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Search Results (1,696)

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Keywords = water quality in the network

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29 pages, 10794 KiB  
Article
Multi-Scale Ecosystem Service Supply–Demand Dynamics and Driving Mechanisms in Mainland China During the Last Two Decades: Implications for Sustainable Development
by Menghao Qi, Mingcan Sun, Qinping Liu, Hongzhen Tian, Yanchao Sun, Mengmeng Yang and Hui Zhang
Sustainability 2025, 17(15), 6782; https://doi.org/10.3390/su17156782 (registering DOI) - 25 Jul 2025
Abstract
The growing mismatch between ecosystem service (ES) supply and demand underscores the importance of thoroughly understanding their spatiotemporal patterns and key drivers to promote ecological civilization and sustainable development at the regional level in China. This study investigates six key ES indicators across [...] Read more.
The growing mismatch between ecosystem service (ES) supply and demand underscores the importance of thoroughly understanding their spatiotemporal patterns and key drivers to promote ecological civilization and sustainable development at the regional level in China. This study investigates six key ES indicators across mainland China—habitat quality (HQ), carbon sequestration (CS), water yield (WY), sediment delivery ratio (SDR), food production (FP), and nutrient delivery ratio (NDR)—by integrating a suite of analytical approaches. These include a spatiotemporal analysis of trade-offs and synergies in supply, demand, and their ratios; self-organizing maps (SOM) for bundle identification; and interpretable machine learning models. While prior research studies have typically examined ES at a single spatial scale, focusing on supply-side bundles or associated drivers, they have often overlooked demand dynamics and cross-scale interactions. In contrast, this study integrates SOM and SHAP-based machine learning into a dual-scale framework (grid and city levels), enabling more precise identification of scale-dependent drivers and a deeper understanding of the complex interrelationships between ES supply, demand, and their spatial mismatches. The results reveal pronounced spatiotemporal heterogeneity in ES supply and demand at both grid and city scales. Overall, the supply services display a spatial pattern of higher values in the east and south, and lower values in the west and north. High-value areas for multiple demand services are concentrated in the densely populated eastern regions. The grid scale better captures spatial clustering, enhancing the detection of trade-offs and synergies. For instance, the correlation between HQ and NDR supply increased from 0.62 (grid scale) to 0.92 (city scale), while the correlation between HQ and SDR demand decreased from −0.03 to −0.58, indicating that upscaling may highlight broader synergistic or conflicting trends missed at finer resolutions. In the spatiotemporal interaction network of supply–demand ratios, CS, WY, FP, and NDR persistently show low values (below –0.5) in western and northern regions, indicating ongoing mismatches and uneven development. Driver analysis demonstrates scale-dependent effects: at the grid scale, HQ and FP are predominantly influenced by socioeconomic factors, SDR and WY by ecological variables, and CS and NDR by climatic conditions. At the city level, socioeconomic drivers dominate most services. Based on these findings, nine distinct supply–demand bundles were identified at both scales. The largest bundle at the grid scale (B3) occupies 29.1% of the study area, while the largest city-scale bundle (B8) covers 26.5%. This study deepens the understanding of trade-offs, synergies, and driving mechanisms of ecosystem services across multiple spatial scales; reveals scale-sensitive patterns of spatial mismatch; and provides scientific support for tiered ecological compensation, integrated regional planning, and sustainable development strategies. Full article
20 pages, 6464 KiB  
Article
Bacterial Communities Respond to Spatiotemporal Fluctuation in Water Quality and Microcystins at Lake Taihu
by Aimin Hao, Dong Xia, Xingping Mou, Sohei Kobayashi, Tomokazu Haraguchi, Yasushi Iseri and Min Zhao
Water 2025, 17(15), 2222; https://doi.org/10.3390/w17152222 - 25 Jul 2025
Abstract
Microbial communities are crucial to maintaining the ecological health of lakes, but their response to water quality and eutrophication is poorly understood. This study analyzed seasonal variation in the effect of water quality parameters on microbial community structure and function at southern Lake [...] Read more.
