Wetland Inventory Research Articles

Building a national wetland inventory: a review and roadmap to move forward

An inventory is internationally recognised as a key tool for informing policy, management and conservation of wetlands. Despite national, state and territory initiatives a comprehensive Australian wetland inventory still does not exist. The primary data requirements for a national wetland inventory are standardised mapping of wetland extent and attribution of typology. Some Australian jurisdictions have ongoing and regularly updated wetland mapping programs that could be consistent with a national inventory framework, whereas others have outdated or scattered information and data. Data requirements for wetland inventories have been reviewed previously, but there have been several recent improvements in technology and available information since those reviews were completed. The focus of this paper is to update previous reviews by outlining the recent opportunities that have emerged, and present a roadmap to support implementation of a jurisdictional wetland inventory program. We applied the process to the Lachlan River catchment in New South Wales, demonstrating the value and relevancy of the roadmap for inventory development. The need for standardised jurisdictional wetland inventories is the first step to their integration into a national wetland inventory. The roadmap and frameworks developed here can guide the development of other programs to overcome impediments and support development of a national wetland inventory. This tool would ultimately support the maintenance, protection and restoration of wetlands at a national level.

DECISION TREE REPOSITORY AND RULE SET BASED MINGJIANG RIVER ESTUARINE WETLANDS CLASSIFACTION

Abstract. The increasing urbanization and industrialization have led to wetland losses in estuarine area of Mingjiang River over past three decades. There has been increasing attention given to produce wetland inventories using remote sensing and GIS technology. Due to inconsistency training site and training sample, traditionally pixel-based image classification methods can’t achieve a comparable result within different organizations. Meanwhile, object-oriented image classification technique shows grate potential to solve this problem and Landsat moderate resolution remote sensing images are widely used to fulfill this requirement. Firstly, the standardized atmospheric correct, spectrally high fidelity texture feature enhancement was conducted before implementing the object-oriented wetland classification method in eCognition. Secondly, we performed the multi-scale segmentation procedure, taking the scale, hue, shape, compactness and smoothness of the image into account to get the appropriate parameters, using the top and down region merge algorithm from single pixel level, the optimal texture segmentation scale for different types of features is confirmed. Then, the segmented object is used as the classification unit to calculate the spectral information such as Mean value, Maximum value, Minimum value, Brightness value and the Normalized value. The Area, length, Tightness and the Shape rule of the image object Spatial features and texture features such as Mean, Variance and Entropy of image objects are used as classification features of training samples. Based on the reference images and the sampling points of on-the-spot investigation, typical training samples are selected uniformly and randomly for each type of ground objects. The spectral, texture and spatial characteristics of each type of feature in each feature layer corresponding to the range of values are used to create the decision tree repository. Finally, with the help of high resolution reference images, the random sampling method is used to conduct the field investigation, achieve an overall accuracy of 90.31 %, and the Kappa coefficient is 0.88. The classification method based on decision tree threshold values and rule set developed by the repository, outperforms the results obtained from the traditional methodology. Our decision tree repository and rule set based object-oriented classification technique was an effective method for producing comparable and consistency wetlands data set.

Wetland hydroperiod classification in the western prairies using multitemporal synthetic aperture radar

