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Remote Sens., Volume 14, Issue 2 (January-2 2022) – 190 articles

Cover Story (view full-size image): In coastal regions of Western Europe, the polychaete Sabellaria alveolata (Linné) builds large intertidal reefs of several hectares on soft-bottom substrates that host a high biodiversity and provide ecological functions, such as protection against coastal erosion. A multispectral UAV-based structure-from-motion photogrammetric survey was carried out to map the complex three-dimensional bioconstructions and the epibionts impacting the reef (oysters, mussels, green macroalgae) at a very high resolution (few cm per pixel). This survey was carried out in October 2020 over the second-largest known European reef on Noirmoutier Island (France). Both topographic and multispectral information was used to derive new variables and indices that can be relevant to describe the health status of large intertidal reefs and further help stakeholders in managing these protected habitats. View this paper.
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21 pages, 8373 KiB  
Article
High-Throughput Legume Seed Phenotyping Using a Handheld 3D Laser Scanner
by Xia Huang, Shunyi Zheng and Ningning Zhu
Remote Sens. 2022, 14(2), 431; https://doi.org/10.3390/rs14020431 - 17 Jan 2022
Cited by 5 | Viewed by 2385
Abstract
High-throughput phenotyping involves many samples and diverse trait types. For the goal of automatic measurement and batch data processing, a novel method for high-throughput legume seed phenotyping is proposed. A pipeline of automatic data acquisition and processing, including point cloud acquisition, single-seed extraction, [...] Read more.
High-throughput phenotyping involves many samples and diverse trait types. For the goal of automatic measurement and batch data processing, a novel method for high-throughput legume seed phenotyping is proposed. A pipeline of automatic data acquisition and processing, including point cloud acquisition, single-seed extraction, pose normalization, three-dimensional (3D) reconstruction, and trait estimation, is proposed. First, a handheld laser scanner is used to obtain the legume seed point clouds in batches. Second, a combined segmentation method using the RANSAC method, the Euclidean segmentation method, and the dimensionality of the features is proposed to conduct single-seed extraction. Third, a coordinate rotation method based on PCA and the table normal is proposed to conduct pose normalization. Fourth, a fast symmetry-based 3D reconstruction method is built to reconstruct a 3D model of the single seed, and the Poisson surface reconstruction method is used for surface reconstruction. Finally, 34 traits, including 11 morphological traits, 11 scale factors, and 12 shape factors, are automatically calculated. A total of 2500 samples of five kinds of legume seeds are measured. Experimental results show that the average accuracies of scanning and segmentation are 99.52% and 100%, respectively. The overall average reconstruction error is 0.014 mm. The average morphological trait measurement accuracy is submillimeter, and the average relative percentage error is within 3%. The proposed method provides a feasible method of batch data acquisition and processing, which will facilitate the automation in high-throughput legume seed phenotyping. Full article
(This article belongs to the Special Issue 3D Modelling and Mapping for Precision Agriculture)
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23 pages, 8791 KiB  
Article
Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof
by Fayez Tarsha Kurdi, Zahra Gharineiat, Glenn Campbell, Mohammad Awrangjeb and Emon Kumar Dey
Remote Sens. 2022, 14(2), 430; https://doi.org/10.3390/rs14020430 - 17 Jan 2022
Cited by 12 | Viewed by 2781
Abstract
This paper suggests a new algorithm for automatic building point cloud filtering based on the Z coordinate histogram. This operation aims to select the roof class points from the building point cloud, and the suggested algorithm considers the general case where high trees [...] Read more.
This paper suggests a new algorithm for automatic building point cloud filtering based on the Z coordinate histogram. This operation aims to select the roof class points from the building point cloud, and the suggested algorithm considers the general case where high trees are associated with the building roof. The Z coordinate histogram is analyzed in order to divide the building point cloud into three zones: the surrounding terrain and low vegetation, the facades, and the tree crowns and/or the roof points. This operation allows the elimination of the first two classes which represent an obstacle toward distinguishing between the roof and the tree points. The analysis of the normal vectors, in addition to the change of curvature factor of the roof class leads to recognizing the high tree crown points. The suggested approach was tested on five datasets with different point densities and urban typology. Regarding the results’ accuracy quantification, the average values of the correctness, the completeness, and the quality indices are used. Their values are, respectively, equal to 97.9%, 97.6%, and 95.6%. These results confirm the high efficacy of the suggested approach. Full article
(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling)
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21 pages, 10837 KiB  
Article
Towards a Multimodal Representation: Claudia Octavia’s Bequeathal
by Sara Gonizzi Barsanti, Santiago Lillo Giner and Adriana Rossi
Remote Sens. 2022, 14(2), 429; https://doi.org/10.3390/rs14020429 - 17 Jan 2022
Cited by 2 | Viewed by 1753
Abstract
Through a non-contact survey methodology, based on image-based techniques, the authors digitally ‘build’ a three-dimensional hypothesis of a monumental complex carved on a first-century AC marble tombstone. Guided by the mathematical rationality recognised in the artefact, the paper illustrates the reasons for the [...] Read more.
Through a non-contact survey methodology, based on image-based techniques, the authors digitally ‘build’ a three-dimensional hypothesis of a monumental complex carved on a first-century AC marble tombstone. Guided by the mathematical rationality recognised in the artefact, the paper illustrates the reasons for the reconstructive choices and then proposes a reflection on the architectural contents. The ultimate goal focuses on the potential use of the digital product, which, thanks to and by virtue of the use of dedicated platforms, promotes strategies that include identity values by superimposing technical, social, and economic aspects. The setting up of collaborative spaces programmed with different strategies can effectively support the cognitive experience by verifying the possibility of “remedying” contents that, in our case, direct the study, dissemination, and protection of cultural heritage according to the most recent UNESCO recommendations. Full article
(This article belongs to the Special Issue 3D Virtual Reconstruction for Cultural Heritage)
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22 pages, 5676 KiB  
Article
Assessing Vegetation Decline Due to Pollution from Solid Waste Management by a Multitemporal Remote Sensing Approach
by Giuseppe Mancino, Rodolfo Console, Michele Greco, Chiara Iacovino, Maria Lucia Trivigno and Antonio Falciano
Remote Sens. 2022, 14(2), 428; https://doi.org/10.3390/rs14020428 - 17 Jan 2022
Cited by 8 | Viewed by 2542
Abstract
Nowadays, the huge production of Municipal Solid Waste (MSW) is one of the most strongly felt environmental issues. Consequently, the European Union (EU) delivers laws and regulations for better waste management, identifying the essential requirements for waste disposal operations and the characteristics that [...] Read more.
