National Wetlands Inventory Research Articles

A “Region-Specific Model Adaptation (RSMA)”-Based Training Data Method for Large-Scale Land Cover Mapping

An accurate and historical land cover monitoring dataset for Alaska could provide fundamental information for a range of studies, such as conservation habitats, biogeochemical cycles, and climate systems, in this distinctive region. This research addresses challenges associated with the extraction of training data for timely and accurate land cover classifications in Alaska over longer time periods (e.g., greater than 10 years). Specifically, we designed the “Region-Specific Model Adaptation (RSMA)” method for training data. The method integrates land cover information from the National Land Cover Database (NLCD), LANDFIRE’s Existing Vegetation Type (EVT), and the National Wetlands Inventory (NWI) and machine learning techniques to generate robust training samples based on the Anderson Level II classification legend. The assumption of the method is that spectral signatures vary across regions because of diverse land surface compositions; however, despite these variations, there are consistent, collective land cover characteristics that span the entire region. Building upon this assumption, this research utilized the classification power of deep learning algorithms and the generalization ability of RSMA to construct a model for the RSMA method. Additionally, we interpreted existing vegetation plot information for land cover labels as validation data to reduce inconsistency in the human interpretation. Our validation results indicate that the RSMA method improved the quality of the training data derived solely from the NLCD by approximately 30% for the overall accuracy. The validation assessment also demonstrates that the RSMA method can generate reliable training data on large scales in regions that lack sufficient reliable data.

The feasibility of using national‐scale datasets for classifying wetlands in Arizona with machine learning

AbstractThe advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national‐scale land cover maps instead of customized sample sets. We created a single map of water and wetland presence by intersecting water and wetland classes from available land cover products (National Wetland Inventory, Gap Analysis Project, National Land Cover Database and Dynamic Surface Water Extent) across the U.S. state of Arizona, which has fewer wetland‐specific mapping products than other parts of the U.S. We derived classified samples for four wetland classes from the combined map: open water, herbaceous wetlands, wooded wetlands and non‐wetland cover. In Google Earth Engine, we developed a random forest model that combined the training data with spatial predictor variables, including vegetation greenness indices, wetness indices, seasonal index variation, topographic parameters and vegetation height metrics. Results show that the final model separates the four classes with an overall accuracy of 86.2%. The accuracy suggests that existing datasets can be effectively used to compile machine learning training samples to map wetlands in arid landscapes in the U.S. These methods hold promise for the generation of wetland inventories at more frequent intervals, which could allow more nuanced investigations of wetland change over time in response to anthropogenic and climatic drivers.

Advancements in mapping areas suitable for wetland habitats across the conterminous United States

Wetland habitats provide critical ecosystem services to the surrounding landscape, including nutrient and pollutant retention, flood mitigation, and carbon storage. Wetland connectivity to water bodies and related ecosystems is critical in habitat sustainability, but there are limited resources for landscape-level wetland planning. Considering the network connectivity of an ecosystem type can derive different benefits to the natural and built environment, as well as human health. The value that wetlands provide, along with incentive programs and conservation goals mandated by the government require new and improved wetland spatial data. Utilizing high quality, publicly available data, this study finds that the amount of land in the United States that could support built or restored wetlands is more than double the area of mapped existing wetlands.This study uses 17 input variables (i.e., features extracted from remotely sensed data and auxiliary datasets) at the 10-m resolution and the National Wetlands Inventory to train a random forest model to identify areas that may support a wetland habitat, or potential wetland areas. Models were calculated for each of 18 two-digit hydrologic units that encompass the conterminous United States, and model overall accuracy ranged from 78.0 % to 89.8 %. The models predicted that 21.1 % of the conterminous United States can be categorized as potential wetland area.Selecting input variables to predict areas with wetland potential, rather than to identify existing wetlands, using the random forest algorithm can be transferred to other locations, scales, and ecosystem types. Visualizing potential wetland areas using input data at the 10-m resolution and enhanced methodology improves previous work, as even slight changes in topography, soils, and landscape features can determine ecosystem connections. This product can be used to better place wetland restoration projects to serve ecosystem- and community-wide health by ensuring ecosystem success and targeting areas that face increased climate change impacts.

