Impacts of Urbanization on the Muthurajawela Marsh and Negombo Lagoon, Sri Lanka: Implications for Landscape Planning towards a Sustainable Urban Wetland Ecosystem

Hotspots of wetland loss to impervious surfaces in the conterminous United States

In this study, a first wall-to-wall comparison between the National Wetlands Inventory (NWI) and the National Land Cover Database (NLCD) was conducted across the entire conterminous United States (CONUS) to evaluate U.S. wetland loss conditions. Annually, around 26 km2 of wetlands are lost to impervious surfaces across the CONUS. Spatially, wetland loss is not evenly distributed, with 90 % of losses occurring in only 9 % of the land area, forming hotspots around expanding urban regions such as Houston, Jacksonville, and Naples. Over the past few decades, Florida experienced the highest wetland loss (5.73 km2/year) among all states, while Houston had the most wetland loss (2.54 km2/year) among all metropolitan regions. Stepwise multiple regression models identified population growth and its associated demand for new housing as the major drivers for wetland loss. Wetland loss per population increase is the highest (>15 m2/person) in most metropolitan regions around the East Coast and Gulf of Mexico. Unfortunately, current wetland loss hotspots will likely suffer further losses in future decades due to projected population growth, with Houston, Cape Coral, and Miami metropolitan regions having the greatest projected wetland loss of 89.15 km2, 34.35 km2, and 28.20 km2, respectively. This study has identified wetland loss hotspots and their drivers across the U.S. that were not possible in previous sample-based studies. The findings are critical in wetland management and protection across the U.S.

Quantifying spatiotemporal dynamics in the Kolonnawa marsh of Colombo, Sri Lanka

Kolonnawa marsh (KM) is an important wetland ecosystem in Colombo district, Sri Lanka that provides essential ecosystem services, and has undergone significant changes over recent decades due to continuous exploitation and reclamation. The values of wetlands are disregarded by decision-makers, despite the fact that they are crucial for improving the quality of water and offer chances for relaxation and amusement in metropolitan areas. Underestimation of the value of wetlands contributes to their continuing deterioration and inevitable loss. Investigating the changes in wetlands can provide crucial information for decision-making. This study aimed to monitor the spatiotemporal land-cover dynamics of KM with the prospect prediction as reduced total extent of KM gradually with time and marsh area being transformed into terrestrial vegetation with time. The collective images from Google Earth (2000 to 2021) and drone data (2022) were analyzed with the GIS application. Subsequently, 50-m2 grid squares with unique cell IDs are designed to link among land cover maps for spatiotemporal land-cover change analysis. Then, we calculate land cover category: surface water, marsh, and terrestrial vegetation proportions for each map in 50-m2 grid cells. Statistical comparison of the land cover changes in grid square cells shows that each land cover category has significant change with the time. The results showed that the reduction of KM marsh resulting in land cover changes has a positive implication on wetland degradation. Thus, interventions should be made for the restoration and sustainable management of KM.

Human health risk attributed to consumption of seafood and recreation swimming in Negombo Lagoon, Sri Lanka: An assessment on lagoon water and inhabitant oysters (Crassostrea cucullata Born, 1778)

The Negombo Lagoon is a coastal lagoon influenced by local communities that introduce waste into its ecosystem. This study examined seven sewage entry points, out of which five sites were chosen for oyster sampling based on availability. Physicochemical and microbiological parameters of water (measured in triplicate at each site, n = 84) and oyster samples (total length, TL > 6 cm, n = 30) were assessed. Variation in regional coliform contamination was analyzed employing a one-way analysis of variance (ANOVA). Results indicated that the northern part of the lagoon exceeded recommended coliform thresholds for swimming (total coliform concentration (TCC) < 126 most probable number (MPN)) and seafood consumption (TCC < 100 MPN/g), indicating the presence of Escherichia coli. Water quality indices affirmed fecal pollution, except in the southern part of the lagoon. Furthermore, the study found high oyster consumption (76.7 %), elucidating that oysters from the northern part of Negombo Lagoon pose health risks.

