land extent that uses model output. LPJ-Bern wetsoils uses a “Hydrological model” to derive “Soil moisture” and “Unsat- urated” MPAs. “Unsaturated” means that the pore-space in the soil is not completely filled with water. This could be the case when – even though a water table position is calculated – it is below the surface or when the soil moisture is esti- mated as a homogeneous average over the soil depth and its values do not reach saturation. Next, we include approaches that comprise of “Topography” in addition to “Hydrological model” as an additional factor to locate “Unsaturated” areas (UVic-ESCM). SDGVM uses a similar approach to UVic- ESCM but simulates “Water table position” before determin- ing “Unsaturated” as well as “Saturated/inundated” MPAs. CLM4Me, DLEM, ORCHIDEE, UW-VIC and LPJ-WSL (Exp. 3) all use “Remotely sensed inundation” (GIEMS) data in their approaches, but they use these data in differ- ent ways: e.g. ORCHIDEE guides the mean simulated wet- land extent over the 1993–2004 period and CLM4Me uses the GIEMS dataset to invert for parameters that allow the hydrological state (i.e. water table depth and runoff) to be used to determine wetlandextent. More details on the use of GIEMS can be found in the description of each model (Sect. 3). Once the “Water table position” is determined, CLM4Me, DLEM, ORCHIDEE, and UW-VIC identify the MPAs that are either “Unsaturated” or “Saturated/inundated” while LPJ-WSL (Experiment 3-opt) determines MPAs which are “Saturated/inundated” only. The UW-VIC model is the most complex model and takes advantage of all of the fea- tures included in Fig. 6, using the fractional peatland cover by Sheng et al. (2004) only as maximal boundaries, rather than as a fixed map.
In summary, nearly continuous wetland monitoring based on dual-co-polarized SAR acquisitions is possible. Beyond monitoring, this enables to define the wetlandextent when including the temporal aspect over the course of a season. Future studies on Lac Bam and further sites in semi-arid regions will focus on the intra- and inter-yearly change of the location and timing of irrigated fields. Regarding the wetland as reservoir, the long-term balance of water availability and water withdrawal is one key feature of wetland conservation. Therefore, the long-term monitoring of selected site is envisaged. Acknowledgments: The authors acknowledge the use of TerraSAR-X data, which have been made available for scientific use via the TerraSAR Science Service proposal “LAN2000 Wetland monitoring and water stress in sub-Saharan West Africa”, RapidEye data has been provided on behalf of the German Aerospace Center through funding of the German Federal Ministry of Economy and Energy with the proposal “Wetland Monitoring of Lac Bam”; and WorldView-2 and GeoEye-1 data, copyright DigitalGlobe provided by European Space Imaging. Great thanks and appreciations go to Raymond Quedraogo who carried out the second fieldwork in October 2015 and provided valuable input to the work alongside local contacts and expertise. The authors acknowledge Francesc Betorz Martínez for accompanying and assisting the main author during the field campaign and Bonda Marcél Somandé from the WASCAL center for assistance in the October 2013 field work. The authors also like to thank the four reviewers for their helpful comments to improve the manuscript.
Wetlands are diverse and fragile ecosystems which are susceptible to anthropogenic and natural perturbations. Globally, wetlands provide several ecosystem goods and services, yet they are increasingly faced with numerous threats from human activities leading to their modification and loss. This chapter aims to assess changes in wetland spatial distribution and areal extent in the Shashe sub-catchment, Zimbabwe, over time. This was achieved through using archival Landsat imagery and Random Forest Image Classification Algorithm using R software. This chapter compares advanced machine learning random forest classifier with traditional supervised Maximum Likelihood algorithm. The results for land change analysis show a decline in woodland and wetland cover, which may be due to both human and natural factors. Major conversions are from wetland cover to crop fields, suggesting agricultural encroachment onto wetland areas. Wetland area thus significantly decreased by 6% (236.52 ha) in the last 30 years (p<0.05). CA-Markov model results for the years 2025, 2035 and 2045 predicted an overall increase in crop fields at the expense of woodland and wetland areas. In particular, the total area of wetlands is expected to shrink by 46% by the year 2045 (72.67 hectares). Quantifying such wetland changes over time is important, not only for pure- scientific purposes, but also for appropriately developing locally relevant and sustainable management strategies.
