Remote Sensing

22 pages, 2453 KiB

Open AccessReview

Remote Sensing Technologies Using UAVs for Pest and Disease Monitoring: A Review Centered on Date Palm Trees

by Bashar Alsadik, Florian J. Ellsäßer, Muheeb Awawdeh, Abdulla Al-Rawabdeh, Lubna Almahasneh, Sander Oude Elberink, Doaa Abuhamoor and Yolla Al Asmar

Remote Sens. 2024, 16(23), 4371; https://doi.org/10.3390/rs16234371 - 22 Nov 2024

Abstract

This review is aimed at exploring the use of remote sensing technology with a focus on Unmanned Aerial Vehicles (UAVs) in monitoring and management of palm pests and diseases with a special focus on date palms. It highlights the most common sensor types, [...] Read more.

This review is aimed at exploring the use of remote sensing technology with a focus on Unmanned Aerial Vehicles (UAVs) in monitoring and management of palm pests and diseases with a special focus on date palms. It highlights the most common sensor types, ranging from passive sensors such as RGB, multispectral, hyperspectral, and thermal as well as active sensors such as light detection and ranging (LiDAR), expounding on their unique functions and gains as far as the detection of pest infestation and disease symptoms is concerned. Indices derived from UAV multispectral and hyperspectral sensors are used to assess their usefulness in vegetation health monitoring and plant physiological changes. Other UAVs are equipped with thermal sensors to identify water stress and temperature anomalies associated with the presence of pests and diseases. Furthermore, the review discusses how LiDAR technology can be used to capture detailed 3D canopy structures as well as volume changes that may occur during the progressing stages of a date palm infection. Besides, the paper examines how machine learning algorithms have been incorporated into remote sensing technologies to ensure high accuracy levels in detecting diseases or pests. This paper aims to present a comprehensive outline for future research focusing on modern methodologies, technological improvements, and direction for the efficient application of UAV-based remote sensing in managing palm tree pests and diseases. Full article

(This article belongs to the Special Issue State-of-the-Art on Satellite and UAV Remote Sensing in Geoscience Research)

52 pages, 7911 KiB

Open AccessReview

Techniques for Canopy to Organ Level Plant Feature Extraction via Remote and Proximal Sensing: A Survey and Experiments

by Prasad Nethala, Dugan Um, Neha Vemula, Oscar Fernandez Montero, Kiju Lee and Mahendra Bhandari

Remote Sens. 2024, 16(23), 4370; https://doi.org/10.3390/rs16234370 - 22 Nov 2024

Abstract

This paper presents an extensive review of techniques for plant feature extraction and segmentation, addressing the growing need for efficient plant phenotyping, which is increasingly recognized as a critical application for remote sensing in agriculture. As understanding and quantifying plant structures become essential [...] Read more.

This paper presents an extensive review of techniques for plant feature extraction and segmentation, addressing the growing need for efficient plant phenotyping, which is increasingly recognized as a critical application for remote sensing in agriculture. As understanding and quantifying plant structures become essential for advancing precision agriculture and crop management, this survey explores a range of methodologies, both traditional and cutting-edge, for extracting features from plant images and point cloud data, as well as segmenting plant organs. The importance of accurate plant phenotyping in remote sensing is underscored, given its role in improving crop monitoring, yield prediction, and stress detection. The review highlights the challenges posed by complex plant morphologies and data noise, evaluating the performance of various techniques and emphasizing their strengths and limitations. The insights from this survey offer valuable guidance for researchers and practitioners in plant phenotyping, advancing the fields of plant science and agriculture. The experimental section focuses on three key tasks: 3D point cloud generation, 2D image-based feature extraction, and 3D shape classification, feature extraction, and segmentation. Comparative results are presented using collected plant data and several publicly available datasets, along with insightful observations and inspiring directions for future research. Full article

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20 pages, 10942 KiB

Open AccessArticle

Changes in Urban Green Spaces in the Pearl River Delta Urban Agglomeration: From the Perspectives of the Area, Spatial Configuration, and Quality

by Tianci Yao, Shengfa Li, Lixin Su and Hongou Zhang

Remote Sens. 2024, 16(23), 4369; https://doi.org/10.3390/rs16234369 - 22 Nov 2024

Abstract

Urban green spaces (UGSs) are integral to urban ecosystems, providing multiple benefits to human well-being. However, previous studies mainly focus on the quantity or quality of UGSs, with less emphasis on a comprehensive analysis. This study systematically examined the spatiotemporal UGS dynamics in [...] Read more.

