Wetland Extent Research Articles

Ecosystem Changes Caused by Hydrological Change are Associated with Population Decline in a Formerly-Common Grazing Herbivore

Few ecosystems are more sensitive to hydrological change than seasonally-flooded wetlands. Here, we investigate how changes in hydrological regimes caused by the construction of two dams have contributed to large changes in the structure and dynamics of the Kafue Flats, an internationally-important, partially-protected wetland ecosystem in Zambia. We use historical (1970) and contemporary (2013) data to describe changes in hydrology due to dam operations, and to compare the areal extent, distribution, and composition of the major wetland and dryland habitats in the pre- and post-dam periods. To understand the seasonal dynamics of the ecosystem and how these dynamics influence the seasonal movement and use of the ecosystem by the dominant grazing herbivore, the endangered Kafue lechwe, we combine data on annual hydrological variation with spatially- and/or temporally-explicit quantitative estimates of: (1) grass biomass and nutrient stocks, (2) distribution of lechwe across habitats, and (3) lechwe nutritional status. The operation of the dams has reduced hydrological variation, causing a loss of more than 50% of seasonally-flooded grasslands and probably promoting woody encroachment in some habitats. Our results demonstrate that Kafue lechwe is highly dependent on these seasonally-flooded grasslands during the dry season, when their nutritional status is otherwise critically low. In combination, we hypothesize that changes in the flooding regime driven by dam construction precipitated ecosystem-level changes that—together with other factors—may have contributed to a rapid historic, as well as ongoing decline in the lechwe population. Our study highlights potential consequences of the spatio-temporal homogenization of a previously highly variable ecosystem.

Possible influence of low latitude wetland area changes on the Holocene global atmospheric methane concentration trend

Understanding the causes of variations in global atmospheric methane concentration (GAMC) is an important issue in the study of global climatic changes. Long-term GAMC varied rhythmically on glacial-interglacial timescales, and broadly followed the orbital/suborbital cycles in northern hemisphere solar insolation. Yet the late Holocene has witnessed an increasing GAMC trend since the mid-Holocene, which decouples with the global atmospheric CO2 concentration trend and the northern hemisphere solar insolation trend. The causes of this decoupling have been extensively studied, but remain highly debated. Here we show that the Holocene GAMC trend closely follows the long-term trend in global low latitude wetland extent as inferred from our lake-level reconstruction and from other existing hydroclimate records. We contend that changes in low latitude wetland extent play an important role in shaping the GAMC trend. We propose that reduced low latitude wetland areas during the mid-Holocene, which were likely due to the submersion of tropical wetlands by rising sea levels, and reduced low latitude wetland areas inferred from lower lake levels, could be responsible for the observed mid-Holocene GAMC drop. Increasing global low latitude wetland areas during the late Holocene are likely responsible for the contemporary increasing GAMC trend.

Mapping coastal wetland changes from 1985 to 2022 in the US Atlantic and Gulf Coasts using Landsat time series and national wetland inventories

The areal extent of coastal wetlands is declining rapidly worldwide, and scientists and land managers need land cover maps that show the magnitude and severity of changes over time to assess impacts and develop effective conservation strategies. Within the United States (US), widely-used, continental-scale wetland land cover data products are either static in time (The National Wetlands Inventory) or have a course temporal resolution and do not distinguish between different types of change (the NOAA Coastal Change Analysis Program, C-CAP). This study presents a new coastal wetland geospatial data product that leverages the Landsat database and maps annual land cover across the US Atlantic and Gulf Coasts from 1985 to 2022. The algorithm was trained on the existing US wetland inventories to make the final maps compatible with products that are used in operational management. A multi-stage classification approach was designed that uses the Continuous Change Detection and Classification (CCDC) algorithm to characterize time series of remote sensing reflectance with fitted harmonic functions and identify when changes likely occurred. The fitted time series models are then input into a random forest classifier to make a class prediction. An annual-scale random forest classification is performed in parallel, and results from both algorithms are combined and analysed to detect both gradual and abrupt changes and to identify transitional time series segments. A time series smoothing procedure is subsequently applied to ensure class transitions are logical and consistent and extract a summative change characterization map that shows the severity and spatial density of change. The final maps distinguish between four homogenous classes and six mixed classes, representing areas that are transitioning between classes and where the boundaries between classes are unstable. The algorithm uses data and tools within the Google Earth Engine platform, making it accessible and scalable. The average overall accuracy is 93.7%, and the average class omission and commission errors are 6.7% and 6.4%, respectively. A variety of change detection comparisons were performed, using the existing wetland inventory that employed a fundamentally different change detection approach, and a more comparable annual-scale, Landsatderived product that estimated changes across the Northeastern Atlantic Coast. These comparisons show that the new products’ severe change magnitude matches that of the existing US inventory and the moderate change magnitude matches that of the Northeastern Coast product. The 2019 Wetland Status and Trends Report estimated that net loss rates in emergent wetlands from 2010 to 2019 amount to 1.7%, and the new maps show an equivalent loss rate of 1.6%, again showing close agreement.

