Changes In Wetland Area Research Articles

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).

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.

Assessment and Driving Factors of Wetland Ecosystem Service Function in Northeast China Based on InVEST-PLUS Model

Wetland ecosystem service function provides and maintains the Earth’s life system, which supports human and social development. However, in recent years, with the intensification of human social activities, the wetland area in northeast China has been reduced, and wetland ecosystem service function has been damaged. This paper evaluates the ecosystem service function of wetlands in northeast China based on the InVEST model, taking 40 prefecture-level cities as the evaluation unit, calculating the carbon stock, soil retention, and habitat quality of the wetlands in the study area and analyzing the drivers of changes in ecosystem service function using the PLUS model. The following results were obtained: temporally, the wetland carbon stock decreased from 754 Tg in 2000 to 688 Tg in 2020; the wetland soil retention increased from 24,424 Tg in 2000 to 33,160 Tg in 2010, and then decreased to 28,765 Tg in 2020; and the quality of wetland habitats was roughly unchanged. The wetland habitats in the study area were categorized into 5 types, classified as I, II, III, IV, or V, and the spatial changes in the 40 prefecture-level cities in northeast China were analyzed. The driving factors affecting the change in the wetland ecosystem service function were further analyzed, mainly focusing on changes in the wetland area itself. The influence of other land-use types and the influence of related policies were analyzed in three aspects, among which the GDP and spatial density of the population are social factors, and the elevation and slope are natural factors that provide larger contributions to the change in wetland area. The reduction in forest and grassland areas and the increase in cultivated land and construction land areas have a negative effect on the ecosystem service function of wetlands, and the implementation of relevant wetland protection policies promotes the ecosystem service function of wetlands. According to the problems faced by wetlands in different regions, the government formulates strategies that are in line with local development, with a view to implementing wetland ecological development in the northeast region in the new context, which will help to realize intensive land use and stimulate the vitality of the region.

Evaluation of ecosystem services vis-à-vis perceptions and attitudes of local communities towards Wetland conservation in Kashmir Himalaya

Valuation of ecosystem services along with the perceptions and attitudes of local communities is crucial for sustainable management of wetlands. This study assesses changes in wetland area and bathymetry, and provides insights into local community's perceptions of ecosystem services, and attitudes towards wetland conservation in Kashmir Himalaya, India. High-resolution satellite data was used to assess land use land cover (LULC) changes within the wetland. The bathymetry changes were assessed by performing depth measurements before and after dredging in 2019 and 2022, respectively. Perceptions of ecosystem services and attitudes of residents towards wetland management were examined through surveys in 182 households from the target population of 334 families. The findings reveal that Khushalsar wetland provides important provisioning (food, fodder, thatching mats, fish, vegetables), regulating (flood control, carbon sequestration, water quality improvement), supporting (habitat, biomass and oxygen production, water and nutrient cycling), and cultural services (aesthetic, education, spiritual). The economic benefits from provisioning services range from 240 USD/yr for minor vegetables to 1201–1802 USD/0.05 ha/yr for Nelumbo nucifera. The analysis of LULC changes indicates a ∼16.5% reduction in wetland area from 1980 to 2017, with a further ∼10% decrease from 2017 to 2021, resulting in an overall loss of ∼25% (27.1 ha) in wetland area from 1980 to 2021. Bathymetric assessments reveal an increase in maximum depth from 427 cm in 2019 to 547 cm in 2022 following dredging activities. Socioeconomic analysis showed the significance of Khushalsar wetland as an important source of livelihood for local communities. Survey results indicate that all the respondents have positive attitudes towards the protection and management of the Khushalsar wetland. The study demonstrates that human conservation activities have significantly contributed to positive changes in the wetland such as increase in depth and flood storage capacity. The study concludes that government protection along with active participation of local communities is indispensable for sustainable management of wetlands.