Microbial communities are crucial to maintaining the ecological health of lakes, but their response to water quality and eutrophication is poorly understood. This study analyzed seasonal variation in the effect of water quality parameters on microbial community structure and function at southern Lake Taihu. We observed poor water quality in autumn (low dissolved oxygen concentration and water transparency) with severe eutrophication (high in nitrogen, phosphorus, and microcystins). Microcystins were a major indicator of water quality that affected total phosphorus and dissolved oxygen concentrations. Redundancy analysis revealed that total nitrogen, total phosphorus, nitrate, ammonium, and microcystins, temperature, and dissolved oxygen all significantly influenced the microbial community. Microbial co-occurrence networks revealed significant seasonal variations, with autumn and winter exhibiting a more complex structure than other seasons. Additionally, we identified microcystin-sensitive microbial species as eutrophication indicators; they are involved in bacterial community components and metabolic function and fluctuate under seasonal changes to water quality. In conclusion, our findings provide insight into the relationship between water quality and microbial communities, offering an empirical basis for improving the sustainable management of Lake Taihu. Full article
(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)
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18 pages, 4024 KiB  
Article
Increasing the Thematic Resolution for Trees and Built Area in a Global Land Cover Dataset Using Class Probabilities
by Daniel T. Myers, Diana Oviedo-Vargas, Melinda Daniels and Yog Aryal
Remote Sens. 2025, 17(15), 2570; https://doi.org/10.3390/rs17152570 - 24 Jul 2025
Abstract
Land cover-based models that rely on purpose-specific thematic details are common in environmental fields such as hydrology, water quality, and ecology. Global remotely sensed land cover from the Dynamic World dataset on Google Earth Engine has trees and built area classes, but enables [...] Read more.
Land cover-based models that rely on purpose-specific thematic details are common in environmental fields such as hydrology, water quality, and ecology. Global remotely sensed land cover from the Dynamic World dataset on Google Earth Engine has trees and built area classes, but enables modelers to create more thematically detailed classifications based on pixel class probabilities from their convolutional neural network (CNN) classifier. However, more information is needed about how these probabilities relate to actual heterogeneity within a land cover class. We used Dynamic World CNN class probabilities to subclassify temperate and tropical forest types from the trees class in the Eastern United States and Costa Rica, as well as developed area intensities from the built class. Subclassifications were evaluated against reference data and in a watershed they were not trained for. The results on dominant temperate forest type user’s accuracy (i.e., of all the pixels classified as a specific land cover type, how many are actually that type) ranged from 43% to 76%, while producer’s accuracy (i.e., of all the actual pixels of a specific land cover type, how many were correctly classified) ranged from 50% to 70%. In the untrained watershed, the overall accuracy was 85% for temperate forest types and 52% for developed areas, demonstrating reliability in classifying forest and developed land cover types. This approach creates opportunities to access up-to-date land cover information with greater thematic detail. Full article
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23 pages, 3301 KiB  
Article
An Image-Based Water Turbidity Classification Scheme Using a Convolutional Neural Network
by Itzel Luviano Soto, Yajaira Concha-Sánchez and Alfredo Raya
Computation 2025, 13(8), 178; https://doi.org/10.3390/computation13080178 - 23 Jul 2025
Viewed by 36
Abstract
Given the importance of turbidity as a key indicator of water quality, this study investigates the use of a convolutional neural network (CNN) to classify water samples into five turbidity-based categories. These classes were defined using ranges inspired by Mexican environmental regulations and [...] Read more.
Given the importance of turbidity as a key indicator of water quality, this study investigates the use of a convolutional neural network (CNN) to classify water samples into five turbidity-based categories. These classes were defined using ranges inspired by Mexican environmental regulations and generated from 33 laboratory-prepared mixtures with varying concentrations of suspended clay particles. Red, green, and blue (RGB) images of each sample were captured under controlled optical conditions, and turbidity was measured using a calibrated turbidimeter. A transfer learning (TL) approach was applied using EfficientNet-B0, a deep yet computationally efficient CNN architecture. The model achieved an average accuracy of 99% across ten independent training runs, with minimal misclassifications. The use of a lightweight deep learning model, combined with a standardized image acquisition protocol, represents a novel and scalable alternative for rapid, low-cost water quality assessment in future environmental monitoring systems. Full article
(This article belongs to the Section Computational Engineering)
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23 pages, 4594 KiB  
Article
Ensemble Machine Learning Approaches for Bathymetry Estimation in Multi-Spectral Images
by Kazi Aminul Islam, Omar Abul-Hassan, Hongfang Zhang, Victoria Hill, Blake Schaeffer, Richard Zimmerman and Jiang Li
Geomatics 2025, 5(3), 34; https://doi.org/10.3390/geomatics5030034 - 22 Jul 2025
Viewed by 92
Abstract
Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named [...] Read more.
Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named CatBoostOpt, to estimate bathymetry based on high-resolution WorldView-2 (WV-2) multi-spectral optical satellite images. CatBoostOpt was demonstrated across the Florida Big Bend coastline, where the model learned correlations between in situ sound Navigation and Ranging (Sonar) bathymetry measurements and the corresponding multi-spectral reflectance values in WV-2 images to map bathymetry. We evaluated three different feature transformations as inputs for bathymetry estimation, including raw reflectance, log-linear, and log-ratio transforms of the raw reflectance value in WV-2 images. In addition, we investigated the contribution of each spectral band and found that utilizing all eight spectral bands in WV-2 images offers the best solution for handling complex water quality conditions. We compared CatBoostOpt with linear regression (LR), support vector machine (SVM), random forest (RF), AdaBoost, gradient boosting, and deep convolutional neural network (DCNN). CatBoostOpt with log-ratio transformed reflectance achieved the best performance with an average root mean square error (RMSE) of 0.34 and coefficient of determination (R2) of 0.87. Full article
(This article belongs to the Special Issue Advances in Ocean Mapping and Hydrospatial Applications)
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22 pages, 5356 KiB  
Article
Seaweed, Used as a Water-Retaining Agent, Improved the Water Distribution and Myofibrillar Protein Properties of Plant-Based Yak Meat Burgers Before and After Freeze–Thaw Cycles
by Yujiao Wang, Xinyi Chang, Yingzhen Wang, Jiahao Xie, Ge Han and Hang Qi
Foods 2025, 14(14), 2541; https://doi.org/10.3390/foods14142541 - 21 Jul 2025
Viewed by 168
Abstract
This study investigated quality changes in seaweed–yak patties before and after freeze–thaw by varying seaweed addition levels (10–70%). Macroscopically, the effects on water-holding capacity, textural properties, and oxidative indices of restructured yak patties were evaluated. Microscopically, the impact of seaweed-derived bioactive ingredients on [...] Read more.
This study investigated quality changes in seaweed–yak patties before and after freeze–thaw by varying seaweed addition levels (10–70%). Macroscopically, the effects on water-holding capacity, textural properties, and oxidative indices of restructured yak patties were evaluated. Microscopically, the impact of seaweed-derived bioactive ingredients on patty microstructure and myofibrillar protein characteristics was examined. LF-NMR and MRI showed that 40% seaweed addition most effectively restricted water migration, reduced thawing loss, and preserved immobilized water content. Texture profile analysis (TPA) revealed that moderate seaweed levels (30–40%) enhanced springiness and minimized post-thaw hardness increases. SEM confirmed that algal polysaccharides formed a denser protective network around the muscle fibers. Lipid oxidation (MDA), free-radical measurements, and non-targeted metabolomics revealed a significant reduction in oxidative damage at 40% seaweed addition, correlating with increased total phenolic content. Protein analyses (particle size, zeta potential, hydrophobicity, and SDS-PAGE) demonstrated a cryoprotective effect of seaweed on myofibrillar proteins, reducing aggregation and denaturation. These findings suggest that approximately 40% seaweed addition can improve the physicochemical stability and antioxidant capacity of frozen seaweed–yak meat products. This work thus identifies the optimal seaweed addition level for enhancing freeze–thaw stability and functional quality, offering practical guidance for the development of healthier, high-value restructured meat products. Full article
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19 pages, 6796 KiB  
Article
Performance Assessment of Advanced Daily Surface Soil Moisture Products in China for Sustainable Land and Water Management
by Dai Chen, Zhounan Dong and Jingnan Chen
Sustainability 2025, 17(14), 6482; https://doi.org/10.3390/su17146482 - 15 Jul 2025
Viewed by 174
Abstract
This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic [...] Read more.