support a variety of vegetation forms (e.g. forest, shrub, tall herb and sedges (Johnson & Gerbeaux 2004). FENZ maps both the current and historic extent of wetlands. It was recognised there are classification errors for some wetland types, such as differentiating swamps from marshes (Ausseil et al. 2008). The current wetlandextent layer in FENZ was derived from satellite images 1999–2003 supplemented with wetland mapping resources obtained from councils (Ausseil et al. 2008). The historic extent relates to the pre-European extent of wetlands, as modelled using landform and soils information (Ausseil et al. 2008). That Maori settlement in New Zealand and corresponding land use change is likely to have increased the extent of wetlands in some regions also needs to be taken into account (McGlone 2009). GIS data on the extent of conservation land administered by DOC at c. 1990 were obtained from geospatial information held by DOC (D. Brown, DOC, pers. comm.). The analysis used 1990 as a baseline as this relates to the establishment of the Resource Management Act (RMA) in 1991 and the Department of Conservation in 1987. Data on the current (2013) extent of conservation areas were obtained from the geospatial layers held in the national information system, known as NATIS, including DOC-administered conservation land and private land conservation areas, such as QEII covenants, Nga Whenua Rahui and DOC covenants. Protected areas administered by local and regional authorities were not included in the analysis.
only spatially comprehensive wetland inventory for our study area, it is now considerably out of date, as it was devel- oped 30 years ago and it does not reflect the wetland tem- poral change (Johnston, 2013). The wetlandextent and type for many wetland patches have changed since its original delineation (e.g., Fig. 2). Nevertheless, NWI does provide valuable information about wetland locations (Tiner, 1997; Huang et al., 2011b). Furthermore, the NWI definition of wetlands requires only one of three wetland indicators (soils, hydrology, or plants) whereas regulatory delineation requires all three – 33 Code of Federal Regulations 328.3(b). In our study, the NWI polygons were primarily used to compare with the wetland depressions delineated from the lidar DEM. The high-resolution NHD data were downloaded from http://nhd.usgs.gov (accessed 30 December 2016). There were 1840 polyline features in the NHD flowline layer for the Pipestem subbasin, with a total length of 1.4 × 10 3 km and an average length of 762 m. The NHD flowlines overlaid on top of the lidar DEM are shown in Fig. 1. It is worth noting that the majority of the NHD flowline features were found in the low-elevation areas in the east. The high-elevation areas in the west, where most NWI wetland polygons are located, have very few NHD flowlines, except for the Little Pipestem Creek. This suggests that a large number of temporary and seasonal flow paths were not captured in the NHD dataset, perhaps due to the fact that the NHD does not try to system- atically measure stream lines < 1.6 km (Stanislawski, 2009; Lane and D’Amico, 2016). In this study, the NHD flowlines were used to compare the lidar-derived potential flow paths using our proposed methodology.
