Search Results (56)

Investigating the coevolution between ecosystem services (ES) and human well-being (HWB) holds significant implications for achieving the sustainable operation of human–environment systems. However, limited research has focused on ES-HWB interactions in ecotourism-dominated counties. To address this gap, this study takes Chun’an County in Zhejiang Province, China, as a case study, with the research objective of exploring the processes, patterns, and mechanisms of the coevolution between ecosystem services (ES) and human well-being (HWB) in ecotourism-dominated counties. By integrating multi-source heterogeneous data, including land use data, the normalized difference vegetation index (NDVI), and statistical records, and employing methods such as the dynamic equivalent factor method, the PLUS model, the coupling coordination degree model, and comprehensive evaluation, we analyzed the synergistic evolution of ES-HWB in Chun’an County from 2000 to 2020. The results indicate that (1) the ecosystem service value (ESV) fluctuated between 30.15 and 36.85 billion CNY, exhibiting a spatial aggregation pattern centered on the Qiandao Lake waterbody, with distance–decay characteristics. The PLUS model confirms ecological conservation policies optimize ES patterns. (2) The HWB index surged from 0.16 to 0.8, driven by tourism-led economic growth, infrastructure investment, and institutional innovation, facilitating a paradigm shift from low to high well-being at the county level. (3) The ES-HWB interaction evolved through three phases—disordered, antagonism, and coordination—revealing tourism as a key mediator driving coupled human–environment system sustainability via a pressure–adaptation–synergy transmission mechanism. This study not only advances the understanding of ES-HWB coevolution in ecotourism-dominated counties, but also provides a transferable methodological framework for sustainable development in similar regions. Full article

Global climate change and human activities are significant threats to biodiversity, contributing to the endangerment of approximately 41% of amphibian species worldwide. In this study, we applied field survey methods, the MaxEnt model, and integrated climate and human activity data to predict potential changes in the diversity and distribution of amphibian species in Huangshan Mountain, China. In this study, we have found 23 amphibian species, belonging to two orders, eight families, and 18 genera. The MaxEnt models showed that the distance from farmland (contributing 26.2%), shrubs (15.6%), and waterbodies (10.6%), as well as the NDVI (Normalized Difference Vegetation Index) (10.1%), significantly influence species distribution and diversity, suggesting that amphibian species prefer habitats with lower levels of human disturbance. Our models also showed that Bio3 (isothermal) (8.9%) and Bio8 (mean temperature of wettest quarter) (8.6%) have a significant impact on the species distribution and diversity, suggesting that amphibians are influenced by temperature and humidity. Our field survey showed that seasonal variation in amphibian diversity revealed significant correlations between climatic factors. Specifically, amphibian species diversity was positively correlated with wind speed, soil moisture, and rainfall (p < 0.05), while amphibian abundance was significantly linked to soil temperature, soil moisture, and rainfall (p < 0.05). These findings underscore the critical role of both climatic conditions and habitat structure in shaping amphibian populations and their distribution in Huangshan Mountain. Therefore, local management authorities should continue to monitor the marginal areas of the region, taking into account key human disturbances and climatic factors that favor the formation of amphibian diversity hotspots. Protective buffer zones should be established to provide effective refuges for amphibians. Full article

Fishponds are regarded as hypertrophic systems accompanied by low biodiversity. We focused on the phytoplankton diversity of 15 fishponds located in Austria. Of the 15 fishponds, 12 waterbodies are aquaculture ponds stocked with common carp, which converted to organic farming some years ago with grain as supplementary feed, and 3 ponds are used for recreational fishing. The trophic state index increased from 59 to 71 in spring to 80 to 93 in autumn and classified the ponds as mid-eutrophic to hypertrophic. The taxa number was surprisingly high (taxa richness up to 100 taxa per pond). The phytoplankton resource use efficiency was in the upper range of eutrophicated waters and did not show seasonal differences (median Chlorophyll-a/total phosphorus = 1.94, Chlorophyll-a/total nitrogen = 0.12). Linking environmental data with the algal community resulted in a distinct temporal community pattern with a significant seasonal shift from the cooler season dominated by Ochrophyta taxa to green algae as the most abundant group in summer and autumn. Our findings challenge general assumptions regarding low phytoplankton diversity with long-lasting Cyanobacteria blooms and conform to the algal dynamics described in the plankton ecology group (PEG) model for temperate shallow lakes. These man-made systems are an ecological asset, highly connected to terrestrial habitats in their vicinity and significantly contributing to the ecological health and long-term sustainability of the region. Full article

