Distribution Of Wetlands Research Articles

Exploring the driving forces of potential marsh wetlands formation and distribution in the Qilian Mountains of Qinghai, China

The formation and distribution of marsh wetlands are influenced by both climate changes and human activities. Quantifying the driving factors of marsh wetlands is essential for understanding the sustainable utilization of wetland ecosystems. The current study simulated the potential marsh wetlands’ spatial distribution in the Qilian Mountains of Qinghai (QMQ) by a random forest (RF) model with a series of environmental indicators, including climate, vegetation, soil, topography, and human activity factors in 2020. Simultaneously, a structural equation modeling (SEM) was utilized to explore driving factors of formation and distribution of marsh wetlands. Results showed that: (1) The potential marsh wetlands area was determined to be 2502.01 km2 in the QMQ, mainly distributed in the watershed source and along the river. (2) Normalized difference vegetation index (NDVI), normalized difference water index (NDWI), digital elevation model (DEM), fraction vegetation coverage (FVC), and ground surface temperature (GST) were the most critical factors in marsh wetland simulation, with the relative importance index of 11.14 %, 9.70 %, 9.07 %, 8.39 %, and 6.50 %, respectively. (3) The topography acted on the potential marsh wetlands’ distribution by influencing the nutrient input function of potential marsh wetlands (indirect effect 0.28). The climate was the fundamental factor that directly controls the distribution of potential marsh wetlands (direct effect 0.44), and vegetation had a total effect of 0.24. The current study may provide information for policy decisions on wetland conservation and restoration.

Evaluation of Remote Sensing Products for Wetland Mapping in the Irtysh River Basin

As a transboundary river with rich and unique wetland types, the Irtysh River faces various challenges and threats from human activities and climate change, which affect area, type, and function of wetland. To accurately obtain information on the spatial and temporal distribution of wetlands in this basin, this study compares and evaluates the consistency and accuracy of a total of eleven remote sensing (RS) based land use/land cover (LULC), and wetland products. The information extraction effect of each RS product was examined through methods such as wetland area and type description, thematic map comparison, and similarity coefficient and Kappa coefficient calculations, which can reflect the wetland distribution characteristics and differences among the RS products in the Irtysh River Basin. The results show that although there is a consensus among the products in the major wetland distribution areas, there are still obvious deviations in detail depiction due to differences in factors such as data sources and methods. The products of Global 30 m Wetland Fine Classification Data (GWL_FCS30) and Global 30 m Land Cover Data (GLC_FCS30-2020) released by the Institute of Space and Astronautical Information Innovation (ISAI) of the Chinese Academy of Sciences (CAS) have a clear advantage in extracting spatial morphology features of wetlands due to the use of multi-source data, while the Esri Global 10 m Land Cover Data (ESRI_Global-LULC_10m) and products such as the global 10 m land cover data (FROM_GLC10_2017) from Tsinghua University have higher classification consistency. Moreover, data resolution, classification scheme design, and validation methods are key factors affecting the quality of wetland information extraction in the Irtysh River Basin. In practical terms, the findings of this study hold significant implications for informed decision-making in wetland conservation and management within the Irtysh River Basin. By advancing wetland monitoring technologies and addressing critical considerations in information extraction, this research effectively bridges the gap between remote sensing technology and practical applications, offering valuable insights for regional wetland protection efforts.

SMALL WETLANDS IN THE BRAZILIAN SAVANNA(CERRADO): AN UNDERESTIMATED FRESHWATER RESOURCE

In Brazil, in scientific research and public policy have largely ignored small wetlands, they are not specifically covered by current environmental laws and reference standards. Furthermore, the lack of information about, small wetlands has made their protection and management difficult. Large-scale land use changes that modify the hydrology of low-order streams often lead to the accelerated degradation and loss of small wetlands. The aim of this study conducted in Brazilian Cerrado Biome area, was to map the type, size, and distribution of small wetlands in ancatchment area). Areas represented in 12 high-resolution multispectral images were individually classified in a geographic object-based image analysis (GEOBIA), resulting in a sensitivity or recall 0.872 (~87%). The 7,132 mapped wetlands had a total area of 163km2, corresponding to a density of 4.27wetlands km−2, with smallest wetlands smaller than 4.0 hectares (90.5%). The following descriptors were applied to map small wetlands: feature: asymmetry, rectangular fit, mean nir band, compactness, density, and length. Seven types of natural wetlands and four types of man-made wetlands were accordingly identified in the study area. Their classification within a system that follows the hierarchy of the Brazilian wetland classification system is proposed. Recommendations for the future study and protection of wetlands was are also provided.