SAR image integration for multi-temporal analysis of Lake Manchar Wetland dynamics using machine learning

The Manchar Lake wetland complex, Pakistan’s largest freshwater-lake, faces unprecedented ecological challenges amidst climate change and human pressures, necessitating urgent, data-driven conservation strategies. This study employs cutting-edge multi-sensor remote sensing techniques to quantify and analyze the dynamic changes in this critical ecosystem from 2015 to 2023, aiming to provide a comprehensive understanding of wetland dynamics for informed management decisions. Integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 multispectral imagery, we assessed changes in wetland extent, vegetation health, and land-use patterns using spectral indices and topographic data. Our methodology achieved classification accuracies exceeding 92% across all study years, revealing significant ecosystem fluctuations. Water body extent exhibited a non-linear trend, expanding from 318.5 km² (5%) in 2015 to 397.0 km² (7%) in 2019, before contracting to 369.9 km² (6%) in 2023. This pattern was corroborated by MNDWI values. Concurrently, vegetation covers dramatically increased from 405.5 km² (7%) in 2019 to 1081.6 km² (18%) in 2023. The Enhanced Vegetation Index (EVI) reflected this trend, decreasing from 0.61 in 2015 to 0.41 in 2019, before recovering to 0.53 in 2023. Land use changes were substantial, with agricultural areas increasing from 118.4 km² (2%) in 2015 to 498.0 km² (8%) in 2023. SAR data consistently supported these observations. Topographic analysis, including the Topographic Wetness Index (TWI), provided crucial insights into wetland distribution and resilience. This comprehensive analysis highlights the complex interplay between natural processes and human influences shaping the Manchar-Lake ecosystem, underscoring the urgent need for adaptive management strategies in the face of rapid environmental change.

Managing the Global Wetland Methane-Climate Feedback: A Review of Potential Options.

Methane emissions by global wetlands are anticipated to increase due to climate warming. The increase in methane represents a sizable emissions source (32-68 Tg CH4 year-1 greater in 2099 than 2010, for RCP2.6-4.5) that threatens long-term climate stability and poses a significant positive feedback that magnifies climate warming. However, management of this feedback, which is ultimately driven by human-caused warming and thus "indirectly" anthropogenic, has been largely unexplored. Here, we review the known range of options for direct management of rising wetland methane emissions, outline contexts for their application, and explore a global scale thought experiment to gauge their potential impact. Among potential management options for methane emissions from wetlands, substrate amendments, particularly sulfate, are the most well studied, although the majority have only been tested in laboratory settings and without considering potential environmental externalities. Using published models, we find that the bulk (64%-80%) of additional wetland methane will arise from hotspots making up only about 8% of global wetland extent, primarily occurring in the tropics and subtropics. If applied to these hotspots, sulfate might suppress 10%-21% of the total additional wetland methane emissions, but this treatment comes with considerable negative consequences for the environment. This thought experiment leverages results from experimental simulations of sulfate from acid rain, as there is essentially no research on the use of sulfate for intentional suppression of additional wetland methane emissions. Given the magnitude of the potential climate forcing feedback of methane from wetlands, it is critical to explore management options and their impacts to ensure that decisions made to directly manage-or not manage-this process be made with the best available science.