Freshwater ecosystems face increasing pressures from human activities, leading to degraded water quality and altered habitats for aquatic species. This study investigates the relationship between water quality and waterbird distribution along the Lieve River, Belgium, based on manually conducted waterbird counts and water quality data collected from 48 transects in March 2024. Localized eutrophication was evident, with TN (2.7–5.6 mg L⁻¹), TP (up to 0.46 mg L⁻¹), and chlorophyll-a (median 70 ppb) exceeding environmental thresholds. Prati index analysis revealed that 58.3% of the sampling points along the Lieve River were categorized as “polluted”, reflecting extensive water quality degradation. Eurasian coots (71.4%) and wild ducks (72.4%) were predominantly found in polluted areas, thriving in nutrient-enriched habitats linked to high TP levels. In contrast, common moorhens (80.3%) preferred acceptable quality areas, indicating higher water quality requirements. These findings indicate that phosphate is a key driver of waterbody eutrophication, as evidenced by the TP concentrations measured on-site, which far exceed the thresholds set by environmental standards. Future research should explore advanced monitoring approaches to improve waterbird and water quality assessments, ensuring the conservation of the Lieve River as one of Europe’s oldest artificial canals, and the protection of its waterbird habitats. Full article

Internal phosphorus (P) loading is a key driver of waterbody eutrophication. Various sediment P indexes are developed to assess sediment P risks by linking them to water column P, but their seasonal reliability remains underexplored. This study evaluated, for the first time, sediment P status in the Xiashan reservoir, a large shallow reservoir in northern China serving 9.4 million people. The ability of three P indexes, including exchangeable P (Ex-P), Olsen P (Olsen-P), and diluted HCl-extractable P (HCl-P), to predict water column P concentrations was tested across February, May, and August. Sediments in the Xiashan reservoir exhibited moderate total P levels (531–650 mg kg⁻¹) but high P availability, with Ex-P, Olsen-P, and HCl-P in ranges of 19–35, 58–101, and 327–444 mg kg⁻¹, respectively, likely due to sandy composition. Water column P concentrations significantly correlate with August sediment P indexes (r = 0.42–0.81) but not with February and May sediments, highlighting the ability of August sediment P indexes to predict water column P across seasons. Sampling in August is recommended to efficiently identify critical zones for internal P loading, with Ex-P as the preferred indicator given its simple extraction and strong correlation with water column P (r = 0.81). Full article

Road dust carries various contaminants and causes urban non-point source pollution in waterbodies through runoff. Road dust samples were collected in each month in two years and then sieved into five particle size fractions. The concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in each fraction were measured. The particle size fraction load index, coefficient of divergence, and Nemerow integrated risk index were used to analyze the temporal variation of pollution load and ecological risk in different particle size fractions. The advanced three-way model and wavelet analysis were used in quantitative identification and time-series analysis of sources. Results showed that both the pollution load and ecological risk of most heavy metals showed a decreasing trend from the finest fraction (P1) to the coarsest fraction (P5). The frequency of heavy metals in P1 posing extreme risk was about two times that of P5. Main types of heavy metal sources were similar among different fractions, whereas the impact intensity of these sources varied among different fractions. Traffic exhaust tended to accumulate in finer particles, and its contribution to Cu in P5 was only 35–55% of that in other fractions. Construction contributed more to coarser particles, and its contribution to Pb was increased from 45.34% in P1 to 65.35% in P5. Wavelet analysis indicated that traffic exhaust showed periodicities of 5–8 and 10–13 months. Fuel combustion displayed the strongest periodicity of 12–15 months, peaking in winter. Full article