AbstractWetlands represent one of the world's most biodiverse and threatened ecosystem types and were diminished globally by about two‐thirds in the 20th century. There is continuing decline in wetland quantity and function due to infilling and other human activities. In addition, with climate change, warmer temperatures and changes in precipitation and evapotranspiration are reducing wetland surface and groundwater supplies, further altering wetland hydrology and vegetation. There is a need to automate inventory and monitoring of wetlands, and as a study system, we investigated the Shepard Slough wetlands complex, which includes numerous wetlands in urban, suburban, and agricultural zones in the prairie pothole region of southern Alberta, Canada. Here, wetlands are generally confined to depressions in the undulating terrain, challenging wetlands inventory and monitoring. This study applied threshold and frequency analysis routines for high‐resolution, single‐polarization (HH) RADARSAT‐2, synthetic aperture radar mapping. This enabled a growing season surface water extent hyroperiod‐based wetland classification, which can support water and wetland resource monitoring. This 3‐year study demonstrated synthetic aperture radar‐derived multitemporal open‐water masks provided an effective index of wetland permanence class, with overall accuracies of 89% to 95% compared with optical validation data, and RMSE between 0.2 and 0.7 m between model and field validation data. This allowed for characterizing the distribution and dynamics of 4 marsh wetlands hydroperiod classes, temporary, seasonal, semipermanent, and permanent, and mapping of the sequential vegetation bands that included emergent, obligate wetland, facultative wetland, and upland plant communities. Hydroperiod variation and surface water extent were found to be influenced by short‐term rainfall events in both wet and dry years. Seasonal hydroperiods in wetlands were particularly variable if there was a decrease in the temporary or semipermanent hydroperiod classes. In years with extreme rain events, the temporary wetlands especially increased relative to longer lasting wetlands (84% in 2015 with significant rainfall events, compared with 42% otherwise).

Fine-Scale Mapping of Coastal Plant Communities in the Northeastern USA

Salt marshes of the northeastern United States are dynamic landscapes where the tidal flooding regime creates patterns of plant zonation based on differences in elevation, salinity, and local hydrology. These patterns of zonation can change quickly due to both natural and anthropogenic stressors, making tidal marshes vulnerable to degradation and loss. We compared several remote sensing techniques to develop a tool that accurately maps high- and low-marsh zonation to use in management and conservation planning for this ecosystem in the northeast USA. We found that random forests (RF) outperformed other classifier tools when applied to the most recent National Agricultural Imagery Program (NAIP) imagery, NAIP derivatives, and elevation data between coastal Maine and Virginia, USA. We then used RF methods to classify plant zonation within a 500-m buffer around coastal marsh delineated in the National Wetland Inventory. We found mean classification accuracies of 94% for high marsh, 76% for low marsh zones, and 90% overall map accuracy. The detailed output is a 3-m resolution continuous map of tidal marsh vegetation communities and cover classes that can be used in habitat modeling of marsh-obligate species or to monitor changes in marsh plant communities over time.

Mapping wetlands in Nova Scotia with multi-beam RADARSAT-2 Polarimetric SAR, optical satellite imagery, and Lidar data

Wetland maps currently in use by the Province of Nova Scotia, namely the Department of Natural Resources (DNR) wetland inventory map and the swamp wetland classes of the DNR forest map, need to be updated. In this study, wetlands were mapped in an area southwest of Halifax, Nova Scotia by classifying a combination of multi-date and multi-beam RADARSAT-2 C-band polarimetric SAR (polSAR) images with spring Lidar, and fall QuickBird optical data using the Random Forests (RF) classifier. The resulting map has five wetland classes (open-water/marsh complex, open bog, open fen, shrub/treed fen/bog, swamp), plus lakes and various upland classes. Its accuracy was assessed using data from 156 GPS wetland sites collected in 2012 and compared to the one obtained with the current wetland map of Nova Scotia. The best overall classification was obtained using a combination of Lidar, RADARSAT-2 HH, HV, VH, VV intensity with polarimetric variables, and QuickBird multispectral (89.2%). The classified image was compared to GPS validation sites to assess the mapping accuracy of the wetlands. It was first done considering a group consisting of all wetland classes including lakes. This showed that only 69.9% of the wetland sites were correctly identified when only the QuickBird classified image was used in the classification. With the addition of variables derived from lidar, the number of correctly identified wetlands increased to 88.5%. The accuracy remained the same with the addition of RADARSAT-2 (88.5%). When we tested the accuracy for identifying wetland classes (e.g. marsh complex vs. open bog) instead of grouped wetlands, the resulting wetland map performed best with either QuickBird and Lidar, or QuickBird, Lidar, and RADARSAT-2 (66%). The Province of Nova Scotia’s current wetland inventory and its associated wetland classes (aerial-photo interpreted) were also assessed against the GPS wetland sites. This provincial inventory correctly identified 62.2% of the grouped wetlands and only 18.6% of the wetland classes. The current inventory’s poor performance demonstrates the value of incorporating a combination of new data sources into the provincial wetland mapping.