Nowadays, the huge production of Municipal Solid Waste (MSW) is one of the most strongly felt environmental issues. Consequently, the European Union (EU) delivers laws and regulations for better waste management, identifying the essential requirements for waste disposal operations and the characteristics that make waste hazardous to human health and the environment. In Italy, environmental regulations define, among other things, the characteristics of sites to be classified as “potentially contaminated”. From this perspective, the Basilicata region is currently one of the Italian regions with the highest number of potentially polluted sites in proportion to the number of inhabitants. This research aimed to identify the possible effects of potentially toxic element (PTE) pollution due to waste disposal activities in three “potentially contaminated” sites in southern Italy. The area was affected by a release of inorganic pollutants with values over the thresholds ruled by national/European legislation. Potential physiological efficiency variations of vegetation were analyzed through the multitemporal processing of satellite images. Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) images were used to calculate the trend in the Normalized Difference Vegetation Index (NDVI) over the years. The multitemporal trends were analyzed using the median of the non-parametric Theil–Sen estimator. Finally, the Mann–Kendall test was applied to evaluate trend significance featuring areas according to the contamination effects on investigated vegetation. The applied procedure led to the exclusion of significant effects on vegetation due to PTEs. Thus, waste disposal activities during previous years do not seem to have significantly affected vegetation around targeted sites. Full article
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20 pages, 27285 KiB  
Article
Multiscale Object Detection in Remote Sensing Images Combined with Multi-Receptive-Field Features and Relation-Connected Attention
by Jiahang Liu, Donghao Yang and Fei Hu
Remote Sens. 2022, 14(2), 427; https://doi.org/10.3390/rs14020427 - 17 Jan 2022
Cited by 27 | Viewed by 2489
Abstract
Object detection is an important task of remote sensing applications. In recent years, with the development of deep convolutional neural networks, object detection in remote sensing images has made great improvements. However, the large variation of object scales and complex scenarios will seriously [...] Read more.
Object detection is an important task of remote sensing applications. In recent years, with the development of deep convolutional neural networks, object detection in remote sensing images has made great improvements. However, the large variation of object scales and complex scenarios will seriously affect the performance of the detectors. To solve these problems, a novel object detection algorithm based on multi-receptive-field features and relation-connected attention is proposed for remote sensing images to achieve more accurate detection results. Specifically, we propose a multi-receptive-field feature extraction module with dilated convolution to aggregate the context information of different receptive fields. This achieves a strong capability of feature representation, which can effectively adapt to the scale changes of objects, either due to various object scales or different resolutions. Then, a relation-connected attention module based on relation modeling is constructed to automatically select and refine the features, which combines global and local attention to make the features more discriminative and can effectively improve the robustness of the detector. We designed these two modules as plug-and-play blocks and integrated them into the framework of Faster R-CNN to verify our method. The experimental results on NWPU VHR-10 and HRSC2016 datasets demonstrate that these two modules can effectively improve the performance of basic deep CNNs, and the proposed method can achieve better results of multiscale object detection in complex backgrounds. Full article
(This article belongs to the Section AI Remote Sensing)
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19 pages, 18725 KiB  
Article
A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery
by You-Hyun Baek, Il-Ju Moon, Jungho Im and Juhyun Lee
Remote Sens. 2022, 14(2), 426; https://doi.org/10.3390/rs14020426 - 17 Jan 2022
Cited by 6 | Viewed by 3325
Abstract
A novel tropical cyclone (TC) size estimation model (TC-SEM) in the western North Pacific was developed based on a convolutional neural network (CNN) using geostationary satellite infrared (IR) images. The proposed TC-SEM was tested using three CNN schemes: a single-task regression model that [...] Read more.
A novel tropical cyclone (TC) size estimation model (TC-SEM) in the western North Pacific was developed based on a convolutional neural network (CNN) using geostationary satellite infrared (IR) images. The proposed TC-SEM was tested using three CNN schemes: a single-task regression model that separately estimated the radius of maximum wind (RMW) and the radius of 34 kt wind (R34) of the TC, a multi-task regression model that estimated the RMW and R34 simultaneously, and a multi-task regression model using best-track TC intensity information. For model training, validation, and testing, 29,730, 2505, and 11,624 geostationary satellite images of the region around the center of the TC, respectively, were used, each containing four IR bands: short-wavelength IR (3.7 µm), water vapor (6.7 µm), IR1 (10.8 µm), and IR2 (12.0 µm). The results showed that the multi-task model performed better than the single-task model due to knowledge sharing and its ability to solve multiple interrelated tasks simultaneously. The inclusion of TC intensity information in the multi-task model further improved the performance of the RMW and R34 estimations, with correlations (mean absolute errors) of 0.95 (2.05 nmi) and 0.93 (9.77 nmi), respectively, which represent significant improvements over the performance of existing linear regression statistical methods. The results suggested that this CNN model using geostationary satellite images may be a powerful tool for estimating TC sizes in operational TC forecasts. Full article
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21 pages, 10794 KiB  
Article
A Fusion-Based Defogging Algorithm
by Ting Chen, Mengni Liu, Tao Gao, Peng Cheng, Shaohui Mei and Yonghui Li
Remote Sens. 2022, 14(2), 425; https://doi.org/10.3390/rs14020425 - 17 Jan 2022
Cited by 9 | Viewed by 2286
Abstract
To solve the problem that traditional dark channel is not suitable for a large sky area and can easyily distort defogged images, we propose a novel fusion-based defogging algorithm. Firstly, an improved remote sensing image segmentation algorithm is introduced to mix the dark [...] Read more.
To solve the problem that traditional dark channel is not suitable for a large sky area and can easyily distort defogged images, we propose a novel fusion-based defogging algorithm. Firstly, an improved remote sensing image segmentation algorithm is introduced to mix the dark channel. Secondly, we establish a dark-light channel fusion model to calculate the atmospheric light map. Furthermore, in order to refine the transmittance image without reducing restoration quality, the grayscale image corresponding to the original image is selected as a guide image. Meanwhile, we optimize the set value of the defogging intensity parameter ω in the transmission estimation formula as the value of atmospheric light. Finally, a brightness/color compensation model based on visual perception is generated for image correction. Experimental results demonstrate that the proposed algorithm achieves superior performance on UAV foggy images in both subjective and objective evaluation, which verifies the effectiveness of the proposed algorithm. Full article
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22 pages, 18342 KiB  
Article
Underground Morphological Detection of Ground Fissures in Collapsible Loess Area Based on Three-Dimensional Laser Scanning Technology
by Yibo He, Zhenqi Hu, Yaokun Fu, Kun Yang, Rui Wang, Guomou Shi, Zhanjie Feng, Qirang Yang and Liang Yu
Remote Sens. 2022, 14(2), 424; https://doi.org/10.3390/rs14020424 - 17 Jan 2022
Cited by 5 | Viewed by 2119
Abstract
Underground coal mining inevitably causes ground fissures, especially permanent cracks that cannot be closed at the boundary of the working face. Studying the underground three-dimensional morphology of the permanent cracks allows one to accurately constrain the formation and development of the ground fissures. [...] Read more.
Underground coal mining inevitably causes ground fissures, especially permanent cracks that cannot be closed at the boundary of the working face. Studying the underground three-dimensional morphology of the permanent cracks allows one to accurately constrain the formation and development of the ground fissures. This information will contribute to reducing mine disasters and is also a prerequisites to avoid environmental pollution. We selected the Zhangjiamao coal mine (China), which is situated in a collapsible loess area, as a case study for deciphering the formation of permanent cracks. After injecting gypsum slurry into the mine, a three-dimensional model of the ground fissures is obtained by three-dimensional (3D) laser scanner technology that records the 3D underground morphology. Integrating the geological context of a collapsible loess area, the characteristics and main processes of the ground fissure development are constrained: (1) The width of the ground fissure decreases to 0 with increasing depth and is strongly affected by the soil composition. (2) Along the vertical extension direction, the ground fissures are generally inclined to the inner-side of the working face, but the direction remains uncertain at different depths. (3) The transverse propagation direction of the ground fissure becomes more complex with increasing depth. (4) Under the influence of soil texture and water, loose soil fills the bottom of the ground fissure, thus affecting the underground 3D morphology. Full article
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24 pages, 37681 KiB  
Article
Evaluation of SMOS L4 Sea Surface Salinity Product in the Western Iberian Coast
by Beatriz Biguino, Estrella Olmedo, Afonso Ferreira, Nuno Zacarias, Luísa Lamas, Luciane Favareto, Carla Palma, Carlos Borges, Ana Teles-Machado, Joaquim Dias, Paola Castellanos and Ana C. Brito
Remote Sens. 2022, 14(2), 423; https://doi.org/10.3390/rs14020423 - 17 Jan 2022
Cited by 2 | Viewed by 1953
Abstract
Salinity is one of the oldest parameters being measured in oceanography and one of the most important to study in the context of climate change. However, its quantification by satellite remote sensing has been a relatively recent achievement. Currently, after over ten years [...] Read more.