Multi-decadal floodplain classification and trend analysis in the Upper Columbia River valley, British Columbia

Abstract. Floodplain wetland ecosystems experience significant seasonal water fluctuation over the year, resulting in a dynamic hydroperiod, with a range of vegetation community responses. This paper assesses trends and changes in land cover and hydroclimatological variables, including air temperature, river discharge, and water level in the Upper Columbia River Wetlands (UCRW), British Columbia, Canada. A land cover classification time series from 1984 to 2022 was generated from the Landsat image archive using a random forest algorithm. Peak river flow timing, duration, and anomalies were examined to evaluate temporal coincidence with observed land cover trends. The land cover classifier used to segment changes in wetland area and open water performed well (kappa of 0.82). Over the last 4 decades, observed river discharge and air temperature have increased, precipitation has decreased, the timing of peak flow is earlier, and the flow duration has been reduced. The frequency of both high-discharge events and dry years have increased, indicating a shift towards more extreme floodplain flow behavior. These hydrometeorological changes are associated with a shift in the timing of snowmelt, from April to mid-May, and with seasonal changes in the vegetative communities over the 39-year period. Thus, woody shrubs (+6 % to +12 %) have expanded as they gradually replaced marsh and wet-meadow land covers with a reduction in open-water area. This suggests that increasing temperatures have already impacted the regional hydrology, wetland hydroperiod, and floodplain land cover in the Upper Columbia River valley. Overall, there is substantial variation in seasonal and annual land cover, reflecting the dynamic nature of floodplain wetlands, but the results show that the wetlands are drying out with increasing areas of woody/shrub habitat and loss of aquatic habitat. The results suggest that floodplain wetlands, particularly marsh and open-water habitats, are vulnerable to climatic and hydrological changes that could further reduce their areal extent in the future.

Changes in wetland landscape and inundation patterns in the middle and lower reaches of the Yangtze River Basin from 1990 to 2020

Wetlands are crucial to diverse societies and ecosystems but are threatened globally. Natural wetlands in the middle and lower reaches of the Yangtze River Basin (MLRYRB) have long been exploited to meet the increasing food and energy demands, especially in recent decades. However, how their landscapes and hydrological patterns changed over time was poorly qualified, which was crucial to conserving biodiversity and ecosystems. In this study, we proposed a new wetland classification procedure taking advantage of the shape, topography, landscape patterns, and phenology to map the distribution of six wetland types in the MLRYRB in 1990 and 2020 with Landsat images. The transitions among different wetland types and associated driving factors were examined. Artificial wetlands expanded by 3907.6 km2 while natural wetlands decreased by 423.2 km2, resulting in a net area increase of 3484.4 km2. Farmland reclamations, aquacultural activities, urban expansion, and dam constructions induced 70 % of the area's loss of natural wetlands. Meanwhile, spatiotemporal patterns in wetland inundation dynamics were determined with agglomerative hierarchical cluster analysis. Widespread transitions from perennial water to seasonal wetlands were detected. Notably, 47.8 % of the natural wetlands experienced significant drying trends, and as a result, the mean yearly inundated periods for natural wetlands decreased from 303 days in 1990 to 272 days in 2020. Given the substantial structural alterations, wetland destruction was more severe than the area reflected. Our results suggested that category-based changes in wetland areas, inundation patterns, associated driving forces, and underlying ecological consequences should be comprehensively assessed to conduct scientific conservation and prevent “paper offsets.”

