Distribution Of Wetlands Research Articles

Evolution of watershed phosphorus buffering capacity and its response to land-use change in Poyang Lake basin, China

The watershed buffering capacity is an important pivot to regulate phosphorus (P) pressure in aquatic environment , and land use is a key factor for its regulation. However, the response mechanism of buffer capacity to land use change remains unknown. Based on the variation of “buffering capacity index” in the Poyang Lake basin from 1990 to 2019, the mechanism of their response relationship was revealed via quantifying watershed P retention threshold and P removal process in different landscapes. Results showed that the watershed P buffering capacity was significantly affected by the scale of landscape subject to certain conditions of net anthropogenic P inputs (NAPI) in the Poyang Lake basin. The decreases in agricultural land, grassland and wetland and the increase of artificial land jointly explained 66.9% of watershed P buffering capacity decrease before accumulative P retention reached the threshold. The contribution of land use to watershed P buffering capacity change decreased to 53.8% when the P retention exceeded the threshold. Decreasing in watershed P buffering capacity was closely related to the increase of P loss in cultivated land, and wetland P removal provided an optimized direction for restoring the watershed buffering capacity. The findings reinforce the understanding of P hysteresis on watershed environment, and provide a policy basis for developing sustainable P management and pollution control from the perspective of optimizing land-use. • The P buffering capacity affected by landscape scale subject to certain conditions of NAPI. • The response of watershed P buffering capacity to land-use changed when legacy P exceeded threshold. • Decreasing in watershed P buffering capacity was related to the landscapes with increased P loss. • Optimizing the distribution of wetlands may be an optimum way to improve buffering capacity.

Global occupation of wetland by artificial impervious surface area expansion and its impact on ecosystem service value for 2001–2018

Wetland is one of the most critical ecosystems with various functions, it produces the highest ecosystem services value (ESV) per unit area among all land types, thus, its decline has become a global concern in recent years. Since the 21st century, many developing countries have been continuously encroaching on wetlands due to rapid economic and population growth, resulting in the replacement of a large number of wetlands by cities, and how to reconcile urban expansion and wetland conservation has become a significant problem that needs to be considered. In this context, we mapped the spatial distribution of wetlands occupied by the expansion of global artificial impervious surface area (ISA) for 2001–2018 and discussed its temporal and spatial change characteristics at the global, continental, and national scales using the annual maps of global annual impervious area data set and global dynamic land cover data set based on Google Earth Engine platform, the loss of ESV due to wetland occupation was also measured at each spatial scale level. The results show that: (1) A total of 1301.24 km2 of wetlands were occupied by ISA expansion globally for 2001–2018, and their interannual variation showed an upward trend for 2001–2015, then began to decline rapidly since 2016. The occupation results in a global loss of US$3.46 billion in ecosystem service value; (2) Asia has the highest loss of ESV due to occupation of wetlands by ISA expansion, 624.18 km2 of occupation of wetlands resulted in a loss of US$1.66 billion in ESV, making it the continent with the largest area of occupied wetlands. Taking into account the different sizes of the continents, Oceania is the continent where ISA expansion occupies the wetlands most, while South America is the continent where it is relatively least severe; (3) China, the USA and Russia account for 50.9 % of global ESV loss due to wetland occupation by ISA expansion, a total of 663.95 km2 of wetlands were occupied. Although countries have made great efforts to protect wetland resources and deal with urban land expansion, our results show that the problem of wetlands occupied by global urban land expansion is still severe.

