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Search Results (7,111)

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23 pages, 3103 KiB  
Article
Estimation of Short-Term Vegetation Recovery in Post-Fire Siberian Dwarf Pine (Pinus pumila) Shrublands Based on Sentinel-2 Data
by Shuo Wang, Xin Zheng, Yang Du, Guoqiang Zhang, Qianxue Wang, Daxiao Han and Jili Zhang
Fire 2025, 8(2), 47; https://doi.org/10.3390/fire8020047 (registering DOI) - 25 Jan 2025
Abstract
The frequency of wildfires ignited by lightning is increasing due to global climate change. Since the forest ecological recovery is influenced by numerous factors, the process of post-fire vegetation recovery in Siberian dwarf pine shrublands remains unclear and demands in-depth study. This paper [...] Read more.
The frequency of wildfires ignited by lightning is increasing due to global climate change. Since the forest ecological recovery is influenced by numerous factors, the process of post-fire vegetation recovery in Siberian dwarf pine shrublands remains unclear and demands in-depth study. This paper explored the short-term recovery process of vegetation after two lightning-ignited fires in the Great Xing’an Mountains that occurred in 2017 and 2020, respectively. The study was aimed at presenting a monitoring approach for estimating the post-fire vegetation state and assessing the influence of various driving factors on vegetation recovery. Spectral indices were computed to evaluate forest vegetation recovery dynamics. The differences in vegetation recovery under various fire severity and topography conditions were also examined. Correlation analysis was employed to assess the influence of moisture content on the recovery of fire sites. The results show that fire severity, topographic features, and moisture content significantly impacted the rate of vegetation recovery. Specifically, regeneration takes place more rapidly on warm, high-altitude, and gentle slopes within highly and moderately burned areas. Additionally, areas marked by high moisture content demonstrate rapid recovery. Our study enriches the research cases of global wildfires and vegetation recovery and provides a scientific basis for forest management and the restoration of post-fire ecosystems. Full article
(This article belongs to the Special Issue Forest Fuel Treatment and Fire Risk Assessment)
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20 pages, 4964 KiB  
Article
Monitoring of Soil Salinity in the Weiku Oasis Based on Feature Space Models with Typical Parameters Derived from Sentinel-2 MSI Images
by Nigara Tashpolat and Abuduwaili Reheman
Land 2025, 14(2), 251; https://doi.org/10.3390/land14020251 (registering DOI) - 25 Jan 2025
Abstract
Soil salinization, as one of the types of land degradation, is a global threat. It not only poses serious ecological problems, but also poses great challenges for the sustainable utilization of land resources, especially in arid and semi-arid areas. The Weiku Oasis is [...] Read more.
Soil salinization, as one of the types of land degradation, is a global threat. It not only poses serious ecological problems, but also poses great challenges for the sustainable utilization of land resources, especially in arid and semi-arid areas. The Weiku Oasis is undoubtedly one of the typical areas under severe salinization. The wide spread of saline soil brings numerous negative impacts to the local region. To prevent the escalation of soil salinization, timely monitoring of soil salinization is urgently needed for informed decision-making. Remote sensing technology can obtain large-scale datasets in a short period, allowing researchers to carry out the rapid and accurate investigation of soil salinization. Sentinel-2 images have a relatively high spatial resolution and provide red-edge bands data, referring to bands 5, 6, and 7, and the use of red-edge bands is a new approach to estimate soil salinization in the Weiku Oasis. In this study, we selected five typical indices (NDre1, RNDSI, MSAVI, NDWI, SI3, with the first two being red-edge indices) from twenty potential indices to construct multiple two-dimensional feature space models. Consequently, an optimal and novel monitoring index for soil salinization in the Weiku Oasis was developed. The result showed that: (1) The monitoring index MSAVI-RNDSI, which includes red-edge indices, had the highest inversion accuracy of R2 = 0.7998 and MAE = 3.3444; (2) The red-edge salinity indices effectively captured the conditions of salinization, with the feature space model composed of red-edge indices achieving an average inversion accuracy of R2 = 0.7902; (3) Land-use type was identified as the primary factor affecting the degree of soil salinization in the study area. The proposed approach provides a highly accurate and high-resolution soil salinity mapping strategy. Full article
17 pages, 1734 KiB  
Article
Incorporating Power-Law Model and ERA-5 Data for InSAR Tropospheric Delay Correction Analysis
by Dongxu Huang, Junyu Wang, Menghua Li, Cheng Huang and Bo-Hui Tang
Sensors 2025, 25(3), 716; https://doi.org/10.3390/s25030716 - 24 Jan 2025
Viewed by 188
Abstract
InSAR technology effectively monitors urban subsidence and evaluates the stability of infrastructure across extensive regions. Atmospheric tropospheric delay constitutes a significant source of error that adversely impacts the accuracy of InSAR deformation measurements. The meteorological conditions in the highland basin region are complex, [...] Read more.