Towards a dynamic effective drainage area map for the Canadian Prairie: Sensitivity of contributing area to wetland storage capacity

Wetlands that occupy topographic depressions are a defining feature of the Canadian Prairie. These features control hydrological connectivity as they contain high storage capacity relative to typically available precipitation and runoff. Altering wetland distribution changes the frequency with which areas can become hydrologically connected to the catchment outlet. There are several methods that have proven successful in estimating contributing area response to changes in wetland storage, but none have been applied widely across the Canadian Prairie. The objective of this study was (1) to determine if the rate at which contributing area changes with loss of wetland storage capacity is related to wetland distribution, and if so, (2) to map sensitivity across the region. To do so, a GIS analysis was employed in which iterative measurements were made of contributing area expansion with simulated wetland storage capacity loss. Results show that those catchments with more small wetlands have more sensitive contributing areas to storage capacity loss. Extrapolation of this relationship across the Canadian Prairie shows that areas in western Manitoba and southeastern Saskatchewan are among the most sensitive. These results provide a better understanding of how contributing area may change with loss of wetland storage capacity. This may inform implementation of beneficial management practices, transportation network design, and management of streamflow and nutrient transport to downstream lakes across the region.

A locally explained heterogeneity model for examining wetland disparity

ABSTRACTIdentifying the factors influencing wetland variations is crucial for understanding the relationship of climate change with wetland conservation and management. The wetland distribution is associated with multiple variables, and the interactions among these variables are complex. In this study, we aim to explore an interpretable and quantitative analysis of factors related to wetland spatiotemporal variations on the Tibetan Plateau (TP). By combining SHapley Additive exPlanations with a spatially stratified heterogeneity model, we propose a locally explained stratified heterogeneity (LESH) model that well reveals the effects of multiple variable interactions on the spatiotemporal variations of wetlands. The results show that topographic variables are the most important variables related to the spatial distribution of wetlands on the TP, and climatic variables are the most relevant factors for the increase in the wetland area on the TP from 2015 to 2019. In addition, the interactions among multiple variables strongly influence wetlands on the TP. Among them, when other geographic variables interact with the evaporation variable, its explanatory power on wetland distribution is significantly increased. Knowledge of wetland distribution determinants can help us understand the evolution of wetlands and the impacts of climate change on wetland variations.

Identifying the main drivers of the spatiotemporal variations in wetland methane emissions during 2001–2020

Wetlands act as an important natural source of global methane (CH4). The emission rate of wetland CH4 is jointly affected by climate change, carbon dioxide (CO2) fertilization, and wetland distribution. In this study, we implemented a wetland CH4 emission module into the Yale Interactive Biosphere (YIBs) model to quantify the spatiotemporal variations of global wetland CH4 emissions in 2001–2020. Site-level validations showed that the YIBs model reasonably captures the seasonality and magnitude of CH4 emissions at 28 out of 33 sites with significantly positive correlations and low relative biases. On the global scale, the YIBs predicts an annual mean wetland CH4 emission of 147.5 Tg yr−1 in 2000–2017, very close to the estimate of 147.9 Tg yr−1 from the ensemble of 13 process-based models. Global wetland CH4 emissions showed a positive trend of 0.74 Tg yr−2 in the past 2 decades, leading to an increase of 7.4 Tg yr−1 (5.2%) in 2008–2017 than 2000–2009. Climate change and CO2 fertilization accounted for over 70% of global wetland CH4 emission changes. Among them, the impact of CO2 grew steadily and became the dominant factor after the year 2008. The most significant changes in wetland CH4 emissions were located in the tropical regions following the perturbations in temperature that drives the ecosystem productivity. We found limited changes in CH4 emissions over high latitudes because of the moderate variations in wetland area fraction. The rise of wetland CH4 emissions poses an emerging threat to the global warming and likely escalates the tropospheric air pollutants.