Impact of flood and seasonality on wetland changing trends in the Diara region of West Bengal, India

The hydrological condition of any floodplain wetland depends on the water availability through surface run-off from adjacent areas. The seasonal variation of rainfall and resultant flood bring massive change in the wetlands of that region. The present study focuses on identifying wetland inventories and trend of wetlands of the Diara region of West Bengal. Diara region of Eastern India is a highly dynamic physical region bounded by two frequently shifting rivers (River Ganga and Kalindri) and relatively tightly bound river Mahananda in its three sides. Two types of Normalised Difference Water Index, water presence frequency and hydrological stability analysis using LANDSAT data of 1973–2016 have been done for this study. The result shows that the wetland area was 14,124.78 ha during 1999 and it has turned into 4885.92 ha during 2016 which indicates the extreme flood event of 1998–1999 over entire region has brought a boost in the wetland scenario for a shorter period but it is in the decaying phase now-a-days. Total 1552.56 ha of wetland in this area is identified as stable based on the regular appearance of water over there.

An examination of the potential wetland development landscape around managed reservoirs in the central U.S. Great Plains

Reservoirs around the world are losing storage capacity due to sediment infilling, leading to reductions in the quality and value of reservoir uses. However, the sediment accumulating in the upper ends of reservoirs, particularly around primary inflows within well-defined floodplains, could potentially be developing into wetland ecosystems that provide services such as sediment filtration, nutrient sequestration, and wildlife habitat. In this study, we examine lake level, wetland, and topographic characteristics for 20 large, federally operated reservoirs in the state of Kansas located in the central U.S. Great Plains. First, daily water level data and empty-basin topography (created by merging LiDAR elevation with bathymetry) are used to delineate the primary fluctuation zone for each reservoir, which we define to be the range of extents between the 25th and 75th lake level percentiles. Next, we use data from the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) to characterize wetland composition in the upper fluctuation zone (extent range between the 50th and 75th lake level percentiles) and the upper flood pool (extent range between the 75th percentile and the maximum designed flood pool elevation). For the final assessment, we examine the relationship between ground slope and NWI in the upper fluctuation zone and the upper flood pool. Results indicate that relatively low-sloped ground is classified as wetland in the upper fluctuation zone at 24% greater frequency than in the upper flood pool.

Evaluating the potential for site-specific modification of LiDAR DEM derivatives to improve environmental planning-scale wetland identification using Random Forest classification

Wetlands are important ecosystems that provide many ecological benefits, and their quality and presence are protected by federal regulations. These regulations require wetland delineations, which can be costly and time-consuming to perform. Computer models can assist in this process, but lack the accuracy necessary for environmental planning-scale wetland identification. In this study, the potential for improvement of wetland identification models through modification of digital elevation model (DEM) derivatives, derived from high-resolution and increasingly available light detection and ranging (LiDAR) data, at a scale necessary for small-scale wetland delineations is evaluated. A novel approach of flow convergence modelling is presented where Topographic Wetness Index (TWI), curvature, and Cartographic Depth-to-Water index (DTW), are modified to better distinguish wetland from upland areas, combined with ancillary soil data, and used in a Random Forest classification. This approach is applied to four study sites in Virginia, implemented as an ArcGIS model. The model resulted in significant improvement in average wetland accuracy compared to the commonly used National Wetland Inventory (84.9% vs. 32.1%), at the expense of a moderately lower average non-wetland accuracy (85.6% vs. 98.0%) and average overall accuracy (85.6% vs. 92.0%). From this, we concluded that modifying TWI, curvature, and DTW provides more robust wetland and non-wetland signatures to the models by improving accuracy rates compared to classifications using the original indices. The resulting ArcGIS model is a general tool able to modify these local LiDAR DEM derivatives based on site characteristics to identify wetlands at a high resolution.