Climate drivers and human impacts shape 35‐year trends of coastal wetland health and composition in an urban region

AbstractThe future of coastal wetlands will depend on the combined effects of climate change and human impacts from urbanization and coastal management. Disentangling the effects of these factors is difficult, but satellite imagery archives provide a way to track biological and physical changes in wetlands over recent decades to reveal how coastal wetlands have been changing in response to climate and human drivers. In this study, we used Landsat to monitor the conditions of 32 coastal wetlands in southern California from 1984 to 2019 and identify environmental and human drivers of these trends. Wetland conditions were characterized by vegetation greenness, using the normalized difference vegetation index (NDVI), and by habitat composition, derived from areal estimates of wetland and subtidal habitats. Overall, wetlands displayed three types of long‐term response: greening and gaining wetland (10), greening and losing wetland (16), and browning and losing wetland (6). Regional environmental drivers with overall positive effects on wetland NDVI were sea level, wave height, and precipitation, whereas stream discharge, vapor pressure deficit, and air temperature had negative or nonlinear effects. Wetland area change was primarily correlated with sea level, but response was highly contextual among sites. Negative trends in wetland NDVI and area were more common in larger sites with low elevations and in sites with open inlets. Restoration had mixed effects, with only half of the restored sites showing positive changes in NDVI and wetland area post‐restoration. The important work of managing and restoring urban coastal wetlands is complicated by variability and context and requires us to account for the influence of humans and climate as we build a regional understanding of historic, present, and future wetland health.

The Value of Surface Freshwater Supply Services in the Wetlands of Jilin Province, China

Wetlands are ecologically and socioeconomically crucial areas. The application of economic valuation methods could ensure the sustainable utilization of wetlands. Utilizing wetland survey data from Jilin Province, China, representative of the years 2013 and 2017, we assessed the market value of water obtained from wetlands. Simultaneously, we employed the PLUS model to predict changes in wetland areas of different types over the next decade and analyzed their impact on the value of freshwater resource supplies. The results indicate the following points: (1) the area of wetlands decreased from 10,852.84 km2 in 2013 to 10,794.46 km2 in 2017 and that, in 2027, this was projected to further decrease to 10,614.37 km2, with river wetlands experiencing the most substantial decline; (2) the freshwater volumes in 2013 and 2017 were 20.81 × 108 and 20.09 × 108 m3, respectively, representing a 3.58% decrease. The volume for 2027 was projected to further reduce to 19.74 × 108 m3, with lake wetlands contributing the most to water resources and marsh wetlands contributing the least; and (3) the obtained total value of freshwater continuously increased, rising from CNY 8.384 billion in 2013 to CNY 8.642 billion in 2017, and this was projected to further increase to CNY 9.101 billion in 2027. There was regional variation in the value of wetland freshwater resource supplies, with differences in the per unit area and per capita value among administrative units. These findings can facilitate the optimal allocation of freshwater resources in Jilin Province, promoting its sustainable development while ensuring wetland conservation.

Attribution Analysis on Areal Change of Main Wetland and Its Influence on Runoff in the Naolihe River Basin

Wetlands have powerful runoff regulation functions, which can effectively store and retain surface runoff. The runoff regulation function of wetlands is affected by wetland areas, which affect the capacity of flood control. To explore the law of the area change of the main wetlands of the Naolihe River Basin (MWNRB), the visual interpretation method was used to extract wetlands. To identify the reasons for area changes in the MWNRB, the maximum likelihood method, minimum distance method, and neural network method were used to classify land use types from remote sensing images; the M-K variation point test and Theil-Sen trend analysis were used to test the variation point and calculate the trend of precipitation and temperature series. To clarify the influence of wetland areas on runoff, the Gini coefficient and SRI of runoff were used to calculate runoff temporal inhomogeneity. The results showed that the area of the MWNRB obviously decreased, with 74.5 × 106 m2/year from 1993 to 2008, and increased slowly from 2008 to 2015, with 27.7 × 106 m2/year. From 1993 to 2008, 50.74% and 38.87% of wetlands were transformed into paddy fields and dry fields, respectively. From 2008 to 2015, 61.69% and 7.76% of wetlands were transformed from paddy fields and dry fields, respectively. The temperature of the MWNRB increased slowly by 0.04 °C/year from 1993 to 2008 and increased obviously by 0.16 °C/year from 2008 to 2015. The precipitation decreased by 5.6–8.1 mm/year and increased by 16.6–41.2 mm/year in 1993–2008 and 2008–2015, respectively. Compared with precipitation and temperature, land use change caused by human activities is the main cause of wetland area change. The area change of the MWNRB has a certain influence on the runoff regulation and storage capacity. The Gini coefficient and SRI index increased from 0.002/year (0.008) to 0.023/year from 1993 to 2008 and decreased from 0.046/year (0.045) to 0.161/year from 2008 to 2015, respectively.

