Remote Sensing

21 pages, 7370 KiB

Open AccessArticle

Submarine Landslide Identification Based on Improved DeepLabv3 with Spatial and Channel Attention

by Jingwen Huang, Weijing Song, Tao Liu, Xiaoyu Cui, Jining Yan and Xiaoyu Wang

Remote Sens. 2024, 16(22), 4205; https://doi.org/10.3390/rs16224205 (registering DOI) - 12 Nov 2024

As one of the most destructive, hazardous, and frequent marine geohazards, correctly recognizing submarine landslides holds substantial importance for regional risk assessment, disaster prevention, and marine resource development. Many conventional approaches to prediction and mapping necessitate the involvement of expert insights, oversight, and [...] Read more.

As one of the most destructive, hazardous, and frequent marine geohazards, correctly recognizing submarine landslides holds substantial importance for regional risk assessment, disaster prevention, and marine resource development. Many conventional approaches to prediction and mapping necessitate the involvement of expert insights, oversight, and extensive field investigations, which can result in significant time and effort invested in the prediction process. This paper focuses on employing a deep neural network semantic segmentation technique to detect submarine landslides to replace previous methods, such as numerical analysis and physical modeling, to predict and identify the landslide areas quickly. The peripheral zone of the western Iberian Sea is selected as the study area. Since the neural network image recognition task usually requires RGB images as input data, factors such as slope, hillshade, and elevation extracted from digital elevation model (DEM) data are used to synthesize RGB images through band synthesis methods, and the number and diversity of data are increased utilizing data enhancement. Based on the classical semantic segmentation model DeepLabV3, this paper proposes an improved deep learning method, which strengthens the ability of model feature extraction for complex situations by adding an attention mechanism module, improving the spatial pyramid pooling module, and improving the landslide intersection over union metric from 0.4257 to 0.5219 and the F1-score metric from 0.609 to 0.6631 to achieve effective identification of submarine landslides. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data for Oceanography)

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18 pages, 10337 KiB

Open AccessArticle

FE-SKViT: A Feature-Enhanced ViT Model with Skip Attention for Automatic Modulation Recognition

by Guangyao Zheng, Bo Zang, Penghui Yang, Wenbo Zhang and Bin Li

Remote Sens. 2024, 16(22), 4204; https://doi.org/10.3390/rs16224204 - 11 Nov 2024

Abstract

Automatic modulation recognition (AMR) is widely employed in communication systems. However, under conditions of low signal-to-noise ratio (SNR), recent studies reveal limitations in achieving high AMR accuracy. In this work, we introduce a novel network architecture that leverages a transformer-inspired approach tailored for [...] Read more.

Automatic modulation recognition (AMR) is widely employed in communication systems. However, under conditions of low signal-to-noise ratio (SNR), recent studies reveal limitations in achieving high AMR accuracy. In this work, we introduce a novel network architecture that leverages a transformer-inspired approach tailored for AMR, called Feature-Enhanced Transformer with skip-attention (FE-SKViT). This innovative design adeptly harnesses the advantages of translation variant convolution and the Transformer framework, handling intra-signal variance and small cross-signal variance to achieve enhanced recognition accuracy. Experimental results on RadioML2016.10a, RadioML2016.10b, and RML22 datasets demonstrate that the Feature-Enhanced Transformer with skip-attention (FE-SKViT) excels over other methods, particularly under low SNR conditions ranging from −4 to 6 dB. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Electromagnetic Spectrum Sensing: Data Acquisition and Signal Processing)

25 pages, 3033 KiB

Open AccessArticle

Quantifying the Geomorphological Susceptibility of the Piping Erosion in Loess Using LiDAR-Derived DEM and Machine Learning Methods

by Sisi Li, Sheng Hu, Lin Wang, Fanyu Zhang, Ninglian Wang, Songbai Wu, Xingang Wang and Zongda Jiang

Remote Sens. 2024, 16(22), 4203; https://doi.org/10.3390/rs16224203 - 11 Nov 2024

Abstract

Soil piping erosion is an underground soil erosion process that is significantly underestimated or overlooked. It can lead to intense soil erosion and trigger surface processes such as landslides, collapses, and channel erosion. Conducting susceptibility mapping is a vital way to identify the [...] Read more.

Soil piping erosion is an underground soil erosion process that is significantly underestimated or overlooked. It can lead to intense soil erosion and trigger surface processes such as landslides, collapses, and channel erosion. Conducting susceptibility mapping is a vital way to identify the potential for soil piping erosion, which is of enormous significance for soil and water conservation as well as geological disaster prevention. This study utilized airborne radar drones to survey and map 1194 sinkholes in Sunjiacha basin, Huining County, on the Loess Plateau in Northwest China. We identified seventeen key hydrogeomorphological factors that influence sinkhole susceptibility and used six machine learning models—support vector machine (SVM), logistic regression (LR), Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), random forest (RF), and gradient boosting decision tree (GBDT)—for the susceptibility assessment and mapping of loess sinkholes. We then evaluated and validated the prediction results of various models using the area under curve (AUC) of the Receiver Operating Characteristic Curve (ROC). The results showed that all six of these machine learning algorithms had an AUC of more than 0.85. The GBDT model had the best predictive accuracy (AUC = 0.94) and model migration performance (AUC = 0.93), and it could find sinkholes with high and very high susceptibility levels in loess areas. This suggests that the GBDT model is well suited for the fine-scale susceptibility mapping of sinkholes in loess regions. Full article

