It is rare to conduct a comparative analysis of precipitation characteristics across regions based on long-term homogeneous active satellite observations. By collocating the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) observations with European Centre for Medium-Range Weather Forecasts 5th Reanalysis (ERA5) data, this study comparatively examines the microphysics of monsoon precipitation in the rainy season over the Yangtze-and-Huai River Basin (YHRB) and South China (SC) from 2014 to 2023. The comparative analysis is made in terms of precipitation types and intensities, precipitation efficiency index (PEI), and ice phase layer (IPL) width. The results show that the mean near-surface precipitation rate and PEI are generally higher over SC (2.87 mm/h, 3.43 h−1) than over YHRB (2.27 mm/h, 3.22 h−1) due to the more frequent occurrence of convective precipitation. The DSD characteristics of heavy precipitation in the wet season for both regions are similar to those of deep ocean convection, which is associated with a greater amount of water vapor. However, over SC, there are larger but fewer raindrops in the near-surface precipitation. Moreover, moderate PEI precipitation is the main contributor to heavy precipitation (>8 mm/h). Stratiform precipitation over YHRB is frequent enough to contribute more than convective precipitation to heavy precipitation (8–20 mm/h). The combined effect of stronger convective available potential energy and low-level vertical wind favors intense convection over SC, resulting in a larger storm top height (STH) than that over YHRB. Consequently, it is conducive to enhancing the microphysical processes of the ice and melt phases within the precipitation. The vertical wind can also influence the liquid phase processes below the melting layer. Collectively, these dynamic microphysical processes are important in shaping the efficiency and intensity of precipitation.
{"title":"Microphysical Characteristics of Monsoon Precipitation over Yangtze-and-Huai River Basin and South China: A Comparative Study from GPM DPR Observation","authors":"Zelin Wang, Xiong Hu, Weihua Ai, Junqi Qiao, Xianbin Zhao","doi":"10.3390/rs16183433","DOIUrl":"https://doi.org/10.3390/rs16183433","url":null,"abstract":"It is rare to conduct a comparative analysis of precipitation characteristics across regions based on long-term homogeneous active satellite observations. By collocating the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) observations with European Centre for Medium-Range Weather Forecasts 5th Reanalysis (ERA5) data, this study comparatively examines the microphysics of monsoon precipitation in the rainy season over the Yangtze-and-Huai River Basin (YHRB) and South China (SC) from 2014 to 2023. The comparative analysis is made in terms of precipitation types and intensities, precipitation efficiency index (PEI), and ice phase layer (IPL) width. The results show that the mean near-surface precipitation rate and PEI are generally higher over SC (2.87 mm/h, 3.43 h−1) than over YHRB (2.27 mm/h, 3.22 h−1) due to the more frequent occurrence of convective precipitation. The DSD characteristics of heavy precipitation in the wet season for both regions are similar to those of deep ocean convection, which is associated with a greater amount of water vapor. However, over SC, there are larger but fewer raindrops in the near-surface precipitation. Moreover, moderate PEI precipitation is the main contributor to heavy precipitation (>8 mm/h). Stratiform precipitation over YHRB is frequent enough to contribute more than convective precipitation to heavy precipitation (8–20 mm/h). The combined effect of stronger convective available potential energy and low-level vertical wind favors intense convection over SC, resulting in a larger storm top height (STH) than that over YHRB. Consequently, it is conducive to enhancing the microphysical processes of the ice and melt phases within the precipitation. The vertical wind can also influence the liquid phase processes below the melting layer. Collectively, these dynamic microphysical processes are important in shaping the efficiency and intensity of precipitation.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Antonio Matellon, Eleonora Maset, Alberto Beinat, Domenico Visintini
The rapid technological development that geomatics has been experiencing in recent years is leading to increasing ease, productivity and reliability of three-dimensional surveys, with portable laser scanner systems based on Simultaneous Localization and Mapping (SLAM) technology, gradually replacing traditional techniques in certain applications. Although the performance of such systems in terms of point cloud accuracy and noise level has been deeply investigated in the literature, there are fewer works about the evaluation of their use for surface reconstruction, cartographic production, and as-built Building Information Model (BIM) creation. The objective of this study is to assess the suitability of SLAM devices for surface modeling in an urban/architectural environment. To this end, analyses are carried out on the datasets acquired by three commercial portable laser scanners in the context of a benchmark organized in 2023 by the Italian Society of Photogrammetry and Topography (SIFET). In addition to the conventional point cloud assessment, we propose a comparison between the reconstructed mesh and a ground-truth model, employing a model-to-model methodology. The outcomes are promising, with the average distance between models ranging from 0.2 to 1.4 cm. However, the surfaces modeled from the terrestrial laser scanning point cloud show a level of detail that is still unmatched by SLAM systems.
