Search Results (391)

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

The northern water hemlock is an endangered species that has been severely diminished in Hungary due to water regulation and river control in the 18th and 19th centuries. We collected data on this highly toxic plant from Hungary using archival sources, including digitized databases of daily and weekly newspapers and books. By exploring historical digital archives, we identified 88 locatable occurrence records spanning 65 flora mapping grids, 52 of which represent new additions to its known distribution. Between 1721 and 1943, a total of 103 records were found relating to its vernacular names. The most widespread names were csomorika (predominantly used in the Berettyó-Sárrét and Hortobágy regions), mételytorzsa (Rétköz, Taktaköz, Ecsedi-láp), and Kónyi gyökér (Fertő-Hanság region). Human poisonings caused by this species were primarily due to confusion with parsley, celery, and, less frequently, parsnips or carrots, occasionally resulting in the deaths of entire families. Children, in particular, were at risk when they accidentally consumed it raw as a snack. There have also been instances of intentional homicidal use and unintentional fatalities associated with its ethnomedicinal application. The plant was primarily used to treat scrofula (a form of tuberculosis-induced lymphoma) and for abortion. Full article

The rapid loss of biodiversity significantly impacts birds’ environments and behaviors, highlighting the importance of analyzing bird behavior for ecological insights. With the growing adoption of Machine Learning (ML) algorithms in the Internet of Things (IoT) domain, edge computing has become essential to ensure data privacy and enable real-time predictions by processing high-dimensional data, such as video streams, efficiently. This paper introduces a set of dimensionality reduction techniques tailored for video sequences based on cutting-edge methods for this data representation. These methods drastically compress video data, reducing bandwidth and storage requirements while enabling the creation of compact ML models with faster inference speeds. Comprehensive experiments on bird behavior classification in rural environments demonstrate the effectiveness of the proposed techniques. The experiments incorporate state-of-the-art deep learning techniques, including pre-trained video vision models, Autoencoders, and single-frame feature extraction. These methods demonstrated superior performance to the baseline, achieving up to a 6000-fold reduction in data size while reaching a classification accuracy of 60.7% on the Visual WetlandBirds Dataset and obtaining state-of-the-art performance on this dataset. These findings underline the potential of using dimensionality reduction to enhance the scalability and efficiency of bird behavior analysis. Full article

Due to the increasing impact of human activities on the environment, habitat loss, fragmentation, and degradation pose significant threats to bird diversity worldwide. Baiyangdian, the largest freshwater lake wetland in North China, is an important habitat for birds. The degradation of water quality caused by decaying reed rhizomes has prompted governmental initiatives for ecological restoration in Baiyangdian. However, it has also led to the significant destruction of reed habitats within the wetlands consequently. Bird species that rely on these reed habitats, especially the reed parrotbill, face a significant threat, necessitating the establishment of species reserves to mitigate the loss of bird diversity. Our research aims to identify the potential suitable habitats for the reed parrotbill in Baiyangdian and establish priority conservation areas. Using the environmental factors determined with Google Earth Engine (GEE), ultimately we designated the following areas as priority conservation zones: the Fuhe Wetland (FHW), the reed area south of Beihezhuang (BHV), both sides of the Baiyangdian Bridge (BYDB), the western shoreline of Shaochedian (SCD), Yannandi Park (YNDP), east of Guangdianzhangzhuang Village (GDZZV), east of Dongtianzhuang (DTV), north of Xilizhuang (XLV), south of Caiputai Village (CPTV), north of Gaolou Village (GLV), and the Xiaoyihe Wetland (XYW). Our findings provide a scientific reference for ecological restoration projects in the Baiyangdian region and offer supporting data for the conservation management of the reed parrotbill. Full article