(This article belongs to the Topic AI for Natural Disasters Detection, Prediction and Modeling)

32 pages, 2219 KiB

Open AccessArticle

SSFAN: A Compact and Efficient Spectral-Spatial Feature Extraction and Attention-Based Neural Network for Hyperspectral Image Classification

by Chunyang Wang, Chao Zhan, Bibo Lu, Wei Yang, Yingjie Zhang, Gaige Wang and Zongze Zhao

Remote Sens. 2024, 16(22), 4202; https://doi.org/10.3390/rs16224202 - 11 Nov 2024

Abstract

Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have [...] Read more.

Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have significantly improved performance due to their strong feature extraction capabilities. However, these improvements often come with increased model complexity, leading to higher computational costs. To address this, we propose a compact and efficient spectral-spatial feature extraction and attention-based neural network (SSFAN) for HSI classification. The SSFAN model consists of three core modules: the Parallel Spectral-Spatial Feature Extraction Block (PSSB), the Scan Block, and the Squeeze-and-Excitation MLP Block (SEMB). After preprocessing the HSI data, it is fed into the PSSB module, which contains two parallel streams, each comprising a 3D convolutional layer and a 2D convolutional layer. The 3D convolutional layer extracts spectral and spatial features from the input hyperspectral data, while the 2D convolutional layer further enhances the spatial feature representation. Next, the Scan Block module employs a layered scanning strategy to extract spatial information at different scales from the central pixel outward, enabling the model to capture both local and global spatial relationships. The SEMB module combines the Spectral-Spatial Recurrent Block (SSRB) and the MLP Block. The SSRB, with its adaptive weight assignment mechanism in the SToken Module, flexibly handles time steps and feature dimensions, performing deep spectral and spatial feature extraction through multiple state updates. Finally, the MLP Block processes the input features through a series of linear transformations, GELU activation functions, and Dropout layers, capturing complex patterns and relationships within the data, and concludes with an argmax layer for classification. Experimental results show that the proposed SSFAN model delivers superior classification performance, outperforming the second-best method by

1.72 %

,

5.19 %

, and

1.94 %

in OA, AA, and Kappa coefficient, respectively, on the Indian Pines dataset. Additionally, it requires less training and testing time compared to other state-of-the-art deep learning methods. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Algorithm for the Analysis of Remote Sensing Images (Third Edition))

26 pages, 33386 KiB

Open AccessArticle

Feature Intensification using Perception-Guided Regional Classification for Remote Sensing Image Super-Resolution

by Yinghua Li, Jingyi Xie, Kaichen Chi, Ying Zhang and Yunyun Dong

Remote Sens. 2024, 16(22), 4201; https://doi.org/10.3390/rs16224201 - 11 Nov 2024

Abstract

In recent years, super-resolution technology has gained widespread attention in the field of remote sensing. Despite advancements, current methods often employ uniform reconstruction techniques across entire remote sensing images, neglecting the inherent variability in spatial frequency distributions, particularly the distinction between high-frequency texture [...] Read more.

In recent years, super-resolution technology has gained widespread attention in the field of remote sensing. Despite advancements, current methods often employ uniform reconstruction techniques across entire remote sensing images, neglecting the inherent variability in spatial frequency distributions, particularly the distinction between high-frequency texture regions and smoother areas, leading to computational inefficiency, which introduces redundant computations and fails to optimize the reconstruction process for regions of higher complexity. To address these issues, we propose the Perception-guided Classification Feature Intensification (PCFI) network. PCFI integrates two key components: a compressed sensing classifier that optimizes speed and performance, and a deep texture interaction fusion module that enhances content interaction and detail extraction. This network mitigates the tendency of Transformers to favor global information over local details, achieving improved image information integration through residual connections across windows. Furthermore, a classifier is employed to segment sub-image blocks prior to super-resolution, enabling efficient large-scale processing. The experimental results on the AID dataset indicate that PCFI achieves state-of-the-art performance, with a PSNR of 30.87 dB and an SSIM of 0.8131, while also delivering a 4.33% improvement in processing speed compared to the second-best method. Full article

21 pages, 62733 KiB

Open AccessArticle

The Seismic Surface Rupture Zone in the Western Segment of the Northern Margin Fault of the Hami Basin and Its Causal Interpretation, Eastern Tianshan

by Hao Sun, Daoyang Yuan, Ruihuan Su, Shuwu Li, Youlin Wang, Yameng Wen and Yanwen Chen

Remote Sens. 2024, 16(22), 4200; https://doi.org/10.3390/rs16224200 (registering DOI) - 11 Nov 2024

Abstract

The Eastern Tianshan region, influenced by the far-field effect of northward compression and expansion of the Qinghai-Xizang block, features highly developed Late Quaternary active faults that exhibit significant neotectonic activity. Historically, the Barkol-Yiwu Basin, located to the north of the Eastern Tianshan, experienced [...] Read more.