InSAR technology effectively monitors urban subsidence and evaluates the stability of infrastructure across extensive regions. Atmospheric tropospheric delay constitutes a significant source of error that adversely impacts the accuracy of InSAR deformation measurements. The meteorological conditions in the highland basin region are complex, and there is a notable deficiency of sufficient sounding balloon observations. This study replaces the sounding balloon data in the power-law model with ERA-5 data (PLE5) to correct the InSAR atmosphere phase delay. This method was tested in Dali utilizing Sentinel-1 data. By comparing its performance against the GACOS model, traditional linear model, and ERA-5 correction, the PLE5 model exhibited the lowest phase standard deviation. This method provides an alternative approach for applying the power-law model in regions with limited sounding balloon data, enhancing the accuracy of InSAR tropospheric delay correction. Full article
21 pages, 4918 KiB  
Article
Identification, Mechanism and Countermeasures of Cropland Abandonment in Northeast Guangdong Province
by Xiaojian Li, Linbing Ma and Xi Liu
Land 2025, 14(2), 246; https://doi.org/10.3390/land14020246 - 24 Jan 2025
Viewed by 207
Abstract
Cropland serves as the most vital resource for agricultural production, while its security is primarily threatened by abandonment. Northeast Guangdong Province features a fragmented terrain and faces a significant issue of farmland abandonment. It is crucial to analyze the phenomenon of cropland abandonment [...] Read more.
Cropland serves as the most vital resource for agricultural production, while its security is primarily threatened by abandonment. Northeast Guangdong Province features a fragmented terrain and faces a significant issue of farmland abandonment. It is crucial to analyze the phenomenon of cropland abandonment to safeguard food security. However, due to limitations in data sources and attribution methods, previous studies struggled to comprehensively characterize the driving mechanisms of abandoned land. Using data from Sentinel time series remote-sensing images, we employed the land use change trajectory method to map cropland abandonment in Jiaoling County from 2019 to 2023. Furthermore, we proposed a novel analytical framework to quantify the influence pathways and interaction effects driving cropland abandonment. The results indicate that: (1) The overall accuracy of the abandoned land extraction is 79.6%. During the study period, the abandonment rate in Jiaoling County showed a trend of a “gradual rise followed by a sharp decline”, and the abandoned area reached its maximum in 2021. The abandonment phenomenon in the southeastern rural areas was serious and stubborn. (2) The slope has the greatest explanatory power for abandonment, followed by the total cultivated area, aggregation index of cropland, and distance to road. Each driving factor has a threshold effect. (3) Topography, location, and agriculture driving factors directly or indirectly affect the abandonment rate, with direct influences of 0.247, 0.255, and −0.256, respectively. The research findings offer valuable scientific guidance for managing abandoned land and deepen our understanding of its formation mechanisms. Full article
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14 pages, 661 KiB  
Article
Local Predictors of Explosive Synchronization with Ordinal Methods
by I. Leyva, Juan A. Almendral, Christophe Letellier and Irene Sendiña-Nadal
Entropy 2025, 27(2), 113; https://doi.org/10.3390/e27020113 - 24 Jan 2025
Viewed by 251
Abstract
We propose using the ordinal pattern transition (OPT) entropy measured at sentinel central nodes as a potential predictor of explosive transitions to synchronization in networks of various dynamical systems with increasing complexity. Our results demonstrate that the OPT entropic measure surpasses traditional early [...] Read more.
