Multispectral remote sensing technology using unmanned aerial vehicles (UAVs) is able to provide fast, large-scale, and dynamic monitoring and management of water environments. We here select multiple water-body indices based on their spectral reflection characteristics, analyze correlations between the reflectance values of water body indices and the water quality parameters of synchronous measured sampling points, and obtain an optimal water body index. A representative selection, such as statistical analysis methods, neural networks, random forest, XGBoost and other schemes are then used to build water-quality parameter inversion models. Results show that the XGBoost model has the highest accuracy for dissolved oxygen parameters (R² = 0.812, RMSE = 0.414 mg L⁻¹, MRE = 0.057) and the random forest model has the highest accuracy for turbidity parameters (R² = 0.753, RMSE = 0.732 NTU, MRE = 0.065). Finally, spatial distribution maps of dissolved oxygen and turbidity of water bodies in the experimental domain are drawn to visualize water-quality parameters. This study provides a detailed comparative analysis of multiple inversion methods, including parameter quantity, processing speed, algorithm rigor, solution accuracy, robustness, and generalization, and further evaluates the technical characteristics and applicability of several inversion methods. Our results can provide guidance for improved small- and medium-sized surface-water quality monitoring, and provide an intuitive data analysis basis for urban water environment management.

The assessment and prediction of regional water quality are fundamental inputs to environmental planning and watershed ecological management. This paper explored spatiotemporal changes in the correlation of water quality parameters (WQPs) and land-use types (LUTs) in a reticular river network area. Water samples of 44 sampling sites were collected every quarter from 2016 to 2018 and evaluated for dissolved oxygen (DO), total phosphorus (TP), ammonia nitrogen (NH₃-N), and permanganate index (COD_Mn). A redundancy analysis (RDA) and stepwise multiple linear regression (SMLR) were applied to analyze the land-use type impacts on seasonal WQPs at five buffer scales (100, 200, 500, 800, and 1000 m). The Kruskal–Wallis test results revealed significant seasonal differences in NH₃-N, TP, COD_Mn, and DO. The area percentages of farmland, water area and built-up land in the study area were 38.96%, 22.75% and16.20%, respectively, for a combined total area percentage of nearly 80%. Our study showed that orchard land had an especially favorable influence on WQPs. Land-use type impacts on WQPs were more significant during the dry season than the wet season. The total variation explained by LUTs regarding WQPs at the 1 km buffer scale was slightly stronger than at smaller buffer scales. Built-up land had a negative effect on WQPs, but orchard and forest-grassland had a positive effect on WQPs. The effects of water area and farmland on WQPs were complex on different buffer scales. These findings are helpful for improving regional water resource management and environmental planning.

Understanding the spatiotemporal patterns of water quality is crucial because it provides essential information for water pollution control. The spatiotemporal variations in water quality for the Nanxi River in the Taihu watershed of China were evaluated by a water quality index (WQI) and multivariate statistical techniques; additionally, the potential sources of contamination were identified. The data set included 22 water quality parameters collected during the monitoring period from 2015 to 2020 for 14 monitoring stations. WQI assessment revealed that approximately 85% of monitoring stations were classified as “medium-low” water quality, and most showed continuous improvement in water quality. Cluster analysis divided the 14 monitoring stations into three clusters (low contamination, medium contamination and high contamination). Discriminant analysis identified pH, petroleum, volatile phenol, chemical oxygen demand, total phosphorus, F, S, fecal coliform, SO₄, Cl, NO₃-N, total hardness, NO₂-N and NH₃ as important parameters affecting spatial variations. Factor analysis identified four potential contamination source types: nutrient, organics, feces and oil. This study demonstrated the usefulness of multivariate statistical techniques in assessing large data sets, identifying contamination source types, and better understanding spatiotemporal variations in water quality to restore and protect water resources.

