Remote Sensing

21 pages, 52503 KiB

Open AccessArticle

Study on the Identification, Failure Mode, and Spatial Distribution of Bank Collapses after the Initial Impoundment in the Head Section of Baihetan Reservoir in Jinsha River, China

by Chuangchuang Yao, Lingjing Li, Xin Yao, Renjiang Li, Kaiyu Ren, Shu Jiang, Ximing Chen and Li Ma

Remote Sens. 2024, 16(12), 2253; https://doi.org/10.3390/rs16122253 - 20 Jun 2024

Cited by 1 | Viewed by 1127

Abstract

After the initial impoundment of the Baihetan Reservoir in April 2021, the water level in front of the dam rose about 200 m. The mechanical properties and effects of the bank slopes in the reservoir area changed significantly, resulting in many bank collapses. [...] Read more.

After the initial impoundment of the Baihetan Reservoir in April 2021, the water level in front of the dam rose about 200 m. The mechanical properties and effects of the bank slopes in the reservoir area changed significantly, resulting in many bank collapses. This study systematically analyzed the bank slope of the head section of the reservoir, spanning 30 km from the dam to Baihetan Bridge, through a comprehensive investigation conducted after the initial impoundment. The analysis utilized UAV flights and ground surveys to interpret the bank slope’s distribution characteristics and failure patterns. A total of 276 bank collapses were recorded, with a geohazard development density of 4.6/km. The slope gradient of 26% of the collapsed banks experienced an increase ranging from 5 to 20° after impoundment, whereas the remaining sites’ inclines remained unchanged. According to the combination of lithology and movement mode, the bank failure mode is divided into six types, which are the surface erosion type, surface collapse type, surface slide type, bedding slip type of clastic rock, toppling type of clastic rock, and cavity corrosion type of carbonate rock. It was found that the collapsed banks in the reservoir area of 85% developed in the reactivation of old landslide deposits, while 15% in the clastic and carbonate rock. This study offers guidance for the next phase of bank collapse regulations and future geohazards prevention strategies in the Baihetan Reservoir area. Full article

(This article belongs to the Topic Natural Hazards and Environmental Challenges in the Anthropocene Age)

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23 pages, 76599 KiB

Open AccessArticle

SRBPSwin: Single-Image Super-Resolution for Remote Sensing Images Using a Global Residual Multi-Attention Hybrid Back-Projection Network Based on the Swin Transformer

by Yi Qin, Jiarong Wang, Shenyi Cao, Ming Zhu, Jiaqi Sun, Zhicheng Hao and Xin Jiang

Remote Sens. 2024, 16(12), 2252; https://doi.org/10.3390/rs16122252 - 20 Jun 2024

Cited by 4 | Viewed by 980

Abstract

Remote sensing images usually contain abundant targets and complex information distributions. Consequently, networks are required to model both global and local information in the super-resolution (SR) reconstruction of remote sensing images. The existing SR reconstruction algorithms generally focus on only local or global [...] Read more.

Remote sensing images usually contain abundant targets and complex information distributions. Consequently, networks are required to model both global and local information in the super-resolution (SR) reconstruction of remote sensing images. The existing SR reconstruction algorithms generally focus on only local or global features, neglecting effective feedback for reconstruction errors. Therefore, a Global Residual Multi-attention Fusion Back-projection Network (SRBPSwin) is introduced by combining the back-projection mechanism with the Swin Transformer. We incorporate a concatenated Channel and Spatial Attention Block (CSAB) into the Swin Transformer Block (STB) to design a Multi-attention Hybrid Swin Transformer Block (MAHSTB). SRBPSwin develops dense back-projection units to provide bidirectional feedback for reconstruction errors, enhancing the network’s feature extraction capabilities and improving reconstruction performance. SRBPSwin consists of the following four main stages: shallow feature extraction, shallow feature refinement, dense back projection, and image reconstruction. Firstly, for the input low-resolution (LR) image, shallow features are extracted and refined through the shallow feature extraction and shallow feature refinement stages. Secondly, multiple up-projection and down-projection units are designed to alternately process features between high-resolution (HR) and LR spaces, obtaining more accurate and detailed feature representations. Finally, global residual connections are utilized to transfer shallow features during the image reconstruction stage. We propose a perceptual loss function based on the Swin Transformer to enhance the detail of the reconstructed image. Extensive experiments demonstrate the significant reconstruction advantages of SRBPSwin in quantitative evaluation and visual quality. Full article

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Graphical abstract

19 pages, 6650 KiB

Open AccessTechnical Note

Innovative Rotating SAR Mode for 3D Imaging of Buildings

by Yun Lin, Ying Wang, Yanping Wang, Wenjie Shen and Zechao Bai

Remote Sens. 2024, 16(12), 2251; https://doi.org/10.3390/rs16122251 - 20 Jun 2024

Cited by 1 | Viewed by 1362

Abstract

Three-dimensional SAR imaging of urban buildings is currently a hotspot in the research area of remote sensing. Synthetic Aperture Radar (SAR) offers all-time, all-weather, high-resolution capacity, and is an important tool for the monitoring of building health. Buildings have geometric distortion in conventional [...] Read more.

Three-dimensional SAR imaging of urban buildings is currently a hotspot in the research area of remote sensing. Synthetic Aperture Radar (SAR) offers all-time, all-weather, high-resolution capacity, and is an important tool for the monitoring of building health. Buildings have geometric distortion in conventional 2D SAR images, which brings great difficulties to the interpretation of SAR images. This paper proposes a novel Rotating SAR (RSAR) mode, which acquires 3D information of buildings from two different angles in a single rotation. This new RSAR mode takes the center of a straight track as its rotation center, and obtains images of the same facade of a building from two different angles. By utilizing the differences in geometric distortion of buildings in the image pair, the 3D structure of the building is reconstructed. Compared to the existing tomographic SAR or circular SAR, this method does not require multiple flights in different elevations or observations from varying aspect angles, and greatly simplifies data acquisition. Furthermore, both simulation analysis and actual data experiment have verified the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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27 pages, 6641 KiB

Open AccessArticle

Biomass Estimation and Saturation Value Determination Based on Multi-Source Remote Sensing Data

by Rula Sa, Yonghui Nie, Sergey Chumachenko and Wenyi Fan

Remote Sens. 2024, 16(12), 2250; https://doi.org/10.3390/rs16122250 - 20 Jun 2024

Cited by 4 | Viewed by 2096

Abstract

Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. [...] Read more.

Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. Based on Landsat 8, Sentinel-2A, and ALOS2 PALSAR data, this paper takes the artificial coniferous forests in the Saihanba Forest of Hebei Province as the object of study, fully explores and establishes remote sensing factors and information related to forest structure, gives full play to the advantages of spectral signals in detecting the horizontal structure and multi-dimensional synthetic aperture radar (SAR) data in detecting the vertical structure, and combines environmental factors to carry out multivariate synergistic methods of estimating the AGB. This paper uses three variable selection methods (Pearson correlation coefficient, random forest significance, and the least absolute shrinkage and selection operator (LASSO)) to establish the variable sets, combining them with three typical non-parametric models to estimate AGB, namely, random forest (RF), support vector regression (SVR), and artificial neural network (ANN), to analyze the effect of forest structure on biomass estimation, explore the suitable AGB of artificial coniferous forests estimation of machine learning models, and develop the method of quantifying saturation value of the combined variables. The results show that the horizontal structure is more capable of explaining the AGB compared to the vertical structure information, and that combining the multi-structure information can improve the model results and the saturation value to a great extent. In this study, different sets of variables can produce relatively superior results in different models. The variable set selected using LASSO gives the best results in the SVR model, with an

R^{2}

values of 0.9998 and 0.8792 for the training and the test set, respectively, and the highest saturation value obtained is 185.73 t/ha, which is beyond the range of the measured data. The problem of saturation in biomass estimation in boreal medium- and high-density forests was overcome to a certain extent, and the AGB of the Saihanba area was better estimated. Full article

(This article belongs to the Special Issue Multi-platform and Multi-scale Forest Inventory from Remote Sensing Perspectives)

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22 pages, 2885 KiB

Open AccessArticle

Exploring Spatial Patterns of Tropical Peatland Subsidence in Selangor, Malaysia Using the APSIS-DInSAR Technique

by Betsabé de la Barreda-Bautista, Martha J. Ledger, Sofie Sjögersten, David Gee, Andrew Sowter, Beth Cole, Susan E. Page, David J. Large, Chris D. Evans, Kevin J. Tansey, Stephanie Evers and Doreen S. Boyd

Remote Sens. 2024, 16(12), 2249; https://doi.org/10.3390/rs16122249 - 20 Jun 2024

Cited by 1 | Viewed by 1417

Abstract

Tropical peatlands in Southeast Asia have experienced widespread subsidence due to forest clearance and drainage for agriculture, oil palm and pulp wood production, causing concerns about their function as a long-term carbon store. Peatland drainage leads to subsidence (lowering of peatland surface), an [...] Read more.