Mapping small inland wetlands in the South-Kivu province by integrating optical and SAR data with statistical models for accurate distribution assessment

There are several techniques for mapping wetlands. In this study, we examined four statistical models to assess the potential distribution of wetlands in the South-Kivu province by combining optical and SAR images. The approach involved integrating topographic, hydrological, and vegetation indices into the four most used classifiers, namely Artificial Neural Network (ANN), Random Forest (RF), Boosted Regression Tree (BRT), and Maximum Entropy (MaxEnt). A wetland distribution map was generated and classified into 'wetland' and 'non-wetland.' The results showed variations in predictions among the different models. RF exhibited the most accurate predictions, achieving an overall classification accuracy of 95.67% and AUC and TSS values of 82.4%. Integrating SAR data improved accuracy and precision, particularly for mapping small inland wetlands. Our estimations indicate that wetlands cover approximately 13.5% (898,690 ha) of the entire province. BRT estimated wetland areas to be ~ 16% (1,106,080 ha), while ANN estimated ~ 14% (967,820 ha), MaxEnt ~ 15% (1,036,950 ha), and RF approximately ~ 10% (691,300 ha). The distribution of these areas varied across different territories, with higher values observed in Mwenga, Shabunda, and Fizi. Many of these areas are permanently flooded, while others experience seasonal inundation. Through digitization, the delineation process revealed variations in wetland areas, ranging from tens to thousands of hectares. The geographical distribution of wetlands generated in this study will serve as an essential reference for future investigations and pave the way for further research on characterizing and categorizing these areas.

Mapping Lotus Wetland Distribution with the Phenology Normalized Lotus Index Using SAR Time-Series Imagery and the Phenology-Based Method

Lotus wetland is a type of wetland that can efficiently purify water. Therefore, rapid and accurate remote sensing monitoring of the distribution of lotus wetland has great significance to their conservation and the promotion of a sustainable and healthy development of ecosystems. The phenology-based method has proven effective in mapping some different types of wetlands. However, because of the serious absence of remote sensing data caused by cloud coverage and the differences in the phenological rhythms of lotus wetlands in different areas, achieving high-precision mapping of different regions using a unified approach is a challenge. To address the issue, this article proposes a Phenology Normalized Lotus Index (PNLI) model that combines SAR time-series imagery and the phenology-based method. The results of this study demonstrate that the PNLI model shows good applicability in different areas and has high mapping accuracy. The model can map the lotus wetland distribu tion in large areas quickly and simultaneously with high precision.

Reorganizing the Waterscape: Asymmetric Loss of Wetlands and Gain of Artificial Water Features in a Mixed-use Watershed

Between the 1780 and 1980s, more than half of the wetlands in the conterminous US were lost. As wetlands have been lost, numerous artificial water features (AWFs), such as stormwater retention ponds, golf course water features, and reservoirs, have been constructed. We contrasted the loss of wetland area and perimeter to the gain of AWF area and perimeter and further explored how this transformation has altered the spatial characteristics of the waterscape. We conducted this analysis in the Tampa Bay Watershed, a large coastal watershed that lost 33% of its wetland area between the 1950s-2007. Trends have been towards fewer, smaller wetlands and more, smaller AWFs. The loss of wetland area far exceeds the gain in AWF area, leading to an overall loss of 23% of the combined wetland and AWF area. However, the loss of wetland perimeter almost equals the gain in AWF perimeter, leading to an overall loss of just 2% of the combined wetland and AWF perimeter. The loss of wetlands and gain of AWFs have predominantly occurred in different geographic locations, with the loss of wetlands predominantly in the headwaters and the gain in AWFs predominantly adjacent to Tampa Bay. Wetlands became further apart, though generally retained their natural distribution, while AWFs became closer to one another and now mirror the more natural wetland distribution. Overall, the physical structure of the waterscape of today is different than in the past, which likely reflects a change in functions performed and related ecological services provided at local and landscape scales.