Chaohu Lake, as an important freshwater lake in China, mainly relies on surface runoff for water replenishment, and the environmental quality of the surrounding towns directly impacts the environment of Chaohu Lake. Given the characteristics of rich water resources and extensive river networks in the lake area, this paper utilizes the GEE platform and selects Landsat data from 1992 to 2022, taking Feidong County, one of the lake’s inlets, as the study area. We used the water benefit-based ecological index (WBEI) to monitor and evaluate the ecological quality of the study area and employ the Sen+MK trend analysis method to analyze the spatial-temporal characteristics of ecological quality changes. To explore the driving forces behind the spatial-temporal changes in the WBEI, this study selects land use type, elevation, slope, aspect, potential evapotranspiration, annual average precipitation, annual average temperature, and five characteristic factors used in the construction of the WBEI as influencing factors. Using the geo-detector method, the study analyzes the driving forces behind the spatial-temporal changes in the WBEI in the study area. Results show that the WBEI, considering water efficiency, integrates waterbody information into regional environmental quality assessments, comprehensively reflecting the ecological environment of lakeside cities. From 1992 to 2022, the WBEI of the study region shows an increasing trend, with an improved area accounting for 1110.42 km², or 51.21% of the total area. Among these, the significantly improved area covers 372.9789 km² or 17.2% of the total area, while the slightly improved area covers 737.4411 km², corresponding to 34.01% of the total area. Interaction types of influencing factors include bivariate enhancement and nonlinear enhancement, with the primary interactive factors affecting the ecological environment quality change in Feidong County being CLCD∩RVI; changes in land use and vegetation cover are the main driving forces behind the changes in ecological and environmental quality in Feidong County. From 1992 to 2022, the main land type transformations in the study area were from arable land to other land types, with a significant conversion of arable land to construction land, which is the main reason for the degradation of local ecological and environmental quality. The results of this study can provide practical references and theoretical support for ecological environment assessment, governance, and improvement in areas with abundant water resources. Full article

The family Chironomidae is speciose and is present in almost all freshwater habitats. Adult non-biting midges emerge from waterbodies and swarm in high numbers, occasionally disrupting people’s outdoor activities. In order to understand the seasonal dynamics of species composition, a continuous observation of non-biting midge diversity was performed. Adult non-biting midges were collected using light traps from the autumn of 2022 to the summer of 2023 in an urban wetland park. Species were identified based on morphological characteristics and DNA barcodes. Alpha diversity was evaluated using Margalef, Pielou, and Shannon–Wiener indexes. Beta diversity was evaluated using unconstrained NMDS analysis and constrained CCA. The impacts of environmental factors, including barometric pressure, temperature, relative humidity, and wind speed, on the variation in species composition were estimated in the constrained analyses. A total of 42 species were identified, with 29 species belonging to Chironominae, 9 species belonging to Orthocladiinae, and 4 species belonging to Tanypodinae. The species composition varied across different seasons. Summer sites and autumn sites shared the highest similarity in diversity, and spring sites presented the lowest diversity. The variation was significantly correlated with environmental conditions. The results showed that seasonality is a factor influencing the diversity of adult non-biting midges. Full article

Ecological sluices were constructed along the Tarim River to supplement the ecosystem’s water supply. However, the impact of water regulation by these sluices on the surface water area (SWA) and its relationship with the vegetation response remain unclear. To increase the efficiency of ecological water use, it is crucial to study the response of SWA to water control by the ecological gates and its relationship with vegetation restoration. We utilized the Google Earth Engine (GEE) cloud platform, which integrates Landsat-5/7/8 satellite imagery and employs methods such as automated waterbody extraction via mixed index rule sets, field investigation data, Sen + MK trend analysis, mutation analysis, and correlation analysis. Through these techniques, the spatiotemporal variations in SWA in the middle reaches of the Tarim River (MROTR) from 1990–2022 were analyzed, along with the relationships between these variations and vegetation restoration. From 1990–2022, the SWA in the MROTR showed an increasing trend, with an average annual growth rate of 12.47 km² per year. After the implementation of ecological gates water regulations, the SWA significantly increased, with an average annual growth rate of 28.8 km² per year, while the ineffective overflow within 8 km of the riverbank notably decreased. The NDVI in the MROTR exhibited an upward trend, with a significant increase in vegetation on the northern bank after ecological sluice water regulation. This intervention also mitigated the downward trend of the medium and high vegetation coverage types. The SWA showed a highly significant negative correlation with low-coverage vegetation within a 5-km range of the river channel in the same year and a significant positive correlation with high-coverage vegetation within a 15-km range. The lag effect of SWA influenced the growth of medium- and high-coverage vegetation. These findings demonstrated that the large increase in SWA induced by ecological gate water regulation positively impacted vegetation restoration. This study provides a scientific basis for water resource regulation and vegetation restoration in arid regions globally. Full article

