Wetland Area Research Articles

Habitat Assessment of Bocachico (Prochilodus magdalenae) in Ciénaga de Betancí, Colombia, Using a Habitat Suitability Index Model

This study evaluates the habitat of the Bocachico fish (Prochilodus magdalenae) in the Ciénaga de Betancí, Colombia, using a habitat suitability index (HSI) model. Wetlands like the Ciénaga de Betancí are under significant pressure from anthropogenic activities, affecting biodiversity and ecosystem health. The Bocachico, a species of immense cultural and economic importance, faces habitat degradation and fragmentation. Using hydrodynamic and water quality data, a numerical model (EFDC+ Explorer 11.5), and field data collected from multiple sampling campaigns, we assessed habitat suitability based on five key parameters: water temperature, dissolved oxygen, ammonia nitrogen, velocity, and depth. The model results indicated that environmental conditions in the wetland remained relatively stable during the dry season, with an average HSI score of 0.67, where 9% of the wetland area displayed acceptable conditions, and the remaining 91% displayed medium conditions. The wet season, on the other hand, had an average HSI score of 0.64, with 7.2% of the area in the acceptable suitability range, and the remaining 92.8% in the medium category. Variations in HSI were primarily driven by ammonia nitrogen levels, water velocity, and depth. Despite limited fluctuations in the HSI, areas of low suitability were identified, particularly in regions impacted by human activities. These findings have practical implications for conservation strategies, providing valuable insights for the sustainable management and conservation of the Ciénaga de Betancí, informing strategies for improving habitat conditions for the Bocachico, and supporting wetland restoration efforts.

Spatial–Temporal Patterns and the Driving Mechanism for the Gross Ecosystem Product of Wetlands in the Middle Reaches of the Yellow River

Wetlands are crucial for sustainable development, and the evaluation of their GEP is a key focus for governments and scientists. This study created a dynamic accounting model for wetland GEP and assessed the GEP of 39 wetlands in the middle reaches of the Yellow River in Ningxia province. The results indicate that Ningxia province’s wetlands have an average annual GEP of CNY 5.24 billion. Haba wetland contributes the most at 0.52, while Qingtongxia, Sha, and Tenggeli wetlands follow with 0.12, 0.04, and 0.03, respectively. Climate regulation is the most valuable function at 38.24%, with species conservation and scientific research/tourism at 24.93% and 15.11%, respectively. Ningxia’s northern wetlands are vast and shaped by the Yellow River, while the smaller, seasonal southern wetlands are more affected by rainfall and mountain groundwater. Southern wetlands show a strong correlation between GEP and precipitation (0.82), whereas northern wetlands have a moderate correlation between GEP and evapotranspiration (0.52). The effective conservation and management of these wetlands require consideration of their locations and weather patterns, along with customized strategies. To maintain the stability of wetland habitats and provide a suitable environment for various species, it is essential to preserve wetlands within a certain size range. Our study found a strong correlation of 0.85 between the wetland area and the GEP value, indicating that the size of wetlands is a key factor in conserving their GEP. The results provide accurate insights for creating a wetland ecological benefit compensation mechanism.

Predicting land cover changes in Xuan Lien Nature Reserve, Thanh Hoa province, Vietnam

The research was carried out in Xuan Lien Nature Reserve, which is a specially protected natural area and one of the five main centers of biodiversity in Vietnam. The work is aimed at analyzing changes and predicting the state of land cover in Xuan Lien Reserve. For these purposes, Landsat-7, Landsat-5 and Landsat-8 images obtained in 2000, 2010 and 2020 were studied. Normalized Difference Vegetation Index (NDVI) method was used to isolate the vegetation cover on the land areas, and Normalized Difference Water Index (NDWI) method was used. The results of the study indicate that the vegetation on the land is transforming in Xuan Lien Reserve. The area of broadleaf forests decreased by 6.2% from 2000 to 2020, and the area of wetlands increased by 4.9% due to the construction of Cua Dat hydroelectric complex in Reserve. The model of Markov chains and cellular automata was used to predict the state of the allocated lands in the study area. The predicted results of the model have been successfully confirmed using the agreement indices. According to the results of the predicted, by 2035 the area of broadleaf forests and wetlands will increase to 86.3% and 7.4%. The change in the structure of the vegetation cover of Reserve's land is explained by the measures taken by the Vietnamese government to regulate the water level in Cua Dat reservoir and expand the forest area.