We propose using the ordinal pattern transition (OPT) entropy measured at sentinel central nodes as a potential predictor of explosive transitions to synchronization in networks of various dynamical systems with increasing complexity. Our results demonstrate that the OPT entropic measure surpasses traditional early warning signal (EWS) measures and could be valuable to the tools available for predicting critical transitions. In particular, we investigate networks of diffusively coupled phase oscillators and chaotic Rössler systems. As maps, we consider a neural network of Chialvo maps coupled in star and scale-free configurations. Furthermore, we apply this measure to time series data obtained from a network of electronic circuits operating in the chaotic regime. Full article
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23 pages, 7919 KiB  
Article
Interpretable LAI Fine Inversion of Maize by Fusing Satellite, UAV Multispectral, and Thermal Infrared Images
by Yu Yao, Hengbin Wang, Xiao Yang, Xiang Gao, Shuai Yang, Yuanyuan Zhao, Shaoming Li, Xiaodong Zhang and Zhe Liu
Agriculture 2025, 15(3), 243; https://doi.org/10.3390/agriculture15030243 - 23 Jan 2025
Viewed by 251
Abstract
Leaf area index (LAI) serves as a crucial indicator for characterizing the growth and development process of maize. However, the LAI inversion of maize based on unmanned aerial vehicles (UAVs) is highly susceptible to various factors such as weather conditions, light intensity, and [...] Read more.
Leaf area index (LAI) serves as a crucial indicator for characterizing the growth and development process of maize. However, the LAI inversion of maize based on unmanned aerial vehicles (UAVs) is highly susceptible to various factors such as weather conditions, light intensity, and sensor performance. In contrast to satellites, the spectral stability of UAV-based data is relatively inferior, and the phenomenon of “spectral fragmentation” is prone to occur during large-scale monitoring. This study was designed to solve the problem that maize LAI inversion based on UAVs is difficult to achieve both high spatial resolution and spectral consistency. A two-stage remote sensing data fusion method integrating coarse and fine fusion was proposed. The SHapley Additive exPlanations (SHAP) model was introduced to investigate the contributions of 20 features in 7 categories to LAI inversion of maize, and canopy temperature extracted from thermal infrared images was one of them. Additionally, the most suitable feature sampling window was determined through multi-scale sampling experiments. The grid search method was used to optimize the hyperparameters of models such as Gradient Boosting, XGBoost, and Random Forest, and their accuracy was compared. The results showed that, by utilizing a 3 × 3 feature sampling window and 9 features with the highest contributions, the LAI inversion accuracy of the whole growth stage based on Random Forest could reach R2 = 0.90 and RMSE = 0.38 m2/m2. Compared with the single UAV data source mode, the inversion accuracy was enhanced by nearly 25%. The R2 in the jointing, tasseling, and filling stages were 0.87, 0.86, and 0.62, respectively. Moreover, this study verified the significant role of thermal infrared data in LAI inversion, providing a new method for fine LAI inversion of maize. Full article
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25 pages, 4935 KiB  
Article
From Air to Space: A Comprehensive Approach to Optimizing Aboveground Biomass Estimation on UAV-Based Datasets
by Muhammad Nouman Khan, Yumin Tan, Lingfeng He, Wenquan Dong and Shengxian Dong
Forests 2025, 16(2), 214; https://doi.org/10.3390/f16020214 - 23 Jan 2025
Viewed by 501
Abstract
Estimating aboveground biomass (AGB) is vital for sustainable forest management and helps to understand the contributions of forests to carbon storage and emission goals. In this study, the effectiveness of plot-level AGB estimation using height and crown diameter derived from UAV-LiDAR, calibration of [...] Read more.
