The Environmental Protection Agency (EPA) defines wetlands as “areas in which water covers the soil, or is present at or near the surface of the soil all year or for varying periods of time during a year, including during the growing season” [1
], yet, “wetlands” is a broad term and might have varied characteristics in terms of hydrology, vegetation, and soils. To standardize the classification of wetlands, a classification system was developed by the United States Fish and Wildlife Service (FWS) and adopted by several agencies within the United States government. This system broadly categorizes wetlands into marine, estuarine, riverine, lacustrine, and palustrine wetlands [2
Wetlands are the most productive ecosystems in the world, providing ecosystem services that benefit humans, such as provision of food, water regulation, aquatic habitat, recreation, and tourism [3
]. Wetland ecosystem services provide an estimated $
4.9 trillion worth of services annually across the globe [11
]. Wetlands also play a vital role in climate change mitigation through carbon sequestration [12
]. Despite the value and importance of wetlands, large-scale wetland loss and degradation has been affecting the overall health of wetlands for hundreds of years, leading to the loss of ecosystem services. It has been estimated that 50% of wetlands have been lost globally since 1900; however, a more recent review estimated a loss of between 64% and 71%, with greater amounts of loss occurring in inland wetlands [13
]. Common factors that lead to degradation of wetlands include eutrophication from urban and agricultural lands, invasive plants, hydrologic alterations, salinization, and filling, while wetland loss is typically a result of conversion of land use and drainage for agriculture [15
Such loss in ecosystem services is a particularly critical environmental issue in a coastal state such as Delaware. Around 25% of Delaware is covered by different wetland types, such as salt marshes, bays, and freshwater wetlands [16
]. Important ecosystem services are provided by these wetlands, such as improving water quality, reducing flood occurrences, providing plant and animal habitat, and providing educational and recreational opportunities. Wetlands in Delaware face many impacts from natural events such as subsidence and storms, but also experience impacts from human activities such as nutrient loading, stream channelization, drainage for agriculture and flood control, urban development, dredging of channels, timber harvest, ground-water extraction, and pollution [17
]. These events or activities that negatively affect wetlands are considered stressors.
The objective of this paper is to focus on human-induced stressors, such as development of land, destruction of vegetation, and alterations in hydrology, as these factors are commonly used in the evaluation of wetlands and can be observed at a landscape scale. The Delaware Rapid Assessment (DERAP) is an example of a wetland-assessment method, assessing wetland conditions based on the total number of stressors within a site, primarily based on field observations rather than remote sensing [18
]. DERAP breaks down these stressors into three categories: habitat/plant community, hydrology, and landscape buffer [19
]. A similar wetland assessment method is the EPA Level 2 assessment, which is a rapid assessment method based on hydrology, soils/substrate, vegetation, and landscape setting [20
]. The EPA also has a Level 1 assessment method that uses landscape-level remote-sensing data, rather than field data, to assess wetland conditions more broadly.
Currently, even rapid assessments, such as DERAP and EPA Level 2, require time, expertise, and equipment, among other resources. The development of a wetland assessment index similar to DERAP and EPA Level 2 that is based on methods more similar to the EPA Level 1 assessment, such as the use of remote sensing and ancillary geographic data, would have the potential to allow for the automation of wetland assessments. Through the use of remotely sensed data, the drawbacks of rapid assessment methods are minimized, as multiple wetland areas can be assessed simultaneously. Additionally, the use of remotely sensed data allows for large areas of wetlands, including those on private lands, to be assessed, which would otherwise not be possible. However, remote-sensing methods have their own disadvantages, such as the lack of information due to limited spectral and spatial resolutions.
Prior studies on wetland remote sensing involve the classification of wetland types with the goal of producing highly accurate wetland maps using hyperspectral and high spatial resolution imagery, including aerial photography [21
]. Rather than identifying wetland types or measuring wetland loss with aerial photography and hyperspectral data, this work uses freely available data, such as Landsat, Sentinel imagery, and United States Geological Survey (USGS) stream data, in an attempt to create a method of assessing wetland health that is robust and easily reproducible. While a wetlands dataset, the National Wetlands Inventory (NWI), already exists, this dataset has known limitations, such as low temporal resolution (updated every 10 years) and underestimation of wetlands [26
]. Other land-cover maps for Delaware contain more general wetland classes, such as the 2007 classification developed by Aerial Information Systems [27
]. However, an up-to-date dataset classifying wetlands and other land covers does not exist.
While there are many stressors that can impact a wetland, we developed an index that quantifies the most prevalent stressors that have the biggest impact on Delaware wetland conditions based on wetland reports and evaluation methods specific to the state [18
]. The metrics used in our index include land cover adjacent to wetlands, change in natural vegetation, and channelization of streams.