近年来，地理信息学技术发展迅速，基于同步定位与绘图（SLAM）技术的便携式激光扫描仪系统在某些应用中逐渐取代了传统技术，从而提高了三维测量的便捷性、生产率和可靠性。虽然文献中已对此类系统在点云精度和噪声水平方面的性能进行了深入研究，但对其在表面重建、制图和竣工建筑信息模型（BIM）创建方面的应用进行评估的著作较少。本研究的目的是评估 SLAM 设备在城市/建筑环境中进行表面建模的适用性。为此，在意大利摄影测量和地形协会（SIFET）于 2023 年组织的基准测试中，对三台商用便携式激光扫描仪获取的数据集进行了分析。除了传统的点云评估外，我们还采用模型对模型的方法，对重建网格和地面实况模型进行了比较。结果很不错，模型之间的平均距离在 0.2 到 1.4 厘米之间。然而，根据地面激光扫描点云建模的表面显示出的细节水平仍然是 SLAM 系统无法比拟的。
{"title":"Surface Reconstruction from SLAM-Based Point Clouds: Results from the Datasets of the 2023 SIFET Benchmark","authors":"Antonio Matellon, Eleonora Maset, Alberto Beinat, Domenico Visintini","doi":"10.3390/rs16183439","DOIUrl":"https://doi.org/10.3390/rs16183439","url":null,"abstract":"The rapid technological development that geomatics has been experiencing in recent years is leading to increasing ease, productivity and reliability of three-dimensional surveys, with portable laser scanner systems based on Simultaneous Localization and Mapping (SLAM) technology, gradually replacing traditional techniques in certain applications. Although the performance of such systems in terms of point cloud accuracy and noise level has been deeply investigated in the literature, there are fewer works about the evaluation of their use for surface reconstruction, cartographic production, and as-built Building Information Model (BIM) creation. The objective of this study is to assess the suitability of SLAM devices for surface modeling in an urban/architectural environment. To this end, analyses are carried out on the datasets acquired by three commercial portable laser scanners in the context of a benchmark organized in 2023 by the Italian Society of Photogrammetry and Topography (SIFET). In addition to the conventional point cloud assessment, we propose a comparison between the reconstructed mesh and a ground-truth model, employing a model-to-model methodology. The outcomes are promising, with the average distance between models ranging from 0.2 to 1.4 cm. However, the surfaces modeled from the terrestrial laser scanning point cloud show a level of detail that is still unmatched by SLAM systems.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joanna Koszyk, Aleksandra Jasińska, Karolina Pargieła, Anna Malczewska, Kornelia Grzelka, Agnieszka Bieda, Łukasz Ambroziński
Precise and complete 3D representations of architectural structures or industrial sites are essential for various applications, including structural monitoring or cadastre. However, acquiring these datasets can be time-consuming, particularly for large objects. Mobile scanning systems offer a solution for such cases. In the case of complex scenes, multiple scanning systems are required to obtain point clouds that can be merged into a comprehensive representation of the object. Merging individual point clouds obtained from different sensors or at different times can be difficult due to discrepancies caused by moving objects or changes in the scene over time, such as seasonal variations in vegetation. In this study, we present the integration of point clouds obtained from two mobile scanning platforms within a built-up area. We utilized a combination of a quadruped robot and an unmanned aerial vehicle (UAV). The PointNet++ network was employed to conduct a semantic segmentation task, enabling the detection of non-ground objects. The experimental tests used the Toronto 3D dataset and DALES for network training. Based on the performance, the model trained on DALES was chosen for further research. The proposed integration algorithm involved semantic segmentation of both point clouds, dividing them into square subregions, and performing subregion selection by checking the emptiness or when both subregions contained points. Parameters such as local density, centroids, coverage, and Euclidean distance were evaluated. Point cloud merging and augmentation enhanced with semantic segmentation and clustering resulted in the exclusion of points associated with these movable objects from the point clouds. The comparative analysis of the method and simple merging was performed based on file size, number of points, mean roughness, and noise estimation. The proposed method provided adequate results with the improvement of point cloud quality indicators.
