Wetland Land Cover Research Articles

Assessing the impact of land use and land cover change on the Densu Delta wetland using Markov chain modeling and artificial neural networks

This study investigates the dynamics of land use and land cover (LULC) changes in the Densu Delta wetlands, a critical ecosystem in Ghana. Here, satellite images spanning from 1998 to 2023 were used to analyse the spatio-temporal patterns of LULC changes and their implications for water bodies, wetlands, vegetations, bare lands and urban areas in the Densu Delta wetland. Employing advanced techniques such as Markov chain modelling and artificial neural networks (ANNs), the research assesses and predicts LULC alterations. Significantly, the largest loss of LULC is observed in the Densu Delta wetland, where wetlands transition to waterbody cover type (14.02 km²). Model validation for 2023 attests to the accuracy of the model, boasting a correctness percentage of 70% and a kappa value of 0.74. In-depth analyses explore regional variations in the Densu Delta wetlands, revealing distinct patterns in the rates of LULC change before and after 2013. Notably, urbanization emerges as a prominent factor post-2013, with urban areas experiencing remarkable rates of change in the wetland. Transition matrices underscore the intricate interplay of different land cover classes over the years. Simulated LULC predictions for 2033 and 2043 highlight the urban land cover type as having the highest positive change, recording approximately 0.39% for the Densu Delta wetland. The wetland land cover in the Densu Delta wetland exhibit negative changes of about −0.52%. The synthesis of LULC data enhances our understanding of the complex interactions shaping these critical ecosystems. This research offers valuable insights for sustainable environmental conservation, emphasizing the pivotal role of informed urban planning policies. It also unveils potential challenges posed by climate change, advocating for a holistic approach to preserve these vital wetland ecosystems.

Wet-ConViT: A Hybrid Convolutional–Transformer Model for Efficient Wetland Classification Using Satellite Data

Accurate and efficient classification of wetlands, as one of the most valuable ecological resources, using satellite remote sensing data is essential for effective environmental monitoring and sustainable land management. Deep learning models have recently shown significant promise for identifying wetland land cover; however, they are mostly constrained in practical issues regarding efficiency while gaining high accuracy with limited training ground truth samples. To address these limitations, in this study, a novel deep learning model, namely Wet-ConViT, is designed for the precise mapping of wetlands using multi-source satellite data, combining the strengths of multispectral Sentinel-2 and SAR Sentinel-1 datasets. Both capturing local information of convolution and the long-range feature extraction capabilities of transformers are considered within the proposed architecture. Specifically, the key to Wet-ConViT’s foundation is the multi-head convolutional attention (MHCA) module that integrates convolutional operations into a transformer attention mechanism. By leveraging convolutions, MHCA optimizes the efficiency of the original transformer self-attention mechanism. This resulted in high-precision land cover classification accuracy with a minimal computational complexity compared with other state-of-the-art models, including two convolutional neural networks (CNNs), two transformers, and two hybrid CNN–transformer models. In particular, Wet-ConViT demonstrated superior performance for classifying land cover with approximately 95% overall accuracy metrics, excelling the next best model, hybrid CoAtNet, by about 2%. The results highlighted the proposed architecture’s high precision and efficiency in terms of parameters, memory usage, and processing time. Wet-ConViT could be useful for practical wetland mapping tasks, where precision and computational efficiency are paramount.

Comparison of sediment and water column nutrient processing rates in agricultural streams of contrasting buffer land use

AbstractWatershed nutrient management often focuses on actions that reduce the movement of nitrogen (N) and phosphorus (P) from agricultural lands into streams. One area of management focus is the buffer of land adjacent to streams. Wetlands and forests in this buffer can intercept and retain N and P from the landscape. In addition to directly intercepting agricultural nutrients, natural habitats in the buffer can alter stream geomorphology and influence the in‐stream processing and transformation of N and P to less labile and mobile forms. Here, we assess the influence of buffer land cover on in‐stream processing of N and P. We measured nutrient dynamics in the water column and sediments of agricultural streams in the Fox River and Duck Creek watersheds (WI, USA) during the growing season. In these streams, water column processing was low, possibly due to a lack of primary producers in the water column. Water column P processing was weakly associated with wetland land cover in the buffer, but buffer land cover had no clear effect on inorganic N processing. On the other hand, sediments were almost always a source of inorganic P and a sink for inorganic N. Sediment P release was higher in streams with more agricultural land cover in the buffer. Sediments in streams with agricultural land cover in the buffer also removed more nitrate, even after accounting for the greater availability of nitrate in those streams. The buffer land cover conditions we quantified occupy a very small portion of the overall watershed (100 m wide, for 1 km upstream of the study site) but nevertheless appear to influence in‐stream cycling of N and P. For P management, reducing agricultural land cover in buffers is already a priority due to the ability of wetlands and forests to intercept nutrients, but this study suggests there may be some additional benefit due to changes in in‐stream P processing.