The Spatiotemporal Evolution of the Mudflat Wetland in the Yellow Sea Using Landsat Time Series

Mudflat wetland, one of the 27 surface elements identified by the International Geographic Data Committee, has undergone substantial transformations with the rapid growth of the social economy and marine hazards, resulting in significant changes in its area and distribution. Quick identification of mudflat wetland evolution is vital to improve the mudflat ecological service value. We employed object-oriented and decision tree classification methods to map the mudflat wetland in the Yellow Sea using the Landsat time series from 1983 to 2020. The Improved Spectral Water Index (IWI) was established by combining the characteristics of many ratio indices and using ratio operation and quadratic power operation. The coefficient of variation (CV) of the IWI was calculated, and the range of the intertidal zone in 1983, 1990, 2000, 2010, and 2020 was obtained by using a threshold method. The results indicate that the mudflat wetland area decreased continuously from 1983 to 2020, with a reduction of 337.38 km2/10a. Among the total area, the natural wetland experienced a decline of 446.9 km2/10a, with the most drastic changes occurring between 2000 and 2010. In contrast, the area of the human-made wetland increased by 109.56 km2/10a. Over the 38 years, the tidal flat has undergone the most drastic reduction, with an average of 157.45 km2/10a. From 1983 to 2020, the intertidal zone area decreased, with a reduction of 429.02 km2/10a. Human activities were the key factors causing mudflat wetland loss. Based on these findings, we propose several policy suggestions. This study provides a scientific basis for understanding the synergetic evolution mechanism of coastal resources utilization and mudflat wetland protection under global change.

A blue–green ratio of urban wetlands as an ecosystem health indicator: the case of urban sprawl in Nagpur, India

ABSTRACT Rampant urban sprawl has affected the structure and function of existing urban green and blue spaces, exposing developing cities to unexpected disaster events. Drawing on a mixture of secondary information supported by GIS and field-based studies, we assessed the impact of growing impervious surfaces on the dynamic zones of urban wetlands in Nagpur, India. We compared decadal data (2000-2020) to understand the pattern of wetland loss and its drivers in the region. Our findings reveal a significant loss of wetland structure and area over the past two decades, raising alarming concerns regarding ecological and economic repercussions. To address these issues, we propose a blue–green buffer-based assessment approach that incorporates local knowledge and observations along with proactive monitoring of ecosystem health. Our study highlights the urgency of reducing the spatial complexity of landscape patches, restoring wetland buffers, declaring wetland buffers as no-go zones, and promoting corridor connectivity to urban wetlands to improve urban blue infrastructure health. Multi-sectoral and stakeholder cooperation, involvement, and strict enforcement of the Wetland Rules (2017) by local governing bodies can help build urban resilience by protecting urban blue–green infrastructure.

Land use modeling and carbon storage projections of the Bosten Lake Basin in China from 1990 to 2050 across multiple scenarios