建筑结构或工业场地精确而完整的三维表示对于结构监测或地籍等各种应用都至关重要。然而，获取这些数据集非常耗时，尤其是对于大型物体。移动扫描系统为这种情况提供了解决方案。对于复杂的场景，需要多个扫描系统来获取点云，并将其合并为物体的综合表征。由于移动物体或场景随时间的变化（如植被的季节性变化）会造成差异，因此很难合并从不同传感器或不同时间获得的单个点云。在本研究中，我们介绍了在一个建筑密集区中整合从两个移动扫描平台获得的点云的方法。我们使用了四足机器人和无人机（UAV）的组合。利用 PointNet++ 网络执行语义分割任务，从而能够检测非地面物体。实验测试使用多伦多 3D 数据集和 DALES 进行网络训练。根据性能，选择了在 DALES 上训练的模型作为进一步研究的对象。所提出的整合算法包括对两个点云进行语义分割，将其划分为正方形子区域，并通过检查空性或当两个子区域都包含点时进行子区域选择。对局部密度、中心点、覆盖率和欧氏距离等参数进行了评估。通过语义分割和聚类增强点云合并和增强功能，可以从点云中排除与这些可移动物体相关的点。根据文件大小、点数、平均粗糙度和噪声估计，对该方法和简单合并进行了比较分析。建议的方法在改善点云质量指标方面提供了充分的结果。
{"title":"Semantic Segmentation-Driven Integration of Point Clouds from Mobile Scanning Platforms in Urban Environments","authors":"Joanna Koszyk, Aleksandra Jasińska, Karolina Pargieła, Anna Malczewska, Kornelia Grzelka, Agnieszka Bieda, Łukasz Ambroziński","doi":"10.3390/rs16183434","DOIUrl":"https://doi.org/10.3390/rs16183434","url":null,"abstract":"Precise and complete 3D representations of architectural structures or industrial sites are essential for various applications, including structural monitoring or cadastre. However, acquiring these datasets can be time-consuming, particularly for large objects. Mobile scanning systems offer a solution for such cases. In the case of complex scenes, multiple scanning systems are required to obtain point clouds that can be merged into a comprehensive representation of the object. Merging individual point clouds obtained from different sensors or at different times can be difficult due to discrepancies caused by moving objects or changes in the scene over time, such as seasonal variations in vegetation. In this study, we present the integration of point clouds obtained from two mobile scanning platforms within a built-up area. We utilized a combination of a quadruped robot and an unmanned aerial vehicle (UAV). The PointNet++ network was employed to conduct a semantic segmentation task, enabling the detection of non-ground objects. The experimental tests used the Toronto 3D dataset and DALES for network training. Based on the performance, the model trained on DALES was chosen for further research. The proposed integration algorithm involved semantic segmentation of both point clouds, dividing them into square subregions, and performing subregion selection by checking the emptiness or when both subregions contained points. Parameters such as local density, centroids, coverage, and Euclidean distance were evaluated. Point cloud merging and augmentation enhanced with semantic segmentation and clustering resulted in the exclusion of points associated with these movable objects from the point clouds. The comparative analysis of the method and simple merging was performed based on file size, number of points, mean roughness, and noise estimation. The proposed method provided adequate results with the improvement of point cloud quality indicators.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peize Li, Yangrui Xu, Yanpeng Zhao, Kun Liang, Yuanjie Si
Spaceborne photon-counting LiDAR holds significant potential for shallow-water bathymetry. However, the received photon data often contain substantial noise, complicating the extraction of elevation information. Currently, a denoising algorithm named ordering points to identify the clustering structure (OPTICS) draws people’s attention because of its strong performance under high background noise. However, this algorithm’s fixed input variables can lead to inaccurate photon distribution parameters in areas near the water bottom, which results in inadequate denoising in these areas, affecting bathymetric accuracy. To address this issue, an Adaptive Variable OPTICS (AV-OPTICS) model is proposed in this paper. Unlike the traditional OPTICS model with fixed input variables, the proposed model dynamically adjusts input variables based on point cloud distribution. This adjustment ensures accurate measurement of photon distribution parameters near the water bottom, thereby enhancing denoising effects in these areas and improving bathymetric accuracy. The findings indicate that, compared to traditional OPTICS methods, AV-OPTICS achieves higher -values and lower cohesions, demonstrating better denoising performance near the water bottom. Furthermore, this method achieves an average of 0.28 m and of 0.31 m, indicating better bathymetric accuracy than traditional OPTICS methods. This study provides a promising solution for shallow-water bathymetry based on photon-counting LiDAR data.