Estimating aboveground biomass (AGB) is vital for sustainable forest management and helps to understand the contributions of forests to carbon storage and emission goals. In this study, the effectiveness of plot-level AGB estimation using height and crown diameter derived from UAV-LiDAR, calibration of GEDI-L4A AGB and GEDI-L2A rh98 heights, and spectral variables derived from UAV-multispectral and RGB data were assessed. These calibrated AGB and height values and UAV-derived spectral variables were used to fit AGB estimations using a random forest (RF) regression model in Fuling District, China. Using Pearson correlation analysis, we identified 10 of the most important predictor variables in the AGB prediction model, including calibrated GEDI AGB and height, Visible Atmospherically Resistant Index green (VARIg), Red Blue Ratio Index (RBRI), Difference Vegetation Index (DVI), canopy cover (CC), Atmospherically Resistant Vegetation Index (ARVI), Red-Edge Normalized Difference Vegetation Index (NDVIre), Color Index of Vegetation (CIVI), elevation, and slope. The results showed that, in general, the second model based on calibrated AGB and height, Sentinel-2 indices, slope and elevation, and spectral variables from UAV-multispectral and RGB datasets with evaluation metric (for training: R2 = 0.941 Mg/ha, RMSE = 13.514 Mg/ha, MAE = 8.136 Mg/ha) performed better than the first model with AGB prediction. The result was between 23.45 Mg/ha and 301.81 Mg/ha, and the standard error was between 0.14 Mg/ha and 10.18 Mg/ha. This hybrid approach significantly improves AGB prediction accuracy and addresses uncertainties in AGB prediction modeling. The findings provide a robust framework for enhancing forest carbon stock assessment and contribute to global-scale AGB monitoring, advancing methodologies for sustainable forest management and ecological research. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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25 pages, 6944 KiB  
Article
Representation Learning of Multi-Spectral Earth Observation Time Series and Evaluation for Crop Type Classification
by Andrea González-Ramírez, Clement Atzberger, Deni Torres-Roman and Josué López
Remote Sens. 2025, 17(3), 378; https://doi.org/10.3390/rs17030378 - 23 Jan 2025
Viewed by 265
Abstract
Remote sensing (RS) spectral time series provide a substantial source of information for the regular and cost-efficient monitoring of the Earth’s surface. Important monitoring tasks include land use and land cover classification, change detection, forest monitoring and crop type identification, among others. To [...] Read more.
Remote sensing (RS) spectral time series provide a substantial source of information for the regular and cost-efficient monitoring of the Earth’s surface. Important monitoring tasks include land use and land cover classification, change detection, forest monitoring and crop type identification, among others. To develop accurate solutions for RS-based applications, often supervised shallow/deep learning algorithms are used. However, such approaches usually require fixed-length inputs and large labeled datasets. Unfortunately, RS images acquired by optical sensors are frequently degraded by aerosol contamination, clouds and cloud shadows, resulting in missing observations and irregular observation patterns. To address these issues, efforts have been made to implement frameworks that generate meaningful representations from the irregularly sampled data streams and alleviate the deficiencies of the data sources and supervised algorithms. Here, we propose a conceptually and computationally simple representation learning (RL) approach based on autoencoders (AEs) to generate discriminative features for crop type classification. The proposed methodology includes a set of single-layer AEs with a very limited number of neurons, each one trained with the mono-temporal spectral features of a small set of samples belonging to a class, resulting in a model capable of processing very large areas in a short computational time. Importantly, the developed approach remains flexible with respect to the availability of clear temporal observations. The signal derived from the ensemble of AEs is the reconstruction difference vector between input samples and their corresponding estimations, which are averaged over all cloud-/shadow-free temporal observations of a pixel location. This averaged reconstruction difference vector is the base for the representations and the subsequent classification. Experimental results show that the proposed extremely light-weight architecture indeed generates separable features for competitive performances in crop type classification, as distance metrics scores achieved with the derived representations significantly outperform those obtained with the initial data. Conventional classification models were trained and tested with representations generated from a widely used Sentinel-2 multi-spectral multi-temporal dataset, BreizhCrops. Our method achieved 77.06% overall accuracy, which is 6% higher than that achieved using original Sentinel-2 data within conventional classifiers and even 4% better than complex deep models such as OmnisCNN. Compared to extremely complex and time-consuming models such as Transformer and long short-term memory (LSTM), only a 3% reduction in overall accuracy was noted. Our method uses only 6.8k parameters, i.e., 400x fewer than OmnicsCNN and 27x fewer than Transformer. The results prove that our method is competitive in terms of classification performance compared with state-of-the-art methods while substantially reducing the computational load. Full article