Tropical peatlands in Southeast Asia have experienced widespread subsidence due to forest clearance and drainage for agriculture, oil palm and pulp wood production, causing concerns about their function as a long-term carbon store. Peatland drainage leads to subsidence (lowering of peatland surface), an indicator of degraded peatlands, while stability/uplift indicates peatland accumulation and ecosystem health. We used the Advanced Pixel System using the Intermittent SBAS (ASPIS-DInSAR) technique with biophysical and geographical data to investigate the impact of peatland drainage and agriculture on spatial patterns of subsidence in Selangor, Malaysia. Results showed pronounced subsidence in areas subjected to drainage for agricultural and oil palm plantations, while stable areas were associated with intact forests. The most powerful predictors of subsidence rates were the distance from the drainage canal or peat boundary; however, other drivers such as soil properties and water table levels were also important. The maximum subsidence rate detected was lower than that documented by ground-based methods. Therefore, whilst the APSIS-DInSAR technique may underestimate absolute subsidence rates, it gives valuable information on the direction of motion and spatial variability of subsidence. The study confirms widespread and severe peatland degradation in Selangor, highlighting the value of DInSAR for identifying priority zones for restoration and emphasising the need for conservation and restoration efforts to preserve Selangor peatlands and prevent further environmental impacts. Full article

(This article belongs to the Section Biogeosciences Remote Sensing)

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Study area. North and South Selangor. Red points represent the points where pixels were extracted for the analysis. Peatlands are enclosed in the black polygon and rivers and canals are represented by blue lines. Full article ">Figure 2
Methodology framework. Full article ">Figure 3
(a) Land cover map from North Selangor; (b) Subsidence over North Selangor; (c) number of coherent pairs per pixel over North Selangor (coherence count); (d) Land cover map from South Selangor; (e) Subsidence over South Selangor; (f) number of coherent pairs per pixel over North Selangor. The subsidence data are in mm yr−1 between 2017 and 2019. A greater negative value (red) indicates a greater subsidence rate. Coherence count data ranges from 71 to 1335, whereby the higher the value, the greater the number of consistently coherent pairs that exist for this pixel. Black and blue lines represent the peatland extent. Areas of notable interest are marked with a red square. Full article ">Figure 4
Rates of subsidence in mm yr−1 computed from the surface motion velocity (2017–2019) among different land cover classes. Mean and SD are shown. A greater negative value indicates greater subsidence rates. (a) North Selangor subsidence rates; (b) South Selangor subsidence rates. Full article ">Figure 5
(a) Variable importance based on MSE; (b) variable importance based on node purity; (c) variable importance in optimum variables selected based on MSE (d) variable importance in optimum variables selected based on node purity. Full article ">Figure 5 Cont.
(a) Variable importance based on MSE; (b) variable importance based on node purity; (c) variable importance in optimum variables selected based on MSE (d) variable importance in optimum variables selected based on node purity. Full article ">Figure A1
Residual plots for multiple regression. Full article ">

18 pages, 5061 KiB

Open AccessArticle

Generating 10-Meter Resolution Land Use and Land Cover Products Using Historical Landsat Archive Based on Super Resolution Guided Semantic Segmentation Network

by Dawei Wen, Shihao Zhu, Yuan Tian, Xuehua Guan and Yang Lu

Remote Sens. 2024, 16(12), 2248; https://doi.org/10.3390/rs16122248 - 20 Jun 2024

Viewed by 1215

Abstract

Generating high-resolution land cover maps using relatively lower-resolution remote sensing images is of great importance for subtle analysis. However, the domain gap between real lower-resolution and synthetic images has not been permanently resolved. Furthermore, super-resolution information is not fully exploited in semantic segmentation [...] Read more.

Generating high-resolution land cover maps using relatively lower-resolution remote sensing images is of great importance for subtle analysis. However, the domain gap between real lower-resolution and synthetic images has not been permanently resolved. Furthermore, super-resolution information is not fully exploited in semantic segmentation models. By solving the aforementioned issues, a deeply fused super resolution guided semantic segmentation network using 30 m Landsat images is proposed. A large-scale dataset comprising 10 m Sentinel-2, 30 m Landsat-8 images, and 10 m European Space Agency (ESA) Land Cover Product is introduced, facilitating model training and evaluation across diverse real-world scenarios. The proposed Deeply Fused Super Resolution Guided Semantic Segmentation Network (DFSRSSN) combines a Super Resolution Module (SRResNet) and a Semantic Segmentation Module (CRFFNet). SRResNet enhances spatial resolution, while CRFFNet leverages super-resolution information for finer-grained land cover classification. Experimental results demonstrate the superior performance of the proposed method in five different testing datasets, achieving 68.17–83.29% and 39.55–75.92% for overall accuracy and kappa, respectively. When compared to ResUnet with up-sampling block, increases of 2.16–34.27% and 8.32–43.97% were observed for overall accuracy and kappa, respectively. Moreover, we proposed a relative drop rate of accuracy metrics to evaluate the transferability. The model exhibits improved spatial transferability, demonstrating its effectiveness in generating accurate land cover maps for different cities. Multi-temporal analysis reveals the potential of the proposed method for studying land cover and land use changes over time. In addition, a comparison of the state-of-the-art full semantic segmentation models indicates that spatial details are fully exploited and presented in semantic segmentation results by the proposed method. Full article

(This article belongs to the Special Issue AI-Driven Mapping Using Remote Sensing Data)

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The network structure of the deeply fused super resolution guided semantic segmentation network (DFSRSSN): Super Resolution Residual Network (SRResNet) and Cross-Resolution Feature Fusion Network (CRFFNet). Full article ">Figure 2
The 10 m land cover results using Landsat-8 images (a) Landsat-8 RGB images; (b) Super-resolution image; (c) ResUnet with up-sampling block (ResUnet_UP); (d) Deeply Fused Super Resolution Guided Semantic Segmentation Network (DFSRSSN); and (e) ESA 2020. Full article ">Figure 3
Comparison of user’s accuracy (UA), producer’s accuracy (PA), and F1 score for the different testing datasets: (a) Dataset I, (b) Dataset II, (c) Dataset III, (d) Dataset IV, and (e) Dataset V. Note: 1 tree cover, 2 shrubland, 3 grassland, 4 cropland, 5 built-up, 6 bare/sparse vegetation, 7 permanent water bodies, 8 herbaceous wetland. Full article ">Figure 4
The land use and land cover map of Wuhan in (a) 2013 and (b) 2020. Full article ">Figure 5
Representative scenes of 10 m multi-temporal results for Wuhan: (a) Landsat image in 2013; (b) Land cover map in 2013; (c) Landsat image in 2020; (d) Land cover map in 2020, and (e) ESA 2020. Full article ">

21 pages, 11309 KiB

Open AccessArticle

LiDAR Point Cloud Augmentation for Adverse Conditions Using Conditional Generative Model

by Yuxiao Zhang, Ming Ding, Hanting Yang, Yingjie Niu, Maoning Ge, Kento Ohtani, Chi Zhang and Kazuya Takeda

Remote Sens. 2024, 16(12), 2247; https://doi.org/10.3390/rs16122247 - 20 Jun 2024

Cited by 1 | Viewed by 1429

Abstract

The perception systems of autonomous vehicles face significant challenges under adverse conditions, with issues such as obscured objects and false detections due to environmental noise. Traditional approaches, which typically focus on noise removal, often fall short in such scenarios. Addressing the lack of [...] Read more.

The perception systems of autonomous vehicles face significant challenges under adverse conditions, with issues such as obscured objects and false detections due to environmental noise. Traditional approaches, which typically focus on noise removal, often fall short in such scenarios. Addressing the lack of diverse adverse weather data in existing automotive datasets, we propose a novel data augmentation method that integrates realistically simulated adverse weather effects into clear condition datasets. This method not only addresses the scarcity of data but also effectively bridges domain gaps between different driving environments. Our approach centers on a conditional generative model that uses segmentation maps as a guiding mechanism to ensure the authentic generation of adverse effects, which greatly enhances the robustness of perception and object detection systems in autonomous vehicles, operating under varied and challenging conditions. Besides the capability of accurately and naturally recreating over 90% of the adverse effects, we demonstrate that this model significantly improves the performance and accuracy of deep learning algorithms for autonomous driving, particularly in adverse weather scenarios. In the experiments employing our augmented approach, we achieved a 2.46% raise in the 3D average precision, a marked enhancement in detection accuracy and system reliability, substantiating the model’s efficacy with quantifiable improvements in 3D object detection compared to models without augmentation. This work not only serves as an enhancement of autonomous vehicle perception systems under adverse conditions but also marked an advancement in deep learning models in adverse condition research. Full article

(This article belongs to the Special Issue Remote Sensing Advances in Urban Traffic Monitoring)

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25 pages, 11675 KiB

Open AccessArticle

An Ensemble Machine Learning Model to Estimate Urban Water Quality Parameters Using Unmanned Aerial Vehicle Multispectral Imagery

by Xiangdong Lei, Jie Jiang, Zifeng Deng, Di Wu, Fangyi Wang, Chengguang Lai, Zhaoli Wang and Xiaohong Chen

Remote Sens. 2024, 16(12), 2246; https://doi.org/10.3390/rs16122246 - 20 Jun 2024

Viewed by 1332

Abstract

Urban reservoirs contribute significantly to human survival and ecological balance. Machine learning-based remote sensing techniques for monitoring water quality parameters (WQPs) have gained increasing prominence in recent years. However, these techniques still face challenges such as inadequate band selection, weak machine learning model [...] Read more.