Anthrax, a zoonotic disease affecting both livestock and humans globally, is caused by Bacillus anthracis. The objectives of this study were the following: (1) to identify environmental risk factors for anthrax and use this information to develop an improved predictive risk map, and (2) to estimate spatial variation in basic reproduction number (Ro) and herd immunity threshold at the village level, which can be used to optimize vaccination policies within high-risk regions. Based on the anthrax incidences from 2000–2023 and vaccine administration figures between 2008 and 2022 in Karnataka, this study depicted spatiotemporal pattern analysis to derive a risk map employing machine learning algorithms and estimate Ro and herd immunity threshold for better vaccination coverage. Risk factors considered were key meteorological, remote sensing, soil, and geographical parameters. Spatial autocorrelation and SaTScan analysis revealed the presence of hotspots and clusters predominantly in the southern, central, and uppermost northern districts of Karnataka and temporal cluster distribution between June and September. Factors significantly associated with anthrax were air temperature, surface pressure, land surface temperature (LST), enhanced vegetation index (EVI), potential evapotranspiration (PET), soil temperature, soil moisture, pH, available potassium, sulphur, and boron, elevation, and proximity to waterbodies and waterways. Ensemble technique with random forest and classification tree models were used to improve the prediction accuracy of anthrax. High-risk areas are expected in villages in the southern, central, and extreme northern districts of Karnataka. The estimated Ro revealed 11 high-risk districts with Ro > 1.50 and respective herd immunity thresholds ranging from 11.24% to 55.47%, and the assessment of vaccination coverage at the 70%, 80%, and 90% vaccine efficacy levels, all serving for need-based strategic vaccine allocation. A comparison analysis of vaccinations administered and vaccination coverage estimated in this study is used to illustrate difference in the supply and vaccine force. The findings from the present study may support in planning preventive interventions, resource allocation, especially of vaccines, and other control strategies against anthrax across Karnataka, specifically focusing on predicted high-risk regions. Full article

The primary objective of this study is to investigate the carbon footprint, resilience levels, and optimal landscape area ratios of various parks. Additionally, it explores the relationships between landscape element proportions (LEP), the normalized difference vegetation index (NDVI), resilience indicators (RI), and the carbon reduction benefits associated with carbon neutrality (CN). Six parks were assessed for resilience, NDVI, LEP, and CN values, with Pearson correlation analysis conducted. The results revealed that parks with or without waterbodies exhibited ideal LEP area ratios of 6.5:2:1.5 (Softscape:Waterbody:Hardscape) and 8.3:1.7 (Softscape:Hardscape), respectively. Enhanced Softscape and reduced Hardscape proportions in parks correlated with increased NDVI and CN. NDVI exhibited a positive correlation with Softscape percentage and a negative correlation with Hardscape percentage. Conversely, CN demonstrated a negative correlation with Hardscape percentage and a positive correlation with Softscape percentage. Suggesting Softscape should constitute over 65%, and Hardscape should be under 15% in parks with water bodies. Waterless parks are advised to maintain a Softscape ratio exceeding 83% and a Hardscape ratio below 17%. Finally, the study extended to assess the LEP of 22 additional parks, validating the suitability of the ideal LEP area ratio. Full article

East African lakes support the food and water security of millions of people. Yet, a lack of continuous long-term water quality data for these waterbodies impedes their sustainable management. While satellite-based water quality retrieval methods have been developed for lakes globally, African lakes are typically underrepresented in training data, limiting the applicability of existing methods to the region. Hence, this study aimed to (1) assess the accuracy of existing and newly developed water quality band algorithms for East African lakes and (2) make satellite-derived water quality information easily accessible through a Google Earth Engine application (app), named LAndsat water QUality retrieval tool for east African lakes (LAQUA). We collated a dataset of existing and newly collected in situ surface water quality samples from seven lakes to develop and test Landsat water quality retrieval models. Twenty-one published algorithms were evaluated and compared with newly developed linear and quadratic regression models, to determine the most suitable Landsat band algorithms for chlorophyll-a, total suspended solids (TSS), and Secchi disk depth (SDD) for East African lakes. The three-band algorithm, parameterised using data for East African lakes, proved the most suitable for chlorophyll-a retrieval (R² = 0.717, p < 0.001, RMSE = 22.917 μg/L), a novel index developed in this study, the Modified Suspended Matter Index (MSMI), was the most accurate for TSS retrieval (R² = 0.822, p < 0.001, RMSE = 9.006 mg/L), and an existing global model was the most accurate for SDD estimation (R² = 0.933, p < 0.001, RMSE = 0.073 m). The LAQUA app we developed provides easy access to the best performing retrieval models, facilitating the use of water quality information for management and evidence-informed policy making for East African lakes. Full article