(This article belongs to the Collection Sentinel-2: Science and Applications)
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15 pages, 4931 KiB  
Article
Water Hyacinth Invasion and Management in a Tropical Hydroelectric Reservoir: Insights from Random Forest and SVM Classification
by Luis Fernando Correa-Mejía and Yeison Alberto Garcés-Gómez
Viewed by 369
Abstract
The rapid proliferation of water hyacinth (Eichhornia crassipes) in newly formed reservoirs poses a significant threat to aquatic ecosystems and hydroelectric operations. The objective of this study was to map and monitor the spatio-temporal distribution of water hyacinth in the Hidroituango [...] Read more.
The rapid proliferation of water hyacinth (Eichhornia crassipes) in newly formed reservoirs poses a significant threat to aquatic ecosystems and hydroelectric operations. The objective of this study was to map and monitor the spatio-temporal distribution of water hyacinth in the Hidroituango reservoir in Colombia from 2018 to 2023, using Sentinel-2 satellite imagery and machine learning algorithms. The Random Forest (RF) and Support Vector Machine (SVM) algorithms were employed for image classification, and their performance was evaluated using various accuracy metrics. The results revealed that both algorithms effectively detected and mapped water hyacinth infestations, with RF demonstrating greater stability in capturing long-term trends and SVM exhibiting higher sensitivity to rapid changes in coverage. The study also highlighted the impact of the COVID-19 pandemic on control efforts, leading to a temporary increase in infestation. The findings underscore the importance of continuous monitoring and adaptive management strategies to mitigate the ecological and economic impacts of water hyacinth in the Hidroituango reservoir and similar environments. Full article
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20 pages, 22102 KiB  
Article
Mapping of Fluvial Morphological Units from Sentinel-1 Data Using a Deep Learning Approach
by Massimiliano Gargiulo, Carmela Cavallo and Maria Nicolina Papa
Remote Sens. 2025, 17(3), 366; https://doi.org/10.3390/rs17030366 - 22 Jan 2025
Viewed by 346
Abstract
The identification of ongoing evolutionary trajectories, the prediction of future changes in the functioning of riverine habitats, and the assessment of flood-related risks to human populations all depend on regular hydro-morphological monitoring of fluvial settings. This paper focuses on the satellite monitoring of [...] Read more.
The identification of ongoing evolutionary trajectories, the prediction of future changes in the functioning of riverine habitats, and the assessment of flood-related risks to human populations all depend on regular hydro-morphological monitoring of fluvial settings. This paper focuses on the satellite monitoring of river macro-morphological units (assemblages of water, sediment, and vegetation units) and their temporal evolution. In particular, we develop a deep-learning semantic segmentation method using Synthetic Aperture Radar (SAR) Sentinel-1 dual-polarized data. The methodology is executed and tested on the Po River, located in Italy. The training of a relatively deep convolutional neural network requires a large amount of ground-truth data, which is often limited and challenging to acquire. To address this limitation, the dataset is augmented using a random forest (RF) classification algorithm. RF parameters are trained with both Sentinel-1 (S1) and Sentinel-2 (S2) data. The RF classification algorithm is very robust and achieves excellent performance. To overcome the limitation linked with the scarce availability of contemporary acquisition by S1 and S2 sensors, the deep learning (DL) model is trained by using only the Sentinel-1 input data and the ground truth from the RF result. The proposed approach achieves promising results in the classification of water, sediments, and vegetation along rivers such as the Italian Po River with low computational costs and no concurrency constraints between S1 and S2. Full article
(This article belongs to the Special Issue Remote Sensing and GIS in Freshwater Environments)
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11 pages, 14092 KiB  
Article
Prevalence of Breast Cancer-Related Lymphedema in Israeli Women Following Axillary Procedures
by Daniel Josef Kedar, Elad Zvi, Oriana Haran, Lior Sherker, Michael Sernitski, Nadav Oppenheim, Eran Nizri, Marian Khatib and Yoav Barnea
J. Clin. Med. 2025, 14(3), 688; https://doi.org/10.3390/jcm14030688 - 22 Jan 2025
Viewed by 261
Abstract
Background/Objectives: Breast cancer-related lymphedema (BCRL) is a chronic complication of breast cancer treatment, leading to physical and psychological morbidity. While widely studied globally, the prevalence and risk factors for BCRL in Israeli patients remain unexplored. This study’s objectives were to determine the prevalence [...] Read more.