Inverse modelling of European CH<sub>4</sub> emissions during 2006–2012 using different inverse models and reassessed atmospheric observations

Abstract. We present inverse modelling (top down) estimates of European methane (CH4) emissions for 2006–2012 based on a new quality-controlled and harmonised in situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.8 (20.2–29.7) Tg CH4 yr−1 (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006–2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom up, based on statistical data and emissions factors) amount to only 21.3 Tg CH4 yr−1 (2006) to 18.8 Tg CH4 yr−1 (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), total wetland emissions of 4.3 (2.3–8.2) Tg CH4 yr−1 from the EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Taking into account the wetland emissions from the WETCHIMP ensemble, the top-down estimates are broadly consistent with the sum of anthropogenic and natural bottom-up inventories. However, the contribution of natural sources and their regional distribution remain rather uncertain. Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon Cycle (IMECC) aircraft campaign. We present a novel approach to estimate the biases in the derived emissions, based on the comparison of simulated and measured enhancements of CH4 compared to the background, integrated over the entire boundary layer and over the lower troposphere. The estimated average regional biases range between −40 and 20 % at the aircraft profile sites in France, Hungary and Poland.

Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory.

Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree (DT), rule-based (RB), and random forest (RF). High-resolution satellite imagery can provide more specificity to the classified end product, and ancillary data layers such as the Normalized Difference Vegetation Index, and hydrogeomorphic layers such as distance-to-a-stream can be coupled to improve overall accuracy (OA) in wetland studies. In this paper, we contrast three nonparametric machine-learning algorithms (DT, RB, and RF) using a large field-based dataset (n = 228) from the Selenga River Delta of Lake Baikal, Russia. We also explore the use of ancillary data layers selected to improve OA, with a goal of providing end users with a recommended classifier to use and the most parsimonious suite of input parameters for classifying wetland-dominated landscapes. Though all classifiers appeared suitable, the RF classification outperformed both the DT and RB methods, achieving OA >81%. Including a texture metric (homogeneity) substantially improved the classification OA. However, including vegetation/soil/water metrics (based on WorldView-2 band combinations), hydrogeomorphic data layers, and elevation data layers to increase the descriptive content of the input parameters surprisingly did not markedly improve the OA. We conclude that, in most cases, RF should be the classifier of choice. The potential exception to this recommendation is under the circumstance where the end user requires narrative rules to best manage his or her resource. Though not useful in this study, continuously increasing satellite imagery resolution and band availability suggests the inclusion of ancillary contextual data layers such as soil metrics or elevation data, the granularity of which may define its utility in subsequent wetland classifications.

Global extent and distribution of wetlands: trends and issues

Herein we review estimates of global and regional wetland area from ‘bottom-up’ approaches of site or national wetland inventories and ‘top-down’ approaches from global mapping and remote sensing. The trend for increasing wetland extent reported in the literature over time is a consequence of improved mapping technologies and methods rather than a real increase in wetland area, because a continuing trend for natural wetland loss and conversion is documented over the same time period. The most recent high-resolution estimate of global wetland area is in excess of 12.1×106km2, of which 54% is permanently inundated and 46% is temporarily inundated. Globally, 92.8% of continental wetland area is inland and only 7.2% is coastal. Regionally, the largest wetland areas are in Asia (31.8%), North America (27.1%) and Latin America and the Caribbean (Neotropics; 15.8%), with smaller areas in Europe (12.5%), Africa (9.9%) and Oceania (2.9%). It is likely that estimates of global wetland area published to date persist in underestimating the true wetland area. The ‘grand challenge’ of a global inventory integrating all types of permanent and temporary wetlands at high spatial resolution has yet to be fully achieved.

Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping

Modern advances in cloud computing and machine-leaning algorithms are shifting the manner in which Earth-observation (EO) data are used for environmental monitoring, particularly as we settle into the era of free, open-access satellite data streams. Wetland delineation represents a particularly worthy application of this emerging research trend, since wetlands are an ecologically important yet chronically under-represented component of contemporary mapping and monitoring programs, particularly at the regional and national levels. Exploiting Google Earth Engine and R Statistical software, we developed a workflow for predicting the probability of wetland occurrence using a boosted regression tree machine-learning framework applied to digital topographic and EO data. Working in a 13,700 km2 study area in northern Alberta, our best models produced excellent results, with AUC (area under the receiver-operator characteristic curve) values of 0.898 and explained-deviance values of 0.708. Our results demonstrate the central role of high-quality topographic variables for modeling wetland distribution at regional scales. Including optical and/or radar variables into the workflow substantially improved model performance, though optical data performed slightly better. Converting our wetland probability-of-occurrence model into a binary Wet-Dry classification yielded an overall accuracy of 85%, which is virtually identical to that derived from the Alberta Merged Wetland Inventory (AMWI): the contemporary inventory used by the Government of Alberta. However, our workflow contains several key advantages over that used to produce the AMWI, and provides a scalable foundation for province-wide monitoring initiatives.

The potential of using LiDAR and color-infrared aerial imagery for palustrine wetland typology and change

Wetlands are dynamic landscapes and their spatial extent and types can change over time. Mapping wetland locations, types, and monitoring wetland typological changes have important ecological significance. The National Wetlands Inventory data suffer from two problems: the omission error that some wetlands are not mapped, and the out-of-date wetland types in many counties of the United States. To address these two problems, we proposed an automatic wetland classification model for newly mapped (or existing) wetland polygons lacking typological information. The research goals in this study were (1) to develop a nonparametric and automatic rule-based model to assign wetland types to palustrine wetlands using high-resolution remotely sensed data and (2) to quantify wetland typological changes based on the wetland types obtained from the previous step. The model is a direct application of the Cowardin et al. (1979) wetland classification system without modification. The input information for the proposed model includes Light Detection and Ranging (LiDAR)-derived vegetation height and color infrared aerial imagery-derived vegetation spectral information. We tested the model for the palustrine wetlands in Horry County, SC, and analyzed 29,090 palustrine wetland polygons (101,427 ha). The model achieved an overall agreement of 87% for wetland-type classification and showed the dynamics of wetland typological changes. This nonparametric model can be easily applied to other areas where wetland inventory needs updating.

Estimating rates of wetland loss using power-law functions

Estimates of rates of wetland loss are important for understanding whether wetland policies meet their objectives. In Alberta, a no-net-area loss interim wetland policy was introduced in 1993. We tested the effectiveness of this interim wetland policy. A historical wetland inventory was established by generating a wetland inventory using digital topographic analysis and calculating a wetland-area vs. wetland-frequency power-law function from these data. Permanent wetland loss (topographic depression no longer exists) was calculated as the deviation from the historical wetland-inventory power-law function (representing the pre-settlement wetland inventory) and was estimated at 32.8% in number and 2.10% in area, with uncertainty estimates well below 1%. Temporary wetland loss (topographic depression remains on the landscape) was calculated as the difference between the historical wetland inventory and a time series of contemporary wetland inventories mapped from aerial photographs. Results indicate that as of 1993, 49.4% of the number of wetlands were temporarily lost (56.6% of wetland area), which increased in 2011 to 56.8% (68.0% of wetland area), with uncertainty estimates well below 1%. From 1993 to 2011, we estimated a rate of loss of 0.63% in wetland area/year. Wetland loss continued despite the introduction of the no-net-area-loss policy in 1993.

Does National Wetland Inventory class consistently identify vegetation and edaphic differences in Oregon tidal wetlands?