Research on urban three-dimensional greening design from the perspective of climate change-a case study of Beilin District, Xi'an, Shaanxi Province, China

Climate change is an important issue for cities today and in the future. At present, China has a large population and complex climate conditions, and cities are also vulnerable to the adverse effects of climate change (Tian, Environ Sustain Dev 6: 153-155 2020). Three-dimensional greening can not only improve the green space system of a city but also have a far-reaching impact on the ecology, image, and economic benefits of a city. Therefore, the study of urban three-dimensional greening is an effective means to deal with climate change strategies. By exploring the influence of traditional greening and three-dimensional greening on Local Climate in Beilin District of Xi'an, Shaanxi Province, the mechanism of three-dimensional greening on urban ecological environment was discussed, and the ecological theory, urban three-dimensional greening theory, and urban local climate zone (LCZ) were referred to. Based on the methods of national climate monitoring, ENVI-met simulation, and field independent measurement, this paper selected a research sample site in the east section of Jianshe Road, Beilin District, Xi'an City, Shaanxi Province, China, and applied ENVI-met software to simulate the thermal stress relationship among building exterior surfaces, plants, and air in the street; quantified the overall ecology of the area; and used measuring instruments. The influence of different types of greening in the base on the site temperature, humidity, CO2 (carbon dioxide) concentration, wind speed, and other climate factors data was, respectively, measured and analyzed. The grid analysis was used to compare the traditional greening and three-dimensional greening, then the numerical differences of each impact factor were sorted out, and the effect of three-dimensional greening on the improvement of urban ecological environment was discussed by analyzing the climate factors with greater impact. The results show that (1) three-dimensional greening plus traditional greening is the most beneficial mode; (2) in the same environment, according to the parameter of 1.5 m from the ground in the model environment, it can be seen that the temperature of the space treated with three-dimensional greening of buildings is reduced by 3.5-3.6 ℃ compared with the control group, the relative humidity is different by 7-8%, the CO2 concentration is reduced by about 5%, and the spatial wind speed is relatively small. (3) When the urban green coverage rate is more than 40%, the improvement of temperature is more obvious, if it reaches 50%, the cool phenomenon in summer can be fundamentally changed. From the perspective of human perception, the PMV index increased by 0.27 on average. This paper discusses and analyzes the three-dimensional greening of urban streets in Beilin District, Xi'an City, Shaanxi Province, China, and studies its influence on urban ecology to different degrees. The conclusions are as follows: Different types of greening have different degrees of influence on urban climate. Meanwhile, the experimental results of this paper show that in cities like Xi'an, Shaanxi Province, China, where summer is hot, adding three-dimensional greening to traditional street greening can significantly improve the environmental microclimate, which is an effective means to cope with climate change, improve the site environment, and stabilize the urban ecosystem.

Dynamic landscapes and the influence of human activities in the Yellow River Delta wetland region

The Yellow River Delta (YRD) wetland is one of the largest and youngest wetland ecosystems in the world. It plays an important role in regulating climate and maintaining ecological balance in the region. This study analyzes the spatiotemporal changes in land use, wetland migration, and landscape pattern from 2013 to 2022 using Landsat-8 and Sentinel-1 data in YRD. Then wetland landscape changes and the impact of human activities are determined by analyzing correlation between landscape and socio-economic indicators including nighttime light centroid, total light intensity, cultivated land area and centroid, building area and centroid, economic and population. The results show that the total wetland area increased 1426 km2 during this decade. However, the wetland landscape pattern tended to be fragmented from 2013 to 2022, with wetlands of different types interlacing and connectivity decreasing, and distribution becoming more concentrated. Different types of human activities had influences on different aspects of wetland landscape, with the expansion of cultivated land mainly compressing the core area of wetlands from the edge, the expansion of buildings mainly disrupting wetland connectivity, and socio-economic indicators such as total light intensity and the centroid mainly causing wetland fragmentation. The results show the changes of the YRD wetland and provide an explanation of how human activities effect the change of its landscape, which provides available data to achieve sustainable development goals 6.6 and may give an access to measure the change of wetland using human-activity data, which could help to adject behaviors to protect wetlands.