{"title":"Denoising of Photon-Counting LiDAR Bathymetry Based on Adaptive Variable OPTICS Model and Its Accuracy Assessment","authors":"Peize Li, Yangrui Xu, Yanpeng Zhao, Kun Liang, Yuanjie Si","doi":"10.3390/rs16183438","DOIUrl":"https://doi.org/10.3390/rs16183438","url":null,"abstract":"Spaceborne photon-counting LiDAR holds significant potential for shallow-water bathymetry. However, the received photon data often contain substantial noise, complicating the extraction of elevation information. Currently, a denoising algorithm named ordering points to identify the clustering structure (OPTICS) draws people’s attention because of its strong performance under high background noise. However, this algorithm’s fixed input variables can lead to inaccurate photon distribution parameters in areas near the water bottom, which results in inadequate denoising in these areas, affecting bathymetric accuracy. To address this issue, an Adaptive Variable OPTICS (AV-OPTICS) model is proposed in this paper. Unlike the traditional OPTICS model with fixed input variables, the proposed model dynamically adjusts input variables based on point cloud distribution. This adjustment ensures accurate measurement of photon distribution parameters near the water bottom, thereby enhancing denoising effects in these areas and improving bathymetric accuracy. The findings indicate that, compared to traditional OPTICS methods, AV-OPTICS achieves higher -values and lower cohesions, demonstrating better denoising performance near the water bottom. Furthermore, this method achieves an average of 0.28 m and of 0.31 m, indicating better bathymetric accuracy than traditional OPTICS methods. This study provides a promising solution for shallow-water bathymetry based on photon-counting LiDAR data.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yingjie Ji, Weiguo Wu, Shiqiang Nie, Jinyu Wang, Song Liu
Remote-sensing technology has gradually become one of the most important ways to extract sea–land boundaries due to its large scale, high efficiency, and low cost. However, sea–land segmentation (SLS) is still a challenging problem because of data diversity and inconsistency, “different objects with the same spectrum” or “the same object with different spectra”, and noise and interference problems, etc. In this paper, a new sea–land segmentation method (PMFormer) for remote-sensing images is proposed. The contributions are mainly two points. First, based on Mask2Former architecture, we introduce the prompt mask by normalized difference water index (NDWI) of the target image and prompt encoder architecture. The prompt mask provides more reasonable constraints for attention so that the segmentation errors are alleviated in small region boundaries and small branches, which are caused by insufficiency of prior information by large data diversity or inconsistency. Second, for the large intra-class difference problem in the foreground–background segmentation in sea–land scenes, we use deep clustering to simplify the query vectors and make them more suitable for binary segmentation. Then, traditional NDWI and eight other deep-learning methods are thoroughly compared with the proposed PMFormer on three open sea–land datasets. The efficiency of the proposed method is confirmed, after the quantitative analysis, qualitative analysis, time consumption, error distribution, etc. are presented by detailed contrast experiments.
{"title":"Sea–Land Segmentation of Remote-Sensing Images with Prompt Mask-Attention","authors":"Yingjie Ji, Weiguo Wu, Shiqiang Nie, Jinyu Wang, Song Liu","doi":"10.3390/rs16183432","DOIUrl":"https://doi.org/10.3390/rs16183432","url":null,"abstract":"Remote-sensing technology has gradually become one of the most important ways to extract sea–land boundaries due to its large scale, high efficiency, and low cost. However, sea–land segmentation (SLS) is still a challenging problem because of data diversity and inconsistency, “different objects with the same spectrum” or “the same object with different spectra”, and noise and interference problems, etc. In this paper, a new sea–land segmentation method (PMFormer) for remote-sensing images is proposed. The contributions are mainly two points. First, based on Mask2Former architecture, we introduce the prompt mask by normalized difference water index (NDWI) of the target image and prompt encoder architecture. The prompt mask provides more reasonable constraints for attention so that the segmentation errors are alleviated in small region boundaries and small branches, which are caused by insufficiency of prior information by large data diversity or inconsistency. Second, for the large intra-class difference problem in the foreground–background segmentation in sea–land scenes, we use deep clustering to simplify the query vectors and make them more suitable for binary segmentation. Then, traditional NDWI and eight other deep-learning methods are thoroughly compared with the proposed PMFormer on three open sea–land datasets. The efficiency of the proposed method is confirmed, after the quantitative analysis, qualitative analysis, time consumption, error distribution, etc. are presented by detailed contrast experiments.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luciane Favareto, Natalia Rudorff, Vanda Brotas, Andreia Tracana, Carolina Sá, Carla Palma, Ana C. Brito