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

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

A hydrologic and land cover‐based habitat model for use in bog turtle (Glyptemys muhlenbergii) conservation

Abstract The endangered bog turtle (Glyptemys muhlenbergii) exemplifies issues related to rare species conservation; presence surveys have low detection and variables used in habitat models can lack relevance to established biological relationships of the species to its environment. The species' use of groundwater saturated soils and stream networks as core habitat and dispersal corridors has been documented. Less is known about the landscape factors that promote the formation and persistence of wetlands used for core habitat. A GIS‐based resource selection function was developed to predict bog turtle habitat use. Trained on 1 ha plots centred on occupied sites and pseudo‐absent plots constrained to areas within 56.4 m to stream network centrelines, the model tested the capacity of a topographic wetness index (TWI), stream order and soil, wetland and land cover type to predict the presence of suitable habitat and turtle occupancy. Landscape variables were sampled at 10 m resolution, but variable selection and model performance were analysed at 100 m resolution to maintain a biologically relevant 1 ha habitat scale and accommodate the resolution of other variables. Suitable habitat and turtle presence were best predicted by intermediate to high values of TWI, land cover with low vegetation height and wetlands, second‐ and third‐order streams and the occurrence of mapped National Wetland Inventory polygons and hydric soils. Very high values of TWI were negatively associated with habitat suitability. The area under the curve of the best model was 0.833. Suitable habitat was found on 88% of 55 independent sites selected using the model and nine new occupied sites were confirmed. Model error is discussed with consideration of human‐altered drainage networks on the agricultural landscape.

Simulation of the Impacts of Sea-Level Rise on Coastal Ecosystems in Benin Using a Combined Approach of Machine Learning and the Sea Level Affecting Marshes Model

Sea-level rise in Benin coastal zones leads to risks of erosion and flooding, which have significant consequences on the socio-economic life of the local population. In this paper, erosion, flood risk, and greenhouse gas sequestration resulting from sea-level rise in the coastal zone of the Benin coast were assessed with the Sea Level Affecting Marshes Model (SLAMM) using ArcGIS Pro 3.1 tools. The input features used were the Digital Elevation Map (DEM), the National Wetland Inventory (NWI) categories, and the slope of each cell. National Wetland Inventory (NWI) categories were then created using Support Vector Machines (SVMs), a supervised machine learning technique. The research simulated the effects of a 1.468 m sea-level rise in the study area from 2021 to 2090, considering wetland types, marsh accretion, wave erosion, and surface elevation changes. The largest land cover increases were observed in Estuarine Open Water and Open Ocean, expanding by approximately 106.2 hectares across different sea-level rise scenarios (RCP 8.5_Upper Limit). These gains were counterbalanced by losses of approximately 106.2 hectares in Inland Open Water, Ocean Beaches, Mangroves, Regularly Flooded Marsh, Swamp, Undeveloped, and Developed Dryland. Notably, Estuarine Open Water (97.7 hectares) and Open Ocean (8.5 hectares) experienced the most significant expansion, indicating submergence and saltwater intrusion by 2090 due to sea-level rise. The largest reductions occurred in less tidally influenced categories like Inland Open Water (−81.4 hectares), Ocean Beach (−7.9 hectares), Swamp (−5.1 hectares), Regularly Flooded Marsh (−4.6 hectares), and Undeveloped Dryland (−2.9 hectares). As the sea-level rises by 1.468 m, these categories are expected to be notably diminished, with Estuarine Open Water and Open Ocean becoming dominant. Erosion and flooding in the coastal zone are projected to have severe adverse impacts, including a gradual decline in greenhouse gas sequestration capacity. The outputs of this research will aid coastal management organizations in evaluating the consequences of sea-level rise and identifying areas with high mitigation requirements.