Background/Objectives: Breast cancer-related lymphedema (BCRL) is a chronic complication of breast cancer treatment, leading to physical and psychological morbidity. While widely studied globally, the prevalence and risk factors for BCRL in Israeli patients remain unexplored. This study’s objectives were to determine the prevalence of BCRL in Israeli women treated for breast cancer, validate the Hebrew-translated Norman Questionnaire (NQ) for BCRL screening, and identify risk factors associated with the condition. Methods: A single-center study was conducted at Tel Aviv Sourasky Medical Center, including 181 patients who underwent unilateral axillary lymph node interventions between 2015 and 2018. Participants completed the Hebrew-translated NQ, which was validated through clinical evaluation and circumference-based volume measurements in a subset of 20 patients. Prevalence rates and risk factors were analyzed using multivariate modeling. Results: The prevalence of BCRL was 20%, with rates varying by procedure: 8.9% for sentinel lymph node biopsy, 19.6% for lymph node sampling, and 37.5% for axillary lymph node dissection (ALND). Of the 35 patients with BCRL, only 14% had been previously diagnosed. Risk factors included ALND (OR = 97.31), a higher lymph node excision count (OR = 0.81), and referral to physiotherapy (OR = 133.50). The Hebrew NQ demonstrated strong validity (rs = 0.852; p < 0.001). Conclusions: This is the first study to estimate BCRL prevalence in Israeli women, highlighting underdiagnosis and the need for improved early detection. The Hebrew NQ is a reliable screening tool, enabling timely referral and intervention. Early diagnosis is crucial for optimizing treatment outcomes and improving the quality of life of BCRL patients. Full article
(This article belongs to the Special Issue Breast Reconstruction: The Current Environment and Future Directions)
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23 pages, 36422 KiB  
Article
Mapping Urban Green Spaces in Indonesian Cities Using Remote Sensing Analysis
by Agustiyara Agustiyara, Dyah Mutiarin, Achmad Nurmandi, Aulia Nur Kasiwi and M. Faisi Ikhwali
Urban Sci. 2025, 9(2), 23; https://doi.org/10.3390/urbansci9020023 - 22 Jan 2025
Viewed by 452
Abstract
This study explores the dynamics of urban green spaces in five major Indonesian cities—Central Jakarta, Bandung, Yogyakarta, Surabaya, and Semarang—using Sentinel-2 satellite imagery and vegetation indices, such as NDVI and EVI. As major urban areas expand and become more densely populated, development activities [...] Read more.