Nature-Based Solutions in “Forest–Wetland” Spatial Planning Strategies to Promote Sustainable City Development in Tianjin, China

Nature-based solutions are some of the most effective strategies to promote sustainable city development; however, existing research on NbS is mostly comprised of single variable studies rather than multiple variables. The purpose of this study was to explore the possibility of extending the NbS of a single variable to two variables for the better development of sustainable cities. Both forestation and wetland restoration are regarded as NbS for sustainable city development. The research approach of “forest–wetland” NbS was proposed and centers on the process and core issues of traditional NbS. Taking Tianjin as an example, the spatial patterns of forests and wetlands, correlation between the spatial distribution of forests and wetlands, and spatial correlation between the areas of forest growth and wetland growth within a certain distance in different years were studied using a spatial distribution pattern analysis, geographic concentration analysis, kernel density estimation and spatial autocorrelation analysis. Based on the core issues of NbS and the above spatial analysis, a “forest–wetland” spatial planning strategy was formulated. The main conclusions are as follows: forest and wetland were negatively correlated in the whole area of Tianjin, forest resources w mainly located in north, while wetland resources were mainly located in south. Compared with forests, the spatial distribution of wetlands in Tianjin was more balanced. There exist synergy and trade-offs between forest and wetland area under certain circumstances. Growth of forests was positively correlated with the growth of wetlands, within a distance of 0–400 m from 2000 to 2010, and within a distance of 0–600 m from 2010 to 2020. An increase in forest area will lead to an increase in evaporation, which in turn will hinder the growth of wetlands in Tianjin. Forest–wetland ecological network could promote synergistic between forest and wetland, and grey infrastructure to reduce potential trade-off between forest and wetland.

Sea-level rise predicted to reduce exotic mangrove distribution and biomass in coastal wetlands in southern China

The adverse effects of sea-level rise on coastal wetlands have been extensively reported, whereas the positive effects on wetland composition have tended to receive less attention. Using a coupled hydro-vegetation model, we modeled the spatial and temporal distribution of native and exotic mangroves for 120 years in a mangrove forest in southern China in scenarios with and without sea-level rise. After approximately 50 years, the self-reinforcing feedback between mangroves and subsurface salinity is expected to create a stable ecosystem. Without sea-level rise, salt-sensitive exotic species will inhibit the growth of native mangroves, potentially leading to a more fragile ecosystem. Native mangroves are predicted to grow best under moderate sea-level rise (0.5 m). If sea level rises by 1 m, exotic mangrove populations in southern China are predicted to drastically decline. Competition between salt-tolerant and salt-intolerant plants is common in coastal wetlands, and the modeling method presented in this study may be globally applicable.

Wetland spectral unmixing using multispectral satellite images

Wetlands are of great importance to the diversity of biota and ecology, thereby to humans. Monitoring such valuable areas is essential for sustainable development. When the sizes, geographic distribution, and total coverage of wetlands across the earth are taken into account, remote sensing shines out as the most economically and technically feasible method to realise the monitoring task. Concerning the utilisation of medium resolution satellite images as the input, the pixel-level approach falls short of understanding the wetland dynamics since vast amounts of pixels in such areas have mixed content. This study proposes a framework for determining the extent of wetlands and extracting their ground characteristics at the sub-pixel level. In the extent determination part, Tasselled Cap Water Index (TCWI) values are calculated on time series, and their variations throughout the year are modelled by fitting a double-sided sigmoid function. This information is coupled with Digital Terrain Model (DTM) thresholding to extract the final extent. A sub-pixel analysis is proposed for the latter part, which includes adopting a systematic approach using a three-element (soil, vegetation, water) scheme for establishing wetland ontology and implementing supervised spectral unmixing enhanced by band weight optimisation. Balıkdamı, one of the most impressive wetlands of Turkey, is chosen as the test area. Open-access optical satellite data acquired by the Sentinel-2 constellation are utilised as the primary data input. Since the abundance values of land cover classes in each Sentinel-2 pixel are estimated, reference abundance data with a 10 m ground sampling distance (GSD) are generated using four-band aerial images having a 30 cm GSD for the verification stage. A new method entitled ‘Abundance Confusion Matrix’ is introduced for comparison and detailed assessment of fractional land cover. Experimental results demonstrate that the extent determination is addressed with a precision of 99.21% and a miss rate of 5.75%. In addition, the abundance values of land cover classes are identified with an overall accuracy of 66.17% after the optimisation step. The proposed method proves to be a valuable tool for the detailed monitoring of wetlands.