Substantial land development has also occurred in the region over the past two decades that is likely to have impacted wetlands. The Land Cover Database (LCDB), which maps land use across New Zealand, shows an increase in land under intensive agriculture (high producing grassland) in Southland by 8600 ha between 2001 and 2012 (Landcare Research 2014). An increase in land used for dairy production was a key driver of this land conversion. Ledgard (2013) reported that the number of dairy cows in Southland increased four-fold between 1995 and 2011, with 614 648 cows in 2011. Dairy farms have subsequently expanded from 87 109 ha in 2000 to 195 500 ha in 2011. A study by Ewans (2016) examined changes in wetlands situated on private land over half of the Southland Region. They estimated that of the 13 120 ha of wetlands present in 2007, 1235 ha (~10%) had been lost, or 1.3% per year. The Ewans study covered a relatively short period (7 years) and does not consider the broader changes in wetlandextent that may have occurred since the enactment of environmental legislation in New Zealand since 1990. The short time-period was due to relying on high resolution aerial photographs, rather than other remote sensing sources, such as Landsat or SPOT satellite imagery. While satellite images are of lower resolution, they enable longer-term change in wetlandextent and larger study areas to be examined. Spatial analysis
The selected study sites also have ponded water and aquatic vegetation in them every year, which fits the definition of prairie pothole wetlands clarified by van der Kamp, Hayashi, Bedard‐Haughn, and Pennock (2016). The monitored drainage ditches were selected on the basis of current and historical maps that confirm their role in the diversion of runoff away from historical wetlands (that have since been drained) towards the creek. It is worth noting that those drainage ditches are now barely incised swales, as they have been subjected to infilling since their creation; there is usually no water in them except during very wet conditions. Landscape characteristics (Table 2-1) were estimated for the targeted wetlands based on the current and historic wetland inventory dataset. Those datasets were provided by Ducks Unlimited Canada and assembled on the basis of Canada Wetland Inventory specifications (Ducks Unlimited Canada, 2016) as well as field reconnaissance information. Volume estimation methods varied depending on wetland condition. For open‐water wetlands with emergent communities and open water, Ducks Unlimited Canada used the v‐a‐h (i.e., volume‐area‐depth) method as described in Pomeroy et al. (2010). The v‐a‐h method was chosen for its ability to account for capacity below the standing water at the time of LiDAR acquisition. For completely drained wetlands, the volume was estimated from LiDAR data by measuring the storage contained below the plane of the historical wetlandextent using the Surface Volume Tool in ESRI ArcGIS. The perimeter of the instrumented open‐water wetlands (i.e., intact and consolidated) varies from 1.0 × 10 2 to 2.8 × 10 3 m, while their area and storage
The wetland vegetation of the high mountain grasslands of Mpumalanga w as sampled by using stratification based on geology and land types. Floristic data were classified by TWINSPAN procedures and refined by using the Braun-Blanquet method. This resulted in the recognition o f four major w etland plant communities w hich are subdiv ided into eleven minor plant communities. The major communities include the Phragmites australis Wetland occurring in relatively deep water, the Miscanthus junceus Wetland from moist river banks and wet drainage lines, the Eragrostis biflora-Stihurus allopecuroides Moist Grassland restricted to moist, poorly drained soils w ith a high water table, and Arundinella nepalensis Moist Grasslands on black v ertic soils.
Amanigaruhanga and Iyango, (2010), conducted an empirical study on socio- economic baseline survey of communities adjacent to Lake Bisina/Opeta and Lake Mburo/Nakivali Wetland systems, found that wetland catchment communities are unsustainably utilizing wetland resources and for growing of crops. Similarly, in a study by Male (2011) on assessing the impact of crop growing on wetland values, services, functions and goods of Nakivubo wetland, observed that there was over use of wetland resources (and estimated that 80% of the wetland was dominated by crop growing). As well, Song et al. (2012), in their study on wetland shrinkage, fragmentation and their links to agriculture in Muleng, Xingkai, and plain China, found out that wetland conversion to cropland accounted for the largest share (83%) which amounted to 1.5million ha of wetland loss. Thawe (2008) on maintaining seasonal wetlands and their livelihood contributions in Central Southern Africa (Malawi), found that active cultivation for crop growing in the dry season, as opposed to having dormant natural vegetation, withdrew more water from the wetland, especially when irrigation by watering can or by treadle pump was undertaken. As a result, the water table would be lowered especially if all of the surface area of a wetland became altered for cultivation, its ability to function as a wetland would be greatly reduced.