Urban reservoirs contribute significantly to human survival and ecological balance. Machine learning-based remote sensing techniques for monitoring water quality parameters (WQPs) have gained increasing prominence in recent years. However, these techniques still face challenges such as inadequate band selection, weak machine learning model performance, and the limited retrieval of non-optical active parameters (NOAPs). This study focuses on an urban reservoir, utilizing unmanned aerial vehicle (UAV) multispectral remote sensing and ensemble machine learning (EML) methods to monitor optically active parameters (OAPs, including Chla and SD) and non-optically active parameters (including COD_Mn, TN, and TP), exploring spatial and temporal variations of WQPs. A framework of Feature Combination and Genetic Algorithm (FC-GA) is developed for feature band selection, along with two frameworks of EML models for WQP estimation. Results indicate FC-GA’s superiority over popular methods such as the Pearson correlation coefficient and recursive feature elimination, achieving higher performance with no multicollinearity between bands. The EML model demonstrates superior estimation capabilities for WQPs like Chla, SD, COD_Mn, and TP, with an R² of 0.72–0.86 and an MRE of 7.57–42.06%. Notably, the EML model exhibits greater accuracy in estimating OAPs (MRE ≤ 19.35%) compared to NOAPs (MRE ≤ 42.06%). Furthermore, spatial and temporal distributions of WQPs reveal nitrogen and phosphorus nutrient pollution in the upstream head and downstream tail of the reservoir due to human activities. TP, TN, and Chla are lower in the dry season than in the rainy season, while clarity and COD_Mn are higher in the dry season than in the rainy season. This study proposes a novel approach to water quality monitoring, aiding in the identification of potential pollution sources and ecological management. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)

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26 pages, 8482 KiB

Open AccessArticle

Adaptive Background Endmember Extraction for Hyperspectral Subpixel Object Detection

by Lifeng Yang, Xiaorui Song, Bin Bai and Zhuo Chen

Remote Sens. 2024, 16(12), 2245; https://doi.org/10.3390/rs16122245 - 20 Jun 2024

Viewed by 1049

Abstract

Subpixel object detection presents a significant challenge within the domain of hyperspectral image (HSI) processing, primarily due to the inherently limited spatial resolution of imaging spectrometers. For subpixel object detection, the dimensional extent of the object of interest is smaller than an individual [...] Read more.

Subpixel object detection presents a significant challenge within the domain of hyperspectral image (HSI) processing, primarily due to the inherently limited spatial resolution of imaging spectrometers. For subpixel object detection, the dimensional extent of the object of interest is smaller than an individual pixel, which significantly diminishes the utility of spatial information pertaining to the object. Therefore, the efficacy of detection algorithms depends heavily on the spectral data inherent in the image. The detection of subpixel objects in hyperspectral imagery primarily relies on the suppression of the background and the enhancement of the object of interest. Hence, acquiring accurate background information from HSI images is a crucial step. In this study, an adaptive background endmember extraction for hyperspectral subpixel object detection is proposed. An adaptive scale constraint is incorporated into the background spectral endmember learning process to improve the adaptability of background endmember extraction, thus further enhancing the algorithm’s generalizability and applicability in diverse analytical scenarios. Experimental results demonstrate that the adaptive endmember extraction-based subpixel object detection algorithm consistently outperforms existing state-of-the-art algorithms in terms of detection efficacy on both simulated and real-world datasets. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)

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18 pages, 5290 KiB

Open AccessArticle

Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling

by Ida Moalemi, Homa Kheyrollah Pour and K. Andrea Scott

Remote Sens. 2024, 16(12), 2244; https://doi.org/10.3390/rs16122244 - 20 Jun 2024

Viewed by 1347

Abstract

The seasonal temperature trends and ice phenology in the Great Slave Lake (GSL) are significantly influenced by inflow from the Slave River. The river undergoes a sequence of mechanical break-ups all the way to the GSL, initiating the GSL break-up process. Additionally, upstream [...] Read more.

The seasonal temperature trends and ice phenology in the Great Slave Lake (GSL) are significantly influenced by inflow from the Slave River. The river undergoes a sequence of mechanical break-ups all the way to the GSL, initiating the GSL break-up process. Additionally, upstream water management practices impact the discharge of the Slave River and, consequently, the ice break-up of the GSL. Therefore, monitoring the break-up process at the Slave River Delta (SRD), where the river meets the lake, is crucial for understanding the cascading effects of upstream activities on GSL ice break-up. This research aimed to use Random Forest (RF) models to monitor the ice break-up processes at the SRD using a combination of satellite images with relatively high spatial resolution, including Landsat-5, Landsat-8, Sentinel-2a, and Sentinel-2b. The RF models were trained using selected training pixels to classify ice, open water, and cloud. The onset of break-up was determined by data-driven thresholds on the ice fraction in images with less than 20% cloud coverage. Analysis of break-up timing from 1984 to 2023 revealed a significant earlier trend using the Mann–Kendall test with a p-value of 0.05. Furthermore, break-up data in recent years show a high degree of variability in the break-up rate using images in recent years with better temporal resolution. Full article

(This article belongs to the Special Issue Advances of Remote Sensing and GIS Technology in Surface Water Bodies)

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18 pages, 9615 KiB

Open AccessArticle

Multi-Scale Window Spatiotemporal Attention Network for Subsurface Temperature Prediction and Reconstruction

by Jiawei Jiang, Jun Wang, Yiping Liu, Chao Huang, Qiufu Jiang, Liqiang Feng, Liying Wan and Xiangguang Zhang

Remote Sens. 2024, 16(12), 2243; https://doi.org/10.3390/rs16122243 - 20 Jun 2024

Cited by 2 | Viewed by 1273

Abstract

In this study, we investigate the feasibility of using historical remote sensing data to predict the future three-dimensional subsurface ocean temperature structure. We also compare the performance differences between predictive models and real-time reconstruction models. Specifically, we propose a multi-scale residual spatiotemporal window [...] Read more.

In this study, we investigate the feasibility of using historical remote sensing data to predict the future three-dimensional subsurface ocean temperature structure. We also compare the performance differences between predictive models and real-time reconstruction models. Specifically, we propose a multi-scale residual spatiotemporal window ocean (MSWO) model based on a spatiotemporal attention mechanism, to predict changes in the subsurface ocean temperature structure over the next six months using satellite remote sensing data from the past 24 months. Our results indicate that predictions made using historical remote sensing data closely approximate those made using historical in situ data. This finding suggests that satellite remote sensing data can be used to predict future ocean structures without relying on valuable in situ measurements. Compared to future predictive models, real-time three-dimensional structure reconstruction models can learn more accurate inversion features from real-time satellite remote sensing data. This work provides a new perspective for the application of artificial intelligence in oceanography for ocean structure reconstruction. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)

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31 pages, 62358 KiB

Open AccessArticle

Comprehensive Ecological Risk Changes and Their Relationship with Ecosystem Services of Alpine Grassland in Gannan Prefecture from 2000–2020

by Zhanping Ma, Jinlong Gao, Tiangang Liang, Zhibin He, Senyao Feng, Xuanfan Zhang and Dongmei Zhang

Remote Sens. 2024, 16(12), 2242; https://doi.org/10.3390/rs16122242 - 20 Jun 2024

Cited by 3 | Viewed by 1391

Abstract

Alpine grassland is one of the most fragile and sensitive ecosystems, and it serves as a crucial ecological security barrier on the Tibetan Plateau. Due to the combined influence of climate change and human activities, the degradation of the alpine grassland in Gannan [...] Read more.

Alpine grassland is one of the most fragile and sensitive ecosystems, and it serves as a crucial ecological security barrier on the Tibetan Plateau. Due to the combined influence of climate change and human activities, the degradation of the alpine grassland in Gannan Prefecture has been increasing recent years, causing increases in ecological risk (ER) and leading to the grassland ecosystem facing unprecedented challenges. In this context, it is particularly crucial to construct a potential grassland damage index (PGDI) and assessment framework that can be used to effectively characterize the damage and risk to the alpine grassland ecosystem. This study comprehensively uses multi-source data to construct a PGDI based on the grassland resilience index, landscape ER index, and grass–livestock balance index. Thereafter, we proposed a feasible framework for assessing the comprehensive ER of alpine grassland and analyzed the responsive relationship between the comprehensive ER and comprehensive ecosystem services (ESs) of the grassland. There are four findings. The first is that the comprehensive ER of the alpine grassland in Gannan Prefecture from 2000–2020 had a low distribution in the southeast and a high distribution trend in the northwest, with medium risk (29.27%) and lower risk (27.62%) dominating. The high-risk area accounted for 4.58% and was mainly in Lintan County, the border between Diebu and Zhuoni Counties, the eastern part of Xiahe County, and the southwest part of Hezuo. Second, the comprehensive ESs showed a pattern of low distribution in the northwest and high distribution in the southeast. The low and lower services accounted for only 9.30% of the studied area and were mainly distributed in the west of Maqu County and central Lintan County. Third, the Moran’s index values for comprehensive ESs and ER for 2000, 2005, 2010, 2015, and 2020 were −0.246, −0.429, −0.348, −0.320, and −0.285, respectively, thereby indicating significant negative spatial autocorrelation for all aspects. Fourth, ER was caused by the combined action of multiple factors. There are significant differences in the driving factors that affect ER. Landscape index is the first dominant factor affecting ER, with q values greater than 0.25, followed by DEM and NDVI. In addition, the interaction between diversity index and NDVI had the greatest impact on ER. Overall, this study offers a new methodological framework for the quantification of comprehensive ER in alpine grasslands. Full article

(This article belongs to the Special Issue Remote Sensing of Ecosystem Structure and Function Dynamics Due to Climate Change and Human Activities)

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20 pages, 6070 KiB

Open AccessArticle

An Improved Propagation Prediction Method of Low-Frequency Skywave Fusing Fine Channel Parameters

by Jian Wang, Chengsong Duan, Yu Chen, Yafei Shi and Cheng Yang

Remote Sens. 2024, 16(12), 2241; https://doi.org/10.3390/rs16122241 - 20 Jun 2024

Cited by 1 | Viewed by 1373

Abstract

Low-frequency communication constitutes a vital component of essential communication systems, serving a pivotal role in remote radio communication, navigation, timing, and seismic analysis. To enhance the predictive precision of low-frequency skywave propagation and address the demands of engineering applications, we propose a high-precision [...] Read more.