We evaluated two machine-learning algorithms, Random Forest (RF) and Support Vector Machine (SVM) to answer the following questions:
Which classification method, between RF and SVM, provides the highest mapping accuracy for Delaware wetlands?
How does wetland stress vary across Delaware, both spatially and by wetland type?
The RAWSI data set, along with the independent stress indicators developed in this study, highlight many land-cover change trajectories in the state of Delaware, often at a regional scale. For example, the trend analysis for natural vegetation does not show statistically significant vegetation loss in most regions. However, landscape managers often need to focus on smaller regions (rather than the entire state) and must take into account notable changes in the region of interest, e.g., as seen in the Prime Hook National Wildlife Refuge (Figure 4
b). This region lost considerable vegetation in 2006 due to a storm-induced flooding of two impoundments located on the refuge. Additionally, flooding from Hurricane Sandy in 2012 resulted in considerable habitat loss [90
]. Such annual analyses are helpful in identifying the effects of recurring events.
In addition to extreme climatic events, ongoing hydrologic changes resulted in complete conversion in land-cover classes. Natural-vegetation loss was the biggest factor contributing to stress in coastal wetlands when compared to inland wetlands (Figure 4
b). Approximately 11.24% of all vegetation loss since 2004 occurred within non-forested coastal wetlands, most likely due to the dynamic nature of coastal areas, resulting from tides, sea level changes, and storms. These hydrologic changes in coastal areas also led to the conversion of wetlands to open water (18.67%). These changes are represented in RAWSI through the NDVI loss analysis and represent wetland stress that occurred in the recent past. Considering annual land-cover maps, following methods presented here, and quantifying the change trajectory of each land-cover class can thus help the landscape managers in their direct planning efforts.
Delaware wetlands, and natural resources overall, are under considerable stress from anthropogenic activities, as well. Most inland vegetation loss in the state can be attributed to the conversion of forested areas to agricultural land (37.87%) or urban development (23%), between 2004 and 2018, predominately in Sussex County. However, the wetland stress is spatially distributed throughout the state, as indicated by the positive Global Moran’s I statistic (Figure 5
). There is a striking difference between the spatial clustering—hot spots are mostly inlands, while cold spots are mostly coastal. This spatial differentiation is likely due to the laws protecting coastal wetlands in Delaware, as we see the land-cover stress being the largest factor impacting inland wetlands when compared to coastal wetlands (Figure 4
a). Tidal wetlands are protected in Delaware, being subjected to either 50- or 100-foot landward buffer protections, thus restricting any development [91
]. However, inland (nontidal) wetlands are only protected when they are larger than 400 acres, allowing for the conversion of these wetlands and their buffer areas. Higher amounts of stress were identified in Delaware’s lowermost county (Sussex), likely due to the dominance of agricultural land (Figure 5
). Between counties, hydrology stress had the biggest impact. Sussex County also had a much higher level of hydrology stress compared to New Castle or Kent County, due to higher agricultural water use (Figure 4
c). The abundance of agriculture may lead to increased stress through the channelization of streams (Figure 3
b) to divert water for agricultural use.
The current literature shows that a combination of SAR and optical data results in improved accuracy of wetland classification [34
]. Our study corroborates existing research for the forested wetland class when used with the RF classifier (Table 3
). While per-class accuracy changed for the two sensor combinations used with the RF classifier, overall accuracies were comparable (Table 3
). This comparison underscores the need to look beyond the overall accuracy and examine per-class accuracy for the classes of interest. While studies comparing RF and SVM show higher accuracy with SVM [65
], our results consistently showed higher accuracy with RF. When choosing which classification method to use, a comparison of both RF and SVM on the training data may be beneficial. The high accuracy of our final wetland maps might be attributed to the simplified classification scheme of forested and non-forested wetlands. Unlike a few other studies [33
], our classification scheme does not provide details on wetland classes, such as bog, fen, swamp, and marsh. However, the objective of our study was to accurately identify wetlands and existing stress factors, rather than identify more-detailed wetland classes.
Due to the inherent property of remote sensing as a tool, many aspects of DERAP/EPA rapid assessment procedures (such as detailed soil or plant characteristics) were not viable in this study. In the future, RAWSI can be further improved with advancements made in freely accessible high-resolution remotely sensed data, by incorporating those variables that were not considered in our study. We recommend the procedure presented in this study as a first step of a more detailed ground study. However, adopting RAWSI on an annual basis will considerably lower the cost of a timely update of wetland inventory, especially in rapidly transforming landscapes, due to ongoing natural and anthropogenic