The Wetland Intrinsic Potential tool: mapping wetland intrinsic potential through machine learning of multi-scale remote sensing proxies of wetland indicators

Abstract. Accurate, unbiased wetland inventories are critical to monitor and protect wetlands from future harm or land conversion. However, most wetland inventories are constructed through manual image interpretation or automated classification of multi-band imagery and are biased towards wetlands that are easy to directly detect in aerial and satellite imagery. Wetlands that are obscured by forest canopy, that occur ephemerally, and that have no visible standing water are, therefore, often missing from wetland maps. To aid in the detection of these cryptic wetlands, we developed the Wetland Intrinsic Potential (WIP) tool, based on a wetland-indicator framework commonly used on the ground to detect wetlands through the presence of hydrophytic vegetation, hydrology, and hydric soils. Our tool uses a random forest model with spatially explicit input variables that represent all three wetland indicators, including novel multi-scale topographic indicators that represent the processes that drive wetland formation, to derive a map of wetland probability. With the ability to include multi-scale topographic indicators that help identify cryptic wetlands, the WIP tool can identify areas conducive to wetland formation while providing a flexible approach that can be adapted to diverse landscapes. For a study area in the Hoh River watershed in western Washington, USA, classification of the output probability with a threshold of 0.5 provided an overall accuracy of 91.97 %. Compared to the National Wetlands Inventory, the classified WIP tool output identified over 2 times the wetland area and reduced errors of omission from 47.5 % to 14.1 % but increased errors of commission from 1.9 % to 10.5 %. The WIP tool is implemented as an ArcGIS toolbox using a combination of R and Python scripts.

Use of High-Resolution Land Cover Maps to Support the Maintenance of the NWI Geospatial Dataset: A Case Study in a Coastal New Orleans Region

The National Wetlands Inventory (NWI) is the most comprehensive wetland geospatial dataset in the United States. However, it can be time-consuming and costly to maintain. This study introduces automated algorithms and methods to support NWI maintenance. Through a wall-to-wall comparison between NWI and Coastal Change Analysis Program (C-CAP) datasets, a pixel-level difference product was generated at 1 m resolution. Building upon this, supplementary attributes describing wetland changes were incorporated into each NWI polygon. Additionally, new water polygons were extracted from C-CAP data, and regional statistics regarding wetland changes were computed for HUC12 watersheds. The 1 m difference product can indicate specific wetland change locations, such as wetland loss to impervious surfaces, the gain of open water bodies from uplands, and the conversion of drier vegetated wetlands to open water. The supplementary attributes can indicate the amount and percentage of wetland loss or water regime change for NWI polygons. Extracted new water polygons can serve as preliminary materials for generating NWI standard-compliant products, expediating NWI maintenance processes while reducing costs. Regional statistics of wetland change can help target watersheds with the most significant changes for maintenance, thereby reducing work areas. The approaches we present hold significant value in supporting NWI maintenance.

Peruvian Wetlands: National Survey, Diagnosis, and Further Steps toward Their Protection

Wetlands are crucial hydrological features that provide benefits, including ecosystem services and habitat conservation, protection from flooding associated with sea level rise and extreme events, water storage and treatment, recreation, and aesthetics, among others. Peru is one of the few countries in Latin America that has not developed a national wetland inventory. While this can in part be attributed to the limited availability of peer-reviewed publications in the English literature, a larger quantity of Peruvian wetland-related publications are available in Spanish, and are less accessible to the global population. In this paper, we developed an extensive review and synthesis of the Spanish literature, including university theses, peer-reviewed articles, and government reports. Our report focuses on evaluating the state of the art of Peruvian wetlands in terms of temporal evolution, geographical distribution, vested institutions, research topics (e.g., water quality, fauna, flora, microorganisms, etc.), and advances toward the conservation of wetlands. The analysis identified 274 wetland-related publications in Spanish (188 theses, 83 peer-reviewed, and 6 government reports) and a temporal increase in dissemination over the past two decades. The reports encompassed 161 distinct wetlands distributed nationwide; however, most of the investigations focused on only a few wetland bodies with a disproportionate concentration in just three administrative regions. This reveals that wetland-related research is unevenly distributed in Peru, and highlights a need to extend this knowledge to underrepresented systems and regions. Although Peru ranks third in South America for protected (Ramsar) wetland areas, case studies have revealed that wetlands in the country are vulnerable to human activities. Recent national legislation established in 2021 should help to address this challenge, as before this time, there was a more decentralized approach, whereby each administrative region held responsibility for the protection of their own wetlands. Collective findings indicate that research activity should be increased nationally in order to better understand the function and benefits of wetlands throughout Peru, in addition to the continued development and enforcement of regulations designed to protect these valuable ecosystems. Finally, since a national Peruvian wetland inventory is urgently needed, this analysis provides a baseline for this development of, as well as identifying gaps in, knowledge needed for appropriate national representation.