Essential Climate Variables (ECVs) like ocean colour provide crucial information on the Optically Active Constituents (OACs) of seawater, such as phytoplankton, non-algal particles, and coloured dissolved organic matter (CDOM). The challenge in estimating these constituents through remote sensing is in accurately distinguishing and quantifying optical and biogeochemical properties, e.g., absorption coefficients and the concentration of chlorophyll a (Chla), especially in complex waters. This study evaluated the temporal and spatial variability of bio-optical properties in the coastal waters of the Western Iberian Coast (WIC), contributing to the assessment of satellite retrievals. In situ data from three oceanographic cruises conducted in 2019–2020 across different seasons were analyzed. Field-measured biogenic light absorption coefficients were compared to satellite estimates from Ocean-Colour Climate Change Initiative (OC-CCI) reflectance data using semi-analytical approaches (QAA, GSM, GIOP). Key findings indicate substantial variability in bio-optical properties across different seasons and regions. New bio-optical coefficients improved satellite data retrieval, reducing uncertainties and providing more reliable phytoplankton absorption estimates. These results highlight the need for region-specific algorithms to accurately capture the unique optical characteristics of coastal waters. Improved comprehension of bio-optical variability and retrieval techniques offers valuable insights for future research and coastal environment monitoring using satellite ocean colour data.
{"title":"Bio-Optical Properties and Ocean Colour Satellite Retrieval along the Coastal Waters of the Western Iberian Coast (WIC)","authors":"Luciane Favareto, Natalia Rudorff, Vanda Brotas, Andreia Tracana, Carolina Sá, Carla Palma, Ana C. Brito","doi":"10.3390/rs16183440","DOIUrl":"https://doi.org/10.3390/rs16183440","url":null,"abstract":"Essential Climate Variables (ECVs) like ocean colour provide crucial information on the Optically Active Constituents (OACs) of seawater, such as phytoplankton, non-algal particles, and coloured dissolved organic matter (CDOM). The challenge in estimating these constituents through remote sensing is in accurately distinguishing and quantifying optical and biogeochemical properties, e.g., absorption coefficients and the concentration of chlorophyll a (Chla), especially in complex waters. This study evaluated the temporal and spatial variability of bio-optical properties in the coastal waters of the Western Iberian Coast (WIC), contributing to the assessment of satellite retrievals. In situ data from three oceanographic cruises conducted in 2019–2020 across different seasons were analyzed. Field-measured biogenic light absorption coefficients were compared to satellite estimates from Ocean-Colour Climate Change Initiative (OC-CCI) reflectance data using semi-analytical approaches (QAA, GSM, GIOP). Key findings indicate substantial variability in bio-optical properties across different seasons and regions. New bio-optical coefficients improved satellite data retrieval, reducing uncertainties and providing more reliable phytoplankton absorption estimates. These results highlight the need for region-specific algorithms to accurately capture the unique optical characteristics of coastal waters. Improved comprehension of bio-optical variability and retrieval techniques offers valuable insights for future research and coastal environment monitoring using satellite ocean colour data.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cesar Ivan Alvarez, Santiago López, David Vásquez, Dayana Gualotuña
This study uses a remote sensing approach to investigate air quality fluctuations during two short-period social upheaval events caused by civil protests in 2019 and the COVID-19 pandemic in 2020 in Quito, Ecuador. We used data from the TROPOMI Sentinel-P5 satellite to evaluate the concentrations of two greenhouse gases, namely O3 and NO2. TROPOMI Sentinel-P5 satellite data are becoming essential in air quality monitoring, particularly for countries that lack ground-based monitoring systems. For a better approximation of satellite data with ground data, we related the remotely sensed data using ground station data and Pearson correlation analysis, which revealed a significant association between the two sources (0.43 ≤ r ≤ 0.78). Using paired t-test comparisons, we evaluated the differences in mean gas concentrations at 30 randomly selected intervals to identify significant changes before and after the events. The results indicate noticeable changes in the two gases over the three analysis periods. O3 significantly decreased between September and November 2019 and between March and May 2020, while NO2 significantly increased. NO2 levels decreased by 18% between February and March 2020 across the study area, as indicated by remote sensing data. The geovisualization of remotely sensed data over these periods supports these patterns, suggesting a potential connection with population density. The results show the complexity of drawing global conclusions about the impact of social disruptions on the atmosphere and emphasize the advantages of using remote sensing as an effective framework to address air quality changes over short periods of time. This study also highlights the advantages of a remote sensing approach to monitor atmospheric conditions in countries with limited air quality monitoring infrastructure and provides a valuable approach for the evaluation of short-term alterations in atmospheric conditions due to social disturbance events.