This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic Soil Moisture Monitoring Network. All products were standardized to a 0.25° × 0.25° grid in the WGS-84 coordinate system through reprojection and resampling for consistent comparison. Daily averaged station observations were matched to product pixels using a 10 km radius buffer, with the mean station value as the reference for each time series after rigorous quality control. Results reveal distinct performance rankings, with SMAP-based products, particularly the SMAP_IB descending orbit variant, achieving the lowest unbiased root mean square deviation (ubRMSD) and highest correlation with in situ data. Blended products like ESA CCI and NOAA SMOPS, alongside reanalysis datasets such as ERA5 and MERRA2, outperformed SMOS and China’s FY3 products. The SoMo.ml product showed the broadest spatial coverage and strong temporal consistency, while FY3-based products showed limitations in spatial reliability and seasonal dynamics capture. These findings provide critical insights for selecting appropriate soil moisture datasets to enhance sustainable agricultural practices, optimize water resource allocation, monitor ecosystem resilience, and support climate adaptation strategies, therefore advancing sustainable development across diverse geographical regions in China. Full article
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19 pages, 3619 KiB  
Article
An Adaptive Underwater Image Enhancement Framework Combining Structural Detail Enhancement and Unsupervised Deep Fusion
by Semih Kahveci and Erdinç Avaroğlu
Appl. Sci. 2025, 15(14), 7883; https://doi.org/10.3390/app15147883 - 15 Jul 2025
Viewed by 157
Abstract
The underwater environment severely degrades image quality by absorbing and scattering light. This causes significant challenges, including non-uniform illumination, low contrast, color distortion, and blurring. These degradations compromise the performance of critical underwater applications, including water quality monitoring, object detection, and identification. To [...] Read more.
The underwater environment severely degrades image quality by absorbing and scattering light. This causes significant challenges, including non-uniform illumination, low contrast, color distortion, and blurring. These degradations compromise the performance of critical underwater applications, including water quality monitoring, object detection, and identification. To address these issues, this study proposes a detail-oriented hybrid framework for underwater image enhancement that synergizes the strengths of traditional image processing with the powerful feature extraction capabilities of unsupervised deep learning. Our framework introduces a novel multi-scale detail enhancement unit to accentuate structural information, followed by a Latent Low-Rank Representation (LatLRR)-based simplification step. This unique combination effectively suppresses common artifacts like oversharpening, spurious edges, and noise by decomposing the image into meaningful subspaces. The principal structural features are then optimally combined with a gamma-corrected luminance channel using an unsupervised MU-Fusion network, achieving a balanced optimization of both global contrast and local details. The experimental results on the challenging Test-C60 and OceanDark datasets demonstrate that our method consistently outperforms state-of-the-art fusion-based approaches, achieving average improvements of 7.5% in UIQM, 6% in IL-NIQE, and 3% in AG. Wilcoxon signed-rank tests confirm that these performance gains are statistically significant (p < 0.01). Consequently, the proposed method significantly mitigates prevalent issues such as color aberration, detail loss, and artificial haze, which are frequently encountered in existing techniques. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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19 pages, 1681 KiB  
Article
Mixed Inoculation with Lacticaseibacillus casei and Staphylococcus carnosus Improves Safety, Gel Properties and Flavor of Giant Squid Surimi Without Added Seasonings
by Hongliang Mu, Peifang Weng and Zufang Wu
Fermentation 2025, 11(7), 404; https://doi.org/10.3390/fermentation11070404 - 14 Jul 2025
Viewed by 287
Abstract
The gel performance of giant squid is weak. Researchers have confirmed that adding some substances could improve the texture. However, the flavor has not been taken into account. In a previous study, we proved that mixed inoculation with Lacticaseibacillus casei and Staphylococcus carnosus [...] Read more.