Random Forest Classification Method for Predicting Intertidal Wetland Migration Under Sea Level Rise

Intertidal wetlands such as mangrove and saltmarsh are increasingly susceptible to areal losses related to sea level rise. This exposure is potentially offset by processes that might enable wetlands to accrete in situ or migrate landward under sea level rise, and planning policies that might open new opportunities for migration. We present and demonstrate a method to predict intertidal wetland distribution in the present-day landscape using random forest classification models, and use these models to predict the intertidal wetland distribution in future landscapes under specified sea level scenarios. The method is demonstrably robust in predicting present-day intertidal wetland distribution, with moderate correlation or better between predicted and mapped wetland distributions occurring in nearly all estuaries and strong correlation or better occurring in more than half of the estuaries. Given the accuracy in predicting present-day wetland distribution the method is assumed to be informative in predicting potential future wetland distribution when combined with best available models of future sea level. The classification method uses a variety of hydro-geomorphological surrogates that are derived from digital elevation models, Quaternary geology or soils mapping and land use mapping, which is then constrained by a representation of the future sea level inside estuaries. It is anticipated that the outputs from applying the method would inform assessments of intertidal wetland vulnerability to sea level rise and guide planning for potential wetland migration pathways.

Assessment of Changes in Key Ecosystem Factors and Water Conservation with Remote Sensing in the Zoige

As the largest alpine peat swamp wetland distribution area in the world, the Zoige has important ecological functions, including water conservation and biodiversity maintenance. In the past 20 years, the regional ecological protection and restoration measures continuously strengthened under the leadership of the local government have led to gradual improvements in the ecological environment of the region. In this study, multisource satellite remote-sensing image data were used to carry out quantitative monitoring and assessment of the main ecological elements (vegetation and water), as well as the regional leading ecosystem service function in the Zoige. Combined with local ecological protection management policies and measures, we analyzed the characteristics and effectiveness of ecological protection. We compared the ecosystem change trends of the Zoige reserve and the county, from 2001 to 2020, and found that the fractional vegetation cover (FVC) of Zoige county has increased at a rate of 0.25%/year. The growth rate was highest between 2015 and 2020, and the growth rate of FVC in the Zoige Wetland National Nature Reserve is approximately 1.89-fold that of the whole county. The water area also shows similar variation characteristics. On the whole, the water conservation capacity of the Zoige showed a significant increase from 2001 to 2020. We used high-resolution satellite remote-sensing images to capture the details of land use changes brought about by local ecological protection policies and measures, and together with macroecological indicators, we reflected on the effectiveness of regional ecological protection measures. We observed that the ecological effects of nature reserves are more direct and rapid, and the amount of water conservation within the nature reserve is about 1 × 104 m3/km2 higher than that of the surrounding grasslands. Satellite remote-sensing images can not only capture the multiscale change information of ecological indicators, such as vegetation and water, in a timely manner, but can also help us to identify the effectiveness of conservation measures by distinguishing and analyzing the causes of these changes.

Temporal and Spatial Variation of Wetland CH4 Emissions from the Qinghai–Tibet Plateau under Future Climate Change Scenarios

Wetlands are an important natural source of methane (CH4), so it is important to quantify how their emissions may vary under future climate change conditions. The Qinghai–Tibet Plateau contains more than a third of China’s wetlands. Here, we simulated temporal and spatial variation in CH4 emissions from natural wetlands on the Qinghai–Tibet Plateau from 2008 to 2100 under Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5. Based on the simulation results of the TRIPLEX-GHG model forced with data from 24 CMIP5 models of global climate, we predict that, assuming no change in wetland distribution on the Plateau, CH4 emissions from natural wetlands will increase by 35%, 98% and 267%, respectively, under RCP 2.6, 4.5 and 8.5. The predicted increase in atmospheric CO2 concentration will contribute 10–28% to the increased CH4 emissions from wetlands on the Plateau by 2100. Emissions are predicted to be majorly in the range of 0 to 30.5 g C m−2·a−1 across the Plateau and higher from wetlands in the southern region of the Plateau than from wetlands in central or northern regions. Under RCP8.5, the methane emissions of natural wetlands on the Qinghai–Tibet Plateau increased much more significantly than that under RCP2.6 and RCP4.5.