{"title":"Assessing Air Quality Dynamics during Short-Period Social Upheaval Events in Quito, Ecuador, Using a Remote Sensing Framework","authors":"Cesar Ivan Alvarez, Santiago López, David Vásquez, Dayana Gualotuña","doi":"10.3390/rs16183436","DOIUrl":"https://doi.org/10.3390/rs16183436","url":null,"abstract":"This study uses a remote sensing approach to investigate air quality fluctuations during two short-period social upheaval events caused by civil protests in 2019 and the COVID-19 pandemic in 2020 in Quito, Ecuador. We used data from the TROPOMI Sentinel-P5 satellite to evaluate the concentrations of two greenhouse gases, namely O3 and NO2. TROPOMI Sentinel-P5 satellite data are becoming essential in air quality monitoring, particularly for countries that lack ground-based monitoring systems. For a better approximation of satellite data with ground data, we related the remotely sensed data using ground station data and Pearson correlation analysis, which revealed a significant association between the two sources (0.43 ≤ r ≤ 0.78). Using paired t-test comparisons, we evaluated the differences in mean gas concentrations at 30 randomly selected intervals to identify significant changes before and after the events. The results indicate noticeable changes in the two gases over the three analysis periods. O3 significantly decreased between September and November 2019 and between March and May 2020, while NO2 significantly increased. NO2 levels decreased by 18% between February and March 2020 across the study area, as indicated by remote sensing data. The geovisualization of remotely sensed data over these periods supports these patterns, suggesting a potential connection with population density. The results show the complexity of drawing global conclusions about the impact of social disruptions on the atmosphere and emphasize the advantages of using remote sensing as an effective framework to address air quality changes over short periods of time. This study also highlights the advantages of a remote sensing approach to monitor atmospheric conditions in countries with limited air quality monitoring infrastructure and provides a valuable approach for the evaluation of short-term alterations in atmospheric conditions due to social disturbance events.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tao Zhang, Xiaogang Yang, Ruitao Lu, Xueli Xie, Siyu Wang, Shuang Su
Ship detection and formation recognition in remote sensing have increasingly garnered attention. However, research remains challenging due to arbitrary orientation, dense arrangement, and the complex background of ships. To enhance the analysis of ship situations in channels, we model the ships as the key points and propose a context-aware DGCN-based ship formation recognition method. First, we develop a center point-based ship detection subnetwork, which employs depth-separable convolution to reduce parameter redundancy and combines coordinate attention with an oriented response network to generate direction-invariant feature maps. The center point of each ship is predicted by regression of the offset, target scale, and angle to realize the ship detection. Then, we adopt the spatial similarity of the ship center points to cluster the ship group, utilizing the Delaunay triangulation method to establish the topological graph structure of the ship group. Finally, we design a context-aware Dense Graph Convolutional Network (DGCN) with graph structure to achieve formation recognition. Experimental results on HRSD2016 and SGF datasets demonstrate that the proposed method can detect arbitrarily oriented ships and identify formations, attaining state-of-the-art performance.