Influence of human activities and climate change on wetland landscape pattern-A review

Wetlands (rivers, lakes, swamps, etc.) are biodiversity hotspots, providing habitats for biota on the earth. In recent years, wetlands have been significantly affected by human activities and climate change, and wetland ecosystems have become one of the most threatened ecosystems in the world. There have been many studies on the impact of human activities and climate change on wetland landscapes, but there is still a lack of relevant reviews. This article summarizes the research on the impact of global human activities and climate change on wetland landscape patterns (vegetation distribution, etc.) from 1996 to 2021. Human activities such as dam construction, urbanization, and grazing will significantly affect the wetland landscape. Generally, dam construction and urbanization are generally believed to harm wetland vegetation, but appropriate human behaviors such as tillage benefit wetland plants' growth on reclaimed land. Prescribed fires in non-inundation periods are one of the ways to increase the vegetation coverage and diversity of wetlands. In addition, some ecological restoration projects have a positive impact on wetland vegetation (quantity, richness, etc.). Under climatic conditions, extreme floods and droughts are likely to change the wetland landscape pattern, and excessively high and low water levels will restrict plants. At the same time, the invasion of alien vegetation will inhibit the growth of native vegetation in the wetland. In an environment of global warming, rising temperatures may be a "double-edged sword" for alpine and higher latitude wetland plants. This review will help researchers better understand the impact of human activities and climate change on wetland landscape patterns and suggests avenues for future studies.

Economic and socioecological perspectives of urban wetland loss and processes: a study from literatures

Existing literatures across the world highlighted the causes and rate of wetland loss; however, so far, no researches tried to analyze how these are guided by the socioeconomic and ecological conditions. The current review work wished to explore how economic and socioecological perspectives could control the rate and drivers of urban wetland loss. Through meta-analysis, this study also intended to explore the changing polarity in research publication and collaborative research. Total 287 original research articles indicating the rates and drivers of wetland loss from 1990 to June 2022 for the first objective and 1500 articles focusing wetland researches from Dimensions AI database for the last objective were taken.Results clearly revealed that the rate of urban wetland loss varies from 0.03 to 3.13% annually, and three main drivers like built-up, agricultural expansions, pollution were identified all across the world. Loss rate was found maximum in the developing and least developed countries. Pollution, built-up expansion, and agriculture expansion, respectively, in developed, developing, and least developed nations were identified as the most dominant drivers of urban wetland loss. Linking loss rate and drivers with socioecological and economic perspectives revealed that human development index (HDI), ecological performance index (EPI), sustainable development goal index (SDGI), and social progress index (SPI) is negatively associated with the rate of urban wetland loss. Contrarily, a poverty rate encouraged higher rate of loss. This study articulated that improving these socioecological and economic conditions could help wetland conservation and restoration to achieve SDGs.

Examining human disturbances and inundation dynamics in China's marsh wetlands by using time series remote sensing data

The hydrological regime is one of the most significant characteristics of wetlands, which maintains the structural and functional integrity of wetland ecosystems. China experienced rapid economic development since the 1990s, which caused severe degradation of all types of wetlands, especially marsh wetlands that are easily converted through filling or draining. Therefore, it is crucial to examine the inundation alterations in marshes as well as the forces behind the changes. In this study, the inundation dynamics in marsh wetlands of China were documented using time-series Landsat observations from 1992 to 2018. Then, nighttime light data was utilized to indicate the intensity of urbanization and infrastructure construction, which was incorporated with historical statistics to conduct attribution analyses of wetland inundation changes. Great spatial heterogeneity in the water distribution and change trajectory was observed in different areas. Severe wetland desiccation took place in Inner Mongolia and East China, in which the inundation area decreased by 51.3 % and 20.9 %, respectively. By contrast, the water area in North China and Tibetan Plateau increased by 58.2 % and 21.0 %, respectively. Behind the tremendous changes, anthropogenic factors played dominant roles. The marsh wetlands in East China, North China, and Southwest China took up only 1.9 % of the total marsh area but accounted for 26.0 % of the entire nighttime light volume. In East China and Southwest China, urbanization and infrastructure construction had significantly negative effects on wetland inundation. Overgrazing or unregulated irrigation altered the original inundation dynamics of marsh wetlands in Inner Mongolia, Southwest China, the Tibetan plateau, and Northeast China. This study illustrated the possible driving forces behind wetland inundation changes, which could help to locate degrading marsh wetlands triggered by anthropogenic activities. Then, targeted management and conservation actions could be implemented.