This study explored the ecological and cultural significance of Thailand’s wetlands, particularly the Nong Bong Kai Non-Hunting Area and the Lower Songkhram River. These areas were essential for biodiversity, flood prevention, and supporting local livelihoods, serving as food sources, biofertilizers, and natural water purifiers. However, these wetlands faced mounting threats from pollution, habitat loss, and climate change, jeopardising their ecological health and biodiversity. Ethnobotanical surveys were conducted to assess regional and demographic patterns in using and recognising algae and aquatic plants. The collected data highlighted traditional ecological knowledge held primarily by older community members. Principal component analysis (PCA) was employed to analyse generational differences in knowledge and utilisation, focusing on the potential generational knowledge gap due to migration trends among younger individuals. The study revealed significant biodiversity differences between the wetland areas, with Nong Bong Kai exhibiting greater diversity in aquatic plant species. Older residents demonstrated higher recognition and use of algae and aquatic plants for sustainable practices, though a generational knowledge gap was evident. This gap suggested that local ecological knowledge may be at risk as younger residents migrate to urban areas. This research underscored the need for conservation and educational initiatives to bridge generational gaps and engage younger generations in resource management. By integrating local cultural practices and focusing on high-impact species like Lemna perpusilla Torr. and Hydrilla verticillate (L.f.) Royle, region-specific conservation efforts could enhance wetland sustainability, preserve biodiversity, and support ecosystem health in Thailand’s wetland regions. Full article

Alpine wetlands are highly vulnerable to changes caused by global warming. Rapidly and accurately mapping alpine wetlands and analyzing the driving factors of their spatiotemporal changes are crucial for protecting and managing these resources. However, few studies have investigated classification methods and attribution analyses for alpine wetlands. To address this gap, a novel classification method has been developed, integrating the Google Earth Engine, alpine wetland features, and a random forest classifier, named GAWRF, to delineate wetlands in alpine regions. Additionally, an improved Partial Least Squares Structural Equation Model (PLS-SEM) was utilized to explore the mechanisms of spatiotemporal changes in wetlands of the Source Region of Three Rivers (SRTR) from 1990 to 2020. The results indicate (1) the high accuracy of the SRTR land cover maps from 1990 to 2020, with an overall accuracy of above 92.48% and a Kappa coefficient of over 0.91, satisfying the subsequent analysis of wetland spatiotemporal changes; (2) a net loss of 3.8% in the SRTR alpine wetlands, with a notable 7.9% net loss in marsh wetlands and nearly 32,010 km² lost by 2015; and (3) topography and permafrost change as key drivers (as identified by the PLS-SEM), with permafrost contributing 52% to the significant marsh wetland loss from 2010 to 2015. This study aims to provide fundamental information that is essential for the monitoring and conservation of alpine wetlands. Full article

Exploring the characteristics and driving factors of carbon storage change in different terrain gradient variations can provide important insights for formulating the agricultural ecological protection policy for regional development. Previous studies have used the fixed value of carbon density to evaluate the change characteristics of carbon storage but ignored the spatio-temporal heterogeneity of carbon storage at the block scale and the impact of policy factors. Thus, this paper takes Sanjiang Plain, Heilongjiang Province, China, as a study area, and the spatio-temporal variation of carbon storage at different topographic gradients was revealed using hot and cold spot analysis and zonal statistics. Through the geographic detector and estimation of the soil carbon density model, the driving factors and intensity of carbon storage spatial distribution are revealed from 1990 to 2020. We conducted analyses on aboveground biomass, underground biomass, and soil carbon storage across three elevation levels (0–200 m, 200–500 m, 500–999 m) to reveal the quantitative distribution features of carbon storage. The study analysis finds that carbon storage indicates a sawtooth evolution during the study period. Carbon storage was dominant at elevation I (range is 0–200 m), slope I (range is 0–2°), and relief amplitude I (range is 0–30 m). Additionally, the carbon storage losses were severe at elevation II (range is 200–500 m), slope II (2–6°), and relief amplitude II (30–70 m). In contrast, the carbon storage losses at elevation III (500–999 m), slope III (6–15°), and relief amplitude III (70–186 m) were insignificant. The spatial pattern of carbon storage varies significantly under different topographic gradients from 1990 to 2020. The most critical driving factors influencing the spatial distribution pattern of carbon storage were land use and annual average temperature. Distance to urban centers and soil texture also moderately influence the distribution of carbon storage. As the topographic gradient increases, the dominant factors of carbon storage gradually change from annual mean temperature and the extent of land use to policy factors and other socio-economic factors. Therefore, this study emphasizes the importance of implementing policies that convert farmland to forests and wetlands and promote the green transformation of agriculture. Full article

