benchmarks for comparison and validation. Furthermore, the ground-based manually observed cloud cover data at seven quintessential stations at 08:00 and 14:00 BJT (Beijing Time) in 2017 are employed to verify the accuracy of cloud cover data derived from FCMA and OCMA. The results show a high consistency with the ground-based data, and the average correlation coefficient (R) is close to 0.85. Remarkably, the detection accuracy of FCMA is slightly higher than that of OCMA, demonstrating the feasibility of FCMA for providing NRT cloud cover at PV plants. Full article

(This article belongs to the Special Issue Remote Sensing for Smart Renewable Cities)

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18 pages, 5114 KiB

Open AccessArticle

Assessing Snow Water Retrievals over Ocean from Coincident Spaceborne Radar Measurements

by Mengtao Yin and Cheng Yuan

Remote Sens. 2023, 15(4), 1140; https://doi.org/10.3390/rs15041140 - 19 Feb 2023

Viewed by 1179

Abstract

Spaceborne snow water retrievals over oceans are assessed using a multiyear coincident dataset of CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR). Various factors contributing to differences in snow water retrievals between CPR and DPR are carefully [...] Read more.

Spaceborne snow water retrievals over oceans are assessed using a multiyear coincident dataset of CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR). Various factors contributing to differences in snow water retrievals between CPR and DPR are carefully considered. A set of relationships between radar reflectivity (Z_e) and snow water content (SWC) at Ku- and W-bands is developed using the same microphysical assumptions. It is found that surface snow water contents from CPR are much larger than those from DPR at latitudes above 60°, while surface snow water contents from DPR slightly exceed those from CPR at latitudes below 50°. Coincident snow water content profiles between CPR and DPR are further divided into two conditions. One is that only CPR detects the falling snow. Another is that both CPR and DPR detect the falling snow. The results indicate that about 88% of all snow water content profiles are under the first condition and usually associated with light snowfall events. The remaining snow water content profiles are generally associated with moderate and heavy snowfall events. Moreover, CPR surface snow water contents are larger than DPR ones at high latitudes because most light snowfall events are misdetected by DPR due to its low sensitivity. DPR surface snow water contents exceed CPR ones at low latitudes because CPR may experience a significant reduction in backscattering efficiency of large particles and attenuation in heavy snowfall events. The low sensitivity of DPR also causes a noticeable decrease in detected snow layer depth. The results presented here can help in developing global snowfall retrieval algorithms using multi-radars. Full article

(This article belongs to the Special Issue Remote Sensing for the Improvement of High-Impact Weather Analyses and Forecasts)

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32 pages, 38904 KiB

Open AccessArticle

A Distributionally Robust Fusion Framework for Autonomous Multisensor Spacecraft Navigation during Entry Phase of Mars Entry, Descent, and Landing

by Natnael S. Zewge and Hyochoong Bang

Remote Sens. 2023, 15(4), 1139; https://doi.org/10.3390/rs15041139 - 19 Feb 2023

Cited by 2 | Viewed by 1911

Abstract

A robust multisensor navigation filter design for the entry phase of next-generation Mars entry, descent, and landing (EDL) is presented. The entry phase is the longest and most uncertain portion of a Mars landing sequence. Navigation performance at this stage determines landing precision [...] Read more.

A robust multisensor navigation filter design for the entry phase of next-generation Mars entry, descent, and landing (EDL) is presented. The entry phase is the longest and most uncertain portion of a Mars landing sequence. Navigation performance at this stage determines landing precision at the end of the powered descent phase of EDL. In the present work, measurements from a ground-based radio beacon array, an inertial measurement unit (IMU), as well as an array of atmospheric and aerothermal sensors on the body of a Mars entry vehicle are fused using an M-estimation-based iterated extended Kalman filtering (MIEKF) framework. The multisensor approach enables an increased positioning accuracy as well as the estimation of parameters that are otherwise unobservable. Furthermore, owing to the proposed statistically robust filter formulation, states and parameters can be accurately estimated in the presence of non-Gaussian measurement noise. Deviations from normally distributed observation noise correspond to outlier events such as sensor faults or other sources of spurious sensor data such as interference. The proposed framework provides a significant reduction in estimation error at the parachute phase of EDL, thereby increasing the likelihood of a pinpoint landing at a chosen landing site. Six states and three parameters are estimated. The suggested method is compared to the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). Detailed simulation results show that the presented fusion architecture is able to meet future pinpoint planetary landing requirements in realistic sensor measurement scenarios. Full article

(This article belongs to the Special Issue Autonomous Space Navigation)

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Remote Sens., Volume 15, Issue 4 (February-2 2023) – 301 articles

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