Changes and driving forces analysis of alpine wetlands in the first meander of the Yellow River based on long-term time series remote sensing data

IntroductionAs a vital component of the ecosystem of the Qinghai-Tibet Plateau, alpine wetlands coexist with their vulnerability, sensitivity, and abundant biodiversity, propelling the material cycle and energy flux of the entire plateau ecosystem. In recent decades, climate change and human activities have significantly altered the regional landscape. Monitoring and assessing changes in the alpine wetlands on the Qinghai-Tibet Plateau requires the efficient and accurate collection of long-term information.MethodsHere, we interpreted the remote sensing data of the first meander of the Yellow River of alpine wetlands from 1990 to 2020 based on Google Earth Engine (GEE) platform, using geographic information system (GIS) and landscape pattern index to analyze the spatial and temporal evolution of wetland landscape patterns, and the primary drivers of changes in wetland area were explored by GeoDetector.ResultsOur result showed that most wetland areas were found in regions with gradients less than 12° and elevations between 3315 and 3600 m. From 1990 to 2010, the area of alpine wetland in the study area decreased by 25.43%. During the period between 2010 and 2020 to the 1990s, the wetland area decreased by 322.9 km2. Conversion to and from grassland was the primary form of wetland transfer out and in, respectively. The overall migration of the wetland centroid in the study area was to the southwest between 1990 and 2010 and to the north between 2010 and 2020. The geometry of the wetland landscape was relatively simple, the landscape was relatively intact, and patches retained a high level of agglomeration and connectivity. However, their level of agglomeration and connectivity was disrupted. A quantitative analysis of the factor detector in GeoDetector revealed that the DEM, slope, and evaporation were the most important driving factors influencing the change of wetland area, with socioeconomic development also influencing changes in the wetland area to a lesser extent.DiscussionUsing interaction detectors, it was discovered that the interaction of various driving factors could better explain the long-term variations in wetland areas, with a greater degree of explanation than that of each driving factor alone.

Tracking economic-driven coastal wetland change along the East China Sea

Coastal wetlands provide various ecological functions but are threatened by land reclamation and urbanization, which cause wetland degradation and loss. Understanding wetland transition trends and driving factors is challenging. Here, we used the Environmental Kuznets curve (EKC) to analyze the relationship between coastal wetlands, as classified by the Google Earth Engine (GEE), and socio-economic indicators along the East China Sea. Structural equation modeling (SEM) was used to explore the driving factors. We found that the (1) wetland transition depended on geomorphic units and socio-economic development; (2) wetland area changes from 1985 to 2020 had a typical EKC pattern and can be divided into three periods due to policy changes; and (3) economic development and urbanization were the primary reasons for wetland changes, with a strong correlation between natural wetland area, GDP per capita, and impervious surface, but a lower correlation with aquacultural area.