Diagnosing climate variability and environmental change in floodable regions is essential for understanding and mitigating impacts on natural ecosystems. Our objective was to characterize environmental degradation in the Brazilian Pantanal by identifying changes in vegetation and water cover over a 30-year period using remote sensing techniques. We evaluated surface physical–hydric parameters, including Land Use and Land Cover (LULC) maps, Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), and precipitation data. There was a decrease in the area of water bodies (−9.9%), wetlands (−5.7%), and forest formation (−3.0%), accompanied by an increase in the area of pastureland (7.4%). The NDVI showed significant changes in vegetation cover (−0.69 to 0.81), while the MNDWI showed a decrease in water surface areas (−0.73 to 0.93) and the NDMI showed a continuous decrease in vegetation moisture (−0.53 to 1). Precipitation also decreased over the years, reaching a minimum of 595 mm. Vegetation indices and land use maps revealed significant changes in vegetation and loss of water bodies in the Pantanal, reinforcing the need for sustainable management, recovery of degraded areas, and promotion of ecotourism to balance environmental conservation and local development. Full article

In the context of global warming and intensified human activities, the loss and fragmentation of species habitats have been exacerbated. In order to clarify the trends in the current and future suitable wintering areas for hooded cranes (Grus monacha), the MaxEnt model was applied to predict the distribution patterns and trends of hooded cranes based on 94 occurrence records and 23 environmental variables during the wintering periods from 2015 to 2024. The results indicated the following. (1) The elevation (Elev, 43.7%), distance to major water (DW, 39.5%), minimum temperature of the coldest month (Bio6, 9.7%), and precipitation of the wettest month (Bio13, 2.6%) were dominant factors influencing the wintering distribution of hooded cranes. (2) Under current climate and land use scenarios, highly suitable areas for hooded cranes in China cover approximately 1.274 × 10⁵ km², primarily located in inland lakes such as Dongting Lake, Liangzi Lake, Poyang Lake, Shengjin Lake, and Caizi Lake in the middle and lower reaches of the Yangtze River, as well as in coastal wetlands such as Chongming East Beach, Shandong Peninsula, Bohai Bay, and Liaodong Peninsula. (3) Under future climate and land use scenarios, the suitable habitat areas (high and moderate suitability) for hooded cranes are projected to contract substantially in the middle and lower reaches of the Yangtze River and expand slightly in the areas of Shandong Peninsula, Bohai Bay, and Liaodong Peninsula. Under the SSP126 (low emissions), SSP245 (medium emissions), and SSP585 (high emissions) scenarios, the average area reduction percentages were 29.1%, 28.8%, and 31.6%, respectively. (4) The increases in Bio6 and water areas in northern China were the main reasons for the shift of the wintering distribution centroid for hooded cranes toward northeastern China. The minor expansion of suitable habitat in the north covers mainly cultivated land, and this singular foraging habitat could intensify both intraspecific and interspecific competition among waterbirds, thus exacerbating the survival risks for hooded cranes. To more effectively protect the wintering population of hooded cranes in China, the restoration of natural habitats and population monitoring in the middle and lower reaches of the Yangtze River should be strengthened. Additionally, nature reserves or protected areas should be established in the northern expansion regions. Full article