MW‐SAM:Mangrove wetland remote sensing image segmentation network based on segment anything model

AbstractMangrove wetlands are important ecosystems in tropical and subtropical coastal areas, providing wind and wave attenuation and embankment protection functions. However, mangrove wetlands worldwide are facing severe loss and degradation. Accurate identification of mangrove wetland extent is crucial for their protection, but traditional methods struggle to meet the requirements of large‐scale, high‐precision identification. Furthermore, field data collection and annotation in wetlands in complex intertidal zones pose challenges, and the lack of high‐quality training samples limits the application of deep learning methods in this field. This paper proposes a novel semantic segmentation framework for mangrove wetlands called mangrove wetland remote sensing image segmentation network based on segment anything model (MW‐SAM) to address these issues. MW‐SAM is based on the pre‐trained SAM and achieves cross‐domain adaptation through parameter‐efficient fine‐tuning techniques. It introduces wetland‐specific prompts, auxiliary branches, semi‐supervised training, and iterative optimization strategies to improve accuracy and tackle the problem of sample scarcity. Moreover, on the mangrove wetland dataset constructed in this paper, MW‐SAM significantly outperforms SAM and other traditional methods. MW‐SAM provides a new technology for monitoring and protecting mangrove wetlands, and the constructed dataset will be made publicly available, which is expected to promote the development of mangrove wetland conservation efforts.

A unique Ruleset for Saltmarsh and Mangrove monitoring using Sentinel-2

Abstract. Mangrove and saltmarsh vegetation are two important communities in the wetland ecosystem that require continuous monitoring considering their ecological threats and status. Remote Sensing observation is one of the tools to monitor this community. However algorithms are also important for the best sensor to map it accurately. Although there is some research about eCognition-based mapping, a unique rule set is important to monitor this community. Considering this research gap, a unique threshold-based ruleset has been generated in the Random Forest Model environment. A range of vegetation indices, individual bands of Sentinel 2 and Digital Elevation Model (DEM) data were tested in the feature selection method to find the best features for a Random Forest (RF) model. Top three variables were selected and those are (a) reNDVI_A, (b) VIRE_A, and (c) SWIR1 which gave 98.37% accuracy for the test data. A similar trend was found for the other three sites when they were compared with observed data. For site 1, Mangrove was 84.73%, Saltmarsh was 60.19%, and Mixed was 70.12% accurate. A similar trend was found for other three sites with overall accuracy 87.74 % for site-2, 66.23% for site 3, and 72.98% for site 4. A unique ruleset for wetland extent estimation will help to map a wide range of areas with a very minimum level of fieldwork. It will also reduce the cost of mapping done by visual observation, measurement and extensive fieldwork. The results of this work will provide the necessary insight and motivation for ecologists, and environmentalists.

Greenhouse gas column observations from a portable spectrometer in Uganda

Abstract. The extensive terrestrial ecosystems of tropical Africa are a significant store of carbon and play a key but uncertain role in the atmospheric budgets of carbon dioxide and methane. As ground-based observations in the tropics are scarce compared with other parts of the world, recent studies have instead made use of satellite observations assimilated into atmospheric chemistry and transport models to conclude that methane emissions from this geographical region have increased since 2010 as a result of increased wetland extent, accounting for up to a third of global methane growth, and that the tropical Africa region dominates net carbon emission across the tropics. These studies critically rely on the accuracy of satellite datasets, such as those from the Orbiting Carbon Observatory-2 (OCO-2), the Greenhouse gases Observing SATellite (GOSAT), and the Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI), along with results from atmospheric transport models, over a geographical region where there are little independent data to test the robustness of published results. In this paper we present the first ground-based observations of greenhouse gas column concentrations over East Africa, obtained using a portable Bruker EM27/SUN Fourier transform infrared (FTIR) spectrometer during a deployment covering the first few months of 2020 in Jinja, Uganda. We operated the instrument near autonomously by way of an automated weatherproof enclosure and observed total atmospheric column concentrations of the greenhouse gases carbon dioxide and methane, as well as carbon monoxide, a useful proxy for emissions from incomplete combustion processes in the region. We discuss the performance of the combined enclosure and spectrometer system that we deployed in Jinja to obtain these data and show comparisons of our ground-based observations with satellite datasets from OCO-2 and Orbiting Carbon Observatory-3 (OCO-3) for carbon dioxide and TROPOMI for methane and carbon monoxide, whilst also comparing our results with concentration data from the GEOS-Chem and Copernicus Atmosphere Monitoring Service (CAMS) atmospheric inversions that provide a means of increasing spatial and temporal coverage where satellite data are not available. For our measurement period, we find mean differences in XCO2 between OCO-2 and the EM27/SUN of −0.29 % and between OCO-3 and the EM27/SUN of −0.28 %. In the case of TROPOMI, the mean difference in XCH4 that we find between TROPOMI and the EM27/SUN is −0.44 %, whilst for XCO the mean difference is −5.65 %. In each of these cases, the mean difference observed between the satellite and ground-based column concentrations is either close to or within the precision and accuracy requirements for the respective missions. With regard to the model and reanalysis comparisons with the EM27/SUN column concentrations, we see mean differences from the EM27/SUN of a global GEOS-Chem inversion for XCO2 of −0.08 %, a regional high-resolution GEOS-Chem inversion for XCH4 of −0.22 %, and the CAMS global reanalysis for XCO of −9.79 %. Our results demonstrate the value of ground-based observations of total column concentrations and show that the combined EM27/SUN and enclosure system employed would be suitable for acquisition of the longer-term observations needed to rigorously evaluate satellite observations and model and reanalysis calculations over tropical Africa.