Salinity is one of the oldest parameters being measured in oceanography and one of the most important to study in the context of climate change. However, its quantification by satellite remote sensing has been a relatively recent achievement. Currently, after over ten years of data gathering, there are still many challenges in quantifying salinity from space, especially when it is intended for coastal environments study. That is mainly due to the spatial resolution of the available products. Recently, a new higher resolution (5 km) L4 SMOS sea surface salinity (SSS) product was developed by the Barcelona Expert Center (BEC). In this study, the quality of this product was tested along the Western Iberian Coast through its comparison with in situ observations and modelled salinity estimates (CMEMS IBI Ocean Reanalysis system). Moreover, several parameters such as the temperature and depth of in situ measurements were tested to identify the variables or processes that induced higher errors in the product or influenced its performance. Lastly, a seasonal and interannual analysis was conducted considering data between 2011 to 2019 to test the product as a potential tool for long-term studies. The results obtained in the present analysis showed a high potential of using the L4 BEC SSS SMOS product in extended temporal and spatial analyses along the Portuguese coast. A good correlation between the satellite and the in situ datasets was observed, and the satellite dataset showed lower errors in retrieving coastal salinities than the oceanic model. Overall, the distance to the coast and the closest rivers were the factors that most influenced the quality of the product. The present analysis showed that great progress has been made in deriving coastal salinity over the years and that the SMOS SSS product is a valuable contribution to worldwide climatological studies. In addition, these results reinforce the need to continue developing satellite remote sensing products as a global and cost-effective methodology for long-term studies. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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31 pages, 29726 KiB  
Article
3D Modeling of Urban Area Based on Oblique UAS Images—An End-to-End Pipeline
by Valeria-Ersilia Oniga, Ana-Ioana Breaban, Norbert Pfeifer and Maximilian Diac
Remote Sens. 2022, 14(2), 422; https://doi.org/10.3390/rs14020422 - 17 Jan 2022
Cited by 9 | Viewed by 3417
Abstract
3D modelling of urban areas is an attractive and active research topic, as 3D digital models of cities are becoming increasingly common for urban management as a consequence of the constantly growing number of people living in cities. Viewed as a digital representation [...] Read more.
3D modelling of urban areas is an attractive and active research topic, as 3D digital models of cities are becoming increasingly common for urban management as a consequence of the constantly growing number of people living in cities. Viewed as a digital representation of the Earth’s surface, an urban area modeled in 3D includes objects such as buildings, trees, vegetation and other anthropogenic structures, highlighting the buildings as the most prominent category. A city’s 3D model can be created based on different data sources, especially LiDAR or photogrammetric point clouds. This paper’s aim is to provide an end-to-end pipeline for 3D building modeling based on oblique UAS images only, the result being a parametrized 3D model with the Open Geospatial Consortium (OGC) CityGML standard, Level of Detail 2 (LOD2). For this purpose, a flight over an urban area of about 20.6 ha has been taken with a low-cost UAS, i.e., a DJI Phantom 4 Pro Professional (P4P), at 100 m height. The resulting UAS point cloud with the best scenario, i.e., 45 Ground Control Points (GCP), has been processed as follows: filtering to extract the ground points using two algorithms, CSF and terrain-mark; classification, using two methods, based on attributes only and a random forest machine learning algorithm; segmentation using local homogeneity implemented into Opals software; plane creation based on a region-growing algorithm; and plane editing and 3D model reconstruction based on piece-wise intersection of planar faces. The classification performed with ~35% training data and 31 attributes showed that the Visible-band difference vegetation index (VDVI) is a key attribute and 77% of the data was classified using only five attributes. The global accuracy for each modeled building through the workflow proposed in this study was around 0.15 m, so it can be concluded that the proposed pipeline is reliable. Full article
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19 pages, 10027 KiB  
Article
An Accurate Digital Subsidence Model for Deformation Detection of Coal Mining Areas Using a UAV-Based LiDAR
by Junliang Zheng, Wanqiang Yao, Xiaohu Lin, Bolin Ma and Lingxiao Bai
Remote Sens. 2022, 14(2), 421; https://doi.org/10.3390/rs14020421 - 17 Jan 2022
Cited by 13 | Viewed by 2643
Abstract
Coal mine surface subsidence detection determines the damage degree of coal mining, which is of great importance for the mitigation of hazards and property loss. Therefore, it is very important to detect deformation during coal mining. Currently, there are many methods used to [...] Read more.
Coal mine surface subsidence detection determines the damage degree of coal mining, which is of great importance for the mitigation of hazards and property loss. Therefore, it is very important to detect deformation during coal mining. Currently, there are many methods used to detect deformations in coal mining areas. However, with most of them, the accuracy is difficult to guarantee in mountainous areas, especially for shallow seam mining, which has the characteristics of active, rapid, and high-intensity surface subsidence. In response to these problems, we made a digital subsidence model (DSuM) for deformation detection in coal mining areas based on airborne light detection and ranging (LiDAR). First, the entire point cloud of the study area was obtained by coarse to fine registration. Second, noise points were removed by multi-scale morphological filtering, and the progressive triangulation filtering classification (PTFC) algorithm was used to obtain the ground point cloud. Third, the DEM was generated from the clean ground point cloud, and an accurate DSuM was obtained through multiple periods of DEM difference calculations. Then, data mining was conducted based on the DSuM to obtain parameters such as the maximum surface subsidence value, a subsidence contour map, the subsidence area, and the subsidence boundary angle. Finally, the accuracy of the DSuM was analyzed through a comparison with ground checkpoints (GCPs). The results show that the proposed method can achieve centimeter-level accuracy, which makes the data a good reference for mining safety considerations and subsequent restoration of the ecological environment. Full article
(This article belongs to the Special Issue Techniques and Applications of UAV-Based Photogrammetric 3D Mapping)
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22 pages, 4276 KiB  
Article
Small Object Detection Method Based on Adaptive Spatial Parallel Convolution and Fast Multi-Scale Fusion
by Guanqiu Qi, Yuanchuan Zhang, Kunpeng Wang, Neal Mazur, Yang Liu and Devanshi Malaviya
Remote Sens. 2022, 14(2), 420; https://doi.org/10.3390/rs14020420 - 17 Jan 2022
Cited by 46 | Viewed by 4579
Abstract
As one type of object detection, small object detection has been widely used in daily-life-related applications with many real-time requirements, such as autopilot and navigation. Although deep-learning-based object detection methods have achieved great success in recent years, they are not effective in small [...] Read more.