A satellite-based monitoring system for quantifying surface water and mesic vegetation dynamics in a semi-arid region

Semi-arid and arid systems cover one third of the earth’s land surface, and are becoming increasingly drier, but existing datasets do not capture all of the types of water resources that sustain these systems. In semi-arid environments, small surface water bodies and areas of mesic vegetation (wetlands, wet meadows, riparian zones) function as critical water resources. However, the most commonly-used maps of water resources are derived from the Landsat time series or single date aerial photographs, and are too coarse either spatially or temporally to effectively monitor water resource dynamics. In this study, we produced a Sentinel Fusion (SF) water resources product for a semi-arid mountainous region of the western United States, which includes monthly maps of both a) surface water and b) mesic vegetation at 10 m spatial resolution using freely available Earth observation data on an open access platform. We applied random forest classifiers to optical data from the Sentinel-2 time series, synthetic aperture radar (SAR) data from the Sentinel-1 time series, and topographic variables. We compared our SF product with three commonly used and publicly available datasets in the western U.S. We found that our surface water class contained fewer omission errors than a leading global surface water product (94 % producer’s accuracy (PA) vs 84 %) and comparable user’s accuracy (UA) (91 % vs 97 %) with commission errors occurring largely in mixed water pixels. Our mesic vegetation class had up to 43 % higher PAs compared to the National Wetlands Inventory (NWI) estimates and up to 78 % higher UAs over the Sage Grouse Initiative mesic resources maps during the most critical part of the water year. We found that while inclusion of SAR data from the C-band Sentinel-1 sensor consistently improved estimates of water resources in each of the last four months of the 2021 water year when compared to optical-only + topographic variables, only in September did those improvements lie outside of the 95 % confidence interval. With nine times finer spatial resolution and more frequent image collection, our SF maps characterize intra-annual dynamics of smaller water bodies (<30 m wide) and mesic vegetation integral to ecosystem functioning in semi-arid systems compared to leading Landsat-derived products. Further, our workflow is easily reproducible using freely available data on an open access platform, and can be adopted to help guide land use decisions related to water resources by farmers, ranchers, and conservationists in semi-arid environments.

Costa Rican wetlands vulnerability index

Costa Rica comprises approximately 6% of the world’s biodiversity. Among these lush ecosystems, wetlands are represented in mangrove forests near the sea, along river lowlands, sedimentary and volcanic mountains, and highland páramo landscapes. In 2018, the Ministry of Environment and Energy (MINAE), through the National System of Conservation Areas (SINAC), the United Nations Development Program (UNDP), and the Global Environment Facility (GEF) carried out the new National Wetlands Inventory (NWI) which identified 10,699 wetland polygons. This assessment collected key information such as location, characteristics of the wetland, land use in the vicinity, threats, and other generalities. Based on these valuable results, we propose a wetland Vulnerability Index composed of a Condition Index and a Hazard Index to determine the different vulnerability conditions of each wetland unit. Our findings provide a better comprehension of the status of wetlands in Costa Rica with an environmental geography perspective. Located in a climate change hotspot, Costa Rica’s conservation policies and actions should consider how to manage the most vulnerable wetlands at different scales. This methodology can improve and generate regional and national wetlands inventories as a basis for evidence-based decision making in other latitudes.