Spatiotemporal Distribution and Habitat Characteristics of Shorebirds in the Coastal Wetlands of Dalian, Liaoning, China

The coast of Dalian in Liaoning Province, China, is one of the most important habitats for migratory shorebirds along the East Asia–Australasian Flyway. However, the coastal areas of Dalian have been modified extensively by various projects, including reclamation and the construction of aquaculture and port facilities, embankment buildings, cross-sea passages, and wind farms, which have led to significant damage to the coastal habitats of shorebirds. Assessing the spatiotemporal distribution and habitat characteristics of shorebirds is of great importance to biodiversity conservation. The present study involved a 15-month-long survey of shorebirds in nine coastal wetlands of Dalian from August 2021 to October 2022. In total, 31 species of shorebirds, belonging to five families, were recorded. Migratory shorebirds were the most frequently observed, accounting for 77% of the recorded shorebirds. Dunlin (Calidris alpina), Kentish plover (Charadrius alexandrinus), Eurasian curlew (Numenius arquata), and Far Eastern curlew (Numenius madagascariensis) were the most abundant species. Both species richness and species abundance peaked in May and again in September. The Zhuanghe Estuary boasted the highest species diversity, while the Huli Estuary wetland exhibited the greatest species abundance. The type of habitat and the spatial extent of natural wetlands were the main variables influencing the spatial distribution of waterbirds. By identifying the population characteristics and habitat status of shorebirds in Dalian, the results provide support for shorebird conservation and habitat management.

Climate‐Driven Projections of Future Global Wetlands Extent

AbstractWetlands are crucial components of the Earth's system, interacting with various processes such as the hydrological cycle, energy exchanges with the atmosphere, and global nitrogen and carbon cycles. The future trajectory of wetlands is anticipated to be influenced not only by direct human activities, but also by climate change. Here we present our assessment of climate‐driven global changes in wetlands extent, focusing on the main wetland complexes. We used an approach based on the Topographic Hydrological model (TOPMODEL) and soil liquid water content projections from 14 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our analysis reveals a consistent decrease in wetlands extent in the Mediterranean, Central America, and northern South America, with a substantial loss of 28% in the western Amazon Basin for the end of the 21st century (2081–2100) under the SSP370 scenario. Conversely, Central Africa exhibits an increase in wetlands extent, except in the Congo Basin. Nevertheless, most of the areas studied (80%) present uncertain results, due to conflicting projections of changes between the models. Notably, we show that there is significant uncertainty among CMIP6 models regarding liquid soil water content in high latitudes. By narrowing our focus to 10 models, which seem to better represent the thawing of permafrost, we obtain a better inter‐model agreement. We then find a modest declines in the overall global area (<5%), but an average loss of 13% beyond 50°N. Specific areas like the Hudson Bay Lowlands experiencing a 21% decrease and the Western Siberian Lowlands a 15% decrease.

Rising seas could cross thresholds for initiating coastal wetland drowning within decades across much of the United States

Accelerated sea-level rise is an existential threat to coastal wetlands, but the timing and extent of wetland drowning are debated. Recent data syntheses have clarified future relative sea-level rise exposure and sensitivity thresholds for drowning. Here, we integrate these advances to estimate when and where rising sea levels could cross thresholds for initiating wetland drowning across the conterminous United States. Our results show that there is much spatial variation in relative sea-level rise rates, which impacts the potential timing and extent of wetlands crossing thresholds. High rates of relative sea-level rise along wetland-rich parts of the Gulf of Mexico and Atlantic coasts highlight areas where wetlands are already drowning or could begin to drown within decades, including large wetland landscapes within the Mississippi River delta, Greater Everglades, Chesapeake Bay, Texas, Georgia, and the Carolinas. Collectively, our results underscore the need to prepare for transformative coastal change.