Given the escalating issue of global climate change, it is imperative to comprehend and quantify the effects of land use change on carbon storage (CS), which pertains not only to the preservation of ecosystem functions but also directly influences the equilibrium and stability of the global carbon cycle. This study examines the correlation between CS and land use change, forecasts the future spatial distribution of CS, and offers a reference for the rational planning of watershed space. Focusing on the Bosten Lake Basin of Xinjiang in China, employing the land use simulation (PLUS) model and the integrated valuation of ecosystem services and trade-offs (InVEST) model to forecast the spatial distribution of carbon stocks across three developmental scenarios, while also examining the shift in the center of gravity of CS and the autocorrelation of their spatial distribution. The findings derived from the study are as follows: (1) From 1990 to 2020, the predominant land use type in the Bosten Lake Basin was grassland, while there was an upward trend in the areas of cropland, forest land, built-up land, and wetland, alongside a downward trend in the areas of grassland, water, and unused land. (2) In the long term, the regional CS exhibits an upward trend, with the most significant increase anticipated in the EPS scenario. Grassland constitutes the most extensive carbon reservoir in the Bosten Lake Basin, while wetlands exhibit the highest carbon sequestration potential. (3) The alteration in the center of gravity of CS is associated with the expansion or reduction of the major regional carbon reservoirs and types characterized by significant carbon sequestration potential. (4) In the long term, the spatial correlation of CS in the Bosten Lake Basin exhibits a consistent upward trend, with the most pronounced spatial correlation observed under EPS.

An improved optical trapezoid model to identify wetlands and irrigation water ponds in the arid irrigation district using Google Earth Engine

ABSTRACT In arid regions, wetlands support ecosystem with high biodiversity and productivity. Meanwhile, irrigation during the fallow period often results in the temporary formation of water ponds in the cropland, which plays a critical role in agricultural productivity. The prompt identification of wetlands and irrigation water ponds in irrigation districts is important for the protection of local wetland ecosystem and the effective management of irrigation water. Utilizing the OPtical TRApezoid Model (OPTRAM) for soil moisture estimation, we have proposed an improved version of OPTRAM aimed to identify wetlands and irrigation water ponds in irrigation district using Sentinel-2 data through Google Earth Engine platform. Different criteria were used for the identification of wetland and irrigation water pond, given the greater diversity associated with wetlands. Parameters for the wet edge determined from OPTRAM were used to identify wetlands, while an additional threshold was added to the model and calibrated in accordance with the irrigated area to identify irrigation water ponds. The improved OPTRAM was applied in Hetao Irrigation District (HID) of Northwest China from 2016 to 2023, where autumn irrigation applied in late autumn after crop harvesting was considered. The identified distributions of wetland and autumn irrigation were validated with observations from field survey and statistical data, and were also compared with other remote sensing products. Results show that the proposed model is effective in identifying both wetlands and irrigation ponds. Considering the wetlands, the distances from identified boundaries to the actual boundaries obtained from field surveys were calculated, revealing the mean absolute error of 6.99 m, alongside a validation accuracy ratio of 0.94 for points located within wetlands. For autumn irrigation extent, the overall accuracy is 0.90 based on the observations from field survey, with mean absolute relative errors for irrigated areas across four sub-irrigation districts recorded as 20.55%, 8.10%, 12.83%, and 11.38%, respectively, when compared with statistical data. The majority of wetlands within HID are small in size and scattered, with different trends observed in the wetland areas of sub-irrigation districts and Wuliangsuhai Lake. Autumn irrigated croplands are mainly concentrated in Jiefangzha sub-irrigation district while being scattered across other sub-irrigation districts, depicting an overall decreasing trend in the autumn irrigated area. In summary, the proposed model performed well in identifying both wetlands and irrigation ponds, and can offer valuable support for the conservation of wetland ecosystem and irrigation management.

Estimating wetland CH4 emissions on the Qinghai-Tibetan Plateau based on a processed model coupled with inundation dynamics between 1960 and 2100

Quantifying the impacts of climate change and wetland area dynamics on methane (CH4) emissions in the Qinghai-Tibetan Plateau (QTP) is crucial for enhancing our understanding of regional and global CH4 budgets. However, a major source of uncertainty in estimating wetland methane emissions stems from the limited delineation of wetland area dynamics. In this study, we integrated dynamic wetland inundation schemes into a process-based model to quantify the contributions of wetland area variations to natural wetland CH4 emissions on the QTP, both historically and in the future. The results revealed that climate change significantly influenced the spatiotemporal distribution of wetland CH4 emissions, with wetland inundation dynamics contributing between 21.84 % and 27.18 % of the increase in CH4 emissions from 1960 to 2020. Under future climate change scenarios, wetland CH4 emissions are projected to exhibit a more pronounced increasing trend, with wetland inundation dynamics projected to contribute 18.94 % to 21.80 % of the increase in CH4 emissions in the QTP under seven Shared Socioeconomic Pathways scenarios.