Spatial Planning Strategies for Wetlands Based on a Multimethod Approach: The Example of Tianjin in China

Wetlands form a crucial component of ecosystems, and wetland restoration serves as an effective strategy for promoting sustainable urban development. Spatial support is essential for wetland restoration, meaning that research on wetland spatial planning is of considerable importance. Existing studies on wetland spatial planning primarily focus on the analysis of wetland spatial distribution characteristics, with limited exploration of wetland spatial relationships. This paper aims to explore the potential of utilizing both spatial distribution characteristics and spatial relationships to identify wetland spatial issues, thereby facilitating the formulation of wetland spatial planning strategies. Using Tianjin City as a case study, this research applies nearest neighbor analysis, the geographic concentration index, the Gini index, and kernel density analysis to identify the spatial distribution characteristics of wetlands in Tianjin. Additionally, spatial autocorrelation analysis and connectivity analysis are employed to identify the interrelationships among wetlands in Tianjin. Based on the results derived from the analysis of spatial distribution characteristics and spatial relationships, wetland spatial planning strategies are proposed. The effectiveness of these strategies is validated using methods that consider both spatial distribution characteristics and spatial relationships. The findings reveal that, although wetlands in Tianjin are widely distributed, large wetland patches are primarily concentrated in areas with abundant water resources, while the six districts within the city have few or no large patches of wetlands. The spatial distribution of wetlands is highly uneven, exhibiting patterns of high–high aggregation and low–low aggregation. The number of connecting paths between wetland patches is relatively low, indicating a generally low overall connectivity. While medium-sized and larger wetland patches maintain the connectivity of existing wetlands in Tianjin, small wetlands that serve as stepping stones are lacking. Following the implementation of planning strategies, there would be an increase in the wetland area in Tianjin, accompanied by significant improvements in the spatial distribution pattern and spatial relationships of the wetlands.

Modeling potential wetland distributions in China based on geographic big data and machine learning algorithms

ABSTRACT Climate change and human activities have reduced the area and degraded the functions and services of wetlands in China. To protect and restore wetlands, it is urgent to predict the spatial distribution of potential wetlands. In this study, the distribution of potential wetlands in China was simulated by integrating the advantages of Google Earth Engine with geographic big data and machine learning algorithms. Based on a potential wetland database with 46,000 samples and an indicator system of 30 hydrologic, soil, vegetation, and topographic factors, a simulation model was constructed by machine learning algorithms. The accuracy of the random forest model for simulating the distribution of potential wetlands in China was good, with an area under the receiver operating characteristic curve value of 0.851. The area of potential wetlands was 332,702 km2, with 39.0% of potential wetlands in Northeast China. Geographic features were notable, and potential wetlands were mainly concentrated in areas with 400–600 mm precipitation, semi-hydric and hydric soils, meadow and marsh vegetation, altitude less than 700 m, and slope less than 3°. The results provide an important reference for wetland remote sensing mapping and a scientific basis for wetland management in China.

The Susceptibility of Wetland Areas in the Yangtze River Basin to Temperature and Vegetation Changes