As one type of object detection, small object detection has been widely used in daily-life-related applications with many real-time requirements, such as autopilot and navigation. Although deep-learning-based object detection methods have achieved great success in recent years, they are not effective in small object detection and most of them cannot achieve real-time processing. Therefore, this paper proposes a single-stage small object detection network (SODNet) that integrates the specialized feature extraction and information fusion techniques. An adaptively spatial parallel convolution module (ASPConv) is proposed to alleviate the lack of spatial information for target objects and adaptively obtain the corresponding spatial information through multi-scale receptive fields, thereby improving the feature extraction ability. Additionally, a split-fusion sub-module (SF) is proposed to effectively reduce the time complexity of ASPConv. A fast multi-scale fusion module (FMF) is proposed to alleviate the insufficient fusion of both semantic and spatial information. FMF uses two fast upsampling operators to first unify the resolution of the multi-scale feature maps extracted by the network and then fuse them, thereby effectively improving the small object detection ability. Comparative experimental results prove that the proposed method considerably improves the accuracy of small object detection on multiple benchmark datasets and achieves a high real-time performance. Full article
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19 pages, 12533 KiB  
Article
Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements
by Chunhui Zhao, Chengxin Zhang, Jinan Lin, Shuntian Wang, Hanyang Liu, Hongyu Wu and Cheng Liu
Remote Sens. 2022, 14(2), 419; https://doi.org/10.3390/rs14020419 - 17 Jan 2022
Cited by 7 | Viewed by 2137
Abstract
Since the COVID-19 outbreak in 2020, China’s air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 [...] Read more.
Since the COVID-19 outbreak in 2020, China’s air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 Chinese megacities by using satellite and ground-based remote sensing as well as in situ measurements. The average satellite tropospheric vertical column density (TVCD) of NO2 by TROPOMI decreased by 39.2–71.93% during the 15 days after Chinese New Year when the lockdown was at its most rigorous compared to that of 2019, while the in situ NO2 concentration measured by China National Environmental Monitoring Centre (CNEMC) decreased by 42.53–69.81% for these cities. Such differences between both measurements were further investigated by using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) remote sensing of NO2 vertical profiles. For instance, in Beijing, MAX-DOAS NO2 showed a decrease of 14.19% (versus 18.63% by in situ) at the ground surface, and 36.24% (versus 36.25% by satellite) for the total tropospheric column. Thus, vertical discrepancies of atmospheric NO2 can largely explain the differences between satellite and in situ NO2 variations. In the post-epidemic era of 2021, satellite NO2 TVCD and in situ NO2 concentrations decreased by 10.42–64.96% and 1.05–34.99% compared to 2019, respectively, possibly related to the reduction of the transportation industry. This study reveals the changes of China’s urban NO2 pollution in the post-epidemic era and indicates that COVID-19 had a profound impact on human social activities and industrial production. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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18 pages, 2765 KiB  
Article
Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning
by Zhenxing Liu, Jianhua Chang, Hongxu Li, Sicheng Chen and Tengfei Dai
Remote Sens. 2022, 14(2), 418; https://doi.org/10.3390/rs14020418 - 17 Jan 2022
Cited by 6 | Viewed by 2135
Abstract
Reliable estimation of the atmospheric boundary layer height (ABLH) is critical for a range of meteorological applications, including air quality assessment and weather forecasting. Several algorithms have been proposed to detect ABLH from aerosol LiDAR backscatter data. However, most of these focus on [...] Read more.
Reliable estimation of the atmospheric boundary layer height (ABLH) is critical for a range of meteorological applications, including air quality assessment and weather forecasting. Several algorithms have been proposed to detect ABLH from aerosol LiDAR backscatter data. However, most of these focus on cloud-free conditions or use other ancillary instruments due to strong interference from clouds or residual layer aerosols. In this paper, a machine learning method named the Mahalanobis transform K-near-means (MKnm) algorithm is first proposed to derive ABLH under complex atmospheric conditions using only LiDAR-based instruments. It was applied to the micro pulse LiDAR data obtained at the Southern Great Plains site of the Atmospheric Radiation Measurement (ARM) program. The diurnal cycles of ABLH from cloudy weather were detected by using the gradient method (GM), wavelet covariance transform method (WM), K-means, and MKnm. Meanwhile, the ABLH obtained by these four methods under cloud or residual layer conditions based on micropulse LiDAR data were compared with the reference height retrieved from radiosonde data. The results show that MKnm was good at tracking the diurnal variation of ABLH, and the ABLHs obtained by it have remarkable correlation coefficients and smaller mean absolute error and mean deviation with the radiosonde-derived ABLHs than those measured by other three methods. We conclude that MKnm is a promising algorithm to estimate ABLH under cloud or residual layer conditions. Full article
(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)
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17 pages, 2235 KiB  
Article
The Impacts of a Large Water Transfer Project on a Waterbird Community in the Receiving Dam: A Case Study of Miyun Reservoir, China
by Waner Liang, Jialin Lei, Bingshu Ren, Ranxing Cao, Zhixu Yang, Niri Wu and Yifei Jia
Remote Sens. 2022, 14(2), 417; https://doi.org/10.3390/rs14020417 - 17 Jan 2022
Cited by 7 | Viewed by 2300
Abstract
As natural wetlands are degrading worldwide, artificial wetlands can operate as a substitute to provide waterbirds with refuge, but they cannot replace natural wetlands. Reservoirs, one of the most common artificial wetlands in China, can be of great importance to waterbirds. Miyun reservoir [...] Read more.
As natural wetlands are degrading worldwide, artificial wetlands can operate as a substitute to provide waterbirds with refuge, but they cannot replace natural wetlands. Reservoirs, one of the most common artificial wetlands in China, can be of great importance to waterbirds. Miyun reservoir in Beijing, China, has undergone a process similar to a natural lake being constructed in a reservoir. In this study, we surveyed waterbird community composition and evaluated the corresponding land cover and land use change with satellite and digital elevation model images of both before and after the water level change. The results showed that in all modelled scenarios, when the water level rises, agricultural lands suffer the greatest loss, with wetlands and forests following. The water level rise also caused a decrease in shallow water areas and a decline in the number and diversity of waterbird communities, as the components shifted from a shallow-water preferring group (waders, geese and dabbling ducks) to a deep-water preferring group (most diving ducks, gulls and terns). Miyun reservoir ceased to be an important waterbird habitat in China and is no longer an important stopover site for white-naped cranes. A similar process is likely to occur when a natural lake is constructed in a reservoir. Therefore, we suggest that policymakers consider the needs of waterbirds when constructing or managing reservoirs. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Migratory Birds Conservation)
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22 pages, 2722 KiB  
Article
Hybrid Compact Polarimetric SAR Calibration Considering the Amplitude and Phase Coefficients Inconsistency
by Wentao Hou, Fengjun Zhao, Xiuqing Liu, Dacheng Liu, Yonghui Han, Yao Gao and Robert Wang
Remote Sens. 2022, 14(2), 416; https://doi.org/10.3390/rs14020416 - 17 Jan 2022
Cited by 2 | Viewed by 1370
Abstract
Calibration using corner reflectors is an effective way to estimate the distortion parameters of hybrid compact polarimetric (HCP) synthetic aperture radar (SAR) systems. However, the existing literature lacks a discussion on the inconsistency of the amplitude and phase coefficients between measured scattering vectors [...] Read more.