The gel performance of giant squid is weak. Researchers have confirmed that adding some substances could improve the texture. However, the flavor has not been taken into account. In a previous study, we proved that mixed inoculation with Lacticaseibacillus casei and Staphylococcus carnosus with several seasonings adding could improve the texture of squid. Whether the addition of seasonings could affect the quality of samples or not and how fermentation affects the texture and flavor were not clear. In present study, we prepared fermented squid without seasonings. The results showed that compared with fermented samples with added seasonings, samples without seasonings might be safer, with fewer types and lower concentrations of biogenic amines. In samples without seasonings, non-inoculation had a higher pH and higher levels of biogenic amines. Meanwhile, mixed inoculation with L. casei and S. carnosus could ensure safety, improve texture and rheological properties. The water state of the fermented sample was also changed. The microstructure indicated that good network was formed in the fermented sample. After fermentation, the contents of several organic acids, free amino acids and volatile flavor compounds increased, and the results of the electronic nose test were also changed. In addition, starters were dominant during fermentation. These results indicated that mixed inoculation without seasonings might be a safer method than that with seasonings. In addition, mixed inoculation without seasonings could improve the texture and flavor of the squid. These results lay the foundation for improving fermented squid quality in further studies. Full article
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23 pages, 10392 KiB  
Article
Dual-Branch Luminance–Chrominance Attention Network for Hydraulic Concrete Image Enhancement
by Zhangjun Peng, Li Li, Chuanhao Chang, Rong Tang, Guoqiang Zheng, Mingfei Wan, Juanping Jiang, Shuai Zhou, Zhenggang Tian and Zhigui Liu
Appl. Sci. 2025, 15(14), 7762; https://doi.org/10.3390/app15147762 - 10 Jul 2025
Viewed by 197
Abstract
Hydraulic concrete is a critical infrastructure material, with its surface condition playing a vital role in quality assessments for water conservancy and hydropower projects. However, images taken in complex hydraulic environments often suffer from degraded quality due to low lighting, shadows, and noise, [...] Read more.
Hydraulic concrete is a critical infrastructure material, with its surface condition playing a vital role in quality assessments for water conservancy and hydropower projects. However, images taken in complex hydraulic environments often suffer from degraded quality due to low lighting, shadows, and noise, making it difficult to distinguish defects from the background and thereby hindering accurate defect detection and damage evaluation. In this study, following systematic analyses of hydraulic concrete color space characteristics, we propose a Dual-Branch Luminance–Chrominance Attention Network (DBLCANet-HCIE) specifically designed for low-light hydraulic concrete image enhancement. Inspired by human visual perception, the network simultaneously improves global contrast and preserves fine-grained defect textures, which are essential for structural analysis. The proposed architecture consists of a Luminance Adjustment Branch (LAB) and a Chroma Restoration Branch (CRB). The LAB incorporates a Luminance-Aware Hybrid Attention Block (LAHAB) to capture both the global luminance distribution and local texture details, enabling adaptive illumination correction through comprehensive scene understanding. The CRB integrates a Channel Denoiser Block (CDB) for channel-specific noise suppression and a Frequency-Domain Detail Enhancement Block (FDDEB) to refine chrominance information and enhance subtle defect textures. A feature fusion block is designed to fuse and learn the features of the outputs from the two branches, resulting in images with enhanced luminance, reduced noise, and preserved surface anomalies. To validate the proposed approach, we construct a dedicated low-light hydraulic concrete image dataset (LLHCID). Extensive experiments conducted on both LOLv1 and LLHCID benchmarks demonstrate that the proposed method significantly enhances the visual interpretability of hydraulic concrete surfaces while effectively addressing low-light degradation challenges. Full article
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31 pages, 6826 KiB  
Article
Machine Learning-Assisted NIR Spectroscopy for Dynamic Monitoring of Leaf Potassium in Korla Fragrant Pear
by Mingyang Yu, Weifan Fan, Junkai Zeng, Yang Li, Lanfei Wang, Hao Wang, Feng Han and Jianping Bao
Agronomy 2025, 15(7), 1672; https://doi.org/10.3390/agronomy15071672 - 10 Jul 2025
Viewed by 225
Abstract
Potassium (K), a critical macronutrient for the growth and development of Korla fragrant pear (Pyrus sinkiangensis Yu), plays a pivotal regulatory role in sugar-acid metabolism. Furthermore, K exhibits a highly specific response in near-infrared (NIR) spectroscopy compared to elements such as nitrogen (N) [...] Read more.