Low-frequency communication constitutes a vital component of essential communication systems, serving a pivotal role in remote radio communication, navigation, timing, and seismic analysis. To enhance the predictive precision of low-frequency skywave propagation and address the demands of engineering applications, we propose a high-precision prediction method based on the ITU-R P.684 wave-hop theory and real-time environmental parameter forecasts. This method features several distinctive attributes. Firstly, it employs real-time ionospheric prediction data instead of relying on long-term ionospheric model predictions. Secondly, it utilizes a detailed map of land–sea surface electrical characteristics, surpassing the simplistic land–sea dichotomy previously employed. Compared with measured data, the findings demonstrate that we attained a reasonable propagation pattern and achieved high-precision field strength predictions. Comparatively, the improved method exhibits an improvement in the time and spatial domains over the ITU-R P.684 standard. Finally, the improved method balances computational efficiency with enhanced prediction accuracy, supporting the advancement of low-frequency communication system design and performance evaluation. Full article

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

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28 pages, 13295 KiB

Open AccessArticle

Optimized Parameters for Detecting Multiple Forest Disturbance and Recovery Events and Spatiotemporal Patterns in Fast-Regrowing Southern China

by Yuwei Tu, Kaiping Liao, Yuxuan Chen, Hongbo Jiao and Guangsheng Chen

Remote Sens. 2024, 16(12), 2240; https://doi.org/10.3390/rs16122240 - 20 Jun 2024

Viewed by 1364

Abstract

The timing, location, intensity, and drivers of forest disturbance and recovery are crucial for developing effective management strategies and policies for forest conservation and ecosystem resilience. Although many algorithms and improvement methods have been developed, it is still difficult to guarantee the detection [...] Read more.

The timing, location, intensity, and drivers of forest disturbance and recovery are crucial for developing effective management strategies and policies for forest conservation and ecosystem resilience. Although many algorithms and improvement methods have been developed, it is still difficult to guarantee the detection accuracy for forest disturbance and recovery patterns in southern China due to the complex climate and topography, faster forest recovery after disturbance, and the low availability of noise-free Landsat images. Here, we improved the LandTrendr parameters for different provinces to detect forest disturbances and recovery trajectories based on the LandTrendr change detection algorithm and time-series Landsat images on the GEE platform, and then applied the secondary random forest classifier to classify the forest disturbance and recovery patterns in southern China during 1990–2020. The accuracy evaluation indicated that our approach and improved parameters of the LandTrendr algorithm can increase the detection accuracy for both the spatiotemporal patterns and multiple events of forest disturbance and recovery, with an overall accuracy greater than 86% and a Kappa coefficient greater than 0.91 for different provinces. The total forest loss area was 1.54 × 10⁵ km² during 1990–2020 (4931 km²/year); however, most of these disturbed forests were recovered and only 6.39 × 10⁴ km² was a net loss area (converted to other land cover types). The area with two or more times of disturbance events accounted for 11.50% of the total forest loss area. The total forest gain area (including gain after loss and the afforestation area) was 5.44 × 10⁵ km², among which, the forest gain area after loss was 8.94 × 10⁴ km², and the net gain area from afforestation was 4.55 × 10⁵ km². The timing of the implementation of forestry policies significantly affected the interannual variations in forest disturbance and recovery, with large variations among different provinces. The detected forest loss and gain area was further compared against with inventory and other geospatial products, and proved the effectiveness of our method. Our study suggests that parameter optimization in the LandTrendr algorithm could greatly increase the accuracy for detecting the multiple and lower rate disturbance/recovery events in the fast-regrowing forested areas. Our findings also offer a long-term, moderate spatial resolution, and precise forest dynamic data for achieving sustainable forest management and the carbon neutrality goal in southern China. Full article

(This article belongs to the Special Issue Natural Hazard Mapping with Google Earth Engine)

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19 pages, 1380 KiB

Open AccessArticle

Highlighting the Use of UAV to Increase the Resilience of Native Hawaiian Coastal Cultural Heritage

by Kainalu K. Steward, Brianna K. Ninomoto, Haunani H. Kane, John H. R. Burns, Luke Mead, Kamala Anthony, Luka Mossman, Trisha Olayon, Cybil K. Glendon-Baclig and Cherie Kauahi

Remote Sens. 2024, 16(12), 2239; https://doi.org/10.3390/rs16122239 - 20 Jun 2024

Cited by 2 | Viewed by 2416

Abstract

The use of Uncrewed Aerial Vehicles (UAVs) is becoming a preferred method for supporting integrated coastal zone management, including cultural heritage sites. Loko i′a, traditional Hawaiian fishponds located along the coastline, have historically provided sustainable seafood sources. These coastal cultural heritage sites are [...] Read more.

The use of Uncrewed Aerial Vehicles (UAVs) is becoming a preferred method for supporting integrated coastal zone management, including cultural heritage sites. Loko i′a, traditional Hawaiian fishponds located along the coastline, have historically provided sustainable seafood sources. These coastal cultural heritage sites are undergoing revitalization through community-driven restoration efforts. However, sea level rise (SLR) poses a significant climate-induced threat to coastal areas globally. Loko i′a managers seek adaptive strategies to address SLR impacts on flooding, water quality, and the viability of raising native fish species. This study utilizes extreme tidal events, known as King Tides, as a proxy to estimate future SLR scenarios and their impacts on loko i′a along the Keaukaha coastline in Hilo, Hawai′i. In situ water level sensors were deployed at each site to assess flooding by the loko i′a type and location. We also compare inundation modeled from UAV-Structure from Motion (SfM) Digital Elevation Models (DEM) to publicly available Light Detection and Ranging (LiDAR) DEMs, alongside observed flooding documented by UAV imagery in real time. The average water levels (0.64 m and 0.88 m) recorded in this study during the 2023 King Tides are expected to reflect the average sea levels projected for 2060–2080 in Hilo, Hawai′i. Our findings indicate that high-resolution UAV-derived DEMs accurately model observed flooding (with 89% or more agreement), whereas LiDAR-derived flood models significantly overestimate observed flooding (by 2–5 times), outlining a more conservative approach. To understand how UAV datasets can enhance the resilience of coastal cultural heritage sites, we looked into the cost, spatial resolution, accuracy, and time necessary for acquiring LiDAR- and UAV-derived datasets. This study ultimately demonstrates that UAVs are effective tools for monitoring and planning for the future impacts of SLR on coastal cultural heritage sites at a community level. Full article

(This article belongs to the Special Issue The Use of UAV Platforms for Cultural Heritage Monitoring and Surveying)

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Examples of different types of loko i′a included in this study that are labeled with significant natural, cultural, and modern features. Loko kuapā (A) have rock wall enclosures, Loko wai (B) are freshwater-dominated ponds, and Kāheka (C) are natural pools with more direct wave exposure. Full article ">Figure 2
Location of where the study sites are located within the Hawai′i archipelago (A). The loko i′a are situated along the Eastern Keaukaha Coastline on Hawai′i island (B). Each marker in both Transects represents the location of an in situ water sensor. The loko i′a within Honohononui (Transect 1) are Laehala (orange), Hale o Lono (green), and Waiāhole (green). Within Hui Ho′oleimaluō (Transect 2), the loko i′a are Honokea (green) and Kaumaui (purple). The location of the barometer (white) is also indicated. Full article ">Figure 3
Observed UAV King Tide flooding (0.64 m) documented on 3 July 2023 at Honokea loko i′a with ground-truthed photos in the field. Observations include inundation at the main freshwater spring (A), high water levels intruding on Native bird habitat (B), and flooding causing debris to spread (C). Full article ">Figure 4
Modeled Inundation Area of Honokea loko i′a with the UAV observed at low tide (dotted line) and at high tide (purple), with UAV-modeled (light blue) and LiDAR-modeled (yellow) flooding overlaid. Areas of agreement between observed and modeled flooding are overlapping (light purple). The in situ water sensor is located near the rock wall (blue dot). Full article ">

17 pages, 5035 KiB

Open AccessArticle

CLIP-Driven Few-Shot Species-Recognition Method for Integrating Geographic Information

by Lei Liu, Linzhe Yang, Feng Yang, Feixiang Chen and Fu Xu

Remote Sens. 2024, 16(12), 2238; https://doi.org/10.3390/rs16122238 - 20 Jun 2024

Cited by 1 | Viewed by 1128

Abstract

Automatic recognition of species is important for the conservation and management of biodiversity. However, since closely related species are visually similar, it is difficult to distinguish them by images alone. In addition, traditional species-recognition models are limited by the size of the dataset [...] Read more.