Constructed wetland (CW) is a critical ecological engineering for wastewater treatment and improvement of water quality. Nitrogen (N) removal is one of the vital functions of CWs during operation, and N treatment in CWs is mainly affected by aquatic plants and denitrification carried out by microbes. However, due to their low efficiency and instability in N removal, further applications of CWs are limited. The review provides a view of two basic characteristics of aquatic plants, radial oxygen loss (ROL) and root exudates, and their coupled effect on denitrification processes in CWs. First, the role of aquatic plants in denitrification is presented. The individual roles of ROL and root exudates in regulating denitrification, as well as their interaction in this process, have been discussed. Also, the limitation of conventional techniques to reveal interaction between the plant and the microbes has been highlighted. Further research on coupling regulatory mechanisms of ROL and root exudates may be conducted to develop an optimal wetland design and improve biological N removal. This review offers new insights and directions for improving N removal in CWs by utilizing the synergistic effects of plant ROL and root exudates. Full article

Sesuvium portulacastrum floating treatment wetlands (FTWs) are effective at removing nitrogen and phosphorus, adsorbing heavy metals, and removing organic pollutants from aquaculture wastewater, and thus improve fish farming productivity. In this study, an S. portulacastrum FTW was used in a simulated grouper aquaculture experiment for 40 days. The FTW removed 1~3 mg/L of dissolved inorganic nitrogen (DIN) throughout the experimental period as well as the following toxic nitrogen species: 88% NO₂⁻-N in the middle stage and 90% TAN (total ammonia nitrogen) in the middle stage. The health of the groupers was promoted and the weight of each grouper was 8% higher than those in the control group in the end. Compared with that of the control group, the carbon sequestration of the aquaculture ecosystem was also increased by S. portulacastrum FTW because more carbon was held in the biomass, including through the growth of the plant mass of the FTW, 109 g C/pond, and a reduction in fishing catch losses, 442 g C/pond. Therefore, S. portulacastrum FTW can serve as a potential technology for improving the water environment quality of feeding ponds and contributing to carbon sequestration in aquaculture systems. Full article

Wetland birds are undergoing severe population declines globally, primarily attributed to extensive wetland loss and degradation. The attributes of the landscape surrounding a focal locality, referred to as ‘landscape context’, have been shown to influence the diversity of wetland birds living in the given area. At a global scale, however, the landscape context effects on wetland birds have not been assessed. Here, we assessed the effect of landscape context on the richness of threatened bird species recorded in 334 inland Ramsar wetland sites across the globe. Generalized linear mixed models were used to quantify the relationship between the richness of these bird species and the landscape context of the Ramsar sites. Variation partitioning was used to quantify the independent explanatory power of landscape context for comparison between migratory and non-migratory species. The overall and independent explanatory power of landscape context for the global-scale richness pattern of threatened avifauna reached ca. 17% and 3%, respectively, with the scale of peak explanatory power being 5 times the area of a focal Ramsar site. The independent explanatory power of landscape context was significantly higher for migratory species (ca. 30%) than for non-migratory ones (ca. 3%). Among the landscape context metrics, wetland habitat loss and fragmentation were most strongly associated with the global-scale richness of threatened migrant species at Ramsar sites. Our results suggest that even at macroecological scales, landscape context contributes to shaping the richness pattern of threatened bird species, especially for migrants. These findings provide useful insight for managing landscapes surrounding Ramsar sites, in order to improve conservation effectiveness for wetland birds worldwide. Full article