Wetlands serve a critical function in water storage and ecological diversity maintenance. However, human activities have resulted in wetland loss in the middle and lower reaches of the Yangtze River Basin (MLYRB), while the wetland distribution in this area shows great discrepancy in previous estimates. It is, therefore, imperative to estimate the distribution of potential wetlands at present and project their variation under future climate change scenarios. In this study, we simulate the wetland distribution in the MLYRB at 15″ resolution using 5 machine learning methods with 19 predicting factors of topographic index, vegetation index, climate data, hydrological data, and soil type data. A 5-fold cross-validation with observed permanent wetlands shows that the reconstructions from Adaptive Boosting tree (AdaBoost) algorithm have the highest accuracy of 97.5%. The potential wetland area in the MLYRB is approximately ~1.25 × 105 km2, accounting for 15.66% of the study region. Direct human activities have led to the loss of nearly half of the potential wetlands. Furthermore, sensitivity experiments with the well-trained models are performed to quantify the response of the total wetland area to each influencing factor. Results indicate vulnerability of wetland areas to increases in leaf area index (LAI), coldest season temperature, warmest season temperature, and solar radiation. By the 2100s, the potential wetland area is expected to decrease by 40.5% and 50.6% under the intermediate and very high emissions scenarios, respectively. The changes in LAI and the coldest season temperature will contribute to 50% and 40% of this loss of potential wetlands, respectively. Wetland loss may further undermine biodiversity, such as waterfowl, and fail to provide functions such as flood protection, and water supply. This work reveals the spatial pattern of potential wetland areas and their sensitivity to climate changes, stressing the need for effective strategies to mitigate wetland loss at specific regions in the MLYRB.

Distribution and Genesis of Wetlands in Nagaon District, Assam: A Geomorphological Perspective

Assam is endowed with a large number of wetlands of different kinds. The number and areas of wetlands are variously distributed over the state. Assam has a total wetlands area of 1012 sq.kms with 3514 wetlands representing the highest area under wetlands among the North Eastern states. More than 80 percent of the wetlands falls in Assam regions, which suggest that majority of the regions wetlands originates in the fluvial domain of the Brahmaputra River system. Wetland distribution in district level reveals that Nagaon District has the highest number of wetlands among all the districts of the state. Depending on their locational characteristics, the wetlands are prone to various kinds of geo-environmental problems. Therefore, with growing need of wetlands management, it has become highly imperative to study genesis and development of wetlands.

Impact of Spartina alterniflora Invasion in Coastal Wetlands of China: Boon or Bane?

Invasive plants, like Spartina alterniflora (SA), have a competitive advantage over native flora due to their rapid utilization of vital soil nutrients. This results in the depletion of resources for native plant species, significantly impacting ecosystem diversity and stability. This comprehensive review addresses several key aspects related to SA's spread and spatial distribution in China's wetlands. The rapid expansion of Spartina alterniflora is attributed to its high reproductive ability, adaptability to environmental factors like elevated salinity, and ability to disperse its seeds via tides. Spartina alterniflora mainly were found in Zhejiang, Jiangsu, Fujian, and Shanghai provinces, accounting for more than 90% of China's total Spartina alterniflora area. Spartina alterniflora rapid growth results in displacement of native species and loss of vital microbial, plant, and animal diversity. Some studies reported that Spartina alterniflora increases carbon storage, while others argue that it weakens this function. The impact of Spartina alterniflora on organic and inorganic carbon requires further research for better understanding dynamics of carbon in coastal wetlands. The controlled growth of Spartina alterniflora can be beneficial in many aspects of the coastal wetlands' ecosystem. In China, various methods have been employed to control the invasion of SA. Physical control, such as removing the plants and converting them into fertilizer or bioenergy, has been commonly used but has limitations like air pollution and the potential for re-invasion. Chemical herbicides like Imazapyr and Haloxyfop-R-methyl have effectively controlled and prevented re-invasion in specific areas, but their potential adverse impacts are still uncertain. Wetland Park construction, aquaculture development, and substituting native or exotic species with mangroves or reed communities have also been successful. It becomes evident that a long-standing and Contextual approach is necessary to effectively manage the advantages and curtail the drawbacks associated with S. alterniflora across China.