Extending the utility of space-borne snow water equivalent observations over vegetated areas with data assimilation

Abstract. Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24∘×37∘ domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.

Iterative Semi-Supervised Learning with Few-Shot Samples for Coastal Wetland Land Cover Classification

Iterative Semi-Supervised Learning with Few-Shot Samples for Coastal Wetland Land Cover Classification

HLEL: A wetland classification algorithm with self-learning capability, taking the Sanjiang Nature Reserve I as an example

Wetland ecosystems have been severely damaged by atmospheric pollution and other environmental problems for decades. Reasonable protection, restoration, and utilization of wetlands have become priorities for environmental protection. Obtaining high-precision wetland map products is essential for ecological research as well as formulation and implementation of conservation policies. This study proposes a hierarchical labeling ensemble learning (HLEL) algorithm with self-learning capabilities to achieve high-precision wetland land cover and land use (LULC) classification by adapting self-learning hierarchical results. This study evaluated the classification performance of HLEL, random forest (RF), support vector machine (SVM), artificial neural network (ANN), and Gaussian Naive Bayes' (GNB) with GF-6 WFV multispectral images as the database and Sanjiang National Nature Reserve I in China as the study area. The results showed that the HLEL algorithm has an advantage over a single algorithm when using the feature set filtered by ReliefF, even if the complete feature set is used without feature selection. The overall accuracy of the classification results is improved by 0.4%, 5.8%, 1.3%, and 9.0% when compared to the four single algorithms RF, SVM, ANN, and GNB, respectively. In robustness tests, HLEL consistently outperformed the other algorithms for different training sample set sizes, with overall accuracy improvements of 0.4–6.9%, 5.8–11.5%, 1.3–4.4%, and 9.0–14.0% compared with RF, SVM, ANN, and GNB, respectively. The overall accuracy standard deviation of HLEL is 1.22%, proving that HLEL has low sensitivity to the size of the training data. The HLEL algorithm demonstrates exceptional precision when applied to diverse datasets. The overall accuracy of HLEL in Sentinel 2, Landsat 8, and GF-6 WFV increased by 3.1% to 22.7%, 3.1% to 39.0%, and 0.4% to 9.0%, respectively, when compared to the overallaccuracy of the basicclassifiers. The results demonstrate the feasibility of HLEL, an algorithm with high accuracy and data robustness, in applying wetland LULC classification and providing a new solution for wetland cover mapping.

Functional connectivity varies across scales in a fragmented landscape.

Species of different sizes interact with the landscape differently because ecological structure varies with scale, as do species movement capabilities and habitat requirements. As such, landscape connectivity is dependent upon the scale at which an animal interacts with its environment. Analyses of landscape connectivity must incorporate ecologically relevant scales to address scale-specific differences. Many evaluations of landscape connectivity utilize incrementally increasing buffer distances or other arbitrary spatial delineations as scales of analysis. Instead, we used a mammalian body mass discontinuity analysis to objectively identify scales in the Central Platte River Valley (CPRV) of Nebraska, U.S.A. We implemented a graph-theoretic network analysis to evaluate the connectivity of two wetland land cover types in the CPRV, wet meadow and emergent marsh, at multiple scales represented by groupings of species with similar body mass. Body mass is allometric with multiple traits of species, including dispersal distances. The landscape was highly connected at larger scales but relatively unconnected at smaller scales. We identified a threshold at which the landscape becomes highly connected between 500 m and 6,500 m dispersal distances. The presence of a connectivity threshold suggests that species with dispersal distances close to the threshold may be most vulnerable to habitat loss or reconfiguration and management should account for the connectivity threshold. Furthermore, we propose that a multiscale approach to management will be necessary to ensure landscape connectivity for diverse species.