{"title":"Context-Aware DGCN-Based Ship Formation Recognition in Remote Sensing Images","authors":"Tao Zhang, Xiaogang Yang, Ruitao Lu, Xueli Xie, Siyu Wang, Shuang Su","doi":"10.3390/rs16183435","DOIUrl":"https://doi.org/10.3390/rs16183435","url":null,"abstract":"Ship detection and formation recognition in remote sensing have increasingly garnered attention. However, research remains challenging due to arbitrary orientation, dense arrangement, and the complex background of ships. To enhance the analysis of ship situations in channels, we model the ships as the key points and propose a context-aware DGCN-based ship formation recognition method. First, we develop a center point-based ship detection subnetwork, which employs depth-separable convolution to reduce parameter redundancy and combines coordinate attention with an oriented response network to generate direction-invariant feature maps. The center point of each ship is predicted by regression of the offset, target scale, and angle to realize the ship detection. Then, we adopt the spatial similarity of the ship center points to cluster the ship group, utilizing the Delaunay triangulation method to establish the topological graph structure of the ship group. Finally, we design a context-aware Dense Graph Convolutional Network (DGCN) with graph structure to achieve formation recognition. Experimental results on HRSD2016 and SGF datasets demonstrate that the proposed method can detect arbitrarily oriented ships and identify formations, attaining state-of-the-art performance.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Keyan Wang, Jia Jia, Peicheng Zhou, Haoyi Ma, Liyun Yang, Kai Liu, Yunsong Li
Due to the fact that invalid cloud-covered regions in remote sensing images consume a considerable quantity of coding bit rates under the limited satellite-to-ground transmission rate, existing image compression methods suffer from low compression efficiency and poor reconstruction quality, especially in cloud-free regions which are generally regarded as regions of interest (ROIs). Therefore, we propose an efficient on-board compression method for remote sensing images with arbitrary-shaped clouds by leveraging the characteristics of cloudy images. Firstly, we introduce two novel spatial preprocessing strategies, namely, the optimized adaptive filling (OAF) strategy and the controllable quantization (CQ) strategy. Specifically, the OAF strategy fills each cloudy region using the contextual information at its inner and outer edge to completely remove the information of cloudy regions and minimize their coding consumption, which is suitable for images with only thick clouds. The CQ strategy implicitly identifies thin and thick clouds and rationally quantifies the data in cloudy regions to alleviate information loss in thin cloud-covered regions, which can achieve the balance between coding efficiency and reconstructed image quality and is more suitable for images containing thin clouds. Secondly, we develop an efficient coding method for a binary cloud mask to effectively save the bit rate of the side information. Our method provides the flexibility for users to choose the desired preprocessing strategy as needed and can be embedded into existing compression framework such as JPEG2000. Experimental results on the GF-1 dataset show that our method effectively reduces the coding consumption of invalid cloud-covered regions and significantly improve the compression efficiency as well as the quality of decoded images.
{"title":"Efficient On-Board Compression for Arbitrary-Shaped Cloud-Covered Remote Sensing Images via Adaptive Filling and Controllable Quantization","authors":"Keyan Wang, Jia Jia, Peicheng Zhou, Haoyi Ma, Liyun Yang, Kai Liu, Yunsong Li","doi":"10.3390/rs16183431","DOIUrl":"https://doi.org/10.3390/rs16183431","url":null,"abstract":"Due to the fact that invalid cloud-covered regions in remote sensing images consume a considerable quantity of coding bit rates under the limited satellite-to-ground transmission rate, existing image compression methods suffer from low compression efficiency and poor reconstruction quality, especially in cloud-free regions which are generally regarded as regions of interest (ROIs). Therefore, we propose an efficient on-board compression method for remote sensing images with arbitrary-shaped clouds by leveraging the characteristics of cloudy images. Firstly, we introduce two novel spatial preprocessing strategies, namely, the optimized adaptive filling (OAF) strategy and the controllable quantization (CQ) strategy. Specifically, the OAF strategy fills each cloudy region using the contextual information at its inner and outer edge to completely remove the information of cloudy regions and minimize their coding consumption, which is suitable for images with only thick clouds. The CQ strategy implicitly identifies thin and thick clouds and rationally quantifies the data in cloudy regions to alleviate information loss in thin cloud-covered regions, which can achieve the balance between coding efficiency and reconstructed image quality and is more suitable for images containing thin clouds. Secondly, we develop an efficient coding method for a binary cloud mask to effectively save the bit rate of the side information. Our method provides the flexibility for users to choose the desired preprocessing strategy as needed and can be embedded into existing compression framework such as JPEG2000. Experimental results on the GF-1 dataset show that our method effectively reduces the coding consumption of invalid cloud-covered regions and significantly improve the compression efficiency as well as the quality of decoded images.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiawei Zou, Hao Li, Chao Ding, Suhong Liu, Qingdong Shi
Populus euphratica is a unique constructive tree species within riparian desert areas that is essential for maintaining oasis ecosystem stability. The Tarim River Basin contains the most densely distributed population of P. euphratica forests in the world, and obtaining accurate distribution data in the mainstream of the Tarim River would provide important support for its protection and restoration. We propose a new method for automatically extracting P. euphratica using Sentinel-1 and 2 and Landsat-8 images based on the Google Earth Engine cloud platform and the random forest algorithm. A mask of the potential distribution area of P. euphratica was created based on prior knowledge to save computational resources. The NDVI (Normalized Difference Vegetation Index) time series was then reconstructed using the preferred filtering method to obtain phenological parameter features, and the random forest model was input by combining the phenological parameter, spectral index, textural, and backscattering features. An active learning method was employed to optimize the model and obtain the best model for extracting P. euphratica. Finally, the map of natural P. euphratica forests with a resolution of 10 m in the mainstream of the Tarim River was obtained. The overall accuracy, producer’s accuracy, user’s accuracy, kappa coefficient, and F1-score of the map were 0.96, 0.98, 0.95, 0.93, and 0.96, respectively. The comparison experiments showed that simultaneously adding backscattering and textural features improved the P. euphratica extraction accuracy, while textural features alone resulted in a poor extraction effect. The method developed in this study fully considered the prior and posteriori information and determined the feature set suitable for the P. euphratica identification task, which can be used to quickly obtain accurate large-area distribution data of P. euphratica. The method can also provide a reference for identifying other typical desert vegetation.