Stable isotope analysis of faunal remains from Bronze Age Kaymakçı, Western Anatolia

This paper presents the results of stable carbon ( δ13C) and nitrogen ( δ15N) isotopic analysis of bulk bone collagen from faunal remains from the Bronze Age site of Kaymakçı in western Anatolia. We use the isotopic values in conjunction with zooarchaeological data and contextual archaeological information to examine human-environment interactions; in particular, animal management and husbandry. Middle and Late Bronze Age agricultural systems in western and central Anatolia remain poorly understood, and this research aims to contribute to rectifying this research gap. We obtained stable isotopic values from both wild and domestic species, including deer, hare, birds, catfish, dogs, pigs, caprids and cattle. The δ13C values range from −22.0‰ to −16.9‰ and the δ15N values range from 2.3‰ to 10.3‰. For cattle, the δ13C values suggest that some specimens were subject to different management strategies, likely related to feeding or herding patterns or even the movement of animals in and around the region through some exchange mechanism. There appears to be no fixed location or strategy for the management of caprids and, furthermore, there is evidence to suggest that the herding/management areas of some caprids overlapped with those of wild animals such as deer and hare. Variation in δ15N values for pigs may indicate that whilst some animals in the sampled assemblage were free-roaming, others were penned. For the pigs and some other herbivorous domesticated species variation in δ15N values may also point to the exploitation of the wetland areas around Lake Marmara.

Integrating the cellular vortex method with remote sensing and geographical information systems in the modelling of coastal flooding around Niger Delta

Coastal areas are increasingly vulnerable to flooding, necessitating accurate simulation methods to understand flood dynamics and their potential impacts. This study employed a Lagrangian framework integrating the cellular vortex method with remote sensing and GIS to simulate flood height distribution in a coastal region. Leveraging climatic and remotely sensed data, alongside ArcMap 10.6.1 for map processing, the research estimated flood magnitude and frequency using the L-moment approach, applied to a forty-year tidal record dataset. Essential input parameters, such as the roughness coefficient and curve number, were derived from land use and land cover characteristics. Additionally, river flow velocity was observed at 0.12m/s, with measured wind speed and direction recorded at 4m/s in the northwest direction. Notably, analysis of the initial flood height distribution map revealed a significant expansion of wetland areas, attributed to observed land use changes between May 2002 and July 2005. Projections for flood height distribution in 2025 and 2050 highlighted the emergence of tidal floods, emphasizing the critical role of considering future climate and land use scenarios in flood dynamics assessment. This research contributes to advancing understanding of flood modeling techniques and underscores the urgency of adaptive measures to mitigate the potential impacts of coastal flooding.

Regional variation in the landscape ecology of West Nile virus sentinel chicken seroconversion in Florida.