Impacts of urban land use and land cover change on wetland dynamics in Jimma city, southwestern Ethiopia

Abstract Wetland has been substantially decreased by anthropogenic activities worldwide, requiring urgent conservation action. The present study aimed to analyze the effects of land use land cover change on wetland dynamics from 1992 to 2022 in Jimma City, Southwestern Ethiopia. Landsat TM of 1992, ETM+ of 2000, ETM+ of 2009 and OLI/TIRS of 2022 were used in this study. Landsat images were classified by using supervised classification with maximum likelihood algorithm. The results revealed that grassland, wetland and vegetation land cover classes declined by 2,304.9 ha, 1410.2 ha and 820.5 ha, respectively whereas farm land, settlement and bare land increased by 2,199 ha, 2,111 ha and 225.4 ha, respectively. Results show that the normalized difference water index (NDWI) has positive and negative strong relationship with normailized difference vegetation index (NDVI) and normalized difference built up index (NDBI) with the coefficent determination (R2) of 0.96 and 0.98, respectively. Due to rapid urbanization and declining vegetation cover in the study area, about 98% of wetland land cover in the study area has been lost over the past decades. Therefore, the governmental and non-governmental organizations should give special attention to wetland restoration and management in the study area.

Scaling waterbody carbon dioxide and methane fluxes in the arctic using an integrated terrestrial-aquatic approach

In the Arctic waterbodies are abundant and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. We demonstrate how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO2 and CH4 fluxes across the YK Delta of south-west AK. We found that waterbody size and contour were strong predictors for aquatic CO2 emissions, attributing greater than two-thirds of the influence to the scaling model. Small ponds (<0.001 km2) were hotspots of emissions, contributing fluxes several times their relative area, but were less than 5% of the total carbon budget. Small to medium lakes (0.001–0.1 km2) contributed the majority of carbon emissions from waterbodies. Waterbody CH4 emissions were predicted by a combination of wetland landcover and related drivers, as well as watershed hydrology, and waterbody surface reflectance related to chromophoric dissolved organic matter. When compared to our machine learning approach, traditional scaling methods that did not account for relevant landscape characteristics overestimated waterbody CO2 and CH4 emissions by 26%–79% and 8%–53% respectively. This study demonstrates the importance of an integrated terrestrial-aquatic approach to improving estimates and uncertainty when scaling C emissions in the arctic.

A Probabilistic Sample Boosting Approach With Adaptive Representation Coefficient Consistency for China Coastal Wetland Land Cover Classification Using GF-5 Hyperspectral Imagery

A Probabilistic Sample Boosting Approach With Adaptive Representation Coefficient Consistency for China Coastal Wetland Land Cover Classification Using GF-5 Hyperspectral Imagery

Extent, patterns, and drivers of hypoxia in the world's streams and rivers

AbstractHypoxia in coastal waters and lakes is widely recognized as a detrimental environmental issue, yet we lack a comparable understanding of hypoxia in rivers. We investigated controls on hypoxia using 118 million paired observations of dissolved oxygen (DO) concentration and water temperature in over 125,000 locations in rivers from 93 countries. We found hypoxia (DO &lt; 2 mg L−1) in 12.6% of all river sites across 53 countries, but no consistent trend in prevalence since 1950. High‐frequency data reveal a 3‐h median duration of hypoxic events which are most likely to initiate at night. River attributes were better predictors of riverine hypoxia occurrence than watershed land cover, topography, and climate characteristics. Hypoxia was more likely to occur in warmer, smaller, and lower‐gradient rivers, particularly those draining urban or wetland land cover. Our findings suggest that riverine hypoxia and the resulting impacts on ecosystems may be more pervasive than previously assumed.

Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery

The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due to the extremely complex spatial patterns and fuzzy boundaries. This study aims to implement a comprehensive and effective classification scheme for wetland land covers. To achieve this goal, a novel object-based multigrained cascade forest (OGCF) method with multisensor data (including Sentinel-2 and Radarsat-2 remote sensing imagery) was proposed to classify the wetlands and their adjacent land cover classes in the wetland National Natural Reserve. Moreover, a hybrid selection method (ReliefF-RF) was proposed to optimize the feature set in which the spectral and polarimetric decomposition features are contained. We obtained six spectral features from visible and shortwave infrared bands and 10 polarimetric decomposition features from the H/A/Alpha, Pauli, and Krogager decomposition methods. The experimental results showed that the OGCF method with multisource features for land cover classification in wetland regions achieved the overall accuracy and kappa coefficient of 88.20% and 0.86, respectively, which outperformed the support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). The accuracy of the wetland classes ranged from 75.00% to 97.53%. The proposed OGCF method exhibits a good application potential for wetland land cover classification. The classification scheme in this study will make a positive contribution to wetland inventory and monitoring and be able to provide technical support for protecting and developing natural resources.