Automatic recognition of species is important for the conservation and management of biodiversity. However, since closely related species are visually similar, it is difficult to distinguish them by images alone. In addition, traditional species-recognition models are limited by the size of the dataset and face the problem of poor generalization ability. Visual-language models such as Contrastive Language-Image Pretraining (CLIP), obtained by training on large-scale datasets, have excellent visual representation learning ability and demonstrated promising few-shot transfer ability in a variety of few-shot species recognition tasks. However, limited by the dataset on which CLIP is trained, the performance of CLIP is poor when used directly for few-shot species recognition. To improve the performance of CLIP for few-shot species recognition, we proposed a few-shot species-recognition method incorporating geolocation information. First, we utilized the powerful feature extraction capability of CLIP to extract image features and text features. Second, a geographic feature extraction module was constructed to provide additional contextual information by converting structured geographic location information into geographic feature representations. Then, a multimodal feature fusion module was constructed to deeply interact geographic features with image features to obtain enhanced image features through residual connection. Finally, the similarity between the enhanced image features and text features was calculated and the species recognition results were obtained. Extensive experiments on the iNaturalist 2021 dataset show that our proposed method can significantly improve the performance of CLIP’s few-shot species recognition. Under ViT-L/14 and 16-shot training species samples, compared to Linear probe CLIP, our method achieved a performance improvement of 6.22% (mammals), 13.77% (reptiles), and 16.82% (amphibians). Our work provides powerful evidence for integrating geolocation information into species-recognition models based on visual-language models. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges II)

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The overall framework of SG-CLIP for few-shot species recognition. It contains three paths for text, image, and geographic information, respectively. The geographic feature is obtained by GFEM. The parameters of GFEM and IGFFM are learnable. Full article ">Figure 2
The structure of GFEM for geographic feature extraction. The dashed box is the structure of the FCResLayer. Full article ">Figure 3
The structure of IGFFM for image and geographic feature fusion, where Fc denotes the fully connected layer, ReLU denotes the ReLU activation function, and LayerNorm denotes layer normalization. DFB denotes the dynamic fusion block. DFB is used recursively, where N is the number of DFB modules. Full article ">Figure 4
Heatmaps of the geolocation distribution. (a) Mammals. (b) Reptiles. (c) Amphibians. Different colors indicate the number of species at different locations. Green indicates relatively little data and red indicates a large number. Full article ">Figure 5
Performance comparison of different methods with different training samples on different datasets. (a) Mammals. (b) Reptiles. (c) Amphibians. Full article ">Figure 6
Comparison of few-shot species recognition accuracy on different datasets under different versions of CLIP. (a) Mammals. (b) Reptiles. (c) Amphibians. Full article ">Figure 7
Visualization of t-SNE representations under different methods. (a) Zero-shot CLIP under ViT-B/32. (b) Zero-shot CLIP under ViT-L/14. (c) SG-CLIP under ViT-B/32. (d) SG-CLIP under ViT-L/14. Full article ">

17 pages, 2403 KiB

Open AccessArticle

Estimating Pavement Condition by Leveraging Crowdsourced Data

by Yangsong Gu, Mohammad Khojastehpour, Xiaoyang Jia and Lee D. Han

Remote Sens. 2024, 16(12), 2237; https://doi.org/10.3390/rs16122237 - 20 Jun 2024

Cited by 1 | Viewed by 1297

Abstract

Monitoring pavement conditions is critical to pavement management and maintenance. Traditionally, pavement distress is mainly identified via accelerometers, videos, and laser scanning. However, the geographical coverage and temporal frequency are constrained by the limited amount of equipment and labor, which sometimes may delay [...] Read more.

Monitoring pavement conditions is critical to pavement management and maintenance. Traditionally, pavement distress is mainly identified via accelerometers, videos, and laser scanning. However, the geographical coverage and temporal frequency are constrained by the limited amount of equipment and labor, which sometimes may delay road maintenance. By contrast, crowdsourced data, in a manner of crowdsensing, can provide real-time and valuable roadway information for extensive coverage. This study exploited crowdsourced Waze pothole and weather reports for pavement condition evaluation. Two surrogate measures are proposed, namely, the Pothole Report Density (PRD) and the Weather Report Density (WRD). They are compared with the Pavement Quality Index (PQI), which is calculated using laser truck data from the Tennessee Department of Transportation (TDOT). A geographically weighted random forest (GWRF) model was developed to capture the complicated relationships between the proposed measures and PQI. The results show that the PRD is highly correlated with the PQI, and the correlation also varies across the routes. It is also found to be the second most important factor (i.e., followed by pavement age) affecting the PQI values. Although Waze weather reports contribute to PQI values, their impact is significantly smaller compared to that of pothole reports. This paper demonstrates that surrogate pavement condition measures aggregated by crowdsourced data could be integrated into the state decision-making process by establishing nuanced relationships between the surrogated performance measures and the state pavement condition indices. The endeavor of this study also has the potential to enhance the granularity of pavement condition evaluation. Full article

(This article belongs to the Special Issue Harnessing the Geospatial Data Revolution for Promoting Sustainable Transport Systems)

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20 pages, 17559 KiB

Open AccessArticle

Assessing Ecological Impacts and Recovery in Coal Mining Areas: A Remote Sensing and Field Data Analysis in Northwest China

by Deyun Song, Zhenqi Hu, Yi Yu, Fan Zhang and Huang Sun

Remote Sens. 2024, 16(12), 2236; https://doi.org/10.3390/rs16122236 - 19 Jun 2024

Cited by 2 | Viewed by 1448

Abstract

In the coal-rich provinces of Shanxi, Shaanxi, and Inner Mongolia, the landscape bears the scars of coal extraction—namely subsidence and deformation—that disrupt both the terrain and the delicate ecological balance. This research delves into the transformative journey these mining regions undergo, from pre-mining [...] Read more.

In the coal-rich provinces of Shanxi, Shaanxi, and Inner Mongolia, the landscape bears the scars of coal extraction—namely subsidence and deformation—that disrupt both the terrain and the delicate ecological balance. This research delves into the transformative journey these mining regions undergo, from pre-mining equilibrium, through the tumultuous phase of extraction, to the eventual restoration of stability post-reclamation. By harnessing a suite of analytical tools, including sophisticated remote sensing, UAV aerial surveys, and the meticulous ground-level sampling of flora and soil, the study meticulously measures the environmental toll of mining activities and charts the path to ecological restoration. The results are promising, indicating that the restoration initiatives are effectively healing the landscapes, with proactive interventions such as seeding, afforestation, and land rehabilitation proving vital in the swift ecological turnaround. Remote sensing technology, in particular, emerges as a robust ally in tracking ecological shifts, supporting sustainable practices and guiding ecological management strategies. This study offers a promising framework for assessing geological environmental shifts, which may guide policymakers in shaping the future of mining rehabilitation in arid and semi-arid regions. Full article

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Location of the trial region. Full article ">Figure 2
Methodological framework of this study. (Soil moisture content (SMC), pH level, total nitrogen (TN), available phosphorus (AP), and available potassium (AK)). Full article ">Figure 3
Indices for various topographic factors before and after mining at the Erlintu coal mine. Full article ">Figure 4
Topographic factors’ distribution before and after mining: (a) slope distribution before and after mining; (b) aspect distribution before and after mining; (c) surface roughness distribution before and after mining; (d) terrain relief distribution before and after mining. Full article ">Figure 5
Extraction of ground fissures in a mining-disturbed area in 2023: (a) UAV data of the mining area in Region R in 2023, with dense fissures. (b) Surface conditions in Region R in May. (c) Extracted distribution of ground fissures in Region R based on May imagery. (d) Surface conditions in Region R in August with the locations of large fissure areas (width > 15 cm) P1 and small fissure areas (width < 15 cm) P2. (e) Surface conditions in P1 area. (f) Surface conditions in P2 area. Full article ">Figure 6
Extraction of ground fissures in mining-disturbed area in 2014: (a) Surface image of the mining area in Region Q in 2014, with dense fissures. (b) Surface conditions in the dense fissure area Q. (c) Extracted distribution of ground fissures in Region Q. Full article ">Figure 7
Long-term NDVI value change in the trial region. Full article ">Figure 8
The evolution of VFC in the Erlintu mining area: (a) evolution in VFC: 2013–2015; (b) evolution in VFC: 2013–2018; (c) evolution in VFC: 2013–2022; (d) evolution in VFC: 2018–2020; (e) evolution in VFC: 2018–2022. Full article ">Figure 9
Vegetation dynamics in areas A1 and A2: (a) proportions of each category; (b) numerical distribution of VFC changes. Full article ">Figure 10
Vegetation dynamics in area B1 and B2: (a) proportions of each category; (b) numerical distribution of VFC changes. Full article ">Figure 11
Transformation of RSEI: (a) 2015 RSEI map; (b) 2018 RSEI map; (c) 2020 RSEI map; (d) 2022 RSEI map. Full article ">Figure 12
Contrasts the change in RSEI values between the mining-affected area and the control area. Full article ">Figure 13
Soil element analysis of different trial regions in Erlintu coal mine. Full article ">

18 pages, 14154 KiB

Open AccessArticle

Three-Dimensional Rockslide Analysis Using Unmanned Aerial Vehicle and LiDAR: The Castrocucco Case Study, Southern Italy

by Antonio Minervino Amodio, Giuseppe Corrado, Ilenia Graziamaria Gallo, Dario Gioia, Marcello Schiattarella, Valentino Vitale and Gaetano Robustelli

Remote Sens. 2024, 16(12), 2235; https://doi.org/10.3390/rs16122235 - 19 Jun 2024

Cited by 1 | Viewed by 1052

Abstract

Rockslides are one of the most dangerous hazards in mountainous and hilly areas. In this study, a rockslide that occurred on 30 November 2022 in Castrocucco, a district located in the Italian municipality of Maratea (Potenza province) in the Basilicata region, was investigated [...] Read more.

Rockslides are one of the most dangerous hazards in mountainous and hilly areas. In this study, a rockslide that occurred on 30 November 2022 in Castrocucco, a district located in the Italian municipality of Maratea (Potenza province) in the Basilicata region, was investigated by using pre- and post-event high-resolution 3D models. The event caused a great social alarm as some infrastructures were affected. The main road to the tourist hub of Maratea was, in fact, destroyed and made inaccessible. Rock debris also affected a beach club and important boat storage for sea excursions to Maratea. This event was investigated by using multiscale and multisensor close-range remote sensing (LiDAR and SfM) to determine rockslide characteristics. The novelty of this work lies in how these data, although not originally acquired for rockslide analysis, have been integrated and utilized in an emergency at an almost inaccessible site. The event was analyzed both through classical geomorphological analysis and through a quantitative comparison of multi-temporal DEMs (DoD) in order to assess (i) all the morphological features involved, (ii) detached volume (approximately 8000 m³), and (iii) the process of redistributing and reworking the landslide deposit in the depositional area. Full article

(This article belongs to the Special Issue Aerial and Drone LiDAR Data for Geomorphological Mapping, Landform Extraction and Landscape Evolution)

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20 pages, 6499 KiB

Open AccessArticle

Tracking Loop Current Eddies in the Gulf of Mexico Using Satellite-Derived Chlorophyll-a

by Corinne B. Trott, Bulusu Subrahmanyam, Luna Hiron and Olmo Zavala-Romero

Remote Sens. 2024, 16(12), 2234; https://doi.org/10.3390/rs16122234 - 19 Jun 2024

Viewed by 1184

Abstract

During the period of 2018–2022, there were six named Loop Current Eddy (LCE) shedding events in the central Gulf of Mexico (GoM). LCEs form when a large anticyclonic eddy (AE) separates from the main Loop Current (LC) and propagates westward. In doing so, [...] Read more.