Spatial and temporal variation and driving factors of wetland in the Amu Darya River Delta, Central Asia

The Aral Sea in Central Asia has shrunk by 90% since 1960. The resulting desertification and salt and dust storms have diminished biodiversity in the Amu Darya River Delta, triggering the Aral Sea Crisis. Timely monitoring of trends and spatial patterns in deltaic wetlands is beneficial for biodiversity conservation and ecological restoration. Existing studies have mainly focused on the response of vegetation and microbial communities to ecological changes while ignoring the indicative role of wetlands-associated changes. This study analyzed the spatial and temporal patterns of long time series wetland changes extracted from remote sensing imagery through cover frequency maps and landscape indices. The hydrological data, land use/land cover change maps, and statistical data were combined to explore the driving factors of wetland change over the past 40 years. The results showed that the wetland area increased by 2242.96 km2 (90.72%) from 1977 to 2019. The wetland area increased at 268.01 km2/a from the 1970s to the early 1990s, with a fluctuating change since 1994. Wetlands are mainly distributed in the transition zone between the delta and the Aral Sea, accounting for approximately 81.61% of the total wetland area. The proportion of wetlands on the east and west sides significantly expanded along the canal system, increasing from 0.13% in 1977 to 9.74% in 2019. The degree of fragmentation of the wetland landscape in the entire delta increased by 1.7 times. With decreasing runoff into the delta, the expansion of cultivated land and the construction of reservoirs and irrigation canals changed the spatio-temporal distribution of water resources. In contrast, agricultural irrigation has raised the groundwater level. Human activities, such as the expansion of cultivated land and the construction of reservoirs and irrigation canals, are the main reasons for the spatio-temporal changes in wetland distribution, and exacerbated the decrease in water flow into the Aral Sea and its shrinkage. The restoration of wetlands is not a proof of ecological improvement, it resulted from the redistribution of water resources in the delta. The increase in wetland area represents an apparent ecological contrast with the drying up of the Aral Sea, and the phenomenon is not conducive to the ecological restoration of the delta.

Species Diversity and Distribution of Macrophytes in Different Wetland Ecosystems

Wetlands are vital and dynamic components of the global ecosystem, providing a wide range of ecosystem services such as water control and biodiversity. During 2015–2019, plant diversity and distribution of wetlands were studied in forty areas of the Mardan division, Khyber Pakhtunkhwa, Pakistan. A total of 282 vascular wetland plant species were identified, divided into 192 genera and 73 families. Poaceae, with 41 species, is the most diverse family of wetland plants, followed by Asteraceae, with 26 species. In the present study, wetland plants are classified based on the Cook classification system. It is interesting to note that all the life forms were present in the study area except Plankton. Helophyte life form was represented by the highest number of plants with 177 plants and 62.5 percent of total wetland plants, followed by Tenagophyte with 42 plant species (14.8%) and Hyperhydate with 27 plant species (9.5%). Based on the distribution pattern of 282 species in 40 wetland sites, the most common plant species found was Euphorbia helioscopia, recorded from 36 locations, followed by Cynodon dactylon which was found in 32 locations. In Mardan division, the Gulshan Abad Khuwar had the highest number of species occurrences (83 species) accounting for 32% of all species, followed by Alo Khwar (59 species, 23%) and Hathian Khwar (50 species, 19%). The current work presents a comprehensive and field-based detailed survey of wetland plant distribution in this region, filling knowledge gaps in the Himalayas in particular, and having broader implications for wetland management policy and practice. Unfortunately, wetlands of the research area are facing high pressure due to an increase in human population, overgrazing, deforestation, construction of buildings and roads. The filling up of these water bodies is resulting in the loss of hydrophytic species diversity. Therefore, these wetlands need proper attention and effective protection from anthropogenic disturbances.