Fusion of Lidar and Aerial Imagery to Map Wetlands and Channels via Deep Convolutional Neural Network

Wetlands and channels provide significant ecological and societal services. Mapping their locations and types at high resolution remains a challenge for scientific communities and regulatory agencies. In the U.S.A., the National Wetland Inventory (NWI) provides the best nationwide wetland maps, but the NWI for some locations has not been updated for up to four decades and includes omission errors. To address these problems, we developed a deep learning framework for identifying wetlands and channels from lidar point clouds and 1 m aerial images. Both deep learning and terrain analysis were applied to classify wetlands and identify channels. The deep learning classifier was a convolutional neural network that utilized both image color information and lidar-derived canopy height. When tested on a 94 km2 Ohio watershed, the method achieved a classification accuracy of 85.6%. Compared with the NWI maps, the results included 76% more forested wetlands by area and 168% more channels by length. It was also found that only leaf-off images were useful for detecting forested wetlands and typical commission errors (i.e., false positives) were attributed to tree shadows. The study demonstrates the advantages of combining lidar structural and aerial spectral information over the use of each alone and exemplifies the utility of deep learning as an effective means to leverage open-source data for high resolution mapping of wetlands.

Soil carbon storage and its economic values of inland wetlands in Korea

Due to the importance of wetlands as a global carbon pool, explicit research on and management of the carbon storage of wetlands have drawn much attention. Within a single climatic zone, South Korea has various types of inland wetlands such as tidal salt marshes, freshwater lakes, rivers and peatlands, thereby providing an excellent platform for scientific investigation to identify patterns and key controlling factors of carbon budget in different inland wetlands. We estimated the amount of soil organic carbon (SOC) of 2444 inland wetlands in South Korea by cluster analysis and Monte-Carlo simulation using data from South Korea's National Wetland Inventory and their economic values by literature review. In addition, we identified key controlling factors that are closely related to SOC of 34 inland wetlands by correlation analysis and ANOVA test. We found that the total SOC stock of inland wetlands in South Korea is 16.3 ± 0.4 TgC (22.3 ± 0.6 kgC m−2), which is 5.3–5.5% of the total SOC stock of the country, and the annual carbon sequestration rate is 60.7 ± 9.8 gC m−2 yr−1. The economic value of the total SOC stock is 2.0 ± 0.1 billion USD (2.8 ± 0.1 USD m−2), and that of the annual sequestration rate is 5.5 ± 0.9 million USD yr−1 (7.5 ± 1.2 10−3 USD m−2 yr−1). The mean atmospheric temperature and soil characteristics including water content, total nitrogen and electrical conductivity showed positive correlations with SOC, suggesting the importance of hydrology and water chemistry as key determinants of SOC. In addition, inland wetlands with different dominant plant species showed significantly different SOC, probably because primary production and chemical composition of vegetation are strongly associated with carbon input into wetlands. However, annual precipitation and soil pH showed no correlation with SOC. Our results demonstrate that the carbon budget of inland wetlands in South Korea is considerable, and its economic value is closely related to characteristics of soil and vegetation.

Determining the distribution of wetlands across Eswatini

ABSTRACT Eswatini became a signatory to the Ramsar Convention on the 15th of June, 2013. To date, the country does not have a national wetland inventory. This study applies a newly developed wetland mapping technique (produced in South Africa) to Eswatini, to provide baseline information on the potential distribution of wetlands across the country. Results of this study show that when applying the mapping technique as it was applied in South Africa, watercourses (rivers, drainage lines, and riparian zones) are more frequently mapped than true wetlands. Given that Eswatini currently uses the broad Ramsar definition of a wetland, the potential wetland map produced in this study is well suited to identify wetlands falling under such definition. However, the technique does not suffice where a more specific definition for wetlands is used. To improve the initial potential wetland map, this study made use of data, obtained from 2 000 points distributed across the initial wetland map, by classifying areas with the highest potential of being wetlands into hydrogeomorphic units. Results indicate that the methods used to improve the initial map areable to distinguish watercourses with a higher potential of being a true wetland and identify certain hydrogeomorphic units. This method can therefore be used to provide baseline data of potential wetland distribution for countries that do not possess the means, or attribute data to produce a comprehensive wetland map.