Regular detection of land-use and land-cover (LULC) changes with high accuracy is necessary for natural resources management and sustainable urban planning. The produced LULC maps from Google Earth Engine (GEE) also illustrate the transformation of the LULC for the respective landscape over time. The selected study area, Cottbus City and the Spree-Neisse district in northeastern Germany, has undergone significant development over the past decades due to various factors, including urbanization and industrialization; also, the landscape has been converted in some areas for post-mining activities. Detection of LULC changes that have taken place over the last few decades thus plays a vital role in quantifying the impact of these factors while improving the knowledge of these developments and supporting the city planners or urban management officials before implementing further long-term development initiatives for the future. Therefore, the study aims to (i) detect LULC changes for the time slices 2002 and 2022, testing machine learning (ML) algorithms in supervised and unsupervised classification for Landsat satellite imageries, and (ii) validate the newly produced LULC maps with the available regional database (RDB) from the federal and state statistical offices, Germany, and the Dynamic World (DW) near real-time 10 m global LULC data set powered by artificial intelligence (AI). The results of the Random Forest (RF) and the Smilecart classifiers of supervised classification using Landsat 9 OLI-2/TIRS-2 in 2022 demonstrated a validation accuracy of 88% for both, with Kappa Index (KI) of 83% and 84%, respectively. Moreover, the Training Overall Accuracy (TOA) was 100% for both years. The wekaKMeans cluster of the unsupervised classification also illustrated a similar transformation pattern in the LULC maps. Overall, the produced LULC maps offered an improved representation of the selected region’s various land-cover classes (i.e., vegetation, waterbodies, built areas, and bare ground) in the last two decades (20022 to 2022). Full article

Land use change monitoring is a common theme in achieving sustainable development, while research on ecological barrier transition zones is relatively scarce. This study quantitatively analyzes the characteristics and patterns of land use change in Western Jilin, located in the transitional zone between the northeast forest belt and the northern sand prevention belt, from 1990 to 2020. Land dynamic change index and transition matrix are used to quantify the rates and intensities, and conversions between different land use types over time, respectively. Geodetector is adopted to analyze the impact of 12 factors on 12 types of land use change, such as using the factor detector to quantify the influence of temperature on the conversion from cropland to unused land. The results indicate that from 1990 to 2020, there have been noticeable changes in the area of various land use types in western Jilin. However, the conversion types are relatively limited, mainly involving interchanges between cropland, grassland, unused land, and water bodies. The cropland has increased by 20% overall, but 16% of that increase occurred from 1990–2000. The woodland area has steadily increased at a growth rate of 5–8% from 2000–2020, aligning with sustainable development strategies. Water bodies and grasslands are undergoing continuous recovery, and a positive growth trend is predicted to emerge by 2030. The built-up land is steadily expanding. The influencing factors vary for different types of land-use change. In a short time, policy factors play a significant role in land use, such as the implementation of the “River-lake Connection Project”, which has helped to reduce water-body fragmentation and enabled the stable recovery of water resources. However, in the long term, multiple topographic, climatic, and anthropogenic factors exhibit interactive effects in the land use change process in the area. Governments can take corresponding measures and management policies based on the influence of these factors to allocate and plan land use rationally. Full article

Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll a, a water-quality and trophic-state indicator, along with fine spatial resolution, enabling the monitoring of small waterbodies. In this study, two algorithms—the Maximum Chlorophyll Index (MCI) and the Normalized Difference Chlorophyll Index (NDCI)—were applied to S2 MSI data. They were calibrated and validated using in situ chlorophyll a measurements for 103 lakes across the contiguous U.S. Both algorithms were tested using top-of-atmosphere reflectances (ρ_t), Rayleigh-corrected reflectances (ρ_s), and remote sensing reflectances (R_rs). MCI slightly outperformed NDCI across all reflectance products. MCI using ρ_t showed the best overall performance, with a mean absolute error factor of 2.08 and a mean bias factor of 1.15. Conversion of derived chlorophyll a to trophic state improved the potential for management applications, with 82% accuracy using a binary classification. We report algorithm-to-chlorophyll-a conversions that show potential for application across the U.S., demonstrating that S2 can serve as a monitoring tool for inland lakes across broad spatial scales. Full article