The Eastern Tianshan region, influenced by the far-field effect of northward compression and expansion of the Qinghai-Xizang block, features highly developed Late Quaternary active faults that exhibit significant neotectonic activity. Historically, the Barkol-Yiwu Basin, located to the north of the Eastern Tianshan, experienced two major earthquakes in 1842 and 1914, each with a magnitude of M7¹/₂. In contrast, the Hami Basin on the southern margin of the Eastern Tianshan has no historical records of any major earthquakes, and its seismic potential, mechanisms, and future earthquake hazards remain unclear. Based on satellite image interpretation and field surveys, this study identified a relatively recent and well-preserved seismic surface rupture zone with good continuity in the Liushugou area of the western segment of the Northern Margin Fault of the Hami Basin (HMNF), which is the seismogenic structure responsible for the rupture. The surface rupture zone originates at Kekejin in the east, extends intermittently westward through Daipuseke Bulake and Liushugou, and terminates at Wuzun Bulake, with a total length of approximately 21 km. The rupture zone traverses the youngest geomorphic surface units, such as river beds or floodplains and first-order terraces (platforms), and is characterized by a series of single or multiple reverse fault scarps. The morphology of fault scarps is clear, presenting a light soil color with heights ranging from 0.15 m to 2.13 m and an average displacement of 0.56 m, suggesting that this surface rupture zone likely represents the most recent seismic event. Comparison with historical earthquake records in the Eastern Tianshan region suggests that the rupture zone may have been formed simultaneously with the Xiongkuer rupture zone by the 1842 M7¹/₂ earthquake along the boundary faults on both sides of the Barkol Mountains, exhibiting a flower-like structural pattern. Alternatively, it might represent a separate, unrecorded seismic event occurring shortly after the 1842 earthquake. The estimated magnitude of the associated earthquake is about 6.6~6.9. Given that surface-rupturing earthquakes have already occurred in the western segment, the study indicates that the Erdaogou–Nanshankou section of the HMNF has surpassed the average recurrence interval for major earthquakes, indicating a potential future earthquake hazard. Full article

(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide (Third Edition))

46 pages, 19002 KiB

Open AccessArticle

³Cat-8 Mission: A 6-Unit CubeSat for Ionospheric Multisensing and Technology Demonstration Test-Bed

by Luis Contreras-Benito, Ksenia Osipova, Jeimmy Nataly Buitrago-Leiva, Guillem Gracia-Sola, Francesco Coppa, Pau Climent-Salazar, Paula Sopena-Coello, Diego Garcín, Juan Ramos-Castro and Adriano Camps

Remote Sens. 2024, 16(22), 4199; https://doi.org/10.3390/rs16224199 - 11 Nov 2024

Abstract

This paper presents the mission analysis of ³Cat-8, a 6-Unit CubeSat mission being developed by the UPC NanoSat Lab for ionospheric research. The primary objective of the mission is to monitor the ionospheric scintillation of the aurora, and to perform several technological [...] Read more.

This paper presents the mission analysis of ³Cat-8, a 6-Unit CubeSat mission being developed by the UPC NanoSat Lab for ionospheric research. The primary objective of the mission is to monitor the ionospheric scintillation of the aurora, and to perform several technological demonstrations. The satellite incorporates several novel systems, including a deployable Fresnel Zone Plate Antenna (FZPA), an integrated PocketQube deployer, a dual-receiver GNSS board for radio occultation and reflectometry experiments, and a polarimetric multi-spectral imager for auroral emission observations. The mission design, the suite of payloads, and the concept of operations are described in detail. This paper discusses the current development status of ³Cat-8, with several subsystems already developed and others in the final design phase. It is expected that the data gathered by ³Cat-8 will contribute to a better understanding of ionospheric effects on radio wave propagation and demonstrate the feasibility of compact remote sensors in a CubeSat platform. Full article

(This article belongs to the Special Issue Advances in CubeSats for Earth Observation)

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18 pages, 5546 KiB

Open AccessArticle

Climatological Evaluation of Three Assimilation and Reanalysis Datasets on Soil Moisture over the Tibetan Plateau

by Yinghan Sang, Hong-Li Ren and Mei Li

Remote Sens. 2024, 16(22), 4198; https://doi.org/10.3390/rs16224198 - 11 Nov 2024

Abstract

Soil moisture is critical in the linkage between the land and atmosphere of energy and water exchange, especially over the Tibetan Plateau (TP). However, due to the lack of in situ plateau soil moisture measurements, the reanalyzed and assimilated data are the major [...] Read more.