Long-Term Wetland Monitoring Using the Landsat Archive: A Review

Wetlands, which provide multiple functions and ecosystem services, have decreased and been degraded worldwide for several decades due to human activities and climate change. Managers and scientists need tools to characterize and monitor wetland areas, structure, and functions in the long term and at regional and global scales and assess the effects of planning policies on their conservation status. The Landsat earth observation program has collected satellite images since 1972, which makes it the longest global earth observation record with respect to remote sensing. In this review, we describe how Landsat data have been used for long-term (≥20 years) wetland monitoring. A total of 351 articles were analyzed based on 5 topics and 22 attributes that address long-term wetland monitoring and Landsat data analysis issues. Results showed that (1) the open access Landsat archive successfully highlights changes in wetland areas, structure, and functions worldwide; (2) recent progress in artificial intelligence (AI) and machine learning opens new prospects for analyzing the Landsat archive; (3) most unexplored wetlands can be investigated using the Landsat archive; (4) new cloud-computing tools enable dense Landsat times-series to be processed over large areas. We recommend that future studies focus on changes in wetland functions using AI methods along with cloud computing. This review did not include reports and articles that do not mention the use of Landsat imagery.

ASSESSMENT OF WETLAND DEGRADATION CONDITION AND TREND BASED ON LAND USE/COVER IN WAMI-RUVU RIVER BASIN USING MULTI-TEMPORAL LANDSAT IMAGES

The understanding of wetlands’ distribution and their level of susceptibility is important to enhance management and conservation efforts. The study aimed to map wetlands and assess their distribution pattern and their degradation based on the LULC by the years 2000, 2010 and 2020. Wetland types i.e., marshes, ponds, lakes, fens, rivers, floodplains, swamps, and open water bodies with other land use such as agriculture land, built-up and natural vegetation land were classified and mapped using random forest algorithm in Arc Map. Multi-temporal Landsat satellite data including thematic Mapper Landsat (+ETM) and Operational Land Imager (OLI) images were used for LULC mapping. The random forest classifier technique is used to analyse the satellite imagery for detecting the LULC changes during the whole study period. To assess the wetland area degradation three aspects were used in this study for the analysis; wetland area change rate, annual land cover change and annual land cover change rate. The study found about -34.41Km2/y of wetlands potentially are degraded annually due to human and natural stressors in 20 years of our study however the basin experienced a higher rate of wetland degradation in the second epoch of 2010-2020 where the basin experience -42.8Km2/y degradation which results to the total loss of -687.75 km2 total coverage of wetland area in the basin. This information could be used to improve wetland planning and management by wetland managers and other stakeholders.

Wetland Area Change in Klaipeda County

Wetlands are the most important natural resources, they are the sources of biological, cultural, and economic diversity. The article presents an analysis of the current situation of the wetland area in Klaipeda county. Analytical, comparative, statistical, and logical analysis methods were used for the investigation. The aim of the investigation is to carry out the analysis of Klaipeda county's wetland area during the period between the years 2002 and 2022. In 2022, wetlands occupied 1.21 percent of Klaipeda's county, i.e. 6,337.53 ha. In 2002, the area of wetlands in Klaipeda county was 1.68 percent and occupied 8,748.33 ha. So, in Klaipeda county during the period between the years 2002 and 2022 wetland area decreased by 2,410.80 ha or 27.56 percent. After the analysis of the change in wetland areas in the districts of Klaipeda county, it can be seen that in all municipalities, wetland areas decreased in the period of 2002 – 2022. Many wetlands in Klaipeda county have been exploited and managed for various purposes. Large wetland areas have been drained and reclaimed mainly for agriculture and the establishment of human settlements. Monitoring and inventory of wetlands are important for of conserving and managing wetland resources. An important element of all peatland restoration projects is a programme of monitoring to check results and progress. The aim of the investigation is to carry out the analysis of the Klaipeda county wetland area during the period between the years 2002 and 2022.