How landscape composition and configuration impact the distribution of multi-vector and multi-host mosquito vector-borne disease systems, such as West Nile virus (WNV), remains challenging because of complex habitat and resource requirements by hosts and vectors that affect transmission opportunities. We examined correlations between landscape composition and configuration and 2018 WNV sentinel chicken seroconversion in Florida, USA across the state and within five National Oceanic Atmospheric Administration (NOAA) bioclimatic regions to understand strength and variation of landscape effects during an elevated transmission year. Although few landscape studies have examined WNV in Florida, we expected higher percentages of residential or medium-developed landscapes and more fragmented landscapes would be positively correlated with WNV seroconversion owing to the main mosquito vector habitats and avian host distributions. However, we expected to find variation in the importance of forest, wetland, and agriculture landscapes across bioclimatic regions in the state. WNV seroconversion rates were calculated using Florida 2018 Department of Health WNV sentinel chicken seroconversion data from 187 flocks maintained by mosquito control programs. Percent land cover and edge density metrics were calculated for multiple land cover classes and within multiple buffer distances from chicken coops using 2019 National Land Cover Data. We used binomial generalized linear mixed effects models to calculate the importance of landscape metrics to WNV seroconversion. We found no statewide predictors of seroconversion, but as expected, the importance of landscape varied across regions. In the north-central part of the state, we found higher seroconversion in less populated suburban areas while higher seroconversion in south-central Florida was correlated with fragmented forested areas within 0.5 km of coops and intact woody wetland areas within 2 km of coops. This work corroborates previous findings that consistent landscape predictors of WNV are difficult to identify across broader geographic areas and sets the stage for additional work that incorporates climate and landscapes interactions for a greater understanding of WNV ecology in this geographic region.

Divergent dynamics of coastal wetlands in the world’s major river deltas during 1990–2019

Coastal wetlands provide vital dynamic ecosystem services. They have become increasingly important after being linked to several sustainable developmental goals, resulting in a focus on their protection, management, and restoration. Therefore, there is an increasing need to detect and compare coastal wetland spatiotemporal dynamics in deltas at a global scale. In this study, we mapped and characterized coastal wetland spatiotemporal patterns for 1990–2019 in the world’s major river deltas using pixel frequency algorithms and Landsat-4/5 (TM), −7 (ETM + ), −8 (OLI), and Sentinel-2 (MSI) time-series imagery obtained from Google Earth Engine (GEE). Our map had a high overall accuracy (91.84 %) for 2019. Tidal flats were primarily distributed in North America (∼6.87 %) and Asia (∼5.91 %), whereas salt marshes were more commonly found in North America (∼45.39 %) and South America (∼10.61 %). Mangroves are more common in South America (∼11.86 %) and Asia (∼5.83 %), primarily because of the Amazon River Delta and tropical and subtropical regions of Asia, which host several large river deltas. South America had the largest coastal delta wetland area (798,569 km2), followed by Asia (640,251 km2), North America (581,977 km2), Africa (181,977 km2), Europe (140,759 km2), and Oceania (15,915 km2). There was a minor difference in the distribution of wetland vegetation and tidal flats in Asian coastal deltas, and the wetland vegetation area in Asia was greater than that in tidal flats on other continents. We found that the coastal wetland areas increased during 1990–2001, decreased during 2001–2012, and steadily increased during 2012–2019. Our study provides a baseline for monitoring the area, status, and health of the coastal wetlands in these river deltas.

Differences in Anthropogenic Impacts of Typical Mid- to High-Latitude Wetlands in the Heilongjiang Basin

Wetlands represent one of the three principal ecosystems and serve a vital function in the protection of water resources and the regulation of climate. However, wetlands are currently experiencing significant challenges, particularly in the agriculturally productive wetlands of the Heilongjiang River Basin, which have been considerably impacted by human activities. This study focuses on three representative wetlands situated within the Heilongjiang River Basin. This study analyses changes in wetland area and landscape patterns from 2002 to 2022, as well as the impact of agriculture and impervious surface expansion on the wetlands. The findings indicate that agricultural expansion is the primary driver of wetland area loss. The wetland area affected by agriculture demonstrates the most significant change, with the largest observed shift reaching 47.2%. The expansion of impervious surfaces was found to have a significant impact on wetland fragmentation, resulting in a notable decrease in wetland connectivity. This was evidenced by a reduction in the average patch size, which decreased by 14.68 ha over the decade from 2007 to 2017, a period during which impervious surfaces expanded. This paper identifies the distinctions in the influence of diverse human activities on wetland landscape patterns in the Heilongjiang Basin and employs natural samples for control, thereby attenuating the impact of the natural environment. This study offers a novel perspective on the processes of wetland change and the maintenance of wetland health, which is crucial for the realisation of clean water and sanitation (Sustainable Development Goal 6.6).