Coastal Wetland Responses to Sea Level Rise: The Losers and Winners Based on Hydro-Geomorphological Settings

Many coastal wetlands are under pressure due to climate change and the associated sea level rise (SLR). Many previous studies suggest that upslope lateral migration is the key adaptive mechanism for saline wetlands, such as mangroves and saltmarshes. However, few studies have explored the long-term fate of other wetland types, such as brackish swamps and freshwater forests. Using the current wetland map of a micro-tidal estuary, the Manning River in New South Wales, Australia, this study built a machine learning model based on the hydro-geomorphological settings of four broad wetland types. The model was then used to predict the future wetland distribution under three sea level rise scenarios. The predictions were compared to compute the persistence, net, swap, and total changes in the wetlands to investigate the loss and gain potential of different wetland classes. Our results for the study area show extensive gains by mangroves under low (0.5 m), moderate (1.0 m), and high (1.5 m) sea level rise scenarios, whereas the other wetland classes could suffer substantial losses. Our findings suggest that the accommodation spaces might only be beneficial to mangroves, and their availability to saltmarshes might be limited by coastal squeeze at saline–freshwater ecotones. Furthermore, the accommodation spaces for freshwater wetlands were also restrained by coastal squeeze at the wetland-upland ecotones. As sea level rises, coastal wetlands other than mangroves could be lost due to barriers at the transitional ecotones. In our study, these are largely manifested by slope impacts on hydrology at a higher sea level. Our approach provides a framework to systematically assess the vulnerability of all coastal wetland types.

From Forest Dynamics to Wetland Siltation in Mountainous Landscapes: A RS-Based Framework for Enhancing Erosion Control

Human activities have caused a significant change in the function and services that ecosystems have provided to society since historical times. In mountainous landscapes, the regulation of services such as water quality or erosion control has been impacted by land use and land cover (LULC) changes, especially the loss and fragmentation of forest patches. In this work, we develop a Remote Sensing (RS)-based modelling approach to identify areas for the implementation of nature-based solutions (NBS) (i.e., natural forest conservation and restoration) that allow reducing the vulnerability of aquatic ecosystems to siltation in mountainous regions. We used time series Landsat 5TM, 7ETM+, 8OLI and Sentinel 2A/2B MSI (S2) imagery to map forest dynamics and wetland distribution in Picos de Europa National Park (Cantabrian Mountains, northern Spain). We fed RS-based models with detailed in situ information based on photo-interpretation and fieldwork completed from 2017 to 2021. We estimated a forest cover increase rate of 2 ha/year comparing current and past LULC maps against external validation data. We applied this forest gain to a scenario generator model to derive a 30-year future LULC map that defines the potential forest extent for the study area in 2049. We then modelled the distribution of wetlands to identify the areas with the greatest potential for moisture accumulation. We used an S2 mosaic and topography-derived data such as the slope and topographic wetness index (TWI), which indicate terrain water accumulation. Overall accuracy scores reached values of 86% for LULC classification and 61% for wetland mapping. At the same time, we obtained the potential erosion using the NetMap software to identify potential sediment production, transport and deposition areas. Finally, forest dynamics, wetland distribution and potential erosion were combined in a multi-criteria analysis aiming to reduce the amount of sediment reaching selected wetlands. We achieved this by identifying the most suitable locations for the conservation and restoration of natural forests on slopes and in riparian areas, which may reduce the risk of soil erosion and maximise sediment filtering, respectively. The results show a network pattern for forest management that would allow for controlling erosion effects across space and time at three levels: one, by reducing the load that originates upslope in the absence of forest cover; two, by intersecting runoff at watercourses related to sediment transport; and three, by a lack of former barriers, by trapping erosion near to the receiving wetland systems, main river axes and contributing streams. In conclusion, the proposed methodology, which could be transferred to other mountain regions, allows to optimise investment for erosion prevention and wetland conservation by using only very specific areas of the landscape for habitat management (e.g., for NBS implementation).