Assessing Hydrological Changes in Wetland Areas of the Russian Arctic, Subarctic, and Northern Taiga Based on Microwave Remote Sensing Data

Assessing Hydrological Changes in Wetland Areas of the Russian Arctic, Subarctic, and Northern Taiga Based on Microwave Remote Sensing Data

Coastal wetland area change for two freshwater diversions in the Mississippi River Delta

Coastal systems around the globe are being re-integrated with adjacent river systems to restore the natural hydrologic connection to riparian wetlands. The Mississippi River sediment diversions or river reconnections are one such tool to combat high rates of wetland loss in coastal Louisiana, USA by providing freshwater, sediment, and nutrients. There has been some disagreement in the published literature whether re-establishing river reconnection is slowing or contributing to coastal wetland loss. This issue is due to the difficulties in the application of remote sensing in low-relief environments where water level changes could indicate either land loss or simply temporary submergence. We analyzed land change at the receiving areas of two existing freshwater river diversions, Davis Pond and Caernarvon, which have been intermittently receiving river water for up to 2+ decades. This study provides a robust analysis of wetland land change rates in proximity these river diversions including years before river reconnection. Our analyses indicate a net land gain since river reconnection operations began at Davis Pond Diversion (+3.42 km2; range: +2.02–4.81 km2) and no statistically significant change at the Caernarvon Diversion. The Davis Pond wetland results are corroborated with data from a decadal field study documenting increased inorganic sedimentation in the soil. It is clear from this study and others, that river reconnection can increase or, in the case of Caernarvon, have no statistical effect on the land change in these systems due to differences in vegetation, hydroperiod, sediment delivery and external factors including hurricane impacts. Our remote sensing analysis was compared with a global water area change analysis mapping tool which also supported our findings.

Spatio-Temporal Variations and Socio-Economic Driving Forces for Wetland Area Changes: Insights from 2008–2017 Data of Yunnan Province, China

An in-depth understanding of the associations between variations in the wetland area and socio-economic driving forces is essential owing to rapid urbanization. However, to date, no study has performed a quantitative study on the relationships between spatio-temporal patterns for wetland area variations and socio-economic driving factors in Yunnan Province. Based on Statistical Yearbook data, we found that during 10 years, different types of wetlands exhibited different change rates, with obvious spatial heterogeneity. The overall increase in wetland area in Yunnan Province was 13.35%, of which the increases in river, lake, and swamp wetland areas were 46.39%, −3.12%, and 295.56%, respectively. At the city level, the maximum decrease and increase in total wetland area were noted in Xishuangbanna (−84.30%) and Diqing (+185.22%), respectively. A total of 9 of 24 factors which were further selected according to collinearity diagnostics might help interpret changes in the wetland area of Yunnan Province according to the regression analysis results (R2 = 0.749, p < 0.01). Moreover, in different city development periods, the key socio-economic factors were different, which should be considered separately when formulating policies. Our results may clarify the socio-economic influencing factors for wetland spatio-temporal changes and help to guide policymakers.

Resilience of U.S. coastal wetlands to accelerating sea level rise

Coastal wetlands provide a wide array of ecosystem services, valued at trillions of dollars per year globally. Although accelerating sea level rise (SLR) poses the long-term threat of inundation to coastal areas, wetlands may be sustained in two ways: by positive net surface-elevation change (SEC) from sediment and organic matter buildup and by accumulation, or horizontal migration into refugia—low-lying, undeveloped upland areas that become inundated. Using a simple model together with high-resolution elevation data, we provide, across the contiguous United States, analysis of the local effects of SLR, maximum SEC rates, and coastal development on the long-term resilience of coastal wetlands. We find that protecting current refugia is a critical factor for retaining wetlands under accelerating SLR. If refugia are conserved under an optimistic scenario (a high universal maximum SEC rate of 8 mm/yr and low greenhouse gas emissions), wetlands may increase by 25.0% (29.4%–21.5%; 50th, 5th–95th percentiles of SLR) by the end of the century. However, if refugia are developed under a more pessimistic scenario (a moderate universal maximum SEC rate of 3 mm/yr, high greenhouse gas emissions, and projections incorporating high ice-sheet contributions to SLR), wetlands may decrease by −97.0% (−82.3%–99.9%). These median changes in wetland area could result in an annual gain of ∼$222 billion compared to an annual loss of ∼$732 billion in ecosystem services in the US alone. Focusing on key management options for sustaining wetlands, we highlight areas at risk of losing wetlands and identify the benefits possible from conserving refugia or managing SEC rates.