Accurately mapping, modeling, and managing the diversity of wetlands present in estuaries often relies on habitat classification systems that consistently identify differences in biotic structure or other ecosystem characteristics between classes. We used field data from four Oregon estuaries to test for differences in vegetation structure and edaphic characteristics among three tidal emergent marsh classes derived from National Wetlands Inventory (NWI) data: low marsh, high marsh, and palustrine tidal marsh. Independently of NWI class, we also evaluated the number and types of plant assemblages present and how edaphic variables, non-native plant cover, and plant species richness varied among them. Pore water salinity varied most strongly across marsh classes, with sediment carbon and nitrogen content, grain size and marsh surface elevation showing smaller differences. Cover of common vascular plant species differed between marsh classes and overall vegetation composition was somewhat distinct among marsh types. High marsh had the largest species pools. However, plot-level plant diversity was similar among marsh classes. Non-native species cover was highest in palustrine and high marshes. The marshes in the study contained a large number of plant assemblages with most occurring across more than one marsh class. The more common assemblages occurred along a continuum of tidal elevation, soil salinity, and edaphic characteristics, with varying plant richness and non-native cover. Our data suggest that NWI classes are useful for differentiating several general features of Oregon tidal marsh structure, but that more detailed information on plant assemblages found within those wetland classes would allow more precise characterization of additional wetland features such as edaphic conditions and plant diversity.

Building a potential wetland restoration indicator for the contiguous United States

Wetlands provide key functions in the landscape from improving water quality, to regulating flows, to providing wildlife habitat. Over half of the wetlands in the contiguous United States (CONUS) have been converted to agricultural and urban land uses. However, over the last several decades, research has shown the benefits of wetlands to hydrologic, chemical, biological processes, spurring the creation of government programs and private initiatives to restore wetlands. Initiatives tend to focus on individual wetland creation, yet the greatest benefits are achieved when strategic restoration planning occurs across a watershed or multiple watersheds. For watershed-level wetland restoration planning to occur, informative data layers on potential wetland areas are needed. We created an indicator of potential wetland areas (PWA), using nationally available datasets to identify characteristics that could support wetland ecosystems, including: poorly drained soils and low-relief landscape positions as indicated by a derived topographic data layer. We compared our PWA with the National Wetlands Inventory (NWI) from 11 states throughout the CONUS to evaluate their alignment. The state-level percentage of NWI-designated wetlands directly overlapping the PWA ranged from 39 to 95%. When we included NWI that was immediately adjacent to the overlapping NWI, our range of correspondence to NWI ranged from 60 to 99%. Wetland restoration is more likely on certain landscapes (e.g., agriculture) than others due to the lack of substantive infrastructure and the potential for the restoration of hydrology; therefore, we combined the National Land Cover Dataset (NLCD) with the PWA to identify potentially restorable wetlands on agricultural land (PRW-Ag). The PRW-Ag identified a total of over 46 million ha with the potential to support wetlands. The largest concentrations of PRW-Ag occurred in the glaciated corn belt of the upper Mississippi River from Ohio to the Dakotas and in the Mississippi Alluvial Valley. The PRW-Ag layer could assist land managers in identifying sites that may qualify for enrollment in conservation programs, where planners can coordinate restoration efforts, or where decision makers can target resources to optimize the services provided across a watershed or multiple watersheds.

Automated Techniques to Identify Lost and Restorable Wetlands in the Prairie Pothole Region

Wetland loss in the Prairie Pothole Region has been substantial, and automated techniques to estimate wetland loss and identify priority wetlands for restoration are crucial if important ecosystem services provided by wetlands are to be maintained. A suite of automated methods was developed to establish a historical wetland inventory and to identify the proportion of permanently and temporarily lost wetlands in a prairie pothole watershed in Alberta, Canada. A power law analysis of area vs. frequency of historical wetlands provided estimates of permanently lost wetlands. Combining the historical wetland inventory with an inventory of existing wetlands provided estimates of temporarily lost wetlands. 22,204 historical wetlands comprising 12,431 ha were estimated in the watershed. Permanently lost wetland number and area were estimated as 11.1% and 0.6% respectively, and temporarily lost wetland number and area were estimated as 61.1% and 78.3% respectively. Existing wetlands represented only 27.8% of the total historical number and 21.2% of the total historical area. 1,588 ditch-drained (relatively easy restore) wetlands were identified from the inventory of temporarily lost wetlands using digital terrain analysis, representing a potential recovery of 7.2% of the historical wetlands by number and 9.8% by area.