Future sea-level rise in northwest Mexico is projected to decrease the distribution and habitat quality of the endangered Calidris canutus roselaari (Red Knot)

Abstract Sea-level rise (SLR) is one of the most unequivocal consequences of climate change, yet the implications for shorebirds and their coastal habitats are not well understood, especially outside of the north temperate zone. Here, we show that by the year 2050, SLR has the potential to cause significant habitat loss and reduce the quality of the remaining coastal wetlands in Northwest Mexico—one of the most important regions for Nearctic breeding migratory shorebirds. Specifically, we used species distribution modeling and a moderate SLR static inundation scenario to assess the effects of future SLR on coastal wetlands in Northwest Mexico and the potential distribution of Calidris canutus roselaari (Red Knot), a threatened long-distance migratory shorebird. Our results suggest that under a moderate SLR scenario, 55% of the current coastal wetland extent in northwest Mexico will be at risk of permanent submergence by 2050, and the high-quality habitat areas that remain will be 20% less suitable for C. c. roselaari. What is more, 8 out of the 10 wetlands currently supporting the largest numbers of C. c. roselaari are predicted to lose, on average, 17.8% of their Highly Suitable Habitat (HSH) areas, with 2 sites completely losing all of their HSH. In combination with increasing levels of coastal development and anthropogenic disturbance in Northwest Mexico, these predicted changes suggest that the potential future distribution of C. c. roselaari (and other shorebirds) will likely contract, exacerbating their ongoing population declines. Our results also make clear that SLR will likely have profound effects on ecosystems outside the north temperate zones, providing a clarion call to natural resource managers. Urgent action is required to begin securing sufficient space to accommodate the natural capacity of wetlands to migrate inland and implement local-scale solutions that strengthen the resilience of wetlands and human populations to SLR.

Spatiotemporal patterns of forest pollinator diversity across the southeastern United States

AbstractAimEfforts to understand how pollinating insect diversity is distributed across large geographic areas are rare despite the importance of such work for conserving regional diversity. We sought to relate the diversity of bees (Hymenoptera: Apoidea), hover flies (Diptera: Syrphidae), and butterflies (Lepidoptera) to ecoregion, landscape context, canopy openness, and forest composition across southeastern U.S. forests.LocationNineteen experimental forests across nine states in the southeastern U.S.MethodsWe established 5–7 plots on each experimental forest. In each, we sampled pollinators monthly (March–September) using coloured pan traps, and collected data on local forest characteristics. We used the National Land Cover Database (NLCD) to quantify surrounding landcover at different spatial scales.ResultsBee richness was negatively correlated with both the amount of conifer (pine) forest and the extent of wetlands in the surrounding landscape but was positively correlated with canopy openness. Hover flies and butterflies were less sensitive to landscape context and stand conditions. Pollinator communities differed considerably among ecoregions, with those of the Central Appalachian and Coastal Plain ecoregions being particularly distinct. Bee richness and abundance peaked 2 months earlier in Central Appalachia than in the Coastal Plain and Southeastern Mixed Forest ecoregions.Main ConclusionsOur findings reveal ecoregional differences in pollinator communities across the southeastern U.S. and highlight the importance of landscape context and local forest conditions to this diverse fauna. The closed broadleaf forests of Appalachia and the open conifer‐dominated forests of the Coastal Plain support particularly distinct pollinator communities with contrasting seasonality. Our results suggest pine forests may reduce pollinator diversity in regions historically dominated by broadleaf forests. However, efforts to create more open canopies can help improve conditions for pollinators in planted pine forests. Research exploring associations between forest pollinators and different broadleaf tree taxa is needed to better anticipate the impacts of various management activities.