Calibration using corner reflectors is an effective way to estimate the distortion parameters of hybrid compact polarimetric (HCP) synthetic aperture radar (SAR) systems. However, the existing literature lacks a discussion on the inconsistency of the amplitude and phase coefficients between measured scattering vectors of different corner reflectors. In response to this problem, this paper first proves that this inconsistency will seriously deteriorate the estimation accuracy of polarimetric distortion parameters. Based on the optimization algorithm, two calibration schemes for simultaneously estimating the traditional distortion parameters and the amplitude/phase coefficients are proposed while ignoring crosstalk (ICT) and considering crosstalk (CCT). In the process of distortion parameter estimation, the idea of “optimizing while compensating” is adopted to eliminate the problem of uneven echo intensity. Simulation results show that both schemes can eliminate the influence of the inconsistency of amplitude and phase coefficients, and estimate distortion parameters accurately. When the received crosstalk level is lower than −30 dB, the ICT scheme can accurately estimate polarimetric distortion parameters. The CCT scheme has a wider application range of crosstalk and can work well when the crosstalk level is lower than −20 dB, but it also has a higher requirement for the signal-to-clutter ratio (SCR). When SCR is greater than 35 dB, the CCT scheme yields higher estimation accuracy than the ICT scheme. In addition, the effectiveness of the calibration schemes is verified based on the L-band measured data acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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25 pages, 60551 KiB  
Article
Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods
by Osman Ilniyaz, Alishir Kurban and Qingyun Du
Remote Sens. 2022, 14(2), 415; https://doi.org/10.3390/rs14020415 - 17 Jan 2022
Cited by 14 | Viewed by 3523
Abstract
The leaf area index (LAI), a valuable variable for assessing vine vigor, reflects nutrient concentrations in vineyards and assists in precise management, including fertilization, improving yield, quality, and vineyard uniformity. Although some vegetation indices (VIs) have been successfully used to assess LAI variations, [...] Read more.
The leaf area index (LAI), a valuable variable for assessing vine vigor, reflects nutrient concentrations in vineyards and assists in precise management, including fertilization, improving yield, quality, and vineyard uniformity. Although some vegetation indices (VIs) have been successfully used to assess LAI variations, they are unsuitable for vineyards of different types and structures. By calibrating the light extinction coefficient of a digital photography algorithm for proximal LAI measurements, this study aimed to develop VI-LAI models for pergola-trained vineyards based on high-resolution RGB and multispectral images captured by an unmanned aerial vehicle (UAV). The models were developed by comparing five machine learning (ML) methods, and a robust ensemble model was proposed using the five models as base learners. The results showed that the ensemble model outperformed the base models. The highest R2 and lowest RMSE values that were obtained using the best combination of VIs with multispectral data were 0.899 and 0.434, respectively; those obtained using the RGB data were 0.825 and 0.547, respectively. By improving the results by feature selection, ML methods performed better with multispectral data than with RGB images, and better with higher spatial resolution data than with lower resolution data. LAI variations can be monitored efficiently and accurately for large areas of pergola-trained vineyards using this framework. Full article
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16 pages, 4730 KiB  
Article
Tomographic Inversion Methods for Retrieving the Tropospheric Water Vapor Content Based on the NDSA Measurement Approach
by Agnese Mazzinghi, Fabrizio Cuccoli, Fabrizio Argenti, Arjan Feta and Luca Facheris
Remote Sens. 2022, 14(2), 414; https://doi.org/10.3390/rs14020414 - 17 Jan 2022
Cited by 1 | Viewed by 1365
Abstract
In this paper, we deal with the problem of retrieving maps of tropospheric Water Vapor (WV) concentration by means of a set of Low Earth Orbit (LEO) satellites orbiting in the same plane and along the same direction. It is assumed that a [...] Read more.
In this paper, we deal with the problem of retrieving maps of tropospheric Water Vapor (WV) concentration by means of a set of Low Earth Orbit (LEO) satellites orbiting in the same plane and along the same direction. It is assumed that a number of microwave links is deployed between a group of satellites with microwave transmitters onboard and another group with receivers. It is also assumed that the Normalized Differential Spectral Absorption (NDSA) approach is used to provide time series of Integrated Water Vapor (IWV) along each link. The set of links scans an annular region belonging to the orbital plane of the LEO satellites, so that the time series of the IWV measurements can be exploited to retrieve the WV concentration in such a region. This is a typical tomographic inversion problem. The geometry of the acquisition system and the path-integrated nature of measurements well fit the application of the Exterior Reconstruction Tomographic Algorithm (ERTA), which was proposed several decades ago in contexts different from remote sensing. In this paper, we investigate the potential of ERTA for the WV retrieval and compare its performance with that of a least square inversion approach already presented in the literature. The compared analysis has been carried out through simulations that have highlighted the peculiarities and retrieval capabilities of the two tomographic methods, as well as the impact of the richness of the satellite constellation on the overall performance. Full article
(This article belongs to the Special Issue Satellite Observation for Atmospheric Modeling)
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22 pages, 5191 KiB  
Article
Air Pollution Monitoring System with Prediction Abilities Based on Smart Autonomous Sensors Equipped with ANNs with Novel Training Scheme
by Marzena Banach, Rafał Długosz, Tomasz Talaśka and Witold Pedrycz
Remote Sens. 2022, 14(2), 413; https://doi.org/10.3390/rs14020413 - 17 Jan 2022
Cited by 3 | Viewed by 1972
Abstract
The paper presents a concept of an air pollution monitoring system with prediction abilities, based on wireless smart sensors, that takes into account local conditions (microclimate) prevailing in particular areas of the city. In most cases reported in the literature, artificial neural networks [...] Read more.
The paper presents a concept of an air pollution monitoring system with prediction abilities, based on wireless smart sensors, that takes into account local conditions (microclimate) prevailing in particular areas of the city. In most cases reported in the literature, artificial neural networks (ANNs) are used to predict future pollution levels. In existing solutions of this type, ANNs are trained with generalized datasets common for larger areas, e.g., cities. Our investigations show, however, that conditions may strongly differ even between particular streets in the city, which may impact prediction quality. This results from varying density of urban development, different levels of insolation, airiness, amounts of greenery, etc. As a result, with similar values of ANN input signals, such as current pollution levels, temperature, pressure, etc., the results of the prediction may differ significantly from reality. For this reason, we propose an innovative solution, in which particular sensors are equipped with miniaturized low-power ANNs, trained with datasets gathered directly from their closest environment, without a need for the obtaining of such data from a base station. This may simplify the installation and maintenance process of a network of such sensors. In a further part of this work, we dealt with solutions that enable the reduction of the computational complexity of ANNs in the case of their implementation on specialized integrated circuits. We propose replacing the most complex mathematical operations used in the learning algorithm with simpler solutions. A prototype chip containing the main blocks of such an ANN was also designed. Full article
(This article belongs to the Special Issue Remote Sensing for Urban Development and Sustainability)
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20 pages, 4821 KiB  
Article
Capability of GPM IMERG Products for Extreme Precipitation Analysis over the Indonesian Maritime Continent
by Ravidho Ramadhan, Marzuki Marzuki, Helmi Yusnaini, Robi Muharsyah, Wiwit Suryanto, Sholihun Sholihun, Mutya Vonnisa, Alessandro Battaglia and Hiroyuki Hashiguchi
Remote Sens. 2022, 14(2), 412; https://doi.org/10.3390/rs14020412 - 17 Jan 2022
Cited by 18 | Viewed by 2772
Abstract
Integrated Multi-satellite Retrievals for GPM (IMERG) data have been widely used to analyze extreme precipitation, but the data have never been validated for the Indonesian Maritime Continent (IMC). This study evaluated the capability of IMERG Early (E), Late (L), and Final (F) data [...] Read more.