Urban green spaces (UGSs) are integral to urban ecosystems, providing multiple benefits to human well-being. However, previous studies mainly focus on the quantity or quality of UGSs, with less emphasis on a comprehensive analysis. This study systematically examined the spatiotemporal UGS dynamics in the Pearl River Delta urban agglomeration (PRDUA) in China from the perspectives of the area, spatial configuration, and quality, using the high spatial resolution (30 m) Landsat-derived land-cover data and Normalized Difference Vegetation Index (NDVI) data during 1985–2021. Results showed the UGS area in both the old urban districts and expanded urban areas across all nine cities in the PRDUA has experienced a dramatic reduction from 1985 to 2021, primarily due to the conversion of cropland and forest into impervious surfaces. Spatially, the fragmentation trend of UGSs initially increased and then weakened around 2010 in nine cities, but with an inconsistent fragmentation process across different urban areas. In the old urban districts, the fragmentation was mainly due to the loss of large patches; in contrast, it was caused by the division of large patches in the expanded urban areas of most cities. The area-averaged NDVI showed a general upward trend in urban areas in nearly all cities, and the greening trend in the old urban districts was more prevalent than that in the expanded urban areas, suggesting the negative impacts of urbanization on NDVI have been balanced by the positive effects of climate change, urbanization, and greening initiatives in the PRDUA. These findings indicate that urban greening does not necessarily correspond to the improvement in UGS states. We therefore recommend incorporating the three-dimensional analytical framework into urban ecological monitoring and construction efforts to obtain a more comprehensive understanding of UGS states and support effective urban green infrastructure stewardship. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities)

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22 pages, 10600 KiB

Open AccessArticle

The Influence of Local Climatic Factors and Water Vapor Transport from North Atlantic Ocean on Winter Snow-Cover Variation on Western Kunlun Mountains and Eastern Pamir Plateau

by Xiaoying Xue, Xiangde Xu, Guoyu Ren, Xiubao Sun and Panfeng Zhang

Remote Sens. 2024, 16(23), 4368; https://doi.org/10.3390/rs16234368 - 22 Nov 2024

Abstract

Snow cover days (SCD) have increased significantly in winter on the Western Kunlun Mountains and Eastern Pamir Plateau (hereafter referred to as KMPP for short), however the causes have not been well understood so far. Here, we use remote sensing data to analyze [...] Read more.

Snow cover days (SCD) have increased significantly in winter on the Western Kunlun Mountains and Eastern Pamir Plateau (hereafter referred to as KMPP for short), however the causes have not been well understood so far. Here, we use remote sensing data to analyze the abnormal increase in SCD on the KMPP and explore its causes from the perspective of the local factors and water vapor transport caused by sea surface temperatures (SST) warming. We discover that the winter SCD on the KMPP increased significantly at a rate of 4.75 days/decade (significant at the 0.01 level) during 1989–2020, while there has been a significant decrease on the Tibetan Plateau (TP), with a rate of −1.50 days/decade (significant at the 0.1 level). Based on ERA5, GPCP, GHCN, and station data, we find that, in contrast to the significant warming observed on the TP, temperature changes on the KMPP are negligible, while precipitation is increasing, differing from the decreasing precipitation trend observed on the TP. The differences in local temperature and precipitation changes cause different variations in SCD between the KMPP and the TP. The increase in SCD on the KMPP is primarily driven by increased precipitation (over 97% contribution), with minimal impact from the more or less unchanged temperature. In contrast, the decline in SCD on the TP results from decreased precipitation and significantly increased temperature. Furthermore, we found that changes in SCD on the KMPP are significantly correlated with SST in the northern North Atlantic Ocean. Based on the correlation vector, the anomaly field in the high/low SCD years of water vapor transport, and the FLEXPART model, we show that the northern North Atlantic Ocean is one of the major water vapor sources affecting the SCD on the KMPP. The warming SST in the northern North Atlantic Ocean enhances water vapor transport to the KMPP in winter, leading to an abnormal increase in the SCD that differs from the overall trend on the TP. The findings are conductive to further understand the peculiarity of winter precipitation and SCD on the KMPP, and the “Western Kunlun Mountains Oddity” in mountain glacial change. Full article

27 pages, 8809 KiB

Open AccessArticle

Trend Analysis of High-Resolution Soil Moisture Data Based on GAN in the Three-River-Source Region During the 21st Century

by Zhuoqun Li, Siqiong Luo, Xiaoqing Tan and Jingyuan Wang

Remote Sens. 2024, 16(23), 4367; https://doi.org/10.3390/rs16234367 - 22 Nov 2024

Abstract

Soil moisture (SM) is a crucial factor in land-atmosphere interactions and climate systems, affecting surface energy, water budgets, and weather extremes. In the Three-River-Source Region (TRSR) of China, rapid climate change necessitates precise SM monitoring. This study employs a novel UNet-Gan model to [...] Read more.

Soil moisture (SM) is a crucial factor in land-atmosphere interactions and climate systems, affecting surface energy, water budgets, and weather extremes. In the Three-River-Source Region (TRSR) of China, rapid climate change necessitates precise SM monitoring. This study employs a novel UNet-Gan model to integrate and downscale SM data from 17 CMIP6 models, producing a high-resolution (0.1°) dataset called CMIP6_UNet-Gan. This dataset includes SM data for five depth layers (0–10 cm, 10–30 cm, 30–50 cm, 50–80 cm, 80–110 cm), four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). The UNet-Gan model demonstrates strong performance in data fusion and downscaling, especially in shallow soil layers. Analysis of the CMIP6_UNet-Gan dataset reveals an overall increasing trend in SM across all layers, with higher rates under more intense emission scenarios. Spatially, moisture increases vary, with significant trends in the western Yangtze and northeastern Yellow River regions. Deeper soils show a slower response to climate change, and seasonal variations indicate that moisture increases are most pronounced in spring and winter, followed by autumn, with the least increase observed in summer. Future projections suggest higher moisture increase rates in the early and late 21st century compared to the mid-century. By the end of this century (2071–2100), compared to the Historical period (1995–2014), the increase in SM across the five depth layers ranges from: 5.5% to 11.5%, 4.6% to 9.2%, 4.3% to 7.5%, 4.5% to 7.5%, and 3.3% to 6.5%, respectively. Full article