Soil moisture is critical in the linkage between the land and atmosphere of energy and water exchange, especially over the Tibetan Plateau (TP). However, due to the lack of in situ plateau soil moisture measurements, the reanalyzed and assimilated data are the major supplements for TP climate research. Based on observations from 1992 to 2013, this study provides a comprehensive evaluation of three sets of assimilation and reanalysis products (GLDAS, ERA5-Land, and MERRA-2) on the climatic mean and variability of soil moisture over the Tibetan Plateau (TPSM). For the climatic mean, GLDAS captures the spatial distribution and annual cycle of TPSM better than other datasets in terms of lower spatial RMSE (0.07 m3×m-3) and bias (0.06 m3×m-3). In terms of the climatic variability of TPSM, the multi-data average (MDA) highlights its advantages in reducing the bias relative to any single data product. MDA describes the TPSM anomalies more stably and accurately in terms of temporal trend and variation (r = 0.94), as well as the dipole spatial pattern in EOF1. When considering both the climatic mean and spatial variability, the performance of MDA is more accurate and balanced than that of a single data product. This study overcomes the deficiency of limited time and space in previous evaluations of TPSM and indicates that multi-data averaging may be a more effective approach in the climate investigation of TPSM. Full article

(This article belongs to the Special Issue Geodetic and Remote Sensing Observations in Tibet, Xinjiang and Siberia for Climate and Environment Change Studies (Second Edition))

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28 pages, 8537 KiB

Open AccessArticle

The Future of Radar Space Observation in Europe—Major Upgrade of the Tracking and Imaging Radar (TIRA)

by Jens Klare, Florian Behner, Claudio Carloni, Delphine Cerutti-Maori, Lars Fuhrmann, Clemens Hoppenau, Vassilis Karamanavis, Marcel Laubach, Alexander Marek, Robert Perkuhn, Simon Reuter and Felix Rosebrock

Remote Sens. 2024, 16(22), 4197; https://doi.org/10.3390/rs16224197 - 11 Nov 2024

Abstract

The use of near-Earth space has grown dramatically during the last decades, resulting in thousands of active and inactive satellites and a huge amount of space debris. To observe and monitor the near-Earth space environment, radar systems play a major role as they [...] Read more.

The use of near-Earth space has grown dramatically during the last decades, resulting in thousands of active and inactive satellites and a huge amount of space debris. To observe and monitor the near-Earth space environment, radar systems play a major role as they can be operated at any time and under any weather conditions. The Tracking and Imaging Radar (TIRA) is one of the largest space observation radars in the world. It consists of a 34

m

Cassegrain antenna, a precise tracking radar, and a high-resolution imaging radar. Since the 1990s, TIRA contributes to the field of space domain awareness by tracking and imaging space objects and by monitoring the debris population. Due to new technologies, modern satellites become smaller, and satellite extensions become more compact. Thus, sensitive high-resolution space observation systems are needed to detect, track, and image these space objects. To fulfill these requirements, TIRA is undergoing a major upgrade. The current imaging radar in the Ku band will be replaced by a new radar with improved geometrical and radiometric resolution operating in the Ka band. Due to its wideband fully polarimetric capability, the new imaging radar will increase the analysis and characterization of space objects. In addition, the tracking radar in the L band is also being currently refurbished. Through its novel modular structure and open design, highly flexible radar modes and precise tracking concepts can be efficiently implemented for enhanced space domain awareness. The new TIRA system will mark the start of a new era for space observation with radar in Europe. Full article

(This article belongs to the Special Issue Radar for Space Observation: Systems, Methods and Applications)

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30 pages, 1419 KiB

Open AccessReview

Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management

by Ying Deng, Yue Zhang, Daiwei Pan, Simon X. Yang and Bahram Gharabaghi

Remote Sens. 2024, 16(22), 4196; https://doi.org/10.3390/rs16224196 - 11 Nov 2024

Abstract

This review examines the integration of remote sensing technologies and machine learning models for efficient monitoring and management of lake water quality. It critically evaluates the performance of various satellite platforms, including Landsat, Sentinel-2, MODIS, RapidEye, and Hyperion, in assessing key water quality [...] Read more.

This review examines the integration of remote sensing technologies and machine learning models for efficient monitoring and management of lake water quality. It critically evaluates the performance of various satellite platforms, including Landsat, Sentinel-2, MODIS, RapidEye, and Hyperion, in assessing key water quality parameters including chlorophyll-a (Chl-a), turbidity, and colored dissolved organic matter (CDOM). This review highlights the specific advantages of each satellite platform, considering factors like spatial and temporal resolution, spectral coverage, and the suitability of these platforms for different lake sizes and characteristics. In addition to remote sensing platforms, this paper explores the application of a wide range of machine learning models, from traditional linear and tree-based methods to more advanced deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). These models are analyzed for their ability to handle the complexities inherent in remote sensing data, including high dimensionality, non-linear relationships, and the integration of multispectral and hyperspectral data. This review also discusses the effectiveness of these models in predicting various water quality parameters, offering insights into the most appropriate model–satellite combinations for different monitoring scenarios. Moreover, this paper identifies and discusses the key challenges associated with data quality, model interpretability, and integrating remote sensing imagery with machine learning models. It emphasizes the need for advancements in data fusion techniques, improved model generalizability, and the developing robust frameworks for integrating multi-source data. This review concludes by offering targeted recommendations for future research, highlighting the potential of interdisciplinary collaborations to enhance the application of these technologies in sustainable lake water quality management. Full article

(This article belongs to the Special Issue Remote Sensing Band Ratios for the Assessment of Water Quality)

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25 pages, 24537 KiB

Open AccessArticle

A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing

by Jiayuan Shen, Bing Han, Yang Li, Zongxu Pan, Di Yin, Yugang Feng and Guangzuo Li

Remote Sens. 2024, 16(22), 4195; https://doi.org/10.3390/rs16224195 - 11 Nov 2024

Abstract

Synthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the task of screening massive [...] Read more.

Synthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the task of screening massive SAR data, there is an urgent need for the global perception and detection of interference. The existing RFI detection method usually only uses a single type of data for detection, ignoring the information association between the data at all levels of the real SAR product, resulting in some computational redundancy. Meanwhile, current deep learning-based algorithms are often unable to locate the range of RFI coverage in the azimuth direction. Therefore, a novel RFI processing framework from quick-looks to single-look complex (SLC) data and then to raw echo is proposed. We take the data of Sentinel-1 terrain observation with progressive scan (TOPS) mode as an example. By combining the statistics-assisted network with the sliding-window algorithm and the error-tolerant training strategy, it is possible to accurately detect and locate RFI in the quick looks of an SLC product. Then, through the analysis of the TOPSAR imaging principle, the position of the RFI in the SLC image is preliminarily confirmed. The possible distribution of the RFI in the corresponding raw echo is further inferred, which is one of the first attempts to use spaceborne SAR data to ‌elucidate the RFI location mapping relationship between image data and raw echo. Compared with directly detecting all of the SLC data, the time for the proposed framework to determine the RFI distribution in the SLC data can be shortened by 53.526%. All the research in this paper is conducted on Sentinel-1 real data, which verify the feasibility and effectiveness of the proposed framework for radio frequency signals monitoring in advanced spaceborne SAR systems. Full article

(This article belongs to the Special Issue Advances in Spaceborne SAR—Technology and Applications (Second Edition))

16 pages, 12747 KiB

Open AccessTechnical Note

FA-HRNet: A New Fusion Attention Approach for Vegetation Semantic Segmentation and Analysis

by Bingnan He, Dongyang Wu, Li Wang and Sheng Xu

Remote Sens. 2024, 16(22), 4194; https://doi.org/10.3390/rs16224194 - 11 Nov 2024

Abstract

Semantic segmentation of vegetation in aerial remote sensing images is a critical aspect of vegetation mapping. Accurate vegetation segmentation effectively informs real-world production and construction activities. However, the presence of species heterogeneity, seasonal variations, and feature disparities within remote sensing images poses significant [...] Read more.

Semantic segmentation of vegetation in aerial remote sensing images is a critical aspect of vegetation mapping. Accurate vegetation segmentation effectively informs real-world production and construction activities. However, the presence of species heterogeneity, seasonal variations, and feature disparities within remote sensing images poses significant challenges for vision tasks. Traditional machine learning-based methods often struggle to capture deep-level features for the segmentation. This work proposes a novel deep learning network named FA-HRNet that leverages the fusion of attention mechanism and a multi-branch network structure for vegetation detection and segmentation. Quantitative analysis from multiple datasets reveals that our method outperforms existing approaches, with improvements in MIoU and PA by 2.17% and 4.85%, respectively, compared with the baseline network. Our approach exhibits significant advantages over the other methods regarding cross-region and cross-scale capabilities, providing a reliable vegetation coverage ratio for ecological analysis. Full article

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20 pages, 8615 KiB

Open AccessArticle

Divergent Drying Mechanisms in Humid and Non-Humid Regions Across China

by Yao Feng and Xuejie Mou

Remote Sens. 2024, 16(22), 4193; https://doi.org/10.3390/rs16224193 - 11 Nov 2024

Abstract

Understanding the drying mechanism is critical for formulating targeted mitigation strategies to combat drought impacts. This study aimed to reveal divergent drying mechanisms in humid and non-humid regions across China from the multidimensional perspectives of climate, vegetation, and energy balance. During the period [...] Read more.

Understanding the drying mechanism is critical for formulating targeted mitigation strategies to combat drought impacts. This study aimed to reveal divergent drying mechanisms in humid and non-humid regions across China from the multidimensional perspectives of climate, vegetation, and energy balance. During the period 1982–2012, the Standardized Precipitation Evapotranspiration Index (SPEI) revealed non-significant drying trends across China. Simultaneously, temperature and precipitation indicated a warming and drying pattern in the humid regions, contrasted with a warming and moistening pattern in the non-humid areas. The coupling effects of declined precipitation, increased vegetation coverage, and elevated temperature exacerbated dryness in the humid regions, while pronounced warming dominantly caused dryness in the non-humid regions. The inverse correlations between the actual evapotranspiration (ET) with precipitation and potential ET (PET) highlighted the principal role of moisture availability in divergent drying mechanisms over humid and non-humid regions. Random Forest models recognized precipitation and PET as the primary factors influencing SPEI in the humid and non-humid regions, respectively. Ongoing warming from 2013 to 2022 mitigated dryness in the humid regions due to the increased latent heat at the expense of sensible heat. Conversely, warming, amplified by the heightened sensible heat, exacerbated drought in the non-humid regions. By identifying the contrasting responses of humid and non-humid regions to warming and moisture availability, this study provides crucial insights for policymakers to mitigate drought impacts and enhance resilience in vulnerable non-humid areas. Full article