The research of different conditions, various forms of nitrogen in vertical flow constructed wetland filling to the vertical concentration distribution at different depth, by measuring the nitrogen removal effect, so as to determine the effective height of packing. Using zeolite and ceramic filler, lythrum salicaria consists of wetland plants of four units of vertical flow constructed wet land system. Setting the 1#: ceramic, planting, no aeration; 2#: zeolite, no plants, no aeration; 3#: zeolite, planting, no aeration; 4#: zeolite, planting, aeration. The depth of the four device at various nitrogen concentration and conversion rate was determined. The 4 processing unit average conversion rates of NH 4
The objective of this work, that belongs to the field of alternative options for drug pollutants degradation, is to design an integrated system of anaerobic reactor and constructed wetland to eliminate paracetamol (N-acetyl-p- aminophenol) and diclofenac (2-[2-(2,6-dichloroanilino)phenyl]acetic acid) residues contained in contaminated waters. The integrated system of anaerobic reactor and constructed wetland consisted of a water tank supplying wastewater to a biodigester through a feed tank, a constructed wetland home to two species of plants Cyperus alternifolius and Typha angustifolia, and a treated water collection tank. The parameters were analyzed pursuant to NOM-001-SEMARNAT-1996, while diclofenac and paracetamol were analyzed through capillary electrophoresis. The results showed a 90% biodegradation of the organic material in the effluent. In the biodigester, the average biodegradation reaction speed was Kt = 4.9 days-1 ± 0.870; with p˂0.05. The drugs were not detected in the effluent from the wetland, and thus the integrated system of anaerobic reactor and constructed wetland represents an alternative for removing pollutants from municipal wastewaters.
As already mentioned, the higher N uptake rates of the wetland soil might be related to the high number of cyanobacteria, which can amount to up to 5% of the microbial community in the sampled soils . An increased input of N by cyanobacteria seems plausible, since this group of organisms finds good living conditions in wetlands due to often occurring anoxic conditions in these ecosystems, which are favorable for N 2 fixation and nitrogenase activity
Wetland systems provides goods and services to the people. They helps to check floods and prevent the coastal erosion. They store water for long periods. Wetlands preserve water quality and increase biological productivity for both aquatic life as well as human communities of the region . Inundated wetlands are very effective in storing rainwater and are the primary source for recharging ground water covers. More over wetlands may have provided a green barrier to protect coastlines and the coastal communities that live there. There were reports from around the Indian ocean region in which the damaging impact of the Tsunami was reduced behind mangrove stands and coral reefs [3, 4].
IMPORTANCE Wetlands are the largest nonanthropogenic source of atmospheric methane but also a key global carbon reservoir. Characterizing belowground microbial communities that mediate carbon cycling in wetlands is critical to accurately predicting their responses to changes in land management and climate. Here, we studied a restored wetland and revealed substantial spatial heterogeneity in biogeochemistry, methane production, and microbial communities, largely associated with the wetland hydrau- lic design. We observed patterns in microbial community composition and functions correlated with biogeochemistry and methane production, including diverse microorganisms involved in methane production and consumption. We found that methanogenesis gene abundance is inversely correlated with genes from pathways exploiting other electron acceptors, yet the ubiquitous presence of genes from all these pathways suggests that diverse electron acceptors contribute to the energetic balance of the ecosystem. These investigations represent an important step toward effective management of wetlands to reduce methane flux to the atmosphere and enhance belowground carbon storage.
This study introduces some case analyses of wetland distribution on various spatial scales, from nationwide to the area of a wetland group, with a focus on geomorphological feature. The nationwide wetland distribution in Japan showed that snow accumulation and topography of volcanic mountains were important for wetland formation in mountainous regions. Secondly, we clarified that wetlands were mainly distributed on the gentle slope of original volcanic surfaces and in landslides in the Hachimantai volcanic groups, in the northern Japan, using 10 m grid DEM and aerial photo interpretation. With higher-resolution data, it was clear that wetlands were arranged depending on the microtopography of landslides and volcanic surfaces and groundwater. Using data with resolution suitable for the target topographical size and combining the results of multiple spatial scales/resolutions, we can deduce the origin of wetlands in more detail.