Using multiple taxa and wetland classification schemes for enhanced detection of biological response signatures to human impairment

Wetland indices of biological integrity (IBIs) are a common component in monitoring the wetland water resources as required by the United States’ Clean Water Act (CWA). The effectiveness of an IBI to monitor disturbance is dependent on the metrics being consistently responsive to measures of human disturbance within a described classification category. We present IBIs designed for two types of commonly used wetland classification systems – the hydrogeomorphic (HGM) and the National Wetlands Inventory (NWI). The metrics making up the IBIs were derived from anuran, avian, macroinvertebrate, and vegetation communities; each representing increasing levels of resources associated with gathering the necessary data. Knowing which communities’ data best corresponds to impairment can maximize limited wetland monitoring resources, especially if the response differs based on the wetland vegetation type NWI or HGM position on the landscape. By combining these two classification schemes together, to better define a wetland’s form and context on the landscape, more of the variability in community metrics are explained by human impairment. Moreover, when multiple taxa are used within a single wetland classification scheme, the response of the multi-taxa community IBI to the human disturbance gradient is often more sensitive than one-taxa group alone. This approach, a combination of taxa, in hybrid 2-system classification schemes, creates additional utility in measuring the effectiveness of wetland assessments and, or restoration success.

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.

Wetland classification and inventory in fluvial basins: the case of the Yaguarí creek, North-Eastern Argentina

In preparation for the National Wetland Inventory, the Secretary for the Environment and Sustainable Development of Argentina defined pilot cases for their characterization and suggested methodologies for their classification. The results obtained on the pilot site of A° Yaguarí fluvial basin, Corrientes, are synthesized and presented in this paper. It is considered a pilot site due to the increasing anthropic activities: rice cultivation/irrigation and cattle grazing over implanted pastures that impact the natural wetlands. The identification of the types of wetlands was accomplished through the analysis of background information, visual interpretation of satellite images, identification of wet hydrologic periods, and wetland identification by direct field observation and reconnaissance. All these aspects were integrated to define the wetland types using a simple,hierarchical and open classification system, to establish the areal extent of the wetlands, and characterize their distinctive features at field scale. The elements of the selected hierarchical structure are: Ambient–Region-System-Unit Element. The ambient is continental, and the region belongs to Chaco-Mesopotamic Fluvial Corridor. Anthropogenic systems (units: ponds, rice fields, irrigation water reservoirs), and fluvial systems (units: floodplains, minor tributaries headwaters) are identified. The elements that can be found are: riparian forests, marshes behind river banks, ditches, paleo-channels, springs and riverbeds. The developed methodology can be replicated in similar basins using these tools for the inventory, evaluation and management of Argentinean wetlands.

Characterizing Wetland Inundation and Vegetation Dynamics in the Arctic Coastal Plain Using Recent Satellite Data and Field Photos

Arctic wetlands play a critical role in the global carbon cycle and are experiencing disproportionate impacts from climate change. Even though Alaska hosts 65% of U.S. wetlands, less than half of the wetlands in Alaska have been mapped by the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) or other high-resolution wetlands protocols. The availability of time series satellite data and the development of machine learning algorithms have enabled the characterization of Arctic wetland inundation dynamics and vegetation types with limited ground data input. In this study, we built a semi-automatic process to generate sub-pixel water fraction (SWF) maps across the Coastal Plain of the Arctic National Wildlife Refuge (ANWR) in Alaska using random forest regression and 139 Sentinel-2 images taken in ice-free seasons from 2016 to 2019. With this, we characterized the seasonal dynamics of wetland inundation and explored their potential usage in determining NWI water regimes. The highest levels of surface water expression were detected in June, resulting from seasonal active layer thaw and snowmelt. Inundation was most variable in riverbeds, lake and pond margins, and depressional wetlands, where water levels fluctuate substantially between dry and wet seasons. NWI water regimes that indicate frequent inundation, such as permanently flooded wetlands, had high SWF values (SWF ≥ 90%), while those with infrequent inundation, such as temporarily flooded wetlands, had low SWF values (SWF &lt; 10%). Vegetation types were also classified through the synergistic use of a vegetation index, water regimes, synthetic-aperture radar (SAR) data, topographic data, and a random forest classifier. The random forest classification algorithms demonstrated good performance in classifying Arctic wetland vegetation types, with an overall accuracy of 0.87. Compared with NWI data produced in the 1980s, scrub-shrub wetlands appear to have increased from 91 to 258 km2 over the last three decades, which is the largest percentage change (182%) among all vegetation types. However, additional field data are needed to confirm this shift in vegetation type. This study demonstrates the potential of using time series satellite data and machine learning algorithms in characterizing inundation dynamics and vegetation types of Arctic wetlands. This approach could aid in the creation and maintenance of wetland inventories, including the NWI, in Arctic regions and enable an improved understanding of long-term wetland dynamics.