Seasonal variations for combined effects of landscape metrics on land surface temperature (LST) and aerosol optical depth (AOD)

Urban heat island (UHI) and air pollution are common environmental problems in urban areas. However, there is no comprehensive analysis of influencing factors and their interactions on land surface temperature (LST) and aerosol optical depth (AOD). To propose strategies to comprehensively mitigate LST and AOD by optimizing the landscape pattern, we used a series of statistical analysis methods to quantify the correlation, interactive contribution, and influence mechanism that how landscape metrics influenced LST and AOD directly or indirectly. In warmer seasons and year, the percentage of landscape (PLAND), largest patch index (LPI), aggregation index (AI) in forest land (FL), and mean shape index distribution (SHAPE_MN) in wetlands (WL) were negatively correlated with LST and AOD consistently. In contrast, landscape shape index (LSI) in FL, patch density (PD) and LSI in WL, edge density (ED), PD, LPI, SHAPE_MN, and LSI in artificial surfaces were positively correlated. Landscape metrics were weakly correlated with LST and AOD in winter. The climate condition layer was dominant overall. The dominant factor layer of landscape composition, landscape configuration and green quantity on LST and AOD differed in different land types and seasons. Landscape metrics and green quantity could directly or indirectly affect LST and AOD, with the direct effects of landscape metrics and green quantity being the primary. Increasing the percentage of FL, configuring large, aggregated and simple FL, regular, simple and better connected wetlands, and increasing patterns in artificial surfaces with simple, regular, small and better connectivity characteristics are effective mitigation measures. These findings can be helpful in developing UHI and air pollution adaptation strategies.

Dynamic identification and health assessment of wetlands in the middle reaches of the Yangtze River basin under changing environment

Recently, the wetland in the middle reaches of the Yangtze River Basin (MYRB) has been impacted by the human activities and climate change, thereby restricting sustainable development. In order to know the distribution and health of wetlands in the MYRB and provide scientific decision support for environmental protection. This study applied the classification and regression tree (CART) method to extract the wetland areas in the MYRB using remote sensing images and constructed a wetland health index system to quantitatively evaluate the health status of wetlands during 2000–2019 based on the pressure-state-response (PSR) model. The main conclusions are as follows: (1) using the CART method, the classification extraction accuracies of wetlands in 2000, 2006, 2009 and 2015 were higher than 85%, and that of 2019 was 93%; (2) wetland area shrinkage was the main change in the wetland pattern in the MYRB, wherein the landscape diversity index and landscape shape index (LSI) showed decreasing trends during 2000–2009. Then, after 2010, the wetland area has increased, but the wetland area in 2019 was 2045 km2 less than that in 2000; and (3) the wetland health experienced a downward trend during 2000–2010 before improving from 2010 to 2019. However, many regions in the Dongting Lake Basin (DLB) remained at sub-health and pathological wetland levels. Therefore, it is necessary to pay attention to the health level of the wetland system in the MYRB especially the DLB to achieve future sustainable development.

Evaluation and analysis of ecosystem service value based on land use/cover change in Dongting Lake wetland

Wetland vegetation has experienced significant loss and degradation over the last few decades. Although volumes of studies involve wetlands, limited attention has been paid to the long-term changes and ecological values of inland lake wetlands in China. In this paper, land use/cover data of Dongting Lake wetland from 1995 to 2020 was generated based on Google Earth engine using Stacking algorithm. Subsequently, the spatial and temporal variation of wetland types was explored using dynamic analysis and correlation indices of land change. The main factors influencing the distribution of wetland vegetation were identified by Geodetector. Finally, the degree of impact of wetland changes on ecosystem service value was quantified based on the equivalence factor method. The results show that, in the past 25 years, wetland land use types had switched with each other more frequently, and a total of 294.94 km2 of wetland area had been lost, mainly due to the shrinkage of water, reeds and sedge. The main factors affecting wetland distribution are population density, GDP, elevation and sunshine duration, and the interaction of the drivers is mostly linearly enhanced with no weakening effect. In addition, the ecosystem service value of wetlands showed a trend of first decreasing and then increasing, with a total loss of ¥87,000.99 million in wetland ESV. The results of the study can provide basic information and data for the preliminary work of planning and design, and demonstrate the importance of ecosystems in different regions. Based on the assessment results, it can provide an important basis for the construction of ecological security patterns and policy formulation for ecological civilization construction.