Integrated Multi-satellite Retrievals for GPM (IMERG) data have been widely used to analyze extreme precipitation, but the data have never been validated for the Indonesian Maritime Continent (IMC). This study evaluated the capability of IMERG Early (E), Late (L), and Final (F) data to observe extreme rain in the IMC using the rain gauge data within five years (2016–2020). The capability of IMERG in the observation of the extreme rain index was evaluated using Kling–Gupta efficiency (KGE) matrices. The IMERG well captured climatologic characteristics of the index of annual total precipitation (PRCPTOT), number of wet days (R85p), number of very wet days (R95p), number of rainy days (R1mm), number of heavy rain days (R10mm), number of very heavy rain days (R20mm), consecutive dry days (CDD), and max 5-day precipitation (RX5day), indicated by KGE value >0.4. Moderate performance (KGE = 0–0.4) was shown in the index of the amount of very extremely wet days (R99p), the number of extremely heavy precipitation days (R50mm), max 1-day precipitation (RX1day), and Simple Daily Intensity Index (SDII). Furthermore, low performance of IMERG (KGE < 0) was observed in the consecutive wet days (CWDs) index. Of the 13 extreme rain indices evaluated, IMERG underestimated and overestimated precipitation of nine and four indexes, respectively. IMERG tends to overestimate precipitation of indexes related to low rainfall intensity (e.g., R1mm). The highest overestimation was observed in the CWD index, related to the overestimation of light rainfall and the high false alarm ratio (FAR) from the daily data. For all indices of extreme rain, IMERG showed good capability to observe extreme rain variability in the IMC. Overall, IMERG-L showed a better capability than IMERG-E and -F but with an insignificant difference. Thus, the data of IMERG-E and IMERG-L, with a more rapid latency than IMERG-F, have great potential to be used for extreme rain observation and flood modeling in the IMC. Full article
(This article belongs to the Special Issue Radar Remote Sensing of Cloud and Precipitation)
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13 pages, 5490 KiB  
Article
Band-to-Band Registration of FY-1C/D Visible-IR Scanning Radiometer High-Resolution Picture Transmission Data
by Hongbo Pan, Jia Tian, Taoyang Wang, Jing Wang, Chengbao Liu and Lei Yang
Remote Sens. 2022, 14(2), 411; https://doi.org/10.3390/rs14020411 - 17 Jan 2022
Cited by 1 | Viewed by 1678
Abstract
The visible-IR scanning radiometer (VIRR) of FY1-C/D meteorological satellites consists of 10 bands with 4 different focal plane assemblies (FPAs). However, there are significant band-to-band registration (BBR) errors between different bands, which cannot be compensated for by a simple shift in the along-scan [...] Read more.
The visible-IR scanning radiometer (VIRR) of FY1-C/D meteorological satellites consists of 10 bands with 4 different focal plane assemblies (FPAs). However, there are significant band-to-band registration (BBR) errors between different bands, which cannot be compensated for by a simple shift in the along-scan direction. A rigorous BBR frame was proposed to analyze the sources of misregistration in the whisk-broom camera. According to theory, the 45° scanning mirror introduces tangent function style misregistration in the along-track direction and secant function style misregistration in the across-track direction between different bands if the bands are not in the same optical axis. As proven by the experiments of both FY-1C and FY-1D, the image rotation caused by the 45° scanning mirrors plays a major role in the misregistration. However, misregistration between different FPAs does not strictly adhere to this theory. Therefore, a polynomial-based co-registration method was proposed to model the BBR errors for the VIRR. To achieve 0.1 pixel accuracy, a fourth-degree polynomial was used for BBR in the along-scan direction, and a fifth-degree polynomial was used for the along-track direction. For the reflective bands, the root-mean-square errors (RMSEs) of misregistration could be improved from 3 pixels to 0.11 pixels. Limited by matching accuracy, the RMSEs of misregistration between thermal bands and reflective bands were approximately 0.2 to 0.4 pixels, depending on the signal-to-noise ratio. Full article
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24 pages, 5802 KiB  
Article
Investigating Surface Fractures and Materials Behavior of Cultural Heritage Buildings Based on the Attribute Information of Point Clouds Stored in the TLS Dataset
by Miktha Farid Alkadri, Syaiful Alam, Herry Santosa, Adipandang Yudono and Sebrian Mirdeklis Beselly
Remote Sens. 2022, 14(2), 410; https://doi.org/10.3390/rs14020410 - 17 Jan 2022
Cited by 9 | Viewed by 3076
Abstract
To date, the potential development of 3D laser scanning has enabled the capture of high-quality and high-precision reality-based datasets for both research and industry. In particular, Terrestrial Laser Scanning (TLS) technology has played a key role in the documentation of cultural heritage. In [...] Read more.
To date, the potential development of 3D laser scanning has enabled the capture of high-quality and high-precision reality-based datasets for both research and industry. In particular, Terrestrial Laser Scanning (TLS) technology has played a key role in the documentation of cultural heritage. In the existing literature, the geometric properties of point clouds are still the main focus for 3D reconstruction, while the surface performance of the dataset is of less interest due to the partial and limited analysis performed by certain disciplines. As a consequence, geometric defects on surface datasets are often identified when visible through physical inspection. In response to that, this study presents an integrated approach for investigating the materials behavior of heritage building surfaces by making use of attribute point cloud information (i.e., XYZ, RGB, reflection intensity). To do so, fracture surface analysis and material properties are computed to identify vulnerable structures on the existing dataset. This is essential for architects or conservators so that they can assess and prepare preventive measures to minimize microclimatic impacts on the buildings. Full article
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17 pages, 28910 KiB  
Article
Weak Mesoscale Variability in the Optimum Interpolation Sea Surface Temperature (OISST)-AVHRR-Only Version 2 Data before 2007
by Yanan Zhu, Yuanlong Li, Fan Wang and Mingkun Lv
Remote Sens. 2022, 14(2), 409; https://doi.org/10.3390/rs14020409 - 17 Jan 2022
Cited by 3 | Viewed by 1799
Abstract
Mesoscale sea surface temperature (SST) variability triggers mesoscale air–sea interactions and is linked to ocean subsurface mesoscale dynamics. The National Oceanic and Atmospheric Administration (NOAA) daily Optimum Interpolation SST (OISST) products, based on various satellite and in situ SST data, are widely utilized [...] Read more.