Estimating Combined Effects of Climate Change and Land Cover Change on Water Regulation Services of Urban Wetlands in Valdivia, Chile

AbstractThe relationship between cities and wetland cover varies across the globe, with some cities converting wetlands to low‐ and high‐density urban cover and others preserving, conserving, or restoring wetlands, or constructing new ones. However, the scientific literature lacks studies relating changes in systemic flood risk in an urban stormwater management systems to changes in wetland cover. Furthermore, whether and how such relationships are affected by changing storm intensity under climate change is unknown. We present a case study on the effects of changes in urban wetland extent and storm intensity on flooding in an urban drainage system in Valdivia, Chile, under several co‐produced future scenarios and historical trends of development. We used data derived from stakeholder workshops and historical landcover to determine four plausible scenarios of urban development, plus one business‐as‐usual scenario, in Valdivia through the year 2080. Additionally, we used historical precipitation data and downscaled climate data to estimate event rainfall from extreme storms in the year 2080. We found that system flood volume and time the system was flooded increased with increasing wetland loss and rainfall volume. Mean rate and hour of peak discharge were unaffected by wetland loss. Infiltration's relative role in reducing flooding diminished as wetland loss increased. Cities may still experience dangerous and/or unacceptable flooding even with extensive wetland coverage and will likely need to pair conservation with additional improvements in their stormwater management systems and contributing watersheds.

Identification and scoring of conservation gaps in wetlands of China's coastal provinces: Implications for extending protected areas

Wetlands in China's coastal provinces are strategically positioned along migratory flyways for waterbirds, serving as essential habitats and stopover sites due to the expansive land area and abundant wetland resources they offer. This study aimed to introduce a simplified index system to enable rapid assessment and prioritization of unprotected areas for wetlands in China's coastal provinces. A spatial analysis was conducted, combining wetland distribution and existing protected areas data and spatial extent of wetlands extracted by remote sensing data. Results indicate substantial gaps in coverage, covering an area of 108.33 × 104 ha, with 76% being natural wetlands. Over half of these gaps are identified as high-value wetlands with significant ecological functions. The uneven distribution of unprotected wetlands reflects a tension between economic development and wetland conservation. Our findings support the expansion of the existing coastal wetland protected areas' coverage, as well as protecting critical habitats in conservation gaps, and establishing of a network-based waterbird protection system. This research contributes to informed decision-making and policy in wetlands' conservation planning.

Species Abundance Modelling of Arctic-Boreal Zone Ducks Informed by Satellite Remote Sensing

The Arctic-Boreal zone (ABZ) covers over 26 million km2 and is home to numerous duck species; however, understanding the spatiotemporal distribution of their populations across this vast landscape is challenging, in part due to extent and data scarcity. Species abundance models for ducks in the ABZ commonly use static (time invariant) habitat covariates to inform predictions, such as wetland type and extent maps. For the first time in this region, we developed species abundance models using high-resolution, time-varying wetland inundation data produced using satellite remote sensing methods. This data captured metrics of surface water extent and inundated vegetation in the Peace Athabasca Delta, Canada, which is within the NASA Arctic Boreal Vulnerability Experiment core domain. We used generalized additive mixed models to demonstrate the improved predictive value of this novel data set over time-invariant data. Our findings highlight both the potential complementarity and efficacy of dynamic wetland inundation information for improving estimation of duck abundance and distribution at high latitudes. Further, these data can be an asset to spatial targeting of biodiversity conservation efforts and developing model-based metrics of their success under rapidly changing climatic conditions.

Climate change will reduce North American inland wetland areas and disrupt their seasonal regimes

Climate change can alter wetland extent and function, but such impacts are perplexing. Here, changes in wetland characteristics over North America from 25° to 53° North are projected under two climate scenarios using a state-of-the-science Earth system model. At the continental scale, annual wetland area decreases by ~10% (6%-14%) under the high emission scenario, but spatiotemporal changes vary, reaching up to ±50%. As the dominant driver of these changes shifts from precipitation to temperature in the higher emission scenario, wetlands undergo substantial drying during summer season when biotic processes peak. The projected disruptions to wetland seasonality cycles imply further impacts on biodiversity in major wetland habitats of upper Mississippi, Southeast Canada, and the Everglades. Furthermore, wetlands are projected to significantly shrink in cold regions due to the increased infiltration as warmer temperature reduces soil ice. The large dependence of the projections on climate change scenarios underscores the importance of emission mitigation to sustaining wetland ecosystems in the future.