Identifying wetland areas in historical maps using deep convolutional neural networks

The local environment and land usages have changed a lot during the past one hundred years. Historical documents and materials are crucial in understanding and following these changes. Historical documents are, therefore, an important piece in the understanding of the impact and consequences of land usage change. This, in turn, is important in the search of restoration projects that can be conducted to turn and reduce harmful and unsustainable effects originating from changes in the land-usage.This work extracts information on the historical location and geographical distribution of wetlands, from hand-drawn maps. This is achieved by using deep learning (DL), and more specifically a convolutional neural network (CNN). The CNN model is trained on a manually pre-labelled dataset on historical wetlands in the area of Jönköping county in Sweden. These are all extracted from the historical map called “Generalstabskartan”.The presented CNN performs well and achieves a F1-score of 0.886 when evaluated using a 10-fold cross validation over the data. The trained models are additionally used to generate a GIS layer of the presumable historical geographical distribution of wetlands for the area that is depicted in the southern collection in Generalstabskartan, which covers the southern half of Sweden. This GIS layer is released as an open resource and can be freely used.To summarise, the presented results show that CNNs can be a useful tool in the extraction and digitalisation of non-textual information in historical documents, such as historical maps. A modern GIS material that can be used to further understand the past land-usage change is produced within this research. Previously, no material of this detail and extent have been available, due to the large effort needed to manually create such. However, with the presented resource better quantifications and estimations of historical wetlands that have been lost can be made.

Determining the distribution of wetlands across Eswatini

ABSTRACT Eswatini became a signatory to the Ramsar Convention on the 15th of June, 2013. To date, the country does not have a national wetland inventory. This study applies a newly developed wetland mapping technique (produced in South Africa) to Eswatini, to provide baseline information on the potential distribution of wetlands across the country. Results of this study show that when applying the mapping technique as it was applied in South Africa, watercourses (rivers, drainage lines, and riparian zones) are more frequently mapped than true wetlands. Given that Eswatini currently uses the broad Ramsar definition of a wetland, the potential wetland map produced in this study is well suited to identify wetlands falling under such definition. However, the technique does not suffice where a more specific definition for wetlands is used. To improve the initial potential wetland map, this study made use of data, obtained from 2 000 points distributed across the initial wetland map, by classifying areas with the highest potential of being wetlands into hydrogeomorphic units. Results indicate that the methods used to improve the initial map areable to distinguish watercourses with a higher potential of being a true wetland and identify certain hydrogeomorphic units. This method can therefore be used to provide baseline data of potential wetland distribution for countries that do not possess the means, or attribute data to produce a comprehensive wetland map.