(This article belongs to the Special Issue Land-Atmosphere Interactions and Effects on the Climate of the Tibetan Plateau and Surrounding Regions III)

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15 pages, 5214 KiB

Open AccessArticle

An Empirical Atmospheric Weighted Average Temperature Enhancement Model in the Yunnan–Guizhou Plateau Considering Surface Temperature

by Yi Shen, Peicheng Li, Bingbing Zhang, Tong Wu, Junkuan Zhu, Qing Li and Wang Li

Remote Sens. 2024, 16(23), 4366; https://doi.org/10.3390/rs16234366 - 22 Nov 2024

Abstract

Atmospheric weighted mean temperature (Tm) is a crucial parameter for retrieving atmospheric precipitation using the Global Navigation Satellite System (GNSS). It plays a significant role in GNSS meteorology research. Although existing empirical models can quickly obtain Tm values for the Yunnan–Guizhou Plateau, their [...] Read more.

Atmospheric weighted mean temperature (Tm) is a crucial parameter for retrieving atmospheric precipitation using the Global Navigation Satellite System (GNSS). It plays a significant role in GNSS meteorology research. Although existing empirical models can quickly obtain Tm values for the Yunnan–Guizhou Plateau, their accuracy is generally low due to the region’s complex environmental and climatic conditions. To address this issue, this study proposes an enhanced empirical Tm model tailored for the Yunnan–Guizhou Plateau. This new model incorporates surface temperature (Ts) data and employs the least squares method to determine model coefficients, thereby improving the accuracy of the Tm empirical model. The research utilizes observational data from 16 radiosonde stations in the Yunnan–Guizhou Plateau from 2010 to 2018. By integrating Ts into the Hourly Global Pressure and Temperature (HGPT2) model, we establish the enhanced empirical Tm model, referred to as YGTm. We evaluate the accuracy of the YGTm model using Tm values obtained from the 2019 radiosonde station measurements as a reference. A comparative analysis is conducted against the Bevis model, the HGPT2 model, and the regional linear model LTm. The results indicate that at the modeling stations, the proposed enhanced model improves Tm prediction accuracy by 24.9%, 16.1%, and 22.4% compared to the Bevis, HGPT2, and LTm models, respectively. At non-modeling stations, the accuracy improvements are 26.2%, 17.1% and 24.4%, respectively. Furthermore, the theoretical root mean square error and relative error from using the YGTm model for GNSS water vapor retrieval are 0.27 mm and 0.93%, respectively, both of which outperform the comparative models. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods (Third Edition))

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23 pages, 8399 KiB

Open AccessArticle

A Neuro-Symbolic Framework for Tree Crown Delineation and Tree Species Classification

by Ira Harmon, Ben Weinstein, Stephanie Bohlman, Ethan White and Daisy Zhe Wang

Remote Sens. 2024, 16(23), 4365; https://doi.org/10.3390/rs16234365 - 22 Nov 2024

Abstract

Neuro-symbolic models combine deep learning and symbolic reasoning to produce better-performing hybrids. Not only do neuro-symbolic models perform better, but they also deal better with data scarcity, enable the direct incorporation of high-level domain knowledge, and are more explainable. However, these benefits come [...] Read more.

Neuro-symbolic models combine deep learning and symbolic reasoning to produce better-performing hybrids. Not only do neuro-symbolic models perform better, but they also deal better with data scarcity, enable the direct incorporation of high-level domain knowledge, and are more explainable. However, these benefits come at the cost of increased complexity, which may deter the uninitiated from using these models. In this work, we present a framework to simplify the creation of neuro-symbolic models for tree crown delineation and tree species classification via the use of object-oriented programming and hyperparameter tuning algorithms. We show that models created using our framework outperform their non-neuro-symbolic counterparts by as much as two F1 points for crown delineation and three F1 points for species classification. Furthermore, our use of hyperparameter tuning algorithms allows users to experiment with multiple formulations of domain knowledge without the burden of manual tuning. Full article

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17 pages, 12379 KiB

Open AccessArticle

Artificial-Intelligence-Based Classification to Unveil Geodynamic Processes in the Eastern Alps

by Christian Bignami, Alessandro Pignatelli, Giulia Romoli and Carlo Doglioni

Remote Sens. 2024, 16(23), 4364; https://doi.org/10.3390/rs16234364 - 22 Nov 2024

Abstract

InSAR has emerged as a leading technique for studying and monitoring ground movements over large areas and across various geodynamic environments. Recent advancements in SAR sensor technology have enabled the acquisition of dense spatial datasets, providing substantial information at regional and national scales. [...] Read more.