During the period of 2018–2022, there were six named Loop Current Eddy (LCE) shedding events in the central Gulf of Mexico (GoM). LCEs form when a large anticyclonic eddy (AE) separates from the main Loop Current (LC) and propagates westward. In doing so, each LCE traps and advects warmer, saltier waters with lower Chlorophyll-a (Chl-a) concentrations than the surrounding Gulf waters. This difference in water mass permits the study of the effectiveness of using Chl-a from satellite-derived ocean color to identify LCEs in the GoM. In this work, we apply an eddy-tracking algorithm to Chl-a to detect LCEs, which we have validated against the traditional sea surface height-(SSH) based eddy-tracking approach with three datasets. We apply a closed-contour eddy-tracking algorithm to the SSH of two model products (HYbrid Coordination Ocean Model; HYCOM and Nucleus for European Modelling of the Ocean; NEMO) and absolute dynamic topography (ADT) from altimetry, as well as satellite-derived Chl-a data to identify the six named LCEs from 2018 to 2022. We find that Chl-a best characterizes LCEs in the summertime due to a basin-wide increase in the horizontal gradient of Chl-a, which permits a more clearly defined eddy edge. This study demonstrates that Chl-a can be effectively used to identify and track LC and LCEs in the GoM, serving as a promising source of information for regional data assimilative models. Full article

(This article belongs to the Special Issue Remote Sensing of Variables and Mesoscale Processes Linking the Ocean and Atmosphere)

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24 pages, 8813 KiB

Open AccessArticle

MSSD-Net: Multi-Scale SAR Ship Detection Network

by Xi Wang, Wei Xu, Pingping Huang and Weixian Tan

Remote Sens. 2024, 16(12), 2233; https://doi.org/10.3390/rs16122233 - 19 Jun 2024

Cited by 2 | Viewed by 1323

Abstract

In recent years, the development of neural networks has significantly advanced their application in Synthetic Aperture Radar (SAR) ship target detection for maritime traffic control and ship management. However, traditional neural network architectures are often complex and resource intensive, making them unsuitable for [...] Read more.

In recent years, the development of neural networks has significantly advanced their application in Synthetic Aperture Radar (SAR) ship target detection for maritime traffic control and ship management. However, traditional neural network architectures are often complex and resource intensive, making them unsuitable for deployment on artificial satellites. To address this issue, this paper proposes a lightweight neural network: the Multi-Scale SAR Ship Detection Network (MSSD-Net). Initially, the MobileOne network module is employed to construct the backbone network for feature extraction from SAR images. Subsequently, a Multi-Scale Coordinate Attention (MSCA) module is designed to enhance the network’s capability to process contextual information. This is followed by the integration of features across different scales using an FPN + PAN structure. Lastly, an Anchor-Free approach is utilized for the rapid detection of ship targets. To evaluate the performance of MSSD-Net, we conducted extensive experiments on the Synthetic Aperture Radar Ship Detection Dataset (SSDD) and SAR-Ship-Dataset. Our experimental results demonstrate that MSSD-Net achieves a mean average precision (mAP) of 98.02% on the SSDD while maintaining a compact model size of only 1.635 million parameters. This indicates that MSSD-Net effectively reduces model complexity without compromising its ability to achieve high accuracy in object detection tasks. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 2nd Edition)

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21 pages, 5043 KiB

Open AccessArticle

Using Sentinel-2 Imagery to Measure Spatiotemporal Changes and Recovery across Three Adjacent Grasslands with Different Fire Histories

by Annalise Taylor, Iryna Dronova, Alexii Sigona and Maggi Kelly

Remote Sens. 2024, 16(12), 2232; https://doi.org/10.3390/rs16122232 - 19 Jun 2024

Viewed by 1164

Abstract

As a result of the advocacy of Indigenous communities and increasing evidence of the ecological importance of fire, California has invested in the restoration of intentional burning (the practice of deliberately lighting low-severity fires) in an effort to reduce the occurrence and severity [...] Read more.

As a result of the advocacy of Indigenous communities and increasing evidence of the ecological importance of fire, California has invested in the restoration of intentional burning (the practice of deliberately lighting low-severity fires) in an effort to reduce the occurrence and severity of wildfires. Recognizing the growing need to monitor the impacts of these smaller, low-severity fires, we leveraged Sentinel-2 imagery to reveal important inter- and intra-annual variation in grasslands before and after fires. Specifically, we explored three methodological approaches: (1) the complete time series of the normalized burn ratio (NBR), (2) annual summary metrics (mean, fifth percentile, and amplitude of NBR), and (3) maps depicting spatial patterns in these annual NBR metrics before and after fire. We also used a classification of pre-fire vegetation to stratify these analyses by three dominant vegetation cover types (grasses, shrubs, and trees). We applied these methods to a unique study area in which three adjacent grasslands had diverging fire histories and showed how grassland recovery from a low-severity intentional burn and a high-severity wildfire differed both from each other and from a reference site with no recent fire. On the low-severity intentional burn site, our results showed that the annual NBR metrics recovered to pre-fire values within one year, and that regular intentional burning on the site was promoting greater annual growth of both grass and shrub species, even in the third growing season following a burn. In the case of the high-severity wildfire, our metrics indicated that this grassland had not returned to its pre-fire phenological signals in at least three years after the fire, indicating that it may be undergoing a longer recovery or an ecological shift. These proposed methods address a growing need to study the effects of small, intentional burns in low-biomass ecosystems such as grasslands, which are an essential part of mitigating wildfires. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Enhancing Wildfire Management and Ecosystem Multifunctionality)

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23 pages, 6378 KiB

Open AccessArticle

Navigation Resource Allocation Algorithm for LEO Constellations Based on Dynamic Programming

by Sixin Wang, Xiaomei Tang, Jingyuan Li, Xinming Huang, Jiyang Liu and Jian Liu

Remote Sens. 2024, 16(12), 2231; https://doi.org/10.3390/rs16122231 - 19 Jun 2024

Viewed by 1117

Abstract

Navigation resource allocation for low-earth-orbit (LEO) constellations refers to the optimal allocation of navigational assets when the number and allocation of satellites in the LEO constellation have been determined. LEO constellations can not only transmit navigation enhancement signals but also enable space-based monitoring [...] Read more.

Navigation resource allocation for low-earth-orbit (LEO) constellations refers to the optimal allocation of navigational assets when the number and allocation of satellites in the LEO constellation have been determined. LEO constellations can not only transmit navigation enhancement signals but also enable space-based monitoring (SBM) for real-time assessment of GNSS signal quality. However, proximity in the frequencies of LEO navigation signals and SBM can lead to significant interference, necessitating isolated transmission and reception. This separation requires that SBM and navigation signal transmission be carried out by different satellites within the constellation, thus demanding a strategic allocation of satellite resources. Given the vast number of satellites and their rapid movement, the visibility among LEO, medium-earth-orbit (MEO), and geostationary orbit (GEO) satellites is highly dynamic, presenting substantial challenges in resource allocation due to the computational intensity involved. Therefore, this paper proposes an optimal allocation algorithm for LEO constellation navigation resources based on dynamic programming. In this algorithm, a network model for the allocation of navigation resources in LEO constellations is initially established. Under the constraints of visibility time windows and onboard transmission and reception isolation, the objective is set to minimize the number of LEO satellites used while achieving effective navigation signal transmission and SBM. The constraints of resource allocation and the mathematical expression of the optimization objective are derived. A dynamic programming approach is then employed to determine the optimal resource allocation scheme. Analytical results demonstrate that compared to Greedy and Divide-and-Conquer algorithms, this algorithm achieves the highest resource utilization rate and the lowest computational complexity, making it highly valuable for future resource allocation in LEO constellations. Full article

(This article belongs to the Special Issue Space-Geodetic Techniques (Third Edition))

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19 pages, 4057 KiB

Open AccessArticle

Global Navigation Satellite System/Inertial Measurement Unit/Camera/HD Map Integrated Localization for Autonomous Vehicles in Challenging Urban Tunnel Scenarios

by Lu Tao, Pan Zhang, Kefu Gao and Jingnan Liu

Remote Sens. 2024, 16(12), 2230; https://doi.org/10.3390/rs16122230 - 19 Jun 2024

Cited by 1 | Viewed by 1712

Abstract

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs’ localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement [...] Read more.

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs’ localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Solving Challenges in Autonomous Driving and Safety Analysis)

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30 pages, 12064 KiB

Open AccessArticle

Inversion of Forest Aboveground Biomass in Regions with Complex Terrain Based on PolSAR Data and a Machine Learning Model: Radiometric Terrain Correction Assessment

by Yonghui Nie, Rula Sa, Sergey Chumachenko, Yifan Hu, Youzhu Wang and Wenyi Fan

Remote Sens. 2024, 16(12), 2229; https://doi.org/10.3390/rs16122229 - 19 Jun 2024

Cited by 2 | Viewed by 1066

Abstract

The accurate estimation of forest aboveground biomass (AGB) in areas with complex terrain is very important for quantifying the carbon sequestration capacity of forest ecosystems and studying the regional or global carbon cycle. In our previous research, we proposed the radiometric terrain correction [...] Read more.