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16 pages, 5136 KiB

Open AccessArticle

Spatiotemporal Dynamic Analysis of Eutrophication Status Based on Machine Learning-Based Retrieval Algorithm: Case Study in Liangzi Lake, Hubei, China

by Peifeng Li, Fanghua Hao, Hao Wu and Hanjiang Nie

Remote Sens. 2024, 16(22), 4192; https://doi.org/10.3390/rs16224192 - 11 Nov 2024

Abstract

The routine monitoring of eutrophication is an important measure for observing the variation in water quality and protecting the ecological health of lakes. However, in situ information reflects eutrophication levels within a limited distance and period. In this study, we retrieved the trophic [...] Read more.

The routine monitoring of eutrophication is an important measure for observing the variation in water quality and protecting the ecological health of lakes. However, in situ information reflects eutrophication levels within a limited distance and period. In this study, we retrieved the trophic level index (TLI) based on Landsat 8 remote sensing images and using a machine learning (ML) method in Liangzi Lake in Hubei Province, China. The results showed that random forest (RF) outperformed other ML algorithms in estimating the TLI, evaluated by its higher fitness through the Monte Carlo method (median values of R², RMSE, and MAE are 0.54, 0.047, and 0.037, respectively). In general, 8% of the areas of Liangzi Lake presented an increasing eutrophication level from 2014 to 2022, and 20.1% of the areas reached a mild eutrophication level in 2022. In addition, we found that temperature and anthropogenic activities may impact the eutrophication conditions of the lake. This work uses remote sensing imagery and a ML method to monitor the dynamics of the lake’s eutrophication status, thereby providing a valuable reference for pollution control measures and enhancing the efficiency of water resource management. Full article

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14 pages, 9736 KiB

Open AccessTechnical Note

The Impact of Firework Ban Relaxation on Variations in SO₂ Emissions in China During the 2023 Chinese New Year

by Xinyu He, Yiwen Hu, Yi Li, Zengliang Zang, Wei You and Lang Liu

Remote Sens. 2024, 16(22), 4191; https://doi.org/10.3390/rs16224191 - 10 Nov 2024

Abstract

During the 2023 Chinese New Year (CNY), many city governments temporarily relaxed firework restrictions, leading to increased sulfur dioxide (SO₂) emissions from the combustion of sulfur-containing fireworks. This study employed the four-dimensional variational (4DVar) assimilation system to examine variations in SO [...] Read more.

During the 2023 Chinese New Year (CNY), many city governments temporarily relaxed firework restrictions, leading to increased sulfur dioxide (SO₂) emissions from the combustion of sulfur-containing fireworks. This study employed the four-dimensional variational (4DVar) assimilation system to examine variations in SO₂ emissions in China by assimilating hourly ground-based observations. Two experiments were conducted during CNY in 2022 and 2023 to quantify the variations in SO₂ emissions. On CNY’s Eve in 2023, following the relaxation of the firework ban, SO₂ emissions surged by 8.22 Gg nationwide compared to the previous day with significant increases in the Energy Golden Triangle (2.037 Gg), the North China Plain (1.709 Gg), and northeast China (0.945 Gg). Emissions peaked on CNY’s Eve and rapidly declined in the following two days but remained elevated compared to the pre-CNY period, indicating lingering effects of firework burning. Compared to the forecasts using the prior emissions, the optimized emissions markedly improved the model forecasts of SO₂ during the 2023 CNY period, with an increase in the correlation coefficient (R) from 0.13 to 0.64 and a reduction in the root mean square error (RMSE) by 49.2%, demonstrating the effectiveness of the optimized emissions. These findings will be useful for local governments in formulating strategies for firework burning during CNY. Full article

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Model domain featuring terrain altitude (filled colors) and spatial distributions of the observation stations (blue circles). The three subregions defined in red rectangles are (a) Energy Golden Triangle (EGT), (b) northeastern China (NEC) and (c), North China Plain (NCP). The solid black lines are province boundaries, and the names of the provinces are marked in red. Units: m. Full article ">Figure 2
Flowchart for optimization of SO2 emissions by assimilating SO2 observations (purple boxes represent updated optimized emissions). Full article ">Figure 3
Hourly average SO2 concentrations in China: (a) during the CNY periods in 2022 (blue) and 2023 (red), (b) at 17:00 UTC on 21 January 2023. (c) Daily average SO2 concentrations during the 2023 CYN period (20−23 January Day 1∼Day 4, red block) and comparison with the same period in 2022 (blue block). Full article ">Figure 4
Spatial distribution of SO2 emission at 00:00 BJT on the lunar January 1st. (a) MEIC_2016, (b) EMI_2022, (c) EMI_2023, (d) EMI_2023 minus MEIC_2016, (e) EMI_2023 minus EMI_2022. Units: mol/km2/h. Full article ">Figure 5
Hourly SO2 emissions for MEIC inventory in January 2016, and hourly SO2 emissions on CNY’s Eve for the EMI_2022 and EMI_2023 in different regions: (a) China, (b) EGT, (c) NEC, (d) NCP. The bars represent emission differences between EMI_2023 and EMI_2022 (units: 103 kg). Full article ">Figure 6
Hourly variations in SO2 concentration during Spring Festival in 2023 for forecast experiments Sim_MEIC_2016 and Sim_EMI_2023 in different regions: (a) China, (b) EGT, (c) NEC, (d) NCP. The shaded areas represent the day of CNY’s Eve. Full article ">