Analysis of Wetland Landcover Change in Great Lakes Urban Areas Using Self-Organizing Maps

Wetland loss and subsequent reduction of wetland ecosystem services in the Great Lakes region has been driven, in part, by changing landcover and increasing urbanization. With landcover change data, digital elevation models (DEM), and self-organizing maps (SOM), this study explores changing landcover and the flood mitigation attributes of wetland areas over a 15-year period in Toronto and Chicago. The results of this analysis show that (1) in the city of Toronto SOM clusters, the landcover change correlations with wetland volume and wetland area range between −0.1 to −0.5, indicating that a more intense landcover change tends to be correlated with small shallow wetlands, (2) in the city of Chicago SOM clusters, the landcover change correlations with wetland area range between −0.1 to −0.7, the landcover change correlations with wetland volume per area range between −0.1 to 0.8, and the landcover change correlations with elevation range between −0.2 to −0.6, indicating that more intense landcover change tends to be correlated with spatially small wetlands that have a relatively high water-storage capacity per area and are located at lower elevations. In both cities, the smallest SOM clusters represent wetland areas where increased landcover change is correlated with wetland areas that have high flood mitigation potential. This study aims to offer a new perspective on changing urban landscapes and urban wetland ecosystem services in Toronto and Chicago.

Seasonal and Spatial Variability of Dissolved Carbon Concentration and Composition in Lake Michigan Tributaries

AbstractDissolved organic matter (DOM) is a complex mixture of many compounds and its composition dictates numerous reactions in the environment. Large lakes and marine coastlines receive DOM from watersheds that differ widely in their land cover, with potential implications for both the quantity and composition of carbon inputs. Seasonal variation in DOM quantity and composition may also differ among tributaries and be mediated by land cover. Here, we quantify spatially (i.e., among tributary) and seasonal variation in DOM concentration, DOM composition based on ultraviolet‐visible spectroscopy, and alkalinity across 101 tributaries of Lake Michigan, one of the world's largest lakes, using a synoptic sampling approach. Wetland land cover has the largest effect on DOM, producing high concentrations of DOM that are more aromatic and larger in apparent molecular weight. Seasonal variation is also pronounced, with concentrations and aromaticity of DOM peaking in fall across most tributaries. Watershed lithology and land cover both affect alkalinity, with higher values associated with the geography of carbonate bedrock and urbanized watersheds. Watershed land cover has a larger effect than season on all organic carbon parameters. However, seasonal variation is especially important for DOM composition. This disparity suggests that the environmental processing of DOM within river channels mediates its composition more than its concentrations. Considering the wide range of land cover and lithology around Lake Michigan and other large water bodies, accounting for both spatial and seasonal dynamics is essential for understanding controls on DOM delivery.

A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping

The long record of Landsat imagery, which is the cornerstone of Earth observation, provides an opportunity to monitor land use and land cover (LULC) change and understand the interactions between the climate and earth system through time. A few change detection algorithms such as Continuous Change Detection and Classification (CCDC) have been developed to utilize all available Landsat images for change detection and characterization at local or global scales. However, the reliable, rapid, and reproducible collection of training samples have become a challenge for time series land cover classification at a large scale. To meet the challenge, we proposed an automatic phenology learning (APL) method with the assumption that the temporal profiles of samples within the same land cover type are the same or similar at a local scale to generate evenly distributed training samples automatically. We designed the method to build land cover patterns for each category based on consensus samples derived from multiple existing scientific datasets including LANDFIRE's (LF) Existing Vegetation Type (EVT), USGS National Land Cover Database (NLCD), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL), and National Wetlands Inventory (NWI). Then we calculated the Time-Weighted Dynamic Time Warping (twDTW) distance between any undefined samples and land cover patterns in the same geographical region as prior knowledge. Finally, we selected the optimal land cover category for each undefined sample from the land cover products based on the designed criteria iteratively using the twDTW distance as an indicator. The method was applied in the footprint of 10 selected Landsat Analysis Ready Data (ARD) tiles in the eastern and western conterminous United States (CONUS) to produce annual land cover maps from 1985 to 2017. The accuracy assessment and visual comparison revealed that the APL method can generate reliable training samples without any manual interpretation, producing better land cover results especially for the grass/shrub and wetland land cover classes. Applying the APL method, the overall accuracy of the annual land cover maps was improved by 2% over the accuracy of Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products in the research regions. Our results also indicate that the APL method provides an approach for best use of different land cover products and meets the requirement of intensive sampling for training data collection.