InSAR has emerged as a leading technique for studying and monitoring ground movements over large areas and across various geodynamic environments. Recent advancements in SAR sensor technology have enabled the acquisition of dense spatial datasets, providing substantial information at regional and national scales. Despite these improvements, classifying and interpreting such vast datasets remains a significant challenge. InSAR analysts and geologists frequently have to manually analyze the time series from Persistent Scatterer Interferometry (PSI) to model the complexity of geological and tectonic phenomena. This process is time-consuming and impractical for large-scale monitoring. Utilizing Artificial Intelligence (AI) to classify and detect deformation processes presents a promising solution. In this study, vertical ground deformation time series from northeastern Italy were obtained from the European Ground Motion Service and classified by experts into different deformation categories. Convolutional and pre-trained neural networks were then trained and tested using both numerical time-series data and trend images. The application of the best performing trained network to test data showed an accuracy of

83 %

. Such a result demonstrates that neural networks can successfully identify areas experiencing distinct geodynamic processes, emphasizing the potential of AI to improve PSI data interpretation. Full article

(This article belongs to the Special Issue Signal Processing and Machine Learning for Space Geodesy Applications)

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29 pages, 3371 KiB

Open AccessArticle

Biodiversity from the Sky: Testing the Spectral Variation Hypothesis in the Brazilian Atlantic Forest

by Tobias Baruc Moreira Pinon, Adriano Ribeiro de Mendonça, Gilson Fernandes da Silva, Emanuel Maretto Effgen, Nívea Maria Mafra Rodrigues, Milton Marques Fernandes, Jerônimo Boelsums Barreto Sansevero, Catherine Torres de Almeida, Henrique Machado Dias, Fabio Guimarães Gonçalves and André Quintão de Almeida

Remote Sens. 2024, 16(23), 4363; https://doi.org/10.3390/rs16234363 - 22 Nov 2024

Abstract

Tropical forests have high species richness, being considered the most diverse and complex ecosystems in the world. Research on the variation and maintenance of biodiversity in these ecosystems is important for establishing conservation strategies. The main objective of this study was to test [...] Read more.

Tropical forests have high species richness, being considered the most diverse and complex ecosystems in the world. Research on the variation and maintenance of biodiversity in these ecosystems is important for establishing conservation strategies. The main objective of this study was to test the Spectral Variation Hypothesis through associations between species diversity and richness measured in the field and hyperspectral data collected by a Remotely Piloted Aircraft (RPA) in areas with secondary tropical forest in the Brazilian Atlantic Forest biome. Specific objectives were to determine which dispersion measurements, standard deviation (SD) or coefficient of variation (CV), estimated for the n pixels occurring within each sampling unit, better explains species diversity; the effects of pixel size on the direction and intensity of this relationship; and the effects of shaded pixels within each sampling unit. The spectral variability hypothesis was confirmed for the Atlantic Forest biome, with R² of 0.83 for species richness and 0.76 and 0.69 for the Shannon and Simpson diversity indices, respectively, using 1.0 m illuminated pixels. The dispersion (CV and SD) of hyperspectral bands were most strongly correlated with taxonomic diversity and richness in the red-edge and near-infrared (NIR) regions of the electromagnetic spectrum. Pixel size affected R² values, which were higher for 1.0 m pixels (0.83) and lower for 10.0 m pixels (0.71). Additionally, illuminated pixels had higher R² values than those under shadow effects. The main dispersion variables selected as metrics for regression models were mean CV, CV for the 726.7 nm band, and SD for the 742.3 and 933.4 nm bands. Our results suggest that spectral diversity can serve as a proxy for species diversity in the Atlantic Forest. However, factors that can affect this relationship, such as taxonomic and spectral diversity metrics used, pixel size, and shadow effects in images, should be considered. Full article

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20 pages, 16875 KiB

Open AccessArticle

Pest Detection in Citrus Orchards Using Sentinel-2: A Case Study on Mealybug (Delottococcus aberiae) in Eastern Spain

by Fàtima Della Bellver, Belen Franch Gras, Italo Moletto-Lobos, César José Guerrero Benavent, Alberto San Bautista Primo, Constanza Rubio, Eric Vermote and Sebastien Saunier

Remote Sens. 2024, 16(23), 4362; https://doi.org/10.3390/rs16234362 - 22 Nov 2024

Abstract

The Delottococcus aberiae is a mealybug pest known as Cotonet de les Valls in the province of Castellón (Spain). This tiny insect is causing large economic losses in the Spanish agricultural sector, especially in the citrus industry. The European Copernicus program encourages the [...] Read more.