19 pages, 4991 KiB

Open AccessArticle

The Spatiotemporal Evolution of the Mudflat Wetland in the Yellow Sea Using Landsat Time Series

by Zicheng Huang, Wei Tang, Chengyi Zhao, Caixia Jiao and Jianting Zhu

Remote Sens. 2024, 16(22), 4190; https://doi.org/10.3390/rs16224190 - 10 Nov 2024

Abstract

Mudflat wetland, one of the 27 surface elements identified by the International Geographic Data Committee, has undergone substantial transformations with the rapid growth of the social economy and marine hazards, resulting in significant changes in its area and distribution. Quick identification of mudflat [...] Read more.

Mudflat wetland, one of the 27 surface elements identified by the International Geographic Data Committee, has undergone substantial transformations with the rapid growth of the social economy and marine hazards, resulting in significant changes in its area and distribution. Quick identification of mudflat wetland evolution is vital to improve the mudflat ecological service value. We employed object-oriented and decision tree classification methods to map the mudflat wetland in the Yellow Sea using the Landsat time series from 1983 to 2020. The Improved Spectral Water Index (IWI) was established by combining the characteristics of many ratio indices and using ratio operation and quadratic power operation. The coefficient of variation (CV) of the IWI was calculated, and the range of the intertidal zone in 1983, 1990, 2000, 2010, and 2020 was obtained by using a threshold method. The results indicate that the mudflat wetland area decreased continuously from 1983 to 2020, with a reduction of 337.38 km²/10a. Among the total area, the natural wetland experienced a decline of 446.9 km²/10a, with the most drastic changes occurring between 2000 and 2010. In contrast, the area of the human-made wetland increased by 109.56 km²/10a. Over the 38 years, the tidal flat has undergone the most drastic reduction, with an average of 157.45 km²/10a. From 1983 to 2020, the intertidal zone area decreased, with a reduction of 429.02 km²/10a. Human activities were the key factors causing mudflat wetland loss. Based on these findings, we propose several policy suggestions. This study provides a scientific basis for understanding the synergetic evolution mechanism of coastal resources utilization and mudflat wetland protection under global change. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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25 pages, 28178 KiB

Open AccessArticle

Assessment of Vegetation Drought Loss and Recovery in Central Asia Considering a Comprehensive Vegetation Index

by Wanqiang Han, Jianghua Zheng, Jingyun Guan, Yujia Liu, Liang Liu, Chuqiao Han, Jianhao Li, Congren Li, Xurui Mao and Ruikang Tian

Remote Sens. 2024, 16(22), 4189; https://doi.org/10.3390/rs16224189 - 10 Nov 2024

Abstract

In the context of drought events caused by global warming, there is limited understanding of vegetation loss caused by drought and the subsequent recovery of vegetation after drought ends. However, employing a single index representing a specific vegetation characteristic to explore drought’s impact [...] Read more.

In the context of drought events caused by global warming, there is limited understanding of vegetation loss caused by drought and the subsequent recovery of vegetation after drought ends. However, employing a single index representing a specific vegetation characteristic to explore drought’s impact on vegetation may overlook vegetation features and introduce increased uncertainty. We applied the enhanced vegetation index (EVI), fraction of vegetation cover (FVC), gross primary production (GPP), leaf area index (LAI), and our constructed remote sensing vegetation index (RSVI) to assess vegetation drought in Central Asia. We analyzed the differences in drought experiences for different climatic regions and vegetation types and vegetation loss and recovery following drought events. The results indicate that during drought years (2012 and 2019), the differences in vegetation drought across climatic regions were considerable. The vegetation in arid, semiarid, and Mediterranean climate regions was more susceptible to drought. The different indices used to assess vegetation loss exhibited varying degrees of dynamic changes, with vegetation in a state of mild drought experiencing more significantly during drought events. The different vegetation assessment indices exhibited significant variations during the drought recovery periods (with a recovery period of 16 days: EVI of 85%, FVC of 50%, GPP of 84%, LAI of 61%, and RSVI of 44%). Moreover, the required recovery periods tended to decrease from arid to humid climates, influenced by both climate regions and vegetation types. Sensitivity analysis indicated that the primary climatic factors leading to vegetation loss varied depending on the assessment indices used. The proposed RSVI demonstrates high sensitivity, correlation, and interpretability to dry–wet variations and can be used to assess the impact of drought on vegetation. These findings are essential for water resource management and the implementation of measures that mitigate vegetation drought. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology (Second Edition))