Kernel low-rank representation with elastic net for China coastal wetland land cover classification using GF-5 hyperspectral imagery

Wetland contains various ground objects with high spectral similarity. How to accurately distinguish complex classes has become a challenge in wetland land cover classification. In this paper, low-rank representation with elastic net (ENLRR) and the kernel version of ENLRR (KENLRR) are proposed for coastal wetland land cover classification by using Gaofen-5 (GF-5) hyperspectral data of China. The main idea of ENLRR is to combine elastic net with low-rank representation, which replaces rank function with the combination of nuclear norm and Frobenius norm when constraining the coefficient matrix. The KENLRR method considers nonlinear characteristics of hyperspectral data, where a neighborhood filter (NF) kernel function is adopted to map the original data space into a higher dimensional feature space for better classification. In the experiments, three typical coastal wetlands in China: Yellow River Delta, Jiangsu Dafeng Natural Reserve, and Yangtze River Delta (Nantong) are adopted, and the proposed methods and seven comparison methods are used to conduct wetland land cover classification. The experimental results demonstrate that the proposed ENLRR and KENLRR are effective in accurately distinguishing wetland ground objects and reliably mapping their distribution. More specifically, the KENLRR method can provide the best performance, and the OAs are 96.63%, 96.76% and 87.67% for the three wetlands, respectively. The land cover distributions and spatial patterns of the three wetlands are studied as well. Yellow River Delta is a typical estuarine wetland with abundant landscapes, Dafeng Nature Reserve is a coastal wetland with the block regular feature distribution in spatial, and Yangtze River Delta (Nantong) mainly includes river and flood plain, whose ecological environment is deeply affected by human activities.

Seasonal habitat selection of cougars Puma concolor by sex and reproductive state in west‐central Alberta, Canada

Resource selection studies are commonly used to assess the landscape features that animals select or avoid in their environment. Selection for certain landscape features and landcover types may vary by sex and reproductive status of an individual, and habitat selection studies should incorporate these factors. Cougars Puma concolor are a wide‐ranging species that live in a diversity of habitats with varying levels of human disturbance. Geographic positioning satellite telemetry collars were deployed on 55 males, single females and females with kittens. We used a two‐stage resource selection function to assess the seasonal habitat characteristics used by adult cougars in west‐central, Alberta, Canada, near the northern extent of the species range, from 2016 to 2018. A latent selection difference function was used to compare differences in habitat selction between groups. All groups selected for similar habitat types including edge habitat, close proximity to water, sloped terrain, forested habitat and avoided roads. During the summer, close proximity to water and wetland land cover were among the most selected features for all groups. Forest and edge habitats also were important for single females and males. During the winter, forested habitat was one of the most important covariates for all groups along with close proximity to water, edge habitat and slope for single females and males. Selection for slope and avoidance of open agricultural land were among the most important for females with kittens. Our results provide insights into those landscape variables that are important for cougars at the northern extent of their geographical range.

Spatial–Temporal Wetland Landcover Changes of Poyang Lake Derived from Landsat and HJ-1A/B Data in the Dry Season from 1973–2019