The Delottococcus aberiae is a mealybug pest known as Cotonet de les Valls in the province of Castellón (Spain). This tiny insect is causing large economic losses in the Spanish agricultural sector, especially in the citrus industry. The European Copernicus program encourages the progress of Earth observation (EO) in relation to the development of agricultural monitoring tools. In this context, this work is based on the analysis of the temporal evolution of spectral surface reflectance data from Sen2Like, analyzing healthy and fields affected by the mealybug. The study area is focused on the surroundings of Vall d’Uixó (Castellón, Spain), involving an approximate area of 25 ha distributed in a total of 21 fields of citrus trees with different mealybug incidence, classified as healthy or unhealthy, during the 2020–2021 season. The relationship between the mealybug infestation level and the Normalized Difference Vegetation Index (NDVI) and other optical bands (Red, NIR, SWIR, derived from Sen2Like) were analyzed by studying the time-series evolution of each parameter across the time period 2017–2022. In this study, we also demonstrate that evergreen fruit trees such as citrus, show a seasonality across the EO-based time series, which is linked to directional effects caused by the sensor–sun geometry. This can be mitigated by using a Bidirectional Reflectance Distribution Function (BRDF) model such as the High-Resolution Adjusted BRDF Algorithm (HABA). To study the infested fields separately from healthy ones and avoid mixing fields with very different spectral responses caused by field type, separation between rows, or age, we studied the evolution of each parcel separately using monthly linear regressions, considering the 2017–2018 seasons as a reference when the pest had not developed yet. The observations indicate the feasibility of the distinction between affected and healthy plots during a year utilizing specific spectral ranges, with SWIR proving a notably effective channel, enabling separability from mid-summer to the fall. Furthermore, the anomaly inspection demonstrates an increase in the effects of the pest from 2020 to 2022 in all spectral regions and enables a first approximation for identifying healthy and affected fields based on negative anomalies in the red and SWIR channels and positive anomalies in the NIR and NDVI. This work contributes to the development of new monitoring tools for efficient and sustainable action in pest control. Full article

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34 pages, 7354 KiB

Open AccessArticle

Analysis of High-Frequency Sea-State Variability Using SWOT Nadir Measurements and Application to Altimeter Sea State Bias Modelling

by Estelle Mazaleyrat, Ngan Tran, Laïba Amarouche, Douglas Vandemark, Hui Feng, Gérald Dibarboure and François Bignalet-Cazalet

Remote Sens. 2024, 16(23), 4361; https://doi.org/10.3390/rs16234361 - 22 Nov 2024

Abstract

The 1-day fast-sampling orbit phase of the Surface Water Ocean Topography (SWOT) satellite mission provides a unique opportunity to analyze high-frequency sea-state variability and its implications for altimeter sea state bias (SSB) model development. Time series with 1-day repeat sampling of sea-level anomaly [...] Read more.

The 1-day fast-sampling orbit phase of the Surface Water Ocean Topography (SWOT) satellite mission provides a unique opportunity to analyze high-frequency sea-state variability and its implications for altimeter sea state bias (SSB) model development. Time series with 1-day repeat sampling of sea-level anomaly (SLA) and SSB input parameters—comprising the significant wave height (SWH), wind speed (WS), and mean wave period (MWP)—are constructed using SWOT’s nadir altimeter data. The analyses corroborate the following key SSB modelling assumption central to empirical developments: the SLA noise due to all factors, aside from sea state change, is zero-mean. Global variance reduction tests on the SSB model’s performance using corrected SLA differences show that correction skill estimation using a specific (1D, 2D, or 3D) SSB model is unstable when using short time difference intervals ranging from 1 to 5 days, reaching a stable asymptotic limit after 5 days. It is proposed that this result is related to the temporal auto- and cross-correlations associated with the SSB model’s input parameters; the present study shows that SSB wind-wave input measurements take time (typically 1–4 days) to decorrelate in any given region. The latter finding, obtained using unprecedented high-frequency satellite data from multiple ocean basins, is shown to be consistent with estimates from an ocean wave model. The results also imply that optimal time-differencing (i.e., >4 days) should be considered when building SSB model data training sets. The SWOT altimeter data analysis of the temporal cross-correlations also permits an evaluation of the relationships between the SSB input parameters (SWH, WS, and MWP), where distinct behaviors are found in the swell- and wind-sea-dominated areas, and associated time scales are less than or on the order of 1 day. Finally, it is demonstrated that computing cross-correlations between the SLA (with and without SSB correction) and the SSB input parameters offers an additional tool for evaluating the relevance of candidate SSB input parameters, as well as for assessing the performance of SSB correction models, which, so far, mainly rely on the reduction in the variance of the differences in the SLA at crossover points. Full article

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23 pages, 10144 KiB

Open AccessArticle

A Fast Algorithm for Matching AIS Trajectories with Radar Point Data in Complex Environments

by Jialuo Xu, Ying Suo, Yuqing Jiang and Qiang Yang

Remote Sens. 2024, 16(23), 4360; https://doi.org/10.3390/rs16234360 - 22 Nov 2024

Abstract

In high-traffic port areas, vessel traffic-management systems (VTMS) are essential for managing ship movements and preventing collisions. However, inaccuracies and omissions in the Automatic Identification System (AIS), along with frequent false tracks generated by radar false alarms in complex environments, can compromise VTMS [...] Read more.