Comparison of the Applicability of J-M Distance Feature Selection Methods for Coastal Wetland Classification

Accurate determination of the spatial distribution of coastal wetlands is crucial for the management and conservation of ecosystems. Feature selection methods based on the Jeffries-Matusita (J-M) method include J-M distance with simple average ranking (JMave), J-M distance based on weights and correlations (JMimproved), and heuristic J-M distance (JMmc). However, as the impacts of these methods on wetland classification are different, their applicability has rarely been investigated. Based on the Google Earth Engine (GEE) and random forest (RF) classifier, this is a comparative analysis of the applicability of the JMave, JMimproved, and JMmc methods. The results show that the three methods compress feature dimensions and retain all feature types as much as possible. JMmc exhibits the most significant compression from a value of 35 to 15 (57.14%), which is 37.14% and 40% more compressed than JMave and JMimproved, respectively. Moreover, they produce comparable classification results, with an overall classification accuracy of 90.20 ± 0.19% and a Kappa coefficient of 88.80 ± 0.22%. However, different methods had their own advantages for the classification of different land classes. Specifically, JMave has a better classification only in cropland, while JMmc is advantageous for recognizing water bodies, tidal flats, and aquaculture. While JMimproved failed to retain vegetation and mangrove features, it enables a better depiction of the mangroves, salt pans, and vegetation classes. Both JMave and JMimproved rearrange features based on J-M distance, while JMmc places more emphasis on feature selection. As a result, there can be significant differences in feature subsets among these three methods. Therefore, the comparative analysis of these three methods further elucidates the importance of J-M distance in feature selection, demonstrating the significant potential of J-M distance-based feature selection methods in wetland classification.

A wetland permanence classification tool to support prairie wetland conservation and policy implementation

AbstractWetland permanence, the duration and frequency that surface water is present, affects biological communities and whether wetlands are protected under legislation in some jurisdictions. Wetland drainage in the Prairie Pothole Region (PPR) has changed the distribution of wetlands because smaller and more temporary wetlands are more likely to be drained. This change in distribution affects biodiversity and other wetland ecosystem services. In Manitoba, Canada, wetlands are treated differently under the Water Rights Act based on permanence classification and can either be drained with a simplified registration (temporary and ephemeral wetlands), drained with a permit requiring mitigation (seasonal wetlands), or are protected from drainage (semipermanent and permanent wetlands). To facilitate implementing a conservation program targeting the most vulnerable wetlands, we built a classification model using LiDAR and Sentinel‐2 data (1312 training observations). Our random forest model had 73% accuracy on 563 test observations and is applicable across the agricultural region of southwestern Manitoba. We predicted the wetland permanence class of 365,499 wetlands and built an online tool to help practitioners implement a conservation program that pays producers to conserve temporary and ephemeral wetlands. Our approach is applicable elsewhere in the PPR and other regions with variation in wetland permanence.

Distribution Characteristics and Influence Factors of Carbon in Coal Mining Subsidence Wetland

Coal mining subsidence wetlands, as important supplementary resources for wetlands, are of great significance for regulating climate change. This study investigated the distribution and influencing factors of carbon in the overlying water and sediment of coal mining subsidence wetlands in Xuzhou, China, during low-water, high-water, and dry seasons. The results revealed significant spatial and temporal variations in the physicochemical properties of the wetlands, with water hypoxia and a trend toward eutrophication due to excess nitrogen and phosphorus. Dissolved organic carbon (WDOC) and dissolved inorganic carbon (WDIC) in water exhibited opposite temporal trends, while sediment organic carbon (SOC) and dissolved organic carbon (SDOC) showed similar temporal and spatial variations. Inorganic carbon in sediment (SIC) and dissolved inorganic carbon (SDIC) showed consistent temporal changes but significant spatial differences. There was a significant positive correlation between WDOC and SDOC, and WDIC was positively correlated with SDOC and SDIC, indicating the interconnection and transformation of dissolved carbon between water and sediment. WDIC was strongly correlated with water temperature and dissolved oxygen, while WDOC was weakly correlated with the physicochemical properties of water. Overall, these findings contribute to our understanding of the carbon distribution and cycling in coal mining subsidence wetlands, which are crucial supplementary resources to natural wetlands for regulating climate change.