As China’s largest freshwater lake and an important wintering ground for white cranes in Asia, the Poyang Lake wetland has unique ecological value. However, wetland cover types have changed dynamically and have attracted the attention of society and researchers over the past few decades. To obtain detailed knowledge and understanding of the long-term landcover dynamics of Poyang Lake and the associated driving forces, Landsat and HJ-1A/B images (31 images) were used to acquire classification and frequency maps of Poyang Lake in the dry season from 1973–2019 based on the random forest (RF) algorithm. In addition, the driving forces were discussed according to the Geodetector model. The results showed that the coverage of water and mudflat showed opposite trends from 1987–2019. Water and vegetation exhibited a significant decreasing trend from 1981–2003 and from 1996–2004 (p &lt; 0.01), respectively. A phenomenon of vegetation expanding from west to east was found, and the expansion areas were mainly concentrated in the central zone of Poyang Lake, while vegetation in the northern mountainous area of Songmen (region 1) and eastern Songmen Mountain (region 2), showed a significantly expanded trend (R2 &gt; 0.6, p &lt; 0.01) during the five-decade period. The year-long dominant distribution of water occurred mainly in the two deltas formed by the Raohe and Tongjin rivers and the Fuhe and Xinjiang rivers, with deep water. In the 1973–2003 and 2003–2019 periods, a total of 313.522 km2 of water turned into swamp and mudflat and 478.453 km2 of swamp and mudflat transitioned into vegetation, respectively. Elevation and temperature appeared to be the main factors affecting the regional wetland evolution in the dry season and should be considered in the management of Poyang Lake. The findings of this work provide detailed information for spatial–temporal landcover changes of Poyang Lake, which could help policymakers to formulate scientific and appropriate policies and achieve restoration of the Poyang Lake wetland.

Assessing and Modeling the Impacts of Wetland Land Cover Changes on Water Provision and Habitat Quality Ecosystem Services

Understanding the spatial–temporal dynamics of wetland land cover (LC) changes and their impacts on ecosystem services (ESs) is essential for wetland conservation and management. This study aimed at quantifying and predicting water provision and habitat quality ecosystem services and analyzing their response to LC changes using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Firstly, LC maps were created for 1987, 1998, 2007, and 2017 using the index-based and supervised approach and were then used to predict LC changes for 2027 and 2047 using the cellular automata and Markov chain (CA–Markov) model. The Shadegan wetland, located in the southwest of Iran, was chosen as the case study. The results showed that, during the study period (1987–2017), the LC with major increasing area was bare land while the LC with major decreasing area was wetland vegetation. In addition, the wetland tended to be a fragmented mosaic with less connectivity. The results of ESs modeling showed that the highest and lowest water yield values, which were almost 68 × 106 m3 and 26 × 106 m3, were observed in 1998 and 2017, respectively. In the meantime, the area with high-quality habitats has reduced (by 33.58%) and has gradually been replaced by moderate- and low-quality habitats, and the same trend will continue in 2027 and 2047. Based on these results, we conclude that changes in wetland LCs have resulted in variations of the wetland ESs. This site-specific study of the InVEST water yield and habitat quality models enables us to explore the applicability of spatially explicit modeling and simulating of wetland ESs to improve wetland conservation and management. The findings on the spatial–temporal analysis of wetland LCs and ESs can promote wetlands decision-making processes and taking effective conservation actions.

Patterns of mosquito and arbovirus community composition and ecological indexes of arboviral risk in the northeast United States.

BackgroundIn the northeast United States (U.S.), mosquitoes transmit a number of arboviruses, including eastern equine encephalitis, Jamestown Canyon, and West Nile that pose an annual threat to human and animal health. Local transmission of each arbovirus may be driven by the involvement of multiple mosquito species; however, the specificity of these vector-virus associations has not been fully quantified.MethodologyWe used long-term surveillance data consistently collected over 18 years to evaluate mosquito and arbovirus community composition in the State of Connecticut (CT) based on land cover classifications and mosquito species-specific natural histories using community ecology approaches available in the R package VEGAN. We then used binomial-error generalized linear mixed effects models to quantify species-specific trends in arbovirus detections.Primary resultsThe composition of mosquito communities throughout CT varied more among sites than among years, with variation in mosquito community composition among sites explained mostly by a forested-to-developed-land-cover gradient. Arboviral communities varied equally among sites and years, and only developed and forested wetland land cover classifications were associated with the composition of arbovirus detections among sites. Overall, the avian host arboviruses, mainly West Nile and eastern equine encephalitis, displayed the most specific associations among mosquito species and sites, while in contrast, the mammalian host arboviruses (including Cache Valley, Jamestown Canyon, and Potosi) associated with a more diverse mix of mosquito species and were widely distributed throughout CT.ConclusionsWe find that avian arboviruses act as vector specialists infecting a few key mosquito species that associate with discrete habitats, while mammalian arboviruses are largely vector generalists infecting a wide diversity of mosquito species and habitats in the region. These distinctions have important implications for the design and implementation of mosquito and arbovirus surveillance programs as well as mosquito control efforts.