Ecological consequences of urban blue space transformation

This study presents the ecological consequences of the blue space conversion and its qualitative degradation in the English Bazar Municipality (EBM) and its surrounding area. The primary blue spaces of the area, the marshy wetland called Chatra and Mohananda river, are the most affected due to urban activities like built-up expansion and sewage and wastewater discharge. Built-up development encroached more than 300 m within wetland territory and caused a 0.57 km² conversion of wetland area. It is also evident within the bed of the Mohananda river. Agriculture also caused the conversion of the blue space. As a result, the wetland's ecosystem service value (ESV) was reduced by 12.7%, along with a reduction of cultural services by 27.86%. The massive pouring of sewage and wastewater caused hyper-eutrophication in almost the entire wetland area. The trophic state index (TSI) value increased significantly in the last 10 years, causing high growth and areal expansion of water hyacinth. The expanding settlements and agricultural land that captured the river channel face inundation vulnerability during peak discharge. Extreme danger level discharge causes floods in the extensive municipality area. The areal encroachment, water extraction, sewage and wastewater discharge, and water quality deterioration caused severe hydro-ecological degradation of the river. Since blue space is critically essential for urban environmental health, these ecological consequences can cause a crisis for urban wellbeing. Therefore, the anthropogenic adversities towards the urban blue space must be restricted, and the blue space's ecological sustenance must be paid enough attention.

Modelling vegetation land fragmentation in urban areas of Western Province, Sri Lanka using an Artificial Intelligence-based simulation technique

Vegetation land fragmentation has had numerous negative repercussions on sustainable development around the world. Urban planners are currently avidly investigating vegetation land fragmentation due to its effects on sustainable development. The literature has identified a research gap in the development of Artificial Intelligence [AI]-based models to simulate vegetation land fragmentation in urban contexts with multiple affecting elements. As a result, the primary aim of this research is to create an AI-based simulation framework to simulate vegetation land fragmentation in metropolitan settings. The main objective is to use non-linear analysis to identify the factors that contribute to vegetation land fragmentation. The proposed methodology is applied for Western Province, Sri Lanka. Accessibility growth, initial vegetation large patch size, initial vegetation land fragmentation, initial built-up land fragmentation, initial vegetation shape irregularity, initial vegetation circularity, initial building density, and initial vegetation patch association are the main variables used to frame the model among the 20 variables related to patches, corridors, matrix and other. This study created a feed-forward Artificial Neural Network [ANN] using R statistical software to analyze non-linear interactions and their magnitudes. The study likewise utilized WEKA software to create a Decision Tree [DT] modeling framework to explain the effect of variables. According to the ANN olden algorithm, accessibility growth has the maximum importance level [44] between -50 and 50, while DT reveals accessibility growth as the root of the Level of Vegetation Land Fragmentation [LVLF]. Small, irregular, and dispersed vegetation patches are especially vulnerable to fragmentation. As a result, study contributes detech and managing vegetation land fragmentation patterns in urban environments, while opening up vegetation land fragmentation research topics to AI applications.

Using Time-Series Remote Sensing Images in Monitoring the Spatial–Temporal Dynamics of LULC in the Msimbazi Basin, Tanzania

The basins containing rivers and wetlands are very significant to the surrounding dwellers in various ways, altogether aiming at boosting the economy for most developing countries. Unfortunately, the benefits are frequently overlooked and lead to basin mismanagement and degradation posed by increasing population. This study used population and satellite data to quantify the extent of land-use and land-cover changes along the Msimbazi valley between 1990 and 2019. Geographic information system and remote sensing techniques were used in the analysis and processing of remotely sensed images acquired in 1990, 2000, 2010 and 2019. The results reveal that the dominant area is built-up land that occupied 39.3% of the total in 1990 and gradually increased to 42.6% in 2000, 54.1% in 2010 and 65.5% in 2019. Moreover, forest and agriculture that in 1990 had been the second and third largest in size, respectively, had been decreasing throughout the entire period. The population increase had been threatening wetland vegetation during the initial 10 years (1990 to 2000); however, the wetland vegetation showed subsequent improvement after the implementation of some government initiatives. Other land cover, such as bush land and grassland, showed minority status with inconsistent changes in either increase or decrease. These findings imply that the Msimbazi Basin suffers much from uncoordinated human activities that consequently degrade its fertility. This degradation can be observed as well from the population distribution maps that show that a huge stress is being exerted along the riverine due to population growth and urbanization. The study also highlights that a lack of intensive management plans that are supported by clear legal commitments for optimal and sustainable resource utilization contributes to wetland deterioration.

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

The Prairie Pothole Region (PPR) of North America is an extremely important habitat for a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described. A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region.