FV-DLV3+: a light-weight flooded vegetation extraction method with attention-based DeepLabv3+

ABSTRACT Climate change and human activities have led to a decline in wetland vegetation and sharp reduction of wetland area, which has caused considerable damage to the hydrological cycle and ecological functions of many wetlands. Flooded vegetation (FV) is an important wetland landscape, and strengthening the research on methods of the extracting FV using remote sensing or aerial photos plays an important role in monitoring changes to wetlands. This paper used optical satellite and synthetic aperture radar satellite images as data sources, construct a set of feature variable sets suitable for FV extraction; in addition, a deep learning method based on the semantic segmentation model DeepLabv3+, FV-DLV3+, is proposed for FV extraction. The method adopts the lightweight MobileNetV2 model as the backbone feature extraction network, introduces the SegFormer network to make up for the feature information loss caused by MobileNetV2, and further captures more effective features and suppresses the interference of the background through an attention mechanism to enhance the ability to extract feature boundaries, thereby improving the accuracy of FV extraction. Taking the wetland of Poyang Lake as the experimental area, Landsat 8 and Sentinel-1A satellite data were selected as the data sources to produce a deep learning dataset, and U-Net, DeepLabv3+, Swin-Unet, Swin Transformer, and CVTNet were selected for comparison when conducting model training and validating experiments. The results show that FV-DLV3+ has an excellent effect on FV extraction of the Poyang Lake wetland, which effectively reduces the computational complexity of the process while improving extraction accuracy; the mean intersection over union of this paper’s method was higher than the five above-mentioned methods, which was 12.93% and 5.85% higher than that of DeepLabv3+ and CVTNet, respectively.

Improving the efficiency of aerial surveys for monitoring North American beaver population dynamics

Context The North American beaver (Castor canadensis) was extirpated from much of its range in the US in the 1800s due to fur trapping and change in land use. However, the species has recolonised much of its former range, including the US state of Ohio. Since 2013, the Ohio Division of Wildlife (ODOW) has monitored trends in beaver colony density via aerial surveys of 40 km × 40 km plots classified as low, medium, or high suitability based on the amount of wetland. Nonetheless, the current classification system may miss important correlates of beaver colony density. Aims Our study aimed to (1) identify predictors of beaver colony density (number of colonies inferred from aerial counts of lodges) across Ohio, and (2) develop a model-based classification system to improve the efficacy of monitoring efforts. Methods To predict beaver colony density in Ohio we used an aerial survey dataset of 54 plots (40 km × 40 km) collected by ODOW annually between 2013 and 2020, along with a suite of environmental, anthropogenic, and climate variables in a mixed effects modelling framework. Key results Beaver colony density was positively associated with wetland and reclaimed surface mine areas and inversely associated with the proportion of agricultural lands. There was a negative interaction between wetland and surface mines; in general, beaver colony density increased with wetland and surface mine area. However, in plots with wetland area >1000 ha, beaver colony density was weakly negatively associated with surface mine area. Using median and interquartile ranges of model-averaged predicted beaver colony density, we developed a new classification of low, moderate and high suitability for both the survey plots and the entire state of Ohio. We found that eastern Ohio had high suitability, while the central and western parts of the state had lower suitability for C. canadensis. Conclusions Our approach to identifying predictors for beaver colony density at broad spatial scales highlights the importance of reclaimed surface mines and wetlands for beaver populations, while the model-based habitat classification provides ODOW additional information for monitoring and beaver management decisions. Implications Improved C. canadensis monitoring at the landscape scale using habitat classifications that consider local conditions can both improve annual survey cost-effectiveness and facilitate the sustainable management of this recovering species.