Potassium (K), a critical macronutrient for the growth and development of Korla fragrant pear (Pyrus sinkiangensis Yu), plays a pivotal regulatory role in sugar-acid metabolism. Furthermore, K exhibits a highly specific response in near-infrared (NIR) spectroscopy compared to elements such as nitrogen (N) and phosphorus (P). Given its fundamental impact on fruit quality parameters, the development of rapid and non-destructive techniques for K determination is of significant importance for precision fertilization management. By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. “Near-infrared spectroscopy coupled with machine learning can enable accurate, non-destructive monitoring of potassium dynamics in Korla pear leaves, with prediction accuracy (R2) exceeding 0.86 under field conditions.” We systematically collected a total of 9000 leaf samples from Korla fragrant pear orchards and acquired spectral data using a benchtop near-infrared spectrometer. After preprocessing and feature extraction, we determined the optimal modeling method for prediction accuracy through comparative analysis of multiple models. Multiplicative scatter correction (MSC) and first derivative (FD) are synergistically employed for preprocessing to eliminate scattering interference and enhance the resolution of characteristic peaks. Competitive adaptive reweighted sampling (CARS) is then utilized to screen five potassium-sensitive bands, specifically in the regions of 4003.5–4034.35 nm, 4458.62–4562.75 nm, and 5145.15–5249.29 nm, among others, which are associated with O-H stretching vibration and changes in water status. A comparison between random forest (RF) and BP neural network indicates that the MSC + FD–CARS–BP model exhibits the optimal performance, achieving coefficients of determination (R2) of 0.96% and 0.86% for the training and validation sets, respectively, root mean square errors (RMSE) of 0.098% and 0.103%, a residual predictive deviation (RPD) greater than 3, and a ratio of performance to interquartile range (RPIQ) of 4.22. Parameter optimization revealed that the BPNN model achieved optimal stability with 10 neurons in the hidden layer. The model facilitates rapid and non-destructive detection of leaf potassium content throughout the entire growth period of Korla fragrant pears, supporting precision fertilization in orchards. Moreover, it elucidates the physiological mechanism by which potassium influences spectral response through the regulation of water metabolism. Full article
(This article belongs to the Section Precision and Digital Agriculture)
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22 pages, 4476 KiB  
Article
A Method for Identifying Key Areas of Ecological Restoration, Zoning Ecological Conservation, and Restoration
by Shuaiqi Chen, Zhengzhou Ji and Longhui Lu
Land 2025, 14(7), 1439; https://doi.org/10.3390/land14071439 - 10 Jul 2025
Viewed by 269
Abstract
Ecological security patterns (ESPs) are fundamental to safeguarding regional ecological integrity and enhancing human well-being. Consequently, research on conservation and restoration in critical regions is vital for ensuring ecological security and optimizing territorial ecological spatial configurations. Focusing on the Henan section of the [...] Read more.