Landscape Pattern Evolution Processes and the Driving Forces in the Wetlands of Lake Baiyangdian

The spatiotemporal features of land use changes and the evolution process of landscape pattern from 1980 to 2017 were investigated using historical satellite images from a Landsat Thematic Mapper (TM) for 1980, 1990, 2000, 2005, 2010 and 2017 in the wetlands of Lake Baiyangdian in the North China Plain (NCP). Landscape pattern indices were used to quantify landscape changes in wetlands, and a redundancy analysis (RDA) was conducted to analyze the driving forces and quantitatively explain the effects of human activities and natural changes on wetland fragmentation. The results showed that the total wetland area was 234.4 km2 in 1980 but it decreased by 8.1% at an average decrease rate of 0.5 km2 per year. The dominant transition between land use types was from natural wetlands to artificial wetlands, and wetland conversion to dry land and residential land. The RDA results suggested that agricultural activities and total population were the main driving factors affecting wetland landscape. Additionally, climate change provided a potentially favorable environment for agricultural development, due to the increased temperatures and decreased wind speeds. Additionally, governmental policy changes and dam construction also played the roles in land use changes.

Urban Heat Island Formation in Greater Cairo: Spatio-Temporal Analysis of Daytime and Nighttime Land Surface Temperatures along the Urban–Rural Gradient

An urban heat island (UHI) is a significant anthropogenic modification of urban land surfaces, and its geospatial pattern can increase the intensity of the heatwave effects. The complex mechanisms and interactivity of the land surface temperature in urban areas are still being examined. The urban–rural gradient analysis serves as a unique natural opportunity to identify and mitigate ecological worsening. Using Landsat Thematic Mapper (TM), Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS), Land Surface Temperature (LST) data in 2000, 2010, and 2019, we examined the spatial difference in daytime and nighttime LST trends along the urban–rural gradient in Greater Cairo, Egypt. Google Earth Engine (GEE) and machine learning techniques were employed to conduct the spatio-temporal analysis. The analysis results revealed that impervious surfaces (ISs) increased significantly from 564.14 km2 in 2000 to 869.35 km2 in 2019 in Greater Cairo. The size, aggregation, and complexity of patches of ISs, green space (GS), and bare land (BL) showed a strong correlation with the mean LST. The average urban–rural difference in mean LST was −3.59 °C in the daytime and 2.33 °C in the nighttime. In the daytime, Greater Cairo displayed the cool island effect, but in the nighttime, it showed the urban heat island effect. We estimated that dynamic human activities based on the urban structure are causing the spatial difference in the LST distribution between the day and night. The urban–rural gradient analysis indicated that this phenomenon became stronger from 2000 to 2019. Considering the drastic changes in the spatial patterns and the density of IS, GS, and BL, urban planners are urged to take immediate steps to mitigate increasing surface UHI; otherwise, urban dwellers might suffer from the severe effects of heatwaves.

Trade-Off and Projecting Effects of Land Use Change on Ecosystem Services under Different Policies Scenarios: A Case Study in Central China

Predicting the spatio-temporal evolution characteristics and trade-off/synergy relationships of ecosystem service value (ESV) under different policy scenarios is of great significance for realizing regional sustainable development. This study established a framework and used the geographical simulation and optimization systems-future land use simulation (GeoSOS-FLUS) model and bivariate local autocorrelation analysis to stimulate and predict the impact of land use change on the ESV of Anyang City from 1995 to 2025. We also explored the trade-offs and synergy among ecosystem services under three policy scenarios (natural evolution, cultivated land protection, and ecological protection) in 2025. Results show that (1) the land use change in Anyang from 1995 to 2025 was significant, and the degree of land use change under the cultivated land and ecological protection scenarios was more moderate than that under the natural evolution scenario; (2) The total ESV decreased between 1995 and 2015, amounting to losses of 1126 million yuan, and the decline from 2015 to 2025 under the natural evolution scenario was more significant than those under the cultivated land protection and ecological protection scenarios; and (3) an obvious synergy was observed between various ecosystem services in Anyang City under different scenarios in 2025, and the most significant synergy was observed under the natural evolution scenario. In terms of spatial distribution, the agglomeration of “high–high” synergy in the west and “low–low” synergy in the central region was significant. Local areas showed “high–low” and “low–high” trade-off relationships scattered between their built land and woodland or cultivated land. The proposed framework can provide certain scientific support for regulating land use and ecosystem services in rapidly urbanized areas.