This study explores the dynamics of urban green spaces in five major Indonesian cities—Central Jakarta, Bandung, Yogyakarta, Surabaya, and Semarang—using Sentinel-2 satellite imagery and vegetation indices, such as NDVI and EVI. As major urban areas expand and become more densely populated, development activities have significantly altered urban green spaces, necessitating comprehensive mapping through remote sensing technologies. The findings reveal significant variability in green space coverage among the cities over three periods (2019–2020, 2021–2022, 2023–2024), ensuring that the findings are comprehensive and up to date. This study demonstrates the utility of remote sensing for detailed urban analysis, emphasizing its effectiveness in identifying, quantifying, and monitoring changes in green spaces. Integrating advanced techniques, such as NDVI and EVI, offers a nuanced understanding of urban vegetation dynamics and their implications for sustainable urban planning. Utilizing Sentinel-2 data within the Google Earth Engine (GEE) framework represents a contemporary and innovative approach to urban studies, particularly in rapidly urbanizing environments. The novelty of this research lies in its method of preserving and enhancing green infrastructure while supporting the development of effective strategies for sustainable urban growth. Full article
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26 pages, 39396 KiB  
Article
Using a Neural Network to Model the Incidence Angle Dependency of Backscatter to Produce Seamless, Analysis-Ready Backscatter Composites over Land
by Claudio Navacchi, Felix Reuß and Wolfgang Wagner
Remote Sens. 2025, 17(3), 361; https://doi.org/10.3390/rs17030361 - 22 Jan 2025
Viewed by 294
Abstract
In order to improve the current standard of analysis-ready Synthetic Aperture Radar (SAR) backscatter data, we introduce a machine learning-based approach to estimate the slope of the backscatter–incidence angle relationship from several backscatter statistics. The method requires information from radiometric terrain-corrected gamma nought [...] Read more.
In order to improve the current standard of analysis-ready Synthetic Aperture Radar (SAR) backscatter data, we introduce a machine learning-based approach to estimate the slope of the backscatter–incidence angle relationship from several backscatter statistics. The method requires information from radiometric terrain-corrected gamma nought time series and overcomes the constraints of a limited orbital coverage, as exemplified with the Sentinel-1 constellation. The derived slope estimates contain valuable information on scattering characteristics of different land cover types, allowing for the correction of strong forward-scattering effects over water bodies and wetlands, as well as moderate surface scattering effects over bare soil and sparsely vegetated areas. Comparison of the estimated and computed slope values in areas with adequate orbital coverage shows good overall agreement, with an average RMSE value of 0.1 dB/° and an MAE of 0.05 dB/°. The discrepancy between RMSE and MAE indicates the presence of outliers in the computed slope, which are attributed to speckle and backscatter fluctuations over time. In contrast, the estimated slope excels with a smooth spatial appearance. After correcting backscatter values by normalising them to a certain reference incidence angle, orbital artefacts are significantly reduced. This becomes evident with differences up to 5 dB when aggregating the normalised backscatter measurements over certain time periods to create spatially seamless radar backscatter composites. Without being impacted by systematic differences in the illumination and physical properties of the terrain, these composites constitute a valuable foundation for land cover and land use mapping, as well as bio-geophysical parameter retrieval. Full article
(This article belongs to the Special Issue Calibration and Validation of SAR Data and Derived Products)
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24 pages, 104480 KiB  
Article
DBCE-Net: A Novel Deep Learning Framework for Annual Mapping of Coastal Aquaculture Ponds in China with Sentinel-2 Data
by Yin Li, Liaoying Zhao, Huaguo Zhang and Wenting Cao
Remote Sens. 2025, 17(3), 362; https://doi.org/10.3390/rs17030362 - 22 Jan 2025
Viewed by 293
Abstract
Despite the promising advancements of deep learning techniques in coastal aquaculture pond extraction, their capacity for large-scale mapping tasks remains relatively limited. To address this challenge, this study developed a novel deep learning framework, Dual-Branch Enhanced Network (DBCE-Net), for mapping the annual aquaculture [...] Read more.