The accurate estimation of forest aboveground biomass (AGB) in areas with complex terrain is very important for quantifying the carbon sequestration capacity of forest ecosystems and studying the regional or global carbon cycle. In our previous research, we proposed the radiometric terrain correction (RTC) process for introducing normalized correction factors, which has strong effectiveness and robustness in terms of the backscattering coefficient of polarimetric synthetic aperture radar (PolSAR) data and the monadic model. However, the impact of RTC on the correctness of feature extraction and the performance of regression models requires further exploration in the retrieval of forest AGB based on a machine learning multiple regression model. In this study, based on PolSAR data provided by ALOS-2, 117 feature variables were accurately extracted using the RTC process, and then Boruta and recursive feature elimination with cross-validation (RFECV) algorithms were used to perform multi-step feature selection. Finally, 10 machine learning regression models and the Optuna algorithm were used to evaluate the effectiveness and robustness of RTC in improving the quality of the PolSAR feature set and the performance of the regression models. The results revealed that, compared with the situation without RTC treatment, RTC can effectively and robustly improve the accuracy of PolSAR features (the Pearson correlation R between the PolSAR features and measured forest AGB increased by 0.26 on average) and the performance of regression models (the coefficient of determination R² increased by 0.14 on average, and the rRMSE decreased by 4.20% on average), but there is a certain degree of overcorrection in the RTC process. In addition, in situations where the data exhibit linear relationships, linear models remain a powerful and practical choice due to their efficient and stable characteristics. For example, the optimal regression model in this study is the Bayesian Ridge linear regression model (R² = 0.82, rRMSE = 18.06%). Full article

(This article belongs to the Special Issue SAR for Forest Mapping III)

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Full article ">Figure 1
Overview of study sites: (a) the location of Saihanba Forest Farm in relation to the provinces and counties in China; (b) the spatial location of ALOS-2 data relative to Weichang County; (c) the Pauli RGB image (R: |HH-VV|, G: |HV|, B: |HH + VV|) based on PolSAR data and the location of the measured samples; the basemap is the optical image of Tianditu. Full article ">Figure 2
A flowchart of the proposed forest AGB mapping scheme. Full article ">Figure 3
Absolute value of Pearson correlation coefficient (R) between forest AGB and the PolSAR features based on the data (25 July 2020) with radiometric terrain correction (RTC, olive) and non-RTC data (NRT, red). Sorted based on R_RTC (i.e., absolute value of R value between forest AGB and SAR features extracted based on RTC data). (a) The first set of the extracted original PolSAR features; (b) the second set of the extracted original PolSAR features (39 in total); (c) derived features based on PolSAR original features (39 in total). Full article ">Figure 4
Taking PolSAR data from 25 July 2020 as an example, we created scatter density plots between the decibel values of the three components (Volume scattering component (Vol), Surface scattering component (Odd), and Double-bounce scattering component (Dbl)) of the Freeman three-decomposition in different topographic correction stages (non-radiometric terrain correction (NRTC), polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC)) and the local incidence angle θloc. (a) NRTC_Vol; (b) POAC_Vol; (c) ESAC_Vol; (d) AVEC_Vol; (e) NRTC_Odd; (f) POAC_Odd; (g) ESAC_Odd; (h) AVEC_Odd; (i) NRTC_Dbl; (j) POAC_Dbl; (k) ESAC_Dbl; (l) AVEC_Dbl. Full article ">Figure 5
Taking PolSAR data from 25 July 2020 as an example, we created a scatter density plot for each component of Freeman three-decomposition (FRE3) at different radiometric terrain correction (RTC) stages (Y-axis) relative to the previous stage (X-axis), and in AVEC stages (that is, after all processing of the RTC was completed) with respect to non-RTC (NRTC). The three components of FRE3 are the Volume scattering component (Vol), Surface scattering component (Odd), and Double-bounce scattering component (Dbl). The three stages of RTC are polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC). The red line is a 1:1 line. (a) NRTC vs. POAC of Vol; (b) POAC vs. ESAC of Vol; (c) ESAC vs. AVEC of Vol; (d) NRTC vs. AVEC of Vol; (e) NRTC vs. POAC of Odd; (f) POAC vs. ESAC of Odd; (g) ESAC vs. AVEC of Odd; (h) NRTC vs. AVEC of Odd; (i) NRTC vs. POAC of Dbl; (j) POAC vs. ESAC of Dbl; (k) ESAC vs. AVEC of Dbl; (l) NRTC vs. AVEC of Dbl. Full article ">Figure 5 Cont.
Taking PolSAR data from 25 July 2020 as an example, we created a scatter density plot for each component of Freeman three-decomposition (FRE3) at different radiometric terrain correction (RTC) stages (Y-axis) relative to the previous stage (X-axis), and in AVEC stages (that is, after all processing of the RTC was completed) with respect to non-RTC (NRTC). The three components of FRE3 are the Volume scattering component (Vol), Surface scattering component (Odd), and Double-bounce scattering component (Dbl). The three stages of RTC are polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC). The red line is a 1:1 line. (a) NRTC vs. POAC of Vol; (b) POAC vs. ESAC of Vol; (c) ESAC vs. AVEC of Vol; (d) NRTC vs. AVEC of Vol; (e) NRTC vs. POAC of Odd; (f) POAC vs. ESAC of Odd; (g) ESAC vs. AVEC of Odd; (h) NRTC vs. AVEC of Odd; (i) NRTC vs. POAC of Dbl; (j) POAC vs. ESAC of Dbl; (k) ESAC vs. AVEC of Dbl; (l) NRTC vs. AVEC of Dbl. Full article ">Figure 6
Analysis of the effectiveness of RTC and the optimal regression model of this study, taking the SAR data from 25 July 2020 as an example. (a) The training results of the NRTC and RTC data, where the black dots are the results of the corresponding single training; (b) scatter plot of the measured forest AGB and the AGB predicted by the optimal regression model (BysRidge); (c) spatial distribution map of forest AGB in the study area based on optimal model prediction. Full article ">Figure A1
The scatter density plot of each component of Yamaguchi three-component (YAM3) at different radiometric terrain correction (RTC) stages (Y-axis) relative to the previous stage (X-axis), and in AVEC stages (that is, after all processing of the RTC) with respect to non-RTC (NRTC). The three components of YAM3 are the Volume scattering component (Vol), Surface scattering component (Odd), and Double-bounce scattering component (Dbl). The three stages of RTC are polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC). The red line is a 1:1 line. (a) NRTC vs. POAC of Vol; (b) POAC vs. ESAC of Vol; (c) ESAC vs. AVEC of Vol; (d) NRTC vs. AVEC of Vol; (e) NRTC vs. POAC of Odd; (f) POAC vs. ESAC of Odd; (g) ESAC vs. AVEC of Odd; (h) NRTC vs. AVEC of Odd; (i) NRTC vs. POAC of Dbl; (j) POAC vs. ESAC of Dbl; (k) ESAC vs. AVEC of Dbl; (l) NRTC vs. AVEC of Dbl. Full article ">Figure A1 Cont.
The scatter density plot of each component of Yamaguchi three-component (YAM3) at different radiometric terrain correction (RTC) stages (Y-axis) relative to the previous stage (X-axis), and in AVEC stages (that is, after all processing of the RTC) with respect to non-RTC (NRTC). The three components of YAM3 are the Volume scattering component (Vol), Surface scattering component (Odd), and Double-bounce scattering component (Dbl). The three stages of RTC are polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC). The red line is a 1:1 line. (a) NRTC vs. POAC of Vol; (b) POAC vs. ESAC of Vol; (c) ESAC vs. AVEC of Vol; (d) NRTC vs. AVEC of Vol; (e) NRTC vs. POAC of Odd; (f) POAC vs. ESAC of Odd; (g) ESAC vs. AVEC of Odd; (h) NRTC vs. AVEC of Odd; (i) NRTC vs. POAC of Dbl; (j) POAC vs. ESAC of Dbl; (k) ESAC vs. AVEC of Dbl; (l) NRTC vs. AVEC of Dbl. Full article ">Figure A2
The result of feature selection: (a) the 32 features selected in preliminary feature selection (Boruta algorithm) based on radiative terrain correction (RTC) data, including the importance score given by the RF of the selected features, and absolute values of Pearson correlation coefficients (R) between the selected features and measured forest AGB; (b) the 21 features selected in preliminary feature selection (Boruta algorithm) based on non-RTC (NRTC) data, including the importance score given by the RF of the selected features, and absolute values of Pearson correlation coefficients (R) between the selected features and measured forest AGB; (c) the number of features selected in the second step feature selection (RFECV algorithm) based on RTC and NRTC data; (d) the features selected in different multivariate linear models and the variance inflation factor (VIF) value corresponding to each feature; (e) the features selected in different non-parametric models. Full article ">Figure A2 Cont.
The result of feature selection: (a) the 32 features selected in preliminary feature selection (Boruta algorithm) based on radiative terrain correction (RTC) data, including the importance score given by the RF of the selected features, and absolute values of Pearson correlation coefficients (R) between the selected features and measured forest AGB; (b) the 21 features selected in preliminary feature selection (Boruta algorithm) based on non-RTC (NRTC) data, including the importance score given by the RF of the selected features, and absolute values of Pearson correlation coefficients (R) between the selected features and measured forest AGB; (c) the number of features selected in the second step feature selection (RFECV algorithm) based on RTC and NRTC data; (d) the features selected in different multivariate linear models and the variance inflation factor (VIF) value corresponding to each feature; (e) the features selected in different non-parametric models. Full article ">Figure A3
Scatter plot of measured forest AGB and predicted forest AGB. The prediction model is an optimal regression model based on the PolSAR data processed by radiometric terrain correction (RTC) from 25 July 2020. (a) The independent variable of the prediction model was derived from the PolSAR data (after RTC processing) from 11 July 2020. (b) The independent variable of the prediction model was derived from the PolSAR data (after RTC processing) from 8 August 2020. Full article ">

18 pages, 1739 KiB

Open AccessArticle

Polar Stratospheric Cloud Observations at Concordia Station by Remotely Controlled Lidar Observatory

by Luca Di Liberto, Francesco Colao, Federico Serva, Alessandro Bracci, Francesco Cairo and Marcel Snels

Remote Sens. 2024, 16(12), 2228; https://doi.org/10.3390/rs16122228 - 19 Jun 2024

Viewed by 876

Abstract

Polar stratospheric clouds (PSCs) form in polar regions, typically between 15 and 25 km above mean sea level, when the local temperature is sufficiently low. PSCs play an important role in the ozone chemistry and the dehydration and denitrification of the stratosphere. Lidars [...] Read more.