Mesoscale sea surface temperature (SST) variability triggers mesoscale air–sea interactions and is linked to ocean subsurface mesoscale dynamics. The National Oceanic and Atmospheric Administration (NOAA) daily Optimum Interpolation SST (OISST) products, based on various satellite and in situ SST data, are widely utilized in the investigation of multi-scale SST variabilities and reconstruction of subsurface and deep-ocean fields. The quality of OISST datasets is subjected to temporal inhomogeneity due to alterations in the merged data. Yet, whether this issue can significantly affect mesoscale SST variability is unknown. The analysis of this study detects an abrupt enhancement of mesoscale SST variability after 2007 in the OISST-AVHRR-only version 2 and version 2.1 datasets (hereafter OI.v2-AVHRR-only and OI.v2.1-AVHRR-only). The contrast is most stark in the subtropical western boundary current (WBC) regions, where the average mesoscale SST variance during 2007–2018 is twofold larger than that during 1993–2006. Further comparisons with other satellite SST datasets (TMI, AMSR-E, and WindSAT) suggest that the OISST-AVHRR-only datasets have severely underestimated mesoscale SST variability before 2007. An evaluation of related documents of the OISST data indicates that this bias is mainly caused by the change of satellite AVHRR instrument in 2007. There are no corresponding changes detected in the associated fields, such as the number and activity of mesoscale eddies or the background SST gradient in these regions, confirming that the underestimation of mesoscale SST variability before 2007 is an artifact. Another OISST product, OI.v2-AVHRR-AMSR, shows a similar abrupt enhancement of mesoscale SST variability in June 2002, when the AMSR-E instrument was incorporated. This issue leaves potential influences on scientific research that utilize the OISST datasets. The composite SST anomalies of mesoscale eddies based on the OI.v2-AVHRR-only data are underestimated by up to 37% before 2007 in the subtropical WBC regions. The underestimation of mesoscale variability also affects the total (full-scale) SST variability, particularly in winter. Other SST data products based on the OISST datasets were also influenced; we identify suspicious changes in J-OFURO3 and CFSR datasets; the reconstructed three-dimensional ocean products using OISST data as input may also be inevitably affected. This study reminds caution in the usage of the OISST and relevant data products in the investigation of mesoscale processes. Full article
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20 pages, 7785 KiB  
Article
A Transmission Tower Tilt State Assessment Approach Based on Dense Point Cloud from UAV-Based LiDAR
by Zhumao Lu, Hao Gong, Qiuheng Jin, Qingwu Hu and Shaohua Wang
Remote Sens. 2022, 14(2), 408; https://doi.org/10.3390/rs14020408 - 17 Jan 2022
Cited by 14 | Viewed by 2901
Abstract
Transmission towers are easily affected by various meteorological and geological disasters. In this paper, a transmission tower tilt state assessment approach—based on high precision and dense point cloud from UAV LiDAR—was proposed. First, the transmission tower point cloud was rapidly located and extracted [...] Read more.
Transmission towers are easily affected by various meteorological and geological disasters. In this paper, a transmission tower tilt state assessment approach—based on high precision and dense point cloud from UAV LiDAR—was proposed. First, the transmission tower point cloud was rapidly located and extracted from the 3D point cloud obtained by UAV-LiDAR line patrol. A robust histogram local extremum extraction method with additional constraints was proposed to achieve adaptive segmentation of the tower head and tower body point cloud. Second, an accurate and efficient extraction and simplification strategy of the contour of the feature plane point cloud was proposed. The central axis of the tower was constrained by the contour of the feature plane through the four-prism structure to calculate the tilt angle of the tower and evaluate the state of the tower. Finally, the point cloud of tower head from UAV-based LiDAR was accurately matched with the designed tower head model from database, and a tower head state evaluation model based on matching offset parameters was proposed to evaluate tower head tilt state. The experimental results of simulation and measured data showed that the calculation accuracy of the tilt parameters of transmission tower body was better than 0.5 degrees, that the proposed method can effectively evaluate the risk of tower head with complex structure, and improve the rapid and mass intelligent perception level of the risk state of the transmission line tower, which has a wide prospects for application. Full article
(This article belongs to the Special Issue Remote Sensing for Power Line Corridor Surveys)
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21 pages, 4814 KiB  
Article
Potential of AOD Retrieval Using Atmospheric Emitted Radiance Interferometer (AERI)
by Jongjin Seo, Haklim Choi and Youngsuk Oh
Remote Sens. 2022, 14(2), 407; https://doi.org/10.3390/rs14020407 - 16 Jan 2022
Cited by 3 | Viewed by 2219
Abstract
Aerosols in the atmosphere play an essential role in the radiative transfer process due to their scattering, absorption, and emission. Moreover, they interrupt the retrieval of atmospheric properties from ground-based and satellite remote sensing. Thus, accurate aerosol information needs to be obtained. Herein, [...] Read more.
Aerosols in the atmosphere play an essential role in the radiative transfer process due to their scattering, absorption, and emission. Moreover, they interrupt the retrieval of atmospheric properties from ground-based and satellite remote sensing. Thus, accurate aerosol information needs to be obtained. Herein, we developed an optimal-estimation-based aerosol optical depth (AOD) retrieval algorithm using the hyperspectral infrared downwelling emitted radiance of the Atmospheric Emitted Radiance Interferometer (AERI). The proposed algorithm is based on the phenomena that the thermal infrared radiance measured by a ground-based remote sensor is sensitive to the thermodynamic profile and degree of the turbid aerosol in the atmosphere. To assess the performance of algorithm, AERI observations, measured throughout the day on 21 October 2010 at Anmyeon, South Korea, were used. The derived thermodynamic profiles and AODs were compared with those of the European center for a reanalysis of medium-range weather forecasts version 5 and global atmosphere watch precision-filter radiometer (GAW-PFR), respectively. The radiances simulated with aerosol information were more suitable for the AERI-observed radiance than those without aerosol (i.e., clear sky). The temporal variation trend of the retrieved AOD matched that of GAW-PFR well, although small discrepancies were present at high aerosol concentrations. This provides a potential possibility for the retrieval of nighttime AOD. Full article
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20 pages, 4316 KiB  
Article
Columnar Aerosol Optical Property Characterization and Aerosol Typing Based on Ground-Based Observations in a Rural Site in the Central Yangtze River Delta Region
by Yong Xie, Yi Su, Xingfa Gu, Tiexi Chen, Wen Shao and Qiaoli Hu
Remote Sens. 2022, 14(2), 406; https://doi.org/10.3390/rs14020406 - 16 Jan 2022
Viewed by 1869
Abstract
Accurate and updated aerosol optical properties (AOPs) are of vital importance to climatology and environment-related studies for assessing the radiative impact of natural and anthropogenic aerosols. We comprehensively studied the columnar AOP observations between January 2019 and July 2020 from a ground-based remote [...] Read more.
Accurate and updated aerosol optical properties (AOPs) are of vital importance to climatology and environment-related studies for assessing the radiative impact of natural and anthropogenic aerosols. We comprehensively studied the columnar AOP observations between January 2019 and July 2020 from a ground-based remote sensing instrument located at a rural site operated by Central China Comprehensive Experimental Sites in the center of the Yangtze River Delta (YRD) region. In order to further study the aerosol type, two threshold-based aerosol classification methods were used to investigate the potential categories of aerosol particles under different aerosol loadings. Based on AOP observation and classification results, the potential relationships between the above-mentioned results and meteorological factors (i.e., humidity) and long-range transportation processes were analyzed. According to the results, obvious variation in aerosol optical depth (AOD) during the daytime, as well as throughout the year, was revealed. Investigation into AOD, single-scattering albedo (SSA), and absorption aerosol optical depth (AAOD) revealed the dominance of fine-mode aerosols with low absorptivity. According to the results of the two aerosol classification methods, the dominant aerosol types were continental (accounting for 43.9%, method A) and non-absorbing aerosols (62.5%, method B). Longer term columnar AOP observations using remote sensing alongside other techniques in the rural areas in East China are still needed for accurate parameterization in the future. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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18 pages, 2437 KiB  
Technical Note
Wavelength-Dependent Seeing Systematically Changes the Normalized Slope of Telescopic Reflectance Spectra of Mercury
by Kay Wohlfarth and Christian Wöhler
Remote Sens. 2022, 14(2), 405; https://doi.org/10.3390/rs14020405 - 16 Jan 2022
Cited by 2 | Viewed by 1439
Abstract
Telescopic observations of Mercury consistently report systematic variations of the normalized spectral slope of visible-to-near-infrared reflectance spectra. This effect was previously assumed to be a photometric property of the regolith, but it is not yet fully understood. After the MESSENGER mission, detailed global [...] Read more.