Spatiotemporal Variation of Tidal Wetlands Affected by Human Activities during the Past 50 Years: A Case Study of Yueqing Bay in Eastern China

Monitoring spatiotemporal changes in tidal wetlands under the disturbance of human activities provides a strong basis for coastal wetland protection and restoration. Long-term serial remote sensing images and other historical data were collected and analysed to investigate the distribution of tidal wetlands of Yueqing Bay in 1969, 1981, 1993, 2003, 2013, and 2021, a case study across the past 50 years. The data revealed that human activities cumulatively caused the net loss of approximately 59.62 km2 in area of tidal wetlands during the observation period. Firstly, between 1969 and 1993, reclamation primarily accounted for the variation of the tidal wetlands. Furthermore, between 1993 and 2013, the invasion of Spartina alterniflora and mudflat aquaculture also became important influencing factors. Between 2013 and 2021, development activities eased, and mangrove planting area increased. Over the analysis period, the overall results revealed that the tidal wetlands in Yueqing Bay underwent a transformation process from the relatively simple effects of exploitation by reclamation to the collective influence of combined exploitation and restoration, increasing the overall diversity of Yueqing Bay tidal wetlands over the study period.

Predicting the regime shift of coastal wetlands based on the bistability features in the intertidal zone: A case study in the Liaohe estuary

Influenced by human activities and natural interference, the worldwide distribution of coastal wetlands is now undergoing rapid evolution. The prediction on the locations of vegetation conversion is greatly important for the management of these coastal ecosystems in terms of early warning. In this paper, a series of waterlines extracted from multiple satellite images were used to generate a high-precision digital elevation model (DEM) in the intertidal zone of the Liaohe estuary. Based on the characteristics of the alternative stable states in elevation and normalized difference vegetation index (NDVI), the Logistic Regression Model was applied to predict the potential locations of vegetation expansion with geomorphic factors, such as elevation, slope, and annual changing rate of elevation. Before the prediction, the existence of two stable states in the landscape was confirmed in the study area, i.e., low-lying tidal flats and high-lying saltmarshes. When the geomorphic parameters exceeded the thresholds, the stable state transition would occur. By using the Logistic Regression Model, the elevation was the best explainer for determining the vegetation conversion in the single-factor simulation, while the slope was the worst. When multiple factors were integrated in simulations, the prediction with the elevation, slope, and annual elevation change rate was the best, with R2 = 0.739, and the overall accuracy of prediction reached 88.6%. The prediction result indicated that the area of saltmarshes in the Liaohe estuary increased by 16.7 km2 at a rate of 0.8% per year between 2011 and 2015. When considering the popularization in restoration practice, it is necessary to evaluate the reliability and flexibility of the Logistic Regression Model in predicting vegetation conversion in more types of estuarine wetlands.

Impacts of climate change on the wetlands in the arid region of Northwestern China over the past 2 decades

Climate change has caused inland wetlands shrinkage and exacerbated problems, such as sustainable development and ecological security, for years. These issues are mainly pronounced in the inland arid area. The ecological environment’s deterioration is especially severe in the drylands of the interior. However, dryland wetland changes and their response to climate are poorly understood. This study uses the K-means algorithm in Google Earth Engine (GEE) to classify two typical dryland wetlands (Ebinur and Bosten Lakes) for rapidly and accurately detecting dryland wetland changes. Moreover, it explores the long-term spatial–temporal variation in wetland distribution. In addition, it investigates the response of various lakes to climate change in northern and southern Xinjiang using wavelet analysis. The study’s results showed that K-means clustering in the GEE platform has a high classification accuracy (Kappa > 0.8) in wetland classification, making it a feasible approach. The terminal lake wetland types, represented by the Ebinur Lake, changed significantly between 2001 and 2021. In contrast, the inflow-outflow lake wetland types, represented by the Bosten Lake, perform more consistently. Significant spatial–temporal variation is observed at Ebinur Lake, with the lake gradually shrinking and transforming into a marsh, where the largest marsh proportion degrades into non-wetland during the year. Bosten Lake experienced frequent conversions between marsh and non-wetland throughout the year. Furthermore, the responses of various dryland lakes to climate change are consistent, and a low precedes precipitation and follows evapotranspiration. However, their sensitivity to climate response varies, with the terminal lake being most affected by climate change. Mastering the dynamic changes and climate response of dryland wetlands achieves the sustainable development goals of drylands, including carbon neutrality and peak carbon dioxide emissions.