A comprehensive exploration of the trade-offs/synergies and drivers of ecosystem services (ESs) is essential for formulating ecological plans. However, owing to the limited attention given to multiple scales, the relationship of ESs still needs to be further explored. Taking the Yangtze River Delta region of China as the study area, a multiscale data framework with a 1 km grid and 10 km grid and county was established, and six ESs were evaluated for 2000, 2010, and 2020. Then, the trade-offs and synergies between ESs were explored by Spearman’s correlation analysis and geographically weighted regression (GWR), and the ecosystem service bundles (ESBs) were identified by self-organizing maps (SOMs). Finally, the socioecological drivers of ESs were further analyzed via GeoDetector. The results showed that (1) the distribution of ESs exhibited spatial heterogeneity. (2) At the grid scale, there were very strong trade-off effects between crop production and the other ESs. The synergistic effects between ESs at the county level were further strengthened. (3) The ESBs identified at different temporal and spatial scales were different. (4) Land use had the strongest explanatory power for all the ESs. At the grid scale, climatic and biophysical factors had great impacts on ESs, whereas population density and night light remote sensing had significant impacts on crop production, carbon storage, and water yield at the county scale.

Understanding the intricate interrelationships among ecosystem services (ESs) is fundamental to advancing sustainable ecological management. This study focuses on the Taihu Basin and examines five representative ESs, including water yield (WY), carbon sequestration (CS), soil retention (SR), habitat quality (HQ), and crop production (CP), for the years 2000, 2010, and 2020. Spatial distribution characteristics and spatiotemporal dynamics were quantified through the combined application of the InVEST model, a food production model, and ArcGIS. Spearman correlation analysis and K-means clustering were then applied to characterize trade-offs and synergies among ESs and to delineate ecosystem service bundles at multiple spatial scales, including 1 km × 1 km grids, 10 km × 10 km grids, and the county level, while GeoDetector was used to identify the associated driving mechanisms. The results indicated that (1) between 2000 and 2020, the spatial distribution pattern of the ESs in the Taihu Basin underwent significant changes, with WY and SR increasing by 48.97% and 51.89%, respectively, while HQ, CS, and CP decreased by 17.2%, 15.5%, and 47.6%. (2) From an overall perspective of trade-offs and synergies, the interactions among ESs shifted from trade-offs (r < 0) to synergies (r > 0) as the scale increased. From the perspective of the spatial characteristics of trade-offs and synergies, the intensity of these interactions varied significantly with increasing scale, but the trend remained relatively stable. (3) The Taihu Basin can be categorized into six ES bundles (ESBs). ESB 1, ESB 3, ESB 4, and ESB 5 have relatively stable ES structures, whereas ESBs 2 and 6 display significant variations. (4) The primary factors influencing ESs vary significantly across different spatial scales, with land use/land cover (LULC) and the proportions of arable land, forestland, and buildings exhibiting strong explanatory power. This highlights the critical role of coupled natural and anthropogenic processes in shaping the spatial patterns of ESs. This study considers the spatiotemporal variation and scale dependence of ecosystem services, providing management recommendations tailored to different regions and spatial scales, and offering a scientific basis for regional ecological planning and watershed governance.

Existing road crack inspection using UAVs and vehicle-mounted systems often relies on local field-of-view observations, requiring repeated scanning in large road scenes. We present CCVS-3ASeg, a framework for a preliminary feasibility evaluation of UAV-based large-scale road crack semantic segmentation. Its design focuses on two coupled challenges: computational overhead from large-scale feature representation and loss of fine crack features during spatial compression. CCVS extracts road regions and performs direction-aligned slicing, while 3ASeg uses an Ultra-Lightweight Adversarial Attention Mechanism (Ultra-LAAM) to assist crack-feature representation. Evaluation comprised CCVS road processing on the Custom-Dataset, 3ASeg-assisted segmentation on Crack500 and a selected public-source Crack-Dataset, and a two-image G94 Expressway case study of the connected workflow. Across test images, CCVS achieved recall of 0.9377 ± 0.1517 and pixel accuracy (PA) of 0.9606 ± 0.1010. On Crack500, the largest observed mIoU gain among evaluated 3ASeg-assisted configurations was 6.93 percentage points, and Ultra-LAAM reached 1132.27 ± 18.15 FPS. These component‑level experiments preliminarily demonstrate that large‑scale road extraction to assist pavement crack segmentation is a viable direction for further exploration and optimization.