The sources of wastewater can be either from the industrial or non-industrial area. The major source of pollution comes from the non-industrial parts and the waste produced by human contributes the largest part of the non-industrial pollution. If it cannot be well handled, many problems will arise including epidemics. Hence, pollution level should be maintained at a very low level or at least controllable. Wastewater with human sources can be efﬁciently treated by oxidation pond. In addition, wetland system can effectively control industrial wastewater; for instance, the wastewater discharging from construction sites. Currently, there are several mathematical models available for simulating oxidation pond process where some important parameters are considered such as bacteria (cleansing agent), pollutants and dissolved oxygen (DO). However, previous results did not provide good approximation on the required parameters. Moreover, stability analysis was rarely considered for constructed wetland models. However, the steady-state and bifurcation analyses are usually crucial in determining the reliability of the models that is under study. Thus, dynamic mathematical models are developed in this study to allow the simulation and prediction of wastewater treatment process for both oxidation pond and CW case studies. Furthermore, the nonlinear system of ordinary differential equations (ODE) using multiple substrate limiting factors with interactive reactions and partial differential equations (PDE) using advection-diffusion-reaction equations are implemented for CW and oxidation pond, respectively.
during each campaign from the three different wetland vege- tation ecotypes. These ecotypes were Juncus kraussii, Phrag- mites australis, and Juncus kraussii amongst Casuarina spp. forest (Fig. 1). In each ecotype, three acrylic bases (65 × 65 × 30 cm) were installed 4 months before the first time series experiment to minimise disturbance to the sediment profile and vegetative rhizosphere. Vegetative flux chambers were constructed of an aluminium frame with clear Perspex walls and a roof that matched the areal footprint of the pre-inserted acrylic bases. The chambers were 100, 150, and 50 cm high at Juncus, Phragmites, and Juncus–forest sites, respectively. The custom sizes were tailored for the different vegetation heights, whilst minimising chamber volume as much as pos- sible. Each chamber was leak-tested under laboratory condi- tions prior to fieldwork.
Spatio-temporal Land-Use and Land-Cover (LULC) changes have been affecting geo- environmental and climate change globally. This study aims to analyze LULC changes in Bahir Dar city and its surrounds. Landsat 5 TM (1987), Landsat 7 ETM+ (2002) and Landsat 8 OLI (2017) and SPOT images, and aerial photographs, master plan map and Google Earth Landsat images were used to analyze changes. In Bahir Dar city and its surrounds, LULC has been changing in space and time. During 1987-2017, more than 50% of the study area was covered with cropland. Settlement areas have increased from 3.3% in 1987 to 9.13% in 2017. However, wetland vegetation, shrubland, grassland, forest, and waterbodies have degraded. These changes are mainly attributed to population growth and its effect on the environment. Land-use and land-cover is a serious problem and it causes land and environmental degradation, climate change and loss of the biological environment.
Abstract: Coastal wetlands are a critical component of the coastal landscape that are increasingly threatened by sea level rise and other human disturbance. Periodically mapping wetland distribution is crucial to coastal ecosystem management. Ensemble algorithms (EL), such as random forest (RF) and gradient boosting machine (GBM) algorithms, are now commonly applied in the field of remote sensing. However, the performance and potential of other EL methods, such as extreme gradient boosting (XGBoost) and bagged trees, are rarely compared and tested for coastal wetland mapping. In this study, we applied the three most widely used EL techniques (i.e., bagging, boosting and stacking) to map wetland distribution in a highly modified coastal catchment, the Manning River Estuary, Australia. Our results demonstrated the advantages of using ensemble classifiers to accurately map wetland types in a coastal landscape. Enhanced bagging decision trees, i.e., classifiers with additional methods to increasing ensemble diversity such as RF and weighted subspace random forest, had comparably high predictive power. For the stacking method evaluated in this study, our results are inconclusive, and further comprehensive quantitative study is encouraged. Our findings also suggested that the ensemble methods were less effective at discriminating minority classes in comparison with more common classes. Finally, the variable importance results indicated that hydro-geomorphic factors, such as tidal depth and distance to water edge, were among the most influential variables across the top classifiers. However, vegetation indices derived from longer time series of remote sensing data that arrest the full features of land phenology are likely to improve wetland type separation in coastal areas. Keywords: coastal wetland; saltmarsh; mangrove; fractional cover; sentinel-2; machine learning