Ecological security patterns (ESPs) are fundamental to safeguarding regional ecological integrity and enhancing human well-being. Consequently, research on conservation and restoration in critical regions is vital for ensuring ecological security and optimizing territorial ecological spatial configurations. Focusing on the Henan section of the Yellow River Basin, this study established the regional ESP and conservation–restoration framework through an integrated approach: (1) assessing four key ecosystem services—soil conservation, water retention, carbon sequestration, and habitat quality; (2) identifying ecological sources based on ecosystem service importance classification; (3) calculating a comprehensive resistance surface using the entropy weight method, incorporating key factors (land cover type, NDVI, topographic relief, and slope); (4) delineating ecological corridors and nodes using Linkage Mapper and the minimum cumulative resistance (MCR) theory; and (5) integrating ecological functional zoning to synthesize the final spatial conservation and restoration strategy. Key findings reveal: (1) 20 ecological sources, totaling 8947 km2 (20.9% of the study area), and 43 ecological corridors, spanning 778.24 km, were delineated within the basin. Nineteen ecological barriers (predominantly located in farmland, bare land, construction land, and low-coverage grassland) and twenty-one ecological pinch points (primarily clustered in forestland, grassland, water bodies, and wetlands) were identified. Collectively, these elements form the Henan section’s Ecological Security Pattern (ESP), integrating source areas, a corridor network, and key regional nodes for ecological conservation and restoration. (2) Building upon the ESP and the ecological baseline, and informed by ecological functional zoning, we identified a spatial framework for conservation and restoration characterized by “one axis, two cores, and multiple zones”. Tailored conservation and restoration strategies were subsequently proposed. This study provides critical data support for reconciling ecological security and economic development in the Henan Yellow River Basin, offering a scientific foundation and practical guidance for regional territorial spatial ecological restoration planning and implementation. Full article
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28 pages, 14588 KiB  
Article
CAU2DNet: A Dual-Branch Deep Learning Network and a Dataset for Slum Recognition with Multi-Source Remote Sensing Data
by Xi Lyu, Chenyu Zhang, Lizhi Miao, Xiying Sun, Xinxin Zhou, Xinyi Yue, Zhongchang Sun and Yueyong Pang
Remote Sens. 2025, 17(14), 2359; https://doi.org/10.3390/rs17142359 - 9 Jul 2025
Viewed by 209
Abstract
The efficient and precise identification of urban slums is a significant challenge for urban planning and sustainable development, as their morphological diversity and complex spatial distribution make it difficult to use traditional remote sensing inversion methods. Current deep learning (DL) methods mainly face [...] Read more.
The efficient and precise identification of urban slums is a significant challenge for urban planning and sustainable development, as their morphological diversity and complex spatial distribution make it difficult to use traditional remote sensing inversion methods. Current deep learning (DL) methods mainly face challenges such as limited receptive fields and insufficient sensitivity to spatial locations when integrating multi-source remote sensing data, and high-quality datasets that integrate multi-spectral and geoscientific indicators to support them are scarce. In response to these issues, this study proposes a DL model (coordinate-attentive U2-DeepLab network [CAU2DNet]) that integrates multi-source remote sensing data. The model integrates the multi-scale feature extraction capability of U2-Net with the global receptive field advantage of DeepLabV3+ through a dual-branch architecture. Thereafter, the spatial semantic perception capability is enhanced by introducing the CoordAttention mechanism, and ConvNextV2 is adopted to optimize the backbone network of the DeepLabV3+ branch, thereby improving the modeling capability of low-resolution geoscientific features. The two branches adopt a decision-level fusion mechanism for feature fusion, which means that the results of each are weighted and summed using learnable weights to obtain the final output feature map. Furthermore, this study constructs the São Paulo slums dataset for model training due to the lack of a multi-spectral slum dataset. This dataset covers 7978 samples of 512 × 512 pixels, integrating high-resolution RGB images, Normalized Difference Vegetation Index (NDVI)/Modified Normalized Difference Water Index (MNDWI) geoscientific indicators, and POI infrastructure data, which can significantly enrich multi-source slum remote sensing data. Experiments have shown that CAU2DNet achieves an intersection over union (IoU) of 0.6372 and an F1 score of 77.97% on the São Paulo slums dataset, indicating a significant improvement in accuracy over the baseline model. The ablation experiments verify that the improvements made in this study have resulted in a 16.12% increase in precision. Moreover, CAU2DNet also achieved the best results in all metrics during the cross-domain testing on the WHU building dataset, further confirming the model’s generalizability. Full article
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20 pages, 2942 KiB  
Article
Zooplankton Community Responses to Eutrophication and TOC: Network Clustering in Regionally Similar Reservoirs
by Yerim Choi, Hye-Ji Oh, Geun-Hyeok Hong, Dae-Hee Lee, Jeong-Hui Kim, Sang-Hyeon Park, Jung-Ho Yun and Kwang-Hyeon Chang
Water 2025, 17(14), 2051; https://doi.org/10.3390/w17142051 - 9 Jul 2025
Viewed by 236
Abstract
This study analyzed the relationship between zooplankton communities and water quality characteristics, with a focus on total organic carbon (TOC), in 22 reservoirs within the Geum River basin that share similar climatic conditions but exhibit varying levels of pollution. Across all reservoirs, zooplankton [...] Read more.