胡杨是沙漠河岸地区独特的建群树种，对维持绿洲生态系统的稳定至关重要。塔里木河流域拥有世界上分布最密集的胡杨林种群，获得塔里木河主流地区的准确分布数据将为胡杨林的保护和恢复提供重要支持。我们基于谷歌地球引擎云平台和随机森林算法，提出了一种利用 Sentinel-1 和 2 以及 Landsat-8 图像自动提取 P. euphratica 的新方法。为了节省计算资源，我们根据先验知识创建了一个 P. euphratica 潜在分布区的掩膜。然后使用优选滤波方法重建归一化植被指数（NDVI）时间序列，以获得物候参数特征，并结合物候参数、光谱指数、纹理和反向散射特征输入随机森林模型。采用主动学习方法对模型进行优化，获得提取极乐鸟的最佳模型。最后，得到了塔里木河主流地区分辨率为 10 米的天然欧鼠李森林分布图。该地图的总体准确度、生产者准确度、用户准确度、卡帕系数和 F1 分数分别为 0.96、0.98、0.95、0.93 和 0.96。对比实验表明，同时添加反向散射特征和纹理特征提高了极乐鸟的提取精度，而单独添加纹理特征则提取效果不佳。本研究建立的方法充分考虑了先验信息和后验信息，确定了适合于极乐鸟识别任务的特征集，可用于快速获取准确的极乐鸟大面积分布数据。该方法还可为识别其他典型沙漠植被提供参考。
{"title":"Mapping Natural Populus euphratica Forests in the Mainstream of the Tarim River Using Spaceborne Imagery and Google Earth Engine","authors":"Jiawei Zou, Hao Li, Chao Ding, Suhong Liu, Qingdong Shi","doi":"10.3390/rs16183429","DOIUrl":"https://doi.org/10.3390/rs16183429","url":null,"abstract":"Populus euphratica is a unique constructive tree species within riparian desert areas that is essential for maintaining oasis ecosystem stability. The Tarim River Basin contains the most densely distributed population of P. euphratica forests in the world, and obtaining accurate distribution data in the mainstream of the Tarim River would provide important support for its protection and restoration. We propose a new method for automatically extracting P. euphratica using Sentinel-1 and 2 and Landsat-8 images based on the Google Earth Engine cloud platform and the random forest algorithm. A mask of the potential distribution area of P. euphratica was created based on prior knowledge to save computational resources. The NDVI (Normalized Difference Vegetation Index) time series was then reconstructed using the preferred filtering method to obtain phenological parameter features, and the random forest model was input by combining the phenological parameter, spectral index, textural, and backscattering features. An active learning method was employed to optimize the model and obtain the best model for extracting P. euphratica. Finally, the map of natural P. euphratica forests with a resolution of 10 m in the mainstream of the Tarim River was obtained. The overall accuracy, producer’s accuracy, user’s accuracy, kappa coefficient, and F1-score of the map were 0.96, 0.98, 0.95, 0.93, and 0.96, respectively. The comparison experiments showed that simultaneously adding backscattering and textural features improved the P. euphratica extraction accuracy, while textural features alone resulted in a poor extraction effect. The method developed in this study fully considered the prior and posteriori information and determined the feature set suitable for the P. euphratica identification task, which can be used to quickly obtain accurate large-area distribution data of P. euphratica. The method can also provide a reference for identifying other typical desert vegetation.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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