Telescopic observations of Mercury consistently report systematic variations of the normalized spectral slope of visible-to-near-infrared reflectance spectra. This effect was previously assumed to be a photometric property of the regolith, but it is not yet fully understood. After the MESSENGER mission, detailed global spectral maps of Mercury are available that better constrain Mercury’s photometry. So far, wavelength-dependent seeing has not been considered in the context of telescopic observations of Mercury. This study investigates the effect of wavelength-dependent seeing on systematic variations of Mercury’s normalized spectral reflectance slope. Therefore, we simulate the disk of Mercury for an idealized scenario, as seen by four different telescopic campaigns using the Hapke and the Kaasalainen–Shkuratov photometric model, the MDIS global mosaic, and a simple wavelength-dependent seeing model. The simulation results are compared with the observations of previous telescopic studies. We find that wavelength-dependent seeing affects the normalized spectral slope in several ways. The normalized slopes are enhanced near the limb, decrease toward the rim of the seeing disk, and even become negative. The decrease of the normalized spectral slope is consistent with previous observations. However, previous studies have associated the spectral slope variations with photometric effects that correlate with the emission angle. Our study suggests that wavelength-dependent seeing may cause these systematic variations. The combined reflectance and seeing model can also account for slope variations between different measurement campaigns. We report no qualitative differences between results based on the Hapke model or the Kaasalainen–Shkuratov model. Full article
(This article belongs to the Special Issue Cartography of the Solar System: Remote Sensing beyond Earth)
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20 pages, 7702 KiB  
Article
Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-Band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model
by Yaqing Gou, Casey M. Ryan and Johannes Reiche
Remote Sens. 2022, 14(2), 404; https://doi.org/10.3390/rs14020404 - 16 Jan 2022
Cited by 2 | Viewed by 2033
Abstract
Soil moisture effects limit radar-based aboveground biomass carbon (AGBC) prediction accuracy as well as lead to stripes between adjacent paths in regional mosaics due to varying soil moisture conditions on different acquisition dates. In this study, we utilised the semi-empirical water cloud model [...] Read more.
Soil moisture effects limit radar-based aboveground biomass carbon (AGBC) prediction accuracy as well as lead to stripes between adjacent paths in regional mosaics due to varying soil moisture conditions on different acquisition dates. In this study, we utilised the semi-empirical water cloud model (WCM) to account for backscattering from soil moisture in AGBC retrieval from L-band radar imagery in central Mozambique, where woodland ecosystems dominate. Cross-validation results suggest that (1) the standard WCM effectively accounts for soil moisture effects, especially for areas with AGBC ≤ 20 tC/ha, and (2) the standard WCM significantly improved the quality of regional AGBC mosaics by reducing the stripes between adjacent paths caused by the difference in soil moisture conditions between different acquisition dates. By applying the standard WCM, the difference in mean predicted AGBC for the tested path with the largest soil moisture difference was reduced by 18.6%. The WCM is a valuable tool for AGBC mapping by reducing prediction uncertainties and striping effects in regional mosaics, especially in low-biomass areas including African woodlands and other woodland and savanna regions. It is repeatable for recent L-band data including ALOS-2 PALSAR-2, and upcoming SAOCOM and NISAR data. Full article
(This article belongs to the Section Forest Remote Sensing)
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15 pages, 5098 KiB  
Article
LEO-Based Satellite Constellation for Moving Target Detection
by Chongdi Duan, Yu Li, Weiwei Wang and Jianguo Li
Remote Sens. 2022, 14(2), 403; https://doi.org/10.3390/rs14020403 - 16 Jan 2022
Cited by 6 | Viewed by 2211
Abstract
With the rapid development of cooperative detection technology, target fusion detection with regard of LEO satellites can be realized by means of their diverse observation configurations. However, the existing constant false alarm ratio (CFAR) detection research rarely involves the space-based target fusion detection [...] Read more.
With the rapid development of cooperative detection technology, target fusion detection with regard of LEO satellites can be realized by means of their diverse observation configurations. However, the existing constant false alarm ratio (CFAR) detection research rarely involves the space-based target fusion detection theory. In this paper, a novel multi-source fusion detection method based on LEO satellites is presented. Firstly, the pre-compensation function is constructed by employing the range and Doppler history of the cell where the antenna beam center is pointed. As a result, not only is the Doppler band broadening problem caused by the high-speed movement of the satellite platform, but the Doppler frequency rate (DFR) offset issue resulted from different observation configurations are alleviated synchronously. Then, the theoretical upper and lower limits of DFR are designed to achieve the effective clutter suppression and the accurate target echo fusion. Finally, the CFAR detection threshold based on the exponential weighted likelihood ratio is derived, which effectively increases the contrast ratio between the target cell and other background cells, and thus to provide an effective multi-source fusion detection method for LEO-based satellite constellation. Simulation results verify the effectiveness of the proposed algorithm. Full article
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25 pages, 16428 KiB  
Article
An In-Orbit Stereo Navigation Camera Self-Calibration Method for Planetary Rovers with Multiple Constraints
by Xinchao Xu, Mingyue Liu, Song Peng, Youqing Ma, Hongxi Zhao and Aigong Xu
Remote Sens. 2022, 14(2), 402; https://doi.org/10.3390/rs14020402 - 16 Jan 2022
Cited by 6 | Viewed by 1817
Abstract
In order to complete the high-precision calibration of the planetary rover navigation camera using limited initial data in-orbit, we proposed a joint adjustment model with additional multiple constraints. Specifically, a base model was first established based on the bundle adjustment model, second-order radial [...] Read more.
In order to complete the high-precision calibration of the planetary rover navigation camera using limited initial data in-orbit, we proposed a joint adjustment model with additional multiple constraints. Specifically, a base model was first established based on the bundle adjustment model, second-order radial and tangential distortion parameters. Then, combining the constraints of collinearity, coplanarity, known distance and relative pose invariance, a joint adjustment model was constructed to realize the in orbit self-calibration of the navigation camera. Given the problem of directionality in line extraction of the solar panel due to large differences in the gradient amplitude, an adaptive brightness-weighted line extraction method was proposed. Lastly, the Levenberg-Marquardt algorithm for nonlinear least squares was used to obtain the optimal results. To verify the proposed method, field experiments and in-orbit experiments were carried out. The results suggested that the proposed method was more accurate than the self-calibration bundle adjustment method, CAHVOR method (a camera model used in machine vision for three-dimensional measurements), and vanishing points method. The average error for the flag of China and the optical solar reflector was only 1 mm and 0.7 mm, respectively. In addition, the proposed method has been implemented in China’s deep space exploration missions. Full article
(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)
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