In high-traffic port areas, vessel traffic-management systems (VTMS) are essential for managing ship movements and preventing collisions. However, inaccuracies and omissions in the Automatic Identification System (AIS), along with frequent false tracks generated by radar false alarms in complex environments, can compromise VTMS stability. To address the challenges of establishing consistent navigation and improving trajectory quality, this study introduces a novel method to directly identify AIS-matched trajectories from radar plots. This approach treats radar points as probability clouds, generating a multi-dimensional information layer by stacking these clouds after differential transformations based on AIS data. The resulting layer undergoes filtering and clustering to extract point sets that align with AIS data, effectively isolating matching trajectories. The algorithm, validated with simulated data, rapidly identifies target trajectories amid extensive interference without requiring strict parameter adjustments. In measured data, the algorithm rapidly provides matching trajectories, although further human judgment is still required due to the potential absence of true values in measured data. Full article

(This article belongs to the Special Issue Innovative Applications of HF Radar (Second Edition))

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18 pages, 13773 KiB

Open AccessArticle

Comparison and Analysis of CALIPSO Aerosol Optical Depth and AERONET Aerosol Optical Depth Products in Asia from 2006 to 2023

by Yinan Zhao, Qingxin Tang, Zhenting Hu, Quanzhou Yu and Tianquan Liang

Remote Sens. 2024, 16(23), 4359; https://doi.org/10.3390/rs16234359 - 22 Nov 2024

Abstract

Aerosol optical depth (AOD) serves as a significant parameter in aerosol research. With the increasing utilization of satellite data in AOD research, it is crucial to evaluate the satellite AOD data. Using Aerosol Robotic Network (AERONET) in situ measurements, this study investigates the [...] Read more.

Aerosol optical depth (AOD) serves as a significant parameter in aerosol research. With the increasing utilization of satellite data in AOD research, it is crucial to evaluate the satellite AOD data. Using Aerosol Robotic Network (AERONET) in situ measurements, this study investigates the accuracy and applicability of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) AOD data in Asia from June 2006 to June 2023. By matching the CALIPSO AOD data in a 1° × 1° area around the selected AERONET sites, various statistical metrics were used to create a comprehensive evaluation system. The results show that: (1) There is a high correlation between the AODs of CALIPSO and AERONET (R = 0.636), and the AOD values of CALIPSO are only 1.7% higher than those of AERONET on average. The MAE (0.215) and RMSE (0.358) suggest that the error level of CALIPSO AOD is relatively low; (2) In most of the 25 sites throughout Asia CALIPSO AOD have high matching accuracies with the AERONET AOD, and only in three sites has a validation accuracy of ‘Poor’; (3) The accuracy varies across the four seasons, ranked as follows: winter demonstrates the highest accuracy, followed by autumn, spring, and summer; (4) The accuracy varies with surface elevation, with better matching in lowest altitude (<50 m) and high altitude (>500 m) areas, but slightly worse matching in medium altitude (200–500 m) areas and low altitude (50–200 m). The uncertainty in the CALIPSO AOD retrievals varies in seasons, altitudes, and aerosol characteristics. Full article

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20 pages, 5217 KiB

Open AccessArticle

A Real-Time Signal Measurement System Using FPGA-Based Deep Learning Accelerators and Microwave Photonic

by Longlong Zhang, Tong Zhou, Jie Yang, Yin Li, Zhiwen Zhang, Xiang Hu and Yuanxi Peng

Remote Sens. 2024, 16(23), 4358; https://doi.org/10.3390/rs16234358 - 22 Nov 2024

Abstract

Deep learning techniques have been widely investigated as an effective method for signal measurement in recent years. However, most existing deep learning-based methods still face difficulty in deploying on embedded platforms and perform poorly in real-time applications. To address this, this paper develops [...] Read more.

Deep learning techniques have been widely investigated as an effective method for signal measurement in recent years. However, most existing deep learning-based methods still face difficulty in deploying on embedded platforms and perform poorly in real-time applications. To address this, this paper develops two accelerators, as the core of the signal measurement system, for intelligent signal processing. Firstly, by introducing the idea of automated framework, we propose a simplest deep neural network (DNN)-based hardware structure, which automatically maps algorithms to hardware modules, supports configurable parameters, and has the advantage of low latency, with an average inference time of only 3.5 μs. Subsequently, another accelerator is designed with the efficient hardware structure of the long short-term memory (LSTM) + DNN model, demonstrating outstanding performance with a classification accuracy of 98.82%, mean absolute error (MAE) of 0.27°, and root mean square errors (RMSE) of 0.392° after model compression. Moreover, parallel optimization strategies are exploited to further reduce latency and support simultaneous frequency and direction measurement tasks. Finally, we test the actual collected signal data on the XCVU13P field programmable gate array (FPGA). The results show that the time of inference saves 28–31% for the DNN model and 71–73% for the LSTM + DNN model compared to running on graphic processing unit (GPU). In addition, the parallel strategies further decrease the delay by 23.9% and 37.5% when processing continuous data. The FPGA-based and deep learning-assisted hardware accelerators significantly improve real-time performance and provide a promising solution for signal measurement. Full article

(This article belongs to the Topic Advanced Array Signal Processing for B5G/6G: Models, Algorithms, and Applications)

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27 pages, 25812 KiB

Open AccessArticle

Forecasting Flood Inundation in U.S. Flood-Prone Regions Through a Data-Driven Approach (FIER): Using VIIRS Water Fractions and the National Water Model

by Amirhossein Rostami, Chi-Hung Chang, Hyongki Lee, Hung-Hsien Wan, Tien Le Thuy Du, Kel N. Markert, Gustavious P. Williams, E. James Nelson, Sanmei Li, William Straka III, Sean Helfrich and Angelica L. Gutierrez

Remote Sens. 2024, 16(23), 4357; https://doi.org/10.3390/rs16234357 - 22 Nov 2024

Abstract

Floods, one of the costliest, and most frequent hazards, are expected to worsen in the U.S. due to climate change. The real-time forecasting of flood inundations is extremely important for proactive decision-making to reduce damage. However, traditional forecasting methods face challenges in terms [...] Read more.