Despite the promising advancements of deep learning techniques in coastal aquaculture pond extraction, their capacity for large-scale mapping tasks remains relatively limited. To address this challenge, this study developed a novel deep learning framework, Dual-Branch Enhanced Network (DBCE-Net), for mapping the annual aquaculture ponds at the national scale using Sentinel-2 imagery. The DBCE-Net framework effectively mitigates the contextual information loss inherent in traditional methods and reduces classification errors by processing both down-sampled large-scale images and block images at their original resolution. The architecture comprises local feature extraction and global feature extraction, along with feature fusion and decoding. The pivotal Multi-scale Dynamic Feature Fusion (DFF) module synthesizes local and global features while incorporating complementary information, demonstrating strong robustness with smaller training areas, compared to previous methods that required a larger number of samples distributed across different regions. By applying the DBCE-Net to Sentinel-2 imagery from 2017 to 2023, we mapped the spatiotemporal distribution of coastal aquaculture ponds across all coastal counties in China, achieving an overall classification accuracy approximately 93%. The results demonstrate substantial changes in the area of coastal aquaculture ponds in China from 2017 to 2023, with the total area declining from 8970.25 km2 to 8261.17 km2, representing a notable decrease of 7.90%. The most pronounced reduction was observed in Shanghai, with a decrease of 38.92%, followed by Zhejiang (31.57%) and Jiangsu (19.07%). These reductions are primarily attributed to policies converting aquaculture ponds into natural wetlands. In contrast, the area of coastal aquaculture ponds in Liaoning Province slightly increased by 5.75%. This DBCE-Net demonstrates good accuracy and generalizability and is promising to further expand its application to the extraction of coastal aquaculture areas worldwide, providing important scientific value and practical significance for the global coastal aquaculture industry. Full article
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21 pages, 8197 KiB  
Article
Quantifying the Impact of Crude Oil Spills on the Mangrove Ecosystem in the Niger Delta Using AI and Earth Observation
by Jemima O’Farrell, Dualta O’Fionnagáin, Abosede Omowumi Babatunde, Micheal Geever, Patricia Codyre, Pearse C. Murphy, Charles Spillane and Aaron Golden
Remote Sens. 2025, 17(3), 358; https://doi.org/10.3390/rs17030358 - 22 Jan 2025
Viewed by 393
Abstract
The extraction, processing and transport of crude oil in the Niger Delta region of Nigeria has long been associated with collateral environmental damage to the largest mangrove ecosystem in Africa. Oil pollution is impacting not only one of the planet’s most ecologically diverse [...] Read more.
The extraction, processing and transport of crude oil in the Niger Delta region of Nigeria has long been associated with collateral environmental damage to the largest mangrove ecosystem in Africa. Oil pollution is impacting not only one of the planet’s most ecologically diverse regions but also the health, livelihoods, and social cohesion of the Delta region inhabitants. Quantifying and directly associating localised oil pollution events to specific petrochemical infrastructure is complicated by the difficulty of monitoring such vast and complex terrain, with documented concerns regarding the thoroughness and impartiality of reported oil pollution events. Earth Observation (EO) offers a means to deliver such a monitoring and assessment capability using Normalised Difference Vegetation Index (NDVI) measurements as a proxy for mangrove biomass health. However, the utility of EO can be impacted by persistent cloud cover in such regions. To overcome such challenges here, we present a workflow that leverages EO-derived high-resolution (10 m) synthetic aperture radar data from the Sentinel-1 satellite constellation combined with machine learning to conduct observations of the spatial land cover changes associated with oil pollution-induced mangrove mortality proximal to pipeline networks in a 9000 km2 region of Rivers State located near Port Harcourt. Our analysis identified significant deforestation from 2016–2024, with an estimated mangrove mortality rate of 5644 hectares/year. Using our empirically derived Pipeline Impact Indicator (PII), we mapped the oil pipeline network to 1 km resolution, highlighting specific pipeline locations in need of immediate intervention and restoration, and identified several new pipeline sites showing evidence of significant oil spill damage that have yet to be formally reported. Our findings emphasise the critical need for the continuous and comprehensive monitoring of oil extractive regions using satellite remote sensing to support decision-making and policies to mitigate environmental and societal damage from pipeline oil spills, particularly in ecologically vulnerable regions such as the Niger Delta. Full article
(This article belongs to the Special Issue Remote Sensing for Oil and Gas Development, Production and Monitoring)
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