Muskrat occurrence in Rhode Island shows little evidence of land use change driving declines

AbstractMuskrat (Ondatra zibethicus) populations have been in apparent decline across their native range in North America for decades. Several hypotheses exist for the causes of these declines, including loss of wetlands. We used time‐to‐detection data from 925 surveys from 276 sites across Rhode Island, USA, between 2021–2023 to fit an occupancy model that related the probability of muskrat occupancy at a site to land cover classification. We found that muskrat occupancy was higher in areas with more open water, urban land cover, or a second‐order or larger stream, and lower in areas with salt water. We estimated changes in wetland area throughout Rhode Island using the National Land Cover Database classifications from 2001 and 2019 and found a net loss in wetland cover of 219 ha. We calculated the distance between wetland patches in each of these periods and found that patches were closer together than the dispersal distance of muskrats, suggesting isolation is unlikely to be driving muskrat declines. Additionally, when we used our model to predict changes in muskrat occupancy between 2001 and 2019, both mean and median predicted occupancy changed by <0.005. These results indicate that muskrat declines are not driven by habitat loss, and suggest future research is needed that focuses on other hypothesized mechanisms of muskrat declines such as disease, declining habitat quality, predation, and competition.

Wetland Landscape Dynamics and Multi-Functional Assessment of Wuhan City, China

Under the dual pressures of rapid urbanization and intensifying global climate change, China has proposed governance policies aimed at promoting ecological urban construction. Wetland landscapes play a key role in sustaining human and social well-being. As a significant city in the middle reaches of the Yangtze River, Wuhan’s wetland resources play an irreplaceable role in maintaining the regional ecological balance and promoting sustainable economic and social development. However, urbanization poses a severe challenge to the ecological service functions of wetlands. Consequently, in this study, we analyzed the spatial–temporal evolution patterns of the sub-functional systems of carbon sequestration, biodiversity conservation, water yield, and water purification in five wetland types in Wuhan City from 2000 to 2020 by using the CASA model and InVEST model. Then, a wetland multi-functional assessment framework was constructed to quantify the comprehensive multi-function score. It is imperative to unravel the underlying mechanisms driving the changes in wetland functions and to explore the equilibrium point between wetland conservation and urban sustainable development. Our results show that the wetland area of Wuhan City decreased from 5077.33 km2 in 2000 to 4696.60 km2 in 2020, and the wetland multi-functions exhibited significant spatial heterogeneity from 2000 to 2020. Wetland carbon sequestration increased from 0.94 Tg in 2000 to 1.11 Tg in 2020. The wetland habitat quality declined from 0.13 in 2000 to 0.11 in 2020. The water production of the wetlands increased from 5.43 × 109 t in 2000 to 22.59 × 109 t in 2020. The wetland N loss decreased from 55,850.58 t in 2000 to 49,209.93 t in 2020. The highest multi-function score was in paddy fields, which increased from 0.41 ± 0.12 to 0.51 ± 0.12, followed by tidal flats, which increased from 0.39 ± 0.14 to 0.50 ± 0.16, and the lowest was rivers, which also increased from 0.33 ± 0.12 to 0.41 ± 0.14. The multi-function score was closely related to the wetland type and urban development direction over the past two decades, and it was negatively correlated with production and living density, providing new ideas for wetland ecological protection and construction in Wuhan City.

Prediction and Simulation for Land Use and Land Cover Change of Paddy Field Influence by Salinization in Coastal Demak Regency

The extent of coastal rice paddy agricultural land is vulnerable to land use and land cover (LULC) changes to non-agricultural uses due to land degradation, one of which is caused by salinity. This study aims to detect and project LULC changes up to 2031, particularly in coastal rice paddy areas affected by salinity, by comparing LULC in 2017, 2019, and 2021. Sentinel-2 Imagery is used for LULC classification, with recordings selected during the generative phase of rice growth to obtain the most optimal rice paddy area. There are six LULC classifications: water, wetland, low-medium-high vegetation cover, and built-up area. To understand the impact of salinity on crops, several vegetation indices (VIs) such as NDVI, SAVI, EVI, and ARVI are used. The LULC changes classified according to VIs are compared with the MOLUSCE plugin based on artificial neural networkmultilayer perceptron (ANN-MLP) and Cellular Automata (CA). The comparison of VIs results shows that NDVI is better at describing LULC changes due to the influence of salinity, with a kappa value of 0.63 and a Correctness of 72.565. The LULC projection using CA in all VIs indicates that wetland areas are more likely to convert into water bodies, suggesting that high salinity land tends to be unproductive for rice paddies, making it prone to conversion.