This study analyzed the relationship between zooplankton communities and water quality characteristics, with a focus on total organic carbon (TOC), in 22 reservoirs within the Geum River basin that share similar climatic conditions but exhibit varying levels of pollution. Across all reservoirs, zooplankton community structures showed the highest correlations with TOC, suspended solids (SS), chlorophyll-a (Chl-a), and Secchi depth (SD), with stronger associations observed for rotifers and cladocerans compared to copepods. The classification of zooplankton community composition patterns, followed by an analysis of their associations with TOC concentrations, revealed relatively distinct differences between high-TOC and low-TOC reservoirs, indicating that TOC functions as a key determinant of community composition. Meanwhile, network analysis based on overall water quality characteristics indicated that patterns of water quality similarity among zooplankton-based communities differed somewhat from those based solely on TOC concentrations, suggesting that TOC may exert an independent influence on zooplankton community structure. In high-TOC reservoirs, typical eutrophic characteristics—such as elevated chlorophyll-a, total phosphorus, and suspended solids, along with reduced water transparency—were observed, accompanied by higher zooplankton abundance and a greater proportion of rotifers within the community. In contrast, low-TOC reservoirs, despite exhibiting no marked differences in other water quality variables, showed higher diversity of cladocerans alongside rotifers, further supporting the independent role of TOC in shaping zooplankton community structures. These findings highlight TOC not only as a general indicator of pollution but also as an ecologically significant factor influencing zooplankton community composition and carbon dynamics in reservoir ecosystems. They suggest that TOC should be considered a key variable in future assessments and management of lentic ecosystems. Full article
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28 pages, 13281 KiB  
Article
Salinity Gradients Override Hydraulic Connectivity in Shaping Bacterial Community Assembly and Network Stability at a Coastal Aquifer–Reservoir Interface
by Cuixia Zhang, Haiming Li, Mengdi Li, Qian Zhang, Sihui Su, Xiaodong Zhang and Han Xiao
Microorganisms 2025, 13(7), 1611; https://doi.org/10.3390/microorganisms13071611 - 8 Jul 2025
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Abstract
The coastal zone presents complex hydrodynamic interactions among inland groundwater, reservoir water, and intruding seawater, with important implications for ecosystem functioning and water quality. However, the relative roles of hydraulic connectivity and seawater-driven salinity gradients in shaping microbial communities at the aquifer–reservoir interface [...] Read more.
The coastal zone presents complex hydrodynamic interactions among inland groundwater, reservoir water, and intruding seawater, with important implications for ecosystem functioning and water quality. However, the relative roles of hydraulic connectivity and seawater-driven salinity gradients in shaping microbial communities at the aquifer–reservoir interface remain unclear. Here, we integrated hydrochemical analyses with high-throughput 16S rRNA gene sequencing to investigate bacterial community composition, assembly processes, and co-occurrence network patterns across groundwater_in (entering the reservoir), groundwater_out (exiting the reservoir), and reservoir water in a coastal system. Our findings reveal that seawater intrusion exerts a stronger influence on groundwater_out, leading to distinct chemical profiles and salinity-driven environmental filtering, whereas hydraulic connectivity promotes greater microbial similarity between groundwater_in and reservoir water. Groundwater samples exhibited higher alpha and beta diversity compared to the reservoir, with dominant taxa such as Comamonadaceae, Flavobacteriaceae, and Rhodobacteraceae serving as indicators of seawater intrusion. Community assembly analyses showed that homogeneous selection predominated, especially under strong salinity gradients, while dispersal limitation and spatial distance also contributed in areas of reduced connectivity. Key chemical factors, including TDS, Na+, Cl, Mg2+, and K+, strongly shaped groundwater communities. Additionally, groundwater bacterial networks were more complex and robust than those in reservoir water, suggesting enhanced resilience to salinity stress. Collectively, this study demonstrates that salinity gradients can override the effects of hydraulic connectivity in structuring bacterial communities and their networks at coastal interfaces. Our findings provide novel microbial insights relevant for understanding biogeochemical processes and support the use of microbial indicators for more sensitive monitoring and management of coastal groundwater resources. Full article
(This article belongs to the Special Issue Microbial Communities in Aquatic Environments)
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