Floods, one of the costliest, and most frequent hazards, are expected to worsen in the U.S. due to climate change. The real-time forecasting of flood inundations is extremely important for proactive decision-making to reduce damage. However, traditional forecasting methods face challenges in terms of implementation and scalability due to computational burdens and data availability issues. Current forecasting services in the U.S. largely rely on hydrodynamic modeling, limited to river reaches near in situ gauges and requiring extensive data for model setup and calibration. Here, we have successfully adapted the Forecasting Inundation Extents using REOF (FIER) analysis framework to produce forecasted water fraction maps in two U.S. flood-prone regions, specifically the Red River of the North Basin and the Upper Mississippi Alluvial Plain, utilizing Visible Infrared Imaging Radiometer Suite (VIIRS) optical imagery and the National Water Model. Comparing against historical VIIRS imagery for the same dates, FIER 1- to 8-day medium-range pseudo-forecasts show that about 70–80% of pixels exhibit absolute errors of less than 30%. Although originally developed utilizing Synthetic Aperture Radar (SAR) images, this study demonstrated FIER’s versatility and effectiveness in flood forecasting by demonstrating its successful adaptation with optical VIIRS imagery which provides daily water fraction product, offering more historical observations to be used as inputs for FIER during peak flood times, particularly in regions where flooding commonly happens in a short period rather than following a broad seasonal pattern. Full article

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

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19 pages, 11478 KiB

Open AccessArticle

A Comparative Study of Methods for Estimating the Thickness of Glacial Debris: A Case Study of the Koxkar Glacier in the Tian Shan Mountains

by Jun Liu, Yan Qin, Haidong Han, Qiudong Zhao and Yongqiang Liu

Remote Sens. 2024, 16(23), 4356; https://doi.org/10.3390/rs16234356 - 22 Nov 2024

Abstract

The local or overall mass balance of a glacier is significantly influenced by the spatial heterogeneity of its overlying debris thickness. Accurately estimating the debris thickness of glaciers is essential for understanding their hydrological processes and the impact of climate change. This study [...] Read more.

The local or overall mass balance of a glacier is significantly influenced by the spatial heterogeneity of its overlying debris thickness. Accurately estimating the debris thickness of glaciers is essential for understanding their hydrological processes and the impact of climate change. This study focuses on the Koxkar Glacier in the Tian Shan Mountains, using debris thickness data to compare the accuracy of three commonly used approaches for estimating the spatial distribution of debris thickness. The three measurement approaches include two empirical relationships between the land surface temperature (LST) and debris thickness approaches, empirical relationship approach 1 and empirical relationship approach 2, and the energy balance of debris approach. The analysis also explores the potential influence of topographic factors on the debris distribution. By incorporating temperature data from the debris profiles, this study examines the applicability of each approach and identifies areas for possible improvement. The results indicate that (1) all three debris thickness estimation approaches effectively capture the distribution characteristics of glacial debris, although empirical relationship approach 2 outperforms the others in describing the spatial patterns; (2) the accuracy of each approach varies depending on the debris thickness, with the energy balance of debris approach being most accurate for debris less than 50 cm thick, while empirical relationship approach 1 performs better for debris thicker than 50 cm and empirical relationship approach 2 demonstrates the highest overall accuracy; and (3) topographic factors, particularly the elevation, significantly influence the accuracy of debris thickness estimates. Furthermore, the empirical relationships between the LST and debris thickness require field data and focus solely on the surface temperature, neglecting other influencing factors. The energy balance of debris approach is constrained by its linear assumption of the temperature profile, which is only valid within a specific range of debris thickness; beyond this range, it significantly underestimates the values. These findings provide evidence-based support for improving remote-sensing methods for debris thickness estimation. Full article

(This article belongs to the Special Issue Earth Observation of Glacier and Snow Cover Mapping in Cold Regions)

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16 pages, 5925 KiB

Open AccessArticle

Revealing Water Storage Changes and Ecological Water Conveyance Benefits in the Tarim River Basin over the Past 20 Years Based on GRACE/GRACE-FO

by Weicheng Sun and Xingfu Zhang

Remote Sens. 2024, 16(23), 4355; https://doi.org/10.3390/rs16234355 - 22 Nov 2024

Abstract

As China’s largest inland river basin and one of the world’s most arid regions, the Tarim River Basin is home to an extremely fragile ecological environment. Therefore, monitoring the water storage changes is critical for enhancing water resources management and improving hydrological policies [...] Read more.