The spatial distribution of soil organic carbon in tidal wetland soils of the continental United States.

Tidal wetlands contain large reservoirs of carbon in their soils and can sequester carbon dioxide (CO2 ) at a greater rate per unit area than nearly any other ecosystem. The spatial distribution of this carbon influences climate and wetland policy. To assist with international accords such as the Paris Climate Agreement, national-level assessments such as the United States (U.S.) National Greenhouse Gas Inventory, and regional, state, local, and project-level evaluation of CO2 sequestration credits, we developed a geodatabase (CoBluCarb) and high-resolution maps of soil organic carbon (SOC) distribution by linking National Wetlands Inventory data with the U.S. Soil Survey Geographic Database. For over 600,000 wetlands, the total carbon stock and organic carbon density was calculated at 5-cm vertical resolution from 0 to 300cm of depth. Across the continental United States, there are 1,153-1,359 Tg of SOC in the upper 0-100cm of soils across a total of 24945.9km2 of tidal wetland area, twice as much carbon as the most recent national estimate. Approximately 75% of this carbon was found in estuarine emergent wetlands with freshwater tidal wetlands holding about 19%. The greatest pool of SOC was found within the Atchafalaya/Vermilion Bay complex in Louisiana, containing about 10% of the U.S. total. The average density across all tidal wetlands was 0.071g cm-3 across 0-15cm, 0.055g cm-3 across 0-100cm, and 0.040g cm-3 at the 100cm depth. There is inherent variability between and within individual wetlands; however, we conclude that it is possible to use standardized values at a range of 0-100cm of the soil profile, to provide first-order quantification and to evaluate future changes in carbon stocks in response to environmental perturbations. This Tier 2-oriented carbon stock assessment provides a scientific method that can be copied by other nations in support of international requirements.

Object-Based Classification of Wetlands in Newfoundland and Labrador Using Multi-Temporal PolSAR Data

ABSTRACTDespite the fact that vast portions of Newfoundland and Labrador (NL) are covered by wetlands, currently there is no provincial inventory of wetlands in the province. In this study, we analyzed multi-temporal synthetic aperture radar (SAR) data for wetland classification at 4 pilot sites across NL. Object-based image analysis (OBIA) using a segmentation method based on optical data (RapidEye image in this study), and well-adjusted to SAR images, was first compared to pixel-based classification. Next, multi-date object-based wetland maps using the Random Forest classifier were compared to single-date classification. Finally, ratio and textural features were evaluated for wetland classification. The OBIA method demonstrated superior results, and the multi-date classification performed better than single-date classification with accuracies ranging from 75% to 95%. The multi-date results showed that the images acquired in August are the most appropriate for classifying wetlands, while the October images are of less value. Also, covariance matrix is a valuable feature set for wetland mapping. Moreover, ratio and textural features slightly increase the overall accuracy when the initial overall accuracy is relatively low. It can be concluded that multi-date SAR classification, with the proposed segmentation method, shows great potential for mapping wetlands and can be applied throughout the province.

China’s wetland databases based on remote sensing technology

Wetland databases can provide the basic data that necessary for the protection and management of wetlands. A large number of wetland databases have been established in the world as well as in China. In this paper, we review China’s wetland databases based on remote sensing (RS) technology after introducing the background theory to the application of RS technology in wetland surveys. A key conclusion is that China’s wetland databases are far from sufficient in fulfilling protection and management needs. Our recommendations focus on the use of the hyper-spectral imagery, microwave data, multi-temporal images, and automatic classifications in order to improve the accuracy and efficiency of wetland inventory. Further, attention should also be paid to detect major biophysical features of wetlands and build wetland databases in years after the 1980s in China. Considering that great gap exists between RS experts and wetland experts, further cooperation between wetland scientists and RS scientists are needed to promote the application of RS in the foundation of wetland databases.