LiDAR-aided hydrogeologic modeling and object-based wetland mapping approach for Pennsylvania

Wetlands for the entire state of Pennsylvania (119 , 282 km2) were mapped using object-based image analysis (OBIA). The OBIA combined vector output from logistic statistical models informed by hydrogeologic variables (e.g., hydrology facets, soils, and climate) with LiDAR-derived terrain variables, high-resolution aerial orthoimagery, and land-cover data through a data-fusion approach. Wetland presence data and hydrogeologic features in a geographic information system were used to develop moderate-resolution (10 m2) maximum-entropy models (Maxent) both for wetlands dominated by low-lying emergent vegetation and woody vegetation, respectively. Terrain variables describing slope, landscape wetness, surface elevation, climate, precipitation, and poorly drained soils were most strongly associated with occurrences of wetlands in this modeling procedure. Maxent derived models accurately predicted validation wetlands: Area under curve of 0.91 for woody wetlands and 0.93 for emergent wetlands. High-resolution (1 m2) mapping was then performed in the OBIA by creating objects based on a compound topographic index (CTI) and leaf-off true-color orthoimagery and then classifying them using a combination of the CTI, imagery, and 10-m2 statistical models. An accuracy assessment found that the OBIA correctly classified 81.5% of validation locations compared to only 59.3% for National Wetlands Inventory (NWI), although commission rates were higher for OBIA (15.6%) versus NWI (3.4%). The user’s accuracy for OBIA was 82.8% compared to 78.1% for NWI. Our work suggests advances in remote sensing techniques and a data-fusion approach may facilitate improved wetland mapping accuracy while reducing processing time relative to traditional approaches.

Coastal Wetlands Exposure to Storm Surge and Waves in the Albemarle-Pamlico Estuarine System during Extreme Events

Coastal wetlands provide a series of ecosystem services, including flood risk reduction. However, the flood risk reduction from such a complex ecosystem is dependent on incoming extreme hurricane-driven hydrodynamic and wave conditions. This study develops a numerical modeling-based approach for investigating coastal wetlands exposure to storm surge and waves during hurricanes by combining maximum water depth (MWD) and significant wave height (MHs) model outputs with the National Wetland Inventory for the Albemarle-Pamlico Estuarine System. Results show that various hurricanes lead to similar bimodal and bidirectional spatiotemporal flood patterns as a function of the lagoon’s geometry and storm track, with most overland hydrodynamic extremes impacting western Pamlico Sound and the bayside-Outer Banks. Clear positive dependency between MWD and MHS were observed over most wetland classes, with significantly higher magnitudes over estuarine emergent vegetation. In contrast to MWD, sharp MHS attenuation was found as the water propagates inland, leading to high MWD but lower MHS eventually reaching palustrine woody vegetation hundreds of meters away from the coastline. Improved understanding of how storm surge dynamics move across the bays and through the different wetland types, demonstrates the importance of considering the bay-side flood impacts over the coastal vegetation. The spatially distributed MWD and MHS estimates, along with the temporal patterns identified through the use of the TMax, allow coastal managers and engineers to better understand where, when, and by how much, the estuarine APES wetlands are more likely to be exposed to high water depths and waves.