Polar stratospheric clouds (PSCs) form in polar regions, typically between 15 and 25 km above mean sea level, when the local temperature is sufficiently low. PSCs play an important role in the ozone chemistry and the dehydration and denitrification of the stratosphere. Lidars with a depolarization channel may be used to detect and classify different classes of PSCs. The main PSC classes are water ice, nitric acid trihydrate (NAT), and supercooled ternary solutions (STSs), the latter being liquid droplets consisting of water, nitric acid, and sulfuric acid. PSCs have been observed at the lidar observatory at Concordia Station from 2014 onward. The harsh environmental conditions at Concordia during winter render successful lidar operation difficult. To facilitate the operation of the observatory, several measures have been put in place to achieve an almost complete remote control of the system. PSC occurrence is strongly correlated with local temperatures and is affected by dynamics, as the PSC coverage during the observation season shows. PSC observations in 2021 are shown as an example of the capability and functionality of the lidar observatory. A comparison of the observations with the satellite-borne CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar has been made to demonstrate the quality of the data and their representativeness for the Antarctic Plateau. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements)

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19 pages, 3232 KiB

Open AccessArticle

Harmonic Source Depth Estimation by a Single Hydrophone under Unknown Seabed Geoacoustic Property

by Xiaolei Li, Yangjin Xu, Wei Gao, Haozhong Wang and Liang Wang

Remote Sens. 2024, 16(12), 2227; https://doi.org/10.3390/rs16122227 - 19 Jun 2024

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Abstract

The passive estimation of harmonic sound source depth is of great significance for underwater target localization and identification. Passive source depth estimation using a single hydrophone with an unknown seabed geoacoustic property is a crucial challenge. To address this issue, a harmonic sound [...] Read more.

The passive estimation of harmonic sound source depth is of great significance for underwater target localization and identification. Passive source depth estimation using a single hydrophone with an unknown seabed geoacoustic property is a crucial challenge. To address this issue, a harmonic sound source depth estimation algorithm, seabed independent depth estimation (SIDE) algorithm, is proposed. This algorithm combines the estimated mode depth functions, modal amplitudes, and the sign of each modal to estimate the sound source depth. The performance of the SIDE algorithm is analyzed by simulations. Results show that the SIDE is insensitive to the initial range of the sound source, the source depth, the hydrophone depth, the source velocity, and the type of the seabed. Finally, the effectiveness of the SIDE algorithm is verified by the SWellEX-96 data. Full article

(This article belongs to the Topic Advances in Underwater Acoustics and Aeroacoustics)

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16 pages, 15964 KiB

Open AccessArticle

Quantifying the Impact of Aerosols on Geostationary Satellite Infrared Radiance Simulations: A Study with Himawari-8 AHI

by Haofei Sun, Deying Wang, Wei Han and Yunfan Yang

Remote Sens. 2024, 16(12), 2226; https://doi.org/10.3390/rs16122226 - 19 Jun 2024

Cited by 2 | Viewed by 1083

Abstract

Aerosols exert a significant influence on the brightness temperature observed in the thermal infrared (IR) channels, yet the specific contributions of various aerosol types remain underexplored. This study integrated the Copernicus Atmosphere Monitoring Service (CAMS) atmospheric composition reanalysis data into the Radiative Transfer [...] Read more.

Aerosols exert a significant influence on the brightness temperature observed in the thermal infrared (IR) channels, yet the specific contributions of various aerosol types remain underexplored. This study integrated the Copernicus Atmosphere Monitoring Service (CAMS) atmospheric composition reanalysis data into the Radiative Transfer for TOVS (RTTOV) model to quantify the aerosol effects on brightness temperature (BT) simulations for the Advanced Himawari Imager (AHI) aboard the Himawari-8 geostationary satellite. Two distinct experiments were conducted: the aerosol-aware experiment (AER), which accounted for aerosol radiative effects, and the control experiment (CTL), in which aerosol radiative effects were omitted. The CTL experiment results reveal uniform negative bias (observation minus background (O-B)) across all six IR channels of the AHI, with a maximum deviation of approximately −1 K. Conversely, the AER experiment showed a pronounced reduction in innovation, which was especially notable in the 10.4 μm channel, where the bias decreased by 0.7 K. The study evaluated the radiative effects of eleven aerosol species, all of which demonstrated cooling effects in the AHI’s six IR channels, with dust aerosols contributing the most significantly (approximately 86%). In scenarios dominated by dust, incorporating the radiative effect of dust aerosols could correct the brightness temperature bias by up to 2 K, underscoring the substantial enhancement in the BT simulation for the 10.4 μm channel during dust events. Jacobians were calculated to further examine the RTTOV simulations’ sensitivity to aerosol presence. A clear temporal and spatial correlation between the dust concentration and BT simulation bias corroborated the critical role of the infrared channel data assimilation on geostationary satellites in capturing small-scale, rapidly developing pollution processes. Full article

(This article belongs to the Special Issue Remote Sensing for High Impact Weather and Extremes)

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22 pages, 49542 KiB

Open AccessArticle

A Robust Target Detection Algorithm Based on the Fusion of Frequency-Modulated Continuous Wave Radar and a Monocular Camera

by Yanqiu Yang, Xianpeng Wang, Xiaoqin Wu, Xiang Lan, Ting Su and Yuehao Guo

Remote Sens. 2024, 16(12), 2225; https://doi.org/10.3390/rs16122225 - 19 Jun 2024

Cited by 4 | Viewed by 1116

Abstract

Decision-level information fusion methods using radar and vision usually suffer from low target matching success rates and imprecise multi-target detection accuracy. Therefore, a robust target detection algorithm based on the fusion of frequency-modulated continuous wave (FMCW) radar and a monocular camera is proposed [...] Read more.

Decision-level information fusion methods using radar and vision usually suffer from low target matching success rates and imprecise multi-target detection accuracy. Therefore, a robust target detection algorithm based on the fusion of frequency-modulated continuous wave (FMCW) radar and a monocular camera is proposed to address these issues in this paper. Firstly, a lane detection algorithm is used to process the image to obtain lane information. Then, two-dimensional fast Fourier transform (2D-FFT), constant false alarm rate (CFAR), and density-based spatial clustering of applications with noise (DBSCAN) are used to process the radar data. Furthermore, the YOLOv5 algorithm is used to process the image. In addition, the lane lines are utilized to filter out the interference targets from outside lanes. Finally, multi-sensor information fusion is performed for targets in the same lane. Experiments show that the balanced score of the proposed algorithm can reach 0.98, which indicates that it has low false and missed detections. Additionally, the balanced score is almost unchanged in different environments, proving that the algorithm is robust. Full article

(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)

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39 pages, 5735 KiB

Open AccessReview

Potential of Earth Observation to Assess the Impact of Climate Change and Extreme Weather Events in Temperate Forests—A Review

by Marco Wegler and Claudia Kuenzer

Remote Sens. 2024, 16(12), 2224; https://doi.org/10.3390/rs16122224 - 19 Jun 2024

Cited by 3 | Viewed by 2133

Abstract

Temperate forests are particularly exposed to climate change and the associated increase in weather extremes. Droughts, storms, late frosts, floods, heavy snowfalls, or changing climatic conditions such as rising temperatures or more erratic precipitation are having an increasing impact on forests. There is [...] Read more.

Temperate forests are particularly exposed to climate change and the associated increase in weather extremes. Droughts, storms, late frosts, floods, heavy snowfalls, or changing climatic conditions such as rising temperatures or more erratic precipitation are having an increasing impact on forests. There is an urgent need to better assess the impacts of climate change and extreme weather events (EWEs) on temperate forests. Remote sensing can be used to map forests at multiple spatial, temporal, and spectral resolutions at low cost. Different approaches to forest change assessment offer promising methods for a broad analysis of the impacts of climate change and EWEs. In this review, we examine the potential of Earth observation for assessing the impacts of climate change and EWEs in temperate forests by reviewing 126 scientific papers published between 1 January 2014 and 31 January 2024. This study provides a comprehensive overview of the sensors utilized, the spatial and temporal resolution of the studies, their spatial distribution, and their thematic focus on the various abiotic drivers and the resulting forest responses. The analysis indicates that multispectral, non-high-resolution timeseries were employed most frequently. A predominant proportion of the studies examine the impact of droughts. In all instances of EWEs, dieback is the most prevailing response, whereas in studies on changing trends, phenology shifts account for the largest share of forest response categories. The detailed analysis of in-depth forest differentiation implies that area-wide studies have so far barely distinguished the effects of different abiotic drivers at the species level. Full article

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Remote Sens., Volume 16, Issue 12 (June-2 2024) – 208 articles

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