The landscape of the Poyang Lake wetland is significantly influenced by changes in water levels, impacting the distribution of habitats for migratory birds. While long-term effects of water level variations have been extensively studied, short-term impacts on Siberian crane habitats and their ecological vulnerability remain poorly understood. This study utilized 35 years (1987–2022) of Landsat remote sensing data and daily water level records from Poyang Lake to examine the effects of short-term water level fluctuations on the spatial distribution and ecological vulnerability of Siberian crane habitats. The geographic detector method was employed to quantify the explanatory power and interaction effects of factors, including short-term water level fluctuations, on ecological vulnerability. The findings reveal significant differences in the habitats of wintering Siberian cranes across various water level intervals and short-term fluctuation patterns. Short-term water level fluctuations can result in the largest suitable wintering habitat area for Siberian cranes, reaching 1856.41 km² in this study. These habitats are highly sensitive to short-term water level changes, with rising and falling trends potentially leading to habitat loss. Oscillating water levels in the short term create broader and more concentrated habitats. Notably, fluctuations at low water levels support the sustainability and stability of crane habitats. Furthermore, short-term water level trends and nature reserves play a critical role in maintaining habitat ecological vulnerability; well-managed and protected nature reserves exhibit significant explanatory power, both in single-factor analysis and in their interaction with other environmental factors. Specifically, these protected areas show explanatory power exceeding the 20% threshold for both water level fluctuations and ranges, highlighting the crucial role of anthropogenic management in mitigating ecological vulnerability. This study emphasizes the necessity of scientifically informed regulation of short-term water level fluctuations to protect Siberian crane habitats and provides a strong scientific basis for decision-making support. Full article

The role of carbon storage in coastal wetlands is crucial to the global carbon cycle and human production activities. An accurate quantitative assessment and understanding of its spatial distribution are essential for informed decision-making on sustainable development goals. This study focuses on the wetlands in the coastal zones of Hebei and Tianjin. It develops a comprehensive assessment model that spans a long time series, covering the past, present, and future, aiming to provide insights into the intrinsic linkages between coastal wetland types and carbon storage, as well as projected future trends under three different scenarios. The results of the study demonstrate the following: (1) between 1990 and 2020, the area of coastal wetlands in Hebei and Tianjin decreased by 365.63 km², resulting in a loss of 5.15 Tg of carbon storage, which represents approximately 20.82% of the total carbon storage; (2) temperature, precipitation, and the intensity of human activities are key factors influencing carbon storage in coastal wetlands; (3) carbon storage in the coastal wetlands of Hebei and Tianjin is primarily composed of natural wetlands, which account for 59.62% to 60.69% of the total carbon storage, and this ratio is not expected to change significantly in the future; (4) under WRS, carbon storage in the Hebei–Tianjin coastal wetlands is projected to increase, reaching 19.76 Tg by 2050—an increase of 0.21 Tg compared to 2020. This growth trend in carbon storage is significantly better than under the natural and EPS scenarios and aligns more closely with dual-carbon goals. This study not only provides managers with valuable insights into land use and urban development planning but also highlights the positive role of WRS in contributing to the growth of carbon storage in coastal wetlands. Full article

The Cape Canaveral Barrier Island, home to the National Aeronautics and Space Administration (NASA)’s Kennedy Space Center and the United States (U.S.) Space Force’s Cape Canaveral Space Force Station, is situated in a unique ecological transition zone that supports diverse wildlife. This study evaluates the recent changes in vegetation cover (2016–2023) and dune elevation (2007–2017) within the Cape Canaveral Barrier Island using high-resolution optical satellite and light detection and ranging (LiDAR) data. The study period was chosen to depict the time period of a recent increase in rocket launches. The study objectives include assessing changes in vegetation communities, identifying detectable impacts of liquid propellant launches on nearby vegetation, and evaluating dune elevation and tide level shifts near launchpads. The results indicate vegetation cover changes, including mangrove expansion in wetland areas and the conversion of coastal strands to denser scrubs and hardwood forests, which were likely influenced by mild winters and fire management. While detectable impacts of rocket launches on nearby vegetation were observed, they were less severe than those caused by solid rocket motors. Compounding challenges, such as rising tide levels, beach erosion, and wetland loss, potentially threaten the resilience of launch operations and the surrounding habitats. The volume and scale of launches continue to increase, and a balance between space exploration and ecological conservation is required in this biodiverse region. This study focuses on the assessment of barrier islands’ shorelines. Full article