Assessing Wetland Health Through Decomposition in Degraded and Semi-Intact Wetlands in Uganda

The assessment of ecological functions of wetland areas offers a more dependable measure of the health of these ecosystems. The study examined the decomposition process within two urban wetlands with different degree of disturbance as a means of evaluating their ecological health and functionality. The study also inspected the influence of various physicochemical parameters on rates of decomposition. 20g of Pennisetum purpureum, an increasingly common invasive species in natural ecosystems due to human activities, was harvested, air dried to a constant weight and utilised in litter bag decomposition experiments within the two wetlands. Invasive species such as Pennisetum purpureum often have similar impacts across different ecosystems, making them useful models for generalizing results. Changes in leaf mass over time and leaf breakdown rates using a single exponential decay model were determined. Wetland physicochemical characteristics (pH, temperature, TDS, dissolved oxygen & conductivity) were also measured and recorded for the two wetlands within the duration of the experiment. Pearson’s correlation test was conducted to validate the relationship between the physico-chemical parameters and decomposition rates of the two different wetlands. Results revealed a significant difference in mean decomposition rates, (p < 0.001) with the degraded wetland exhibiting a higher value (0.20 kd-1) compared to the semi-intact wetland (0.015 kd-1). The rates of decomposition were also found to positively correlate with temperature (r = 0.54), conductivity and pH, while showing a slight negative correlation with TDS and dissolved oxygen. The study concluded that rapid decomposition in degraded wetlands is primarily due to human disturbances that alter their physical characteristics indicating deteriorating ecological health inform of eutrophication, altered native plant growth, subsidence, decreased carbon storage, oxygen depletion and so forth. Additionally, the alternating periods of inundation within the wetlands, caused by the wet-dry cycles is a primary influencer of decomposition rates in wetlands especially within the tropical region

Land Cover Simulation and Carbon Storage Assessment in Daqing City based on FLUS-InVEST Model

Considering Daqing City as the research area, the impact of land cover change on carbon storage in the future was discussed, and the hot spots of carbon sequestration capacity were identified. The future land use simulation （FLUS） model was used to simulate the land cover pattern of a natural succession scenario, ecological protection scenario, urban development scenario, and comprehensive development scenario in 2030, and the integrated valuation of ecosystem services and trade-offs （InVEST） model was combined to estimate carbon storage in 2010, 2020, and 2030. Finally, the hot spot analysis tool was used to identify the cold hot spots of carbon sequestration capacity. The results showed the following： ① From 2010 to 2020, the area of cultivated land, water, and artificial surface increased, whereas the area of other land cover types decreased, and the total carbon storage decreased by 8.6×105 t. ② The land cover change of the natural succession scenario and urban development scenario in 2030 was similar to that of 2010-2020, with carbon storage decreasing by 1.16×106 t and 1.20×106 t, respectively. The carbon storage of the comprehensive development scenario decreased by 1.00×106 t compared with that in 2020, and carbon storage of the ecological protection scenario was 5.677 7×108 t, which increased by 2.53×106 t compared with that in 2020. ③ The conversion of grassland and wetland to cultivated land was the main cause of carbon storage loss, and the main contributor of carbon storage in the ecological protection scenarios was wetland. ④ The hot spots of carbon sequestration capacity were mainly located in the wetland area, and the cold spots were mainly distributed in the central part of Daqing City. The carbon sequestration capacity of cultivated land was not significant. According to the research results, to realize the urban transformation of Daqing City, we should insist on returning farmland to forest and grass, increase the intensity of returning moisture, improve the utilization rate of urban land, and increase green infrastructure in the main urban area.