Dynamics of land cover change in the Anambra River Basin of Nigeria and implications for sustainable land management

Land cover change and its consequences such as environmental degradation and biodiversity loss pose significant global challenges, including in Nigeria’s Anambra River Basin. This study focuses on monitoring, predicting and understanding land cover changes in the basin from 1987 to 2018, with projections up to 2030. It explores the intricate relationship between population growth and land cover dynamics, aiming to contribute to sustainable land management practices and align with the Sustainable Development Goals (SDGs) for 2030. Using a combination of neural network classification and the CA-Markov model, the study analyses historical land cover data to identify significant transformations. Between 1987 and 2018, bare lands increased by 29%, vegetation increased by 14%, built-up areas increased by 128% and waterbodies increased by 10%, whereas there was a 58% decline in the extent of wetlands. The most significant transformation occurred in the wetlands, with a total of 1819.46 km2 being converted to various land cover types. The results demonstrate remarkable shifts characterised by rapid urbanisation, substantial wetland loss and a decline in vegetation cover. Expectedly, population growth is found to be closely linked to the expansion of built-up areas while negatively impacting other land cover types. These findings underscore the urgent need for sustainable land management strategies that balance the demands of growing populations with the preservation of natural ecosystems and biodiversity. Furthermore, the study provides future projections that offer crucial insights for decision-makers involved in land use planning, biodiversity conservation and sustainable development.

Long-term series wetland classification of Guangdong-Hong Kong-Macao Greater Bay Area based on APSMnet

Wetlands play a crucial role in achieving carbon peak and carbon neutrality goals. Exploring spatiotemporal distribution is one of the fundamental task in wetland research. However, existing large-scale and long-term series wetland mapping methods have challenges related to classification accuracy and obtaining inter-annual wetland samples. Therefore, a rapid sample collection and precise classification method are needed to support wetland resource assessment, conservation, and ecological restoration. In this paper, we propose a novel deep learning method suitable for large-scale and long-term series wetland classification. First, we acquire and preprocess the long-term series remote sensing data on the Google Earth Engine (GEE) platform. Second, the maximum wetland extent is extracted using decision trees and the Otsu algorithm (OTSU). Third, the attention and pyramid structure-based multidimensional feature extraction network (APSMnet) and the inter-annual sample migration algorithm (ISM) are proposed for realizing inter-annual wetland classification and wetland change analysis. Experiments conducted on Landsat 7 data of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2022 demonstrate that the overall accuracy (OA), average accuracy (AA), and kappa coefficient (Kappa) achieved by the proposed wetland classification method are 0.9664, 0.9396, and 0.9570, respectively. Long-term series trend analysis reveals that mangrove/swamp areas experience fluctuations influenced by urbanization and ecological policies, ultimately undergoing restoration and becoming stable. The codes and datasets are made available publicly at https://github.com/louanjun/APSMnet.

Monitoring landuse change in Uchhali and Khabeki wetland lakes, Pakistan using remote sensing data

Although it is a globally significant wetland, the ecological environment of Soon valley (Ucchali and Khabeki wetland lakes, Pakistan) is being negatively impacted by the rapid transformation of its land-use change brought about by economic and human activities. Through supervised classification and interpretation, we extract land use categories from Landsat remote sensing images acquired between 1990 and 2020. Using Google Earth Engine with machine learning algorithms (e.g., Smile CART Classifier), we generate four maps depicting different types of land use with a 10-year time horizon. In statistics, Built-up area increased by 18 % between 1990 and 2020, while barren lands declined by 31 %. Wetland and mudflat extent exhibited fluctuations but demonstrated an overall 21 % reduction since 1990. Adopting a land use transfer matrix and a dynamic stance toward land use, this paper examines the factors that led to the change in wetland landuse in Soon Valley (Ucchali and Khabeki wetland lakes, Pakistan). The results show that between 1990 and 2020, there will be a significant shift in the wetland land use types in Soon valley, with a relatively wide change range for builtup land, mudflat and barren land, and an upward trend. Overall, we saw a reduction in wetland and grassland areas, and a sizable amount of land was diverted from other uses. This land was used primarily for building sites, rice paddies, and other dry purposes. From a dynamic standpoint, the range of change was relatively small as compared to the woodland area expanded. Overall, results indicating a gentle transitioning of one type of land use to another, with the mudflat wetland area decreasing first and then increasing. Although the population was not much larger, but played a significant role with precipitation which may be another driver of land use land cover (LULC) change in the Uchhali-Khabeki wetland lakes, as implied by correlation analysis. The foregoing study of LULC remote sensing is valuable for the preservation of Uchhali and khabeki Lake's wetlands and the administration of the lake's land resources.