THE IMPORTANCE OF SEASONAL TEXTURAL FEATURES FOR OBJECT-BASED CLASSIFICATION OF WETLANDS: NEW YORK STATE CASE STUDY

Abstract. Seasonal variations result in hydrophytes and undrained hydric soil changes in wetland areas, which lead to a dynamic environment that makes wetland classification challenging. This study aims to explore the applicability of multi-seasonal Gray-Level Co-Occurrence Matrix (GLCM) texture-derived features for object-based wetland classification over large-extent for the first time. We attempted to enhance the performance of the random forest classifier by incorporating multi-source remote sensing data, including Sentinel-2, Sentinel-1, Alos-Palsar, and topographic features. A total of 47 features were extracted from multi-source remote sensing data. In this context, we assessed the applicability of multi- versus mono-seasonal derived features for the wetland's classes with low within-class separability. We investigated the mean decrease in the Gini impurity index for each GLCM feature. We observed that including GLCM features enhanced overall accuracy by 7.38% when using imagery from one season and 4.21% for multi-season imagery. The multi-season scenario that included GLCM measures (93.49%) attained the highest overall accuracy. For this scenario, the means of decrease in Gini impurity index suggested that Soil Adjusted Vegetation Index, Modified Normalized Difference Water Index, slope, correlation in summer (GLCM feature), and Sentinel-1 VH are the most important features in increasing the random forest's classifier performance. In looking at the GLCM features, the separability analysis suggested that Entropy, Sum of Average, and Variance calculated from the summer imagery improve the classifier's performance while other textural features from spring imagery better contributed to classifier accuracy.

SDG-oriented multi-scenario sustainable land-use simulation under the background of urban expansion.

With the continuous improvement of urbanization level and the continuous expansion of city scale, there are some unreasonable land development and utilization problems, which will make the contradiction between people and land more prominent and the risk of ecological environment deterioration more serious. It hinders the sustainable land use (SLU) and then affects the realization of regional Sustainable Development Goals (SDGs). SDGs have become a programmatic document for all countries in the world to implement sustainable development. It provides a guideline and direction for the sustainable urban expansion. The sustainable urban expansion promotes the realization of SDGs worldwide. By combining the SDGs with the urban SLU, this paper obtains the optimized future land-use demand of the multi-scenario city and the urban expansion simulation scenario to the SDGs under the multi-scenario through the uncertain mathematical model (MIFCCP) and the spatial simulation model (PLUS). We find that firstly, the net profit of land use (LNB) reaches the highest value under the economic development scenario (ED), when the probability of environmental constraint violation p = 0.01 and p = 0.15, LNB = [2625.48, 3244.98] × 109 CNY, and [2646.95, 3271.51] × 109 CNY. Ecosystem service value (ESV) reached the highest value under the sustainable development scenario (SD), when p = 0.01 and p = 0.15, ESV = [75.34, 93.12] × 109 CNY, and [72.62, 95.56] × 109 CNY. The net carbon emissions from land use (LNC) reached the minimum value in SD scenario, and when p = 0.01 and p = 0.15, the LNC reached [57.46, 71.02] × 106 ton and [56.12, 76.04] × 106 ton. Secondly, the contribution degree of 15 driving factors to the change of local types is excavated, among which, the driving factors of traffic stations have the highest contribution degree to the change of construction land and cultivated land, and the third-class roads have the highest contribution degree to the change of wetland area. Furthermore, by analyzing the indicators related to SLU in SDGs, we can build an optimization model of land use quantity structure under uncertain conditions, and the optimized results can meet the targets of economic benefits, ecological benefits, and net carbon emissions of land use under different development plans. By linking SDGs and SLU, the coupled model framework can provide scientific basis for urban land expansion strategy based on ecological environment constraints and scientific support for sustainable management of land use.