As China’s largest inland river basin and one of the world’s most arid regions, the Tarim River Basin is home to an extremely fragile ecological environment. Therefore, monitoring the water storage changes is critical for enhancing water resources management and improving hydrological policies to ensure sustainable development. This study reveals the spatiotemporal changes of water storage and its driving factors in the Tarim River Basin from 2002 to 2022, utilizing data from GRACE, GRACE-FO (GFO), GLDAS, the glacier model, and measured hydrological data. In addition, we validate GRACE/GFO data as a novel resource that can monitor the ecological water conveyance (EWC) benefits effectively in the lower reaches of the basin. The results reveal that (1) the northern Tarim River Basin has experienced a significant decline in terrestrial water storage (TWS), with an overall deficit that appears to have accelerated in recent years. From April 2002 to December 2009, the groundwater storage (GWS) anomaly accounted for 87.5% of the TWS anomaly, while from January 2010 to January 2020, the ice water storage (IWS) anomaly contributed 57.1% to the TWS anomaly. (2) The TWS changes in the Tarim River Basin are primarily attributed to the changes of GWS and IWS, and they have the highest correlation with precipitation and evapotranspiration, with grey relation analysis (GRA) coefficients of 0.74 and 0.68, respectively, while the human factors mainly affect GWS, with an average GRA coefficient of 0.64. (3) In assessing ecological water conveyance (EWC) benefits, the GRACE/GFO-derived TWS anomaly in the lower reaches of the Tarim River exhibits a good correspondence with the changes of EWC, NDVI, and groundwater levels. Full article

(This article belongs to the Special Issue Remote Sensing for Groundwater Hydrology)

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23 pages, 6340 KiB

Open AccessReview

A Review of Lidar Technology in China’s Lunar Exploration Program

by Genghua Huang and Weiming Xu

Remote Sens. 2024, 16(23), 4354; https://doi.org/10.3390/rs16234354 - 22 Nov 2024

Abstract

Lidar technology plays a pivotal role in lunar exploration, particularly in terrain mapping, 3D topographic surveying, and velocity measurement, which are crucial for guidance, navigation, and control. This paper reviews the current global research and applications of lidar technology in lunar missions, noting [...] Read more.

Lidar technology plays a pivotal role in lunar exploration, particularly in terrain mapping, 3D topographic surveying, and velocity measurement, which are crucial for guidance, navigation, and control. This paper reviews the current global research and applications of lidar technology in lunar missions, noting that existing efforts are primarily focused on 3D terrain mapping and velocity measurement. The paper also discusses the detailed system design and key results of the laser altimeter, laser ranging sensor, laser 3D imaging sensor, and laser velocity sensor used in the Chang’E lunar missions. By comparing and analyzing similar foreign technologies, this paper identifies future development directions for lunar laser payloads. The evolution towards multi-beam single-photon detection technology aims to enhance the point cloud density and detection efficiency. This manuscript advocates that China actively advance new technologies and conduct space application research in areas such as multi-beam single-photon 3D terrain mapping, lunar surface water ice measurement, and material composition analysis, to elevate the use of laser pay-loads in lunar and space exploration. Full article

(This article belongs to the Special Issue Laser and Optical Remote Sensing for Planetary Exploration)

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16 pages, 2661 KiB

Open AccessArticle

Identifying the Interactive Coercive Relationships Between Urbanization and Eco-Environmental Quality in the Yangtze and Yellow River Basins, China

by Liang Zheng, Jiahui Wu, Qian Chen, Jianpeng Wang, Wanxu Chen and Sipei Pan

Remote Sens. 2024, 16(23), 4353; https://doi.org/10.3390/rs16234353 - 21 Nov 2024

Abstract

Urbanization, as an important engine of modernization, plays an important role in promoting regional economy and improving living standards. Nevertheless, unchecked urban expansion over recent decades has strained natural resources and the environment, leading to crises, especially in densely populated urban areas that [...] Read more.

Urbanization, as an important engine of modernization, plays an important role in promoting regional economy and improving living standards. Nevertheless, unchecked urban expansion over recent decades has strained natural resources and the environment, leading to crises, especially in densely populated urban areas that act as ecological barriers within river basins. The investigation of the interactive coercive relationship between the urbanization level (UL) and eco-environmental quality (EEQ) can facilitate the identification of sustainable pathways towards regional sustainability. Therefore, this study employed a set of multidisciplinary approaches, integrating simple linear regression, bivariate spatial autocorrelation, and coupling coordination degree (CCD) models, alongside multi-source remote sensing data to analyze the interactive coercive relationship between UL and EEQ in the Yangtze and Yellow River basins (YYRBs) in China. Key findings included a 6.97% improvement in EEQ in the Yellow River basin (YLRB) from 2001 to 2020, with higher values in the southeastern and southwestern regions and lower values in the central region, while the Yangtze River basin (YTRB) saw only a 1.28% increase, characterized by a lower EEQ in the west and higher levels in the middle and east, although the Yangtze River Delta showed a decline and significant variation among tributaries. UL rose steadily in both basins, especially in the middle reaches of the YLRB. Spatial autocorrelation analysis revealed a positive correlation between UL and EEQ in the YLRB, whereas a negative correlation was found in the YTRB. The CCD between UL and EEQ in the YYRBs improved, particularly in the middle and lower reaches, indicating the need for integrated urban development strategies that consider regional ecological capacities. These findings provided a scientific basis for ecological protection and sustainable urban development at a large river basin scale. Full article

(This article belongs to the Section Remote Sensing and Geo-Spatial Science)

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