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27 pages, 21954 KiB

Open AccessArticle

Long-Term Ground Deformation Monitoring and Quantitative Interpretation in Shanghai Using Multi-Platform TS-InSAR, PCA, and K-Means Clustering

by Yahui Chong and Qiming Zeng

Remote Sens. 2024, 16(22), 4188; https://doi.org/10.3390/rs16224188 - 10 Nov 2024

Abstract

Ground subsidence in urban areas is mainly due to natural or anthropogenic activities, and it seriously threatens the healthy and sustainable development of the city and the security of individuals’ lives and assets. Shanghai is a megacity of China, and it has a [...] Read more.

Ground subsidence in urban areas is mainly due to natural or anthropogenic activities, and it seriously threatens the healthy and sustainable development of the city and the security of individuals’ lives and assets. Shanghai is a megacity of China, and it has a long history of ground subsidence due to the overexploitation of groundwater and urban expansion. Time Series Synthetic Aperture Radar Interferometry (TS-InSAR) is a highly effective and widely used approach for monitoring urban ground deformation. However, it is difficult to obtain long-term (such as over 10 years) deformation results using single-platform SAR satellite in general. To acquire long-term surface deformation monitoring results, it is necessary to integrate data from multi-platform SAR satellites. Furthermore, the deformations are the result of multiple factors that are superimposed, and relevant studies that quantitatively separate the contributions from different driving factors to subsidence are rare. Moreover, the time series cumulative deformation results of massive measurement points also bring difficulties to the deformation interpretation. In this study, we have proposed a long-term surface deformation monitoring and quantitative interpretation method that integrates multi-platform TS-InSAR, PCA, and K-means clustering. SAR images from three SAR datasets, i.e., 19 L-band ALOS-1 PALSAR, 22 C-band ENVISAT ASAR, and 20 C-band Sentinel-1A, were used to retrieve annual deformation rates and time series deformations in Shanghai from 2007 to 2018. The monitoring results indicate that there is serious uneven settlement in Shanghai, with a spatial pattern of stability in the northwest and settlement in the southeast of the study area. Then, we selected Pudong International Airport as the area of interest and quantitatively analyzed the driving factors of land subsidence in this area by using PCA results, combining groundwater exploitation and groundwater level change, precipitation, temperature, and engineering geological and human activities. Finally, the study area was divided into four sub-regions with similar time series deformation patterns using the K-means clustering. This study helps to understand the spatiotemporal evolution of surface deformation and its driving factors in Shanghai, and provides a scientific basis for the formulation and implementation of precise prevention and control strategies for land subsidence disasters, and it can also provide reference for monitoring in other urban areas. Full article

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20 pages, 3018 KiB

Open AccessArticle

Global Semantic Localization from Abstract Ellipse-Ellipsoid Model and Object-Level Instance Topology

by Heng Wu, Yanjie Liu, Chao Wang and Yanlong Wei

Remote Sens. 2024, 16(22), 4187; https://doi.org/10.3390/rs16224187 - 10 Nov 2024

Abstract

Robust and highly accurate localization using a camera is a challenging task when appearance varies significantly. In indoor environments, changes in illumination and object occlusion can have a significant impact on visual localization. In this paper, we propose a visual localization method based [...] Read more.

Robust and highly accurate localization using a camera is a challenging task when appearance varies significantly. In indoor environments, changes in illumination and object occlusion can have a significant impact on visual localization. In this paper, we propose a visual localization method based on an ellipse-ellipsoid model, combined with object-level instance topology and alignment. First, we develop a CNN-based (Convolutional Neural Network) ellipse prediction network, DEllipse-Net, which integrates depth information with RGB data to estimate the projection of ellipsoids onto images. Second, we model environments using 3D (Three-dimensional) ellipsoids, instance topology, and ellipsoid descriptors. Finally, the detected ellipses are aligned with the ellipsoids in the environment through semantic object association, and 6-DoF (Degree of Freedom) pose estimation is performed using the ellipse-ellipsoid model. In the bounding box noise experiment, DEllipse-Net demonstrates higher robustness compared to other methods, achieving the highest prediction accuracy for 11 out of 23 objects in ellipse prediction. In the localization test with 15 pixels of noise, we achieve

A T E

(Absolute Translation Error) and

A R E

(Absolute Rotation Error) of 0.077 m and

2 . 70^{\circ}

in the

f r 2_d e s k

sequence. Additionally, DEllipse-Net is lightweight and highly portable, with a model size of only 18.6 MB, and a single model can handle all objects. In the object-level instance topology and alignment experiment, our topology and alignment methods significantly enhance the global localization accuracy of the ellipse-ellipsoid model. In experiments involving lighting changes and occlusions, our method achieves more robust global localization compared to the classical bag-of-words based localization method and other ellipse-ellipsoid localization methods. Full article

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