西藏年楚河流域湿地土壤纤毛虫群落特征

PDF HTML阅读 XML下载 导出引用 引用提醒 西藏年楚河流域湿地土壤纤毛虫群落特征 DOI: 10.5846/stxb202203290781 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: “一江四河”流域生物多样性调研与维持机制评价综合科学考察项目（藏财科教指【2021】1号和藏财教指【2019】01号）；西藏大学研究生"高水平人才培养计划"项目资助（2020-GSP-S042） Characteristics of soil ciliate community in wetland of the Nianchu River Basin, Tibet, China Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为了研究西藏年楚河流域湿地土壤纤毛虫群落特征及其影响因素，于2020年5月、8月和10月春夏秋三个季节，根据年楚河流域湿地分布特征选取了5个湿地样地共计75个土壤样品，采用"非淹没培养皿法"和"活体观察法"对土壤纤毛虫进行了培养和鉴定，共鉴定出土壤纤毛虫154种，隶属于9纲、19目、43科、70属。结果表明土壤纤毛虫优势类群为前管目（Prorodontida），占11.69%；刺钩目（Haptorida）为次优势类群，占11.04%；罕见类群为游仆虫目（Euplotidae）、瓶纤目（Armophorida）、肾形目（Colpodida）、嗜污目（Philaslasterida）、齿管目（Chlamydodontida）和吸管目（Succorida），共占16.23%。湿地土壤纤毛虫多样性指数在三个季节间均无显著性差异（P>0.05），SD4样地的多样性指数在5个样地中最高；湿地土壤纤毛虫C/P（0.35）<1，表明其土壤环境未污染；土壤纤毛虫群落相似性位于极不相似到中等不相似之间，说明不同样地的物种组成差异性较大；土壤含水量（SWC）、土壤温度（ST）和速效钾（RAK）是影响土壤纤毛虫群落分布的因素。 Abstract:In order to study the characteristics of soil ciliate community and influencing factors in wetland of the Nianchu River Basin, Tibet, China. In May, August and October of 2020, i.e spring summer and autumn three seasons, 75 soil samples from five plots were selected based on the distribution of wetland in the Nianchu River Basin, A total of 154 species of soil ciliates, belonging to 70 genera, 43 families,19 orders and 9 classes, were identified by "non-flooded petri dish method" and "in vivo observation method". The results showed that Prorodontida (11.69%) was the dominant group, Haptorida (11.04%) was the subdominant group, and rare taxa were Euplotidae, Armophorida, Colpodida, Philaslasterida, Chlamydodontida and Succorida, accounting for 16.23% of the total. There was no significant difference in soil ciliate diversity index between the three seasons (P>0.05) in wetland, and the diversity index of the SD4 was the highest among the five plots. The soil ciliates(C/P(0.35)<1), indicating that the soil environment in wetland was not polluted. The similarity of soil ciliate communities ranged from extremely dissimilar to moderately dissimilar, showed that species had great differences in different sites. Soil water content (SWC), soil temperature (ST) and available potassium (RAK) were the main factors affecting soil ciliate community distribution. 参考文献 相似文献 引证文献

Remote sensing to characterize inundation and vegetation dynamics of upland lagoons

AbstractUnderstanding broad trends in the distribution and composition of wetlands is essential for making evidence‐based management decisions. Determining temporal change in the extent of inundation in wetlands using remote sensing remains challenging and requires on‐ground verification to determine accuracy and precision. Therefore, optimization and validation of remote sensing methods in threatened wetlands is a high priority for their conservation. Despite their ecological importance in the landscape, we have little knowledge of the variation in the spatial extent of inundation in upland lagoons, a threatened ecological community in New South Wales, Australia. Our project developed locally trained algorithms to predict the extent of water and emergent vegetation using imagery from the Landsat‐5, ‐7, and ‐8 satellites. The best model for upland lagoons used shortwave infrared reflectance (performing better than normalized difference spectral indices), with model accuracy against validation transects greater than 95%. We applied the model to images from 1988 to 2020 across 58 lagoons to generate a dataset that demonstrates the variable water regime and vegetation change in response to local rainfall over 32 years such as in the lagoons. Our results reduce threats to a dynamic threatened ecological community by filling an important knowledge gap and demonstrate a valuable method to understand historical and current changes in the hydrology of dynamic wetland systems more broadly.

Mapping the spatial distribution of wetlands in Argentina (South America) from a fusion of national databases

Context There a large information gap on the spatial distribution and diversity of wetland types in South America. Aims We focus on mapping the spatial distribution of broad wetland types in Argentina, based on the integration of open spatial data sources developed by national government agencies. Methods We designed a two-tier process, as follows: we filtered broad wetland types described in the attributes of the spatial datasets and created a separate vector layer for each wetland class; we then ensembled the layers by populating a 25-m cell raster template. Key results Our WetCarto_AR layer indicates that wetlands cover 13.5% of mainland Argentina, being distributed throughout the country with a greater concentration towards the north-east, but patchy in the rest of the country. Palustrine is the dominant wetlands class followed by Riparian and Lacustrine. Global datasets underestimated wetland coverage, although the same large wetlands are recognised in all. Conclusions Our results make visible the known spatial extent of wetlands in Argentina and provide information to feed or validate global models. Implications Results stress the importance of existing local databases, which, even when generated for other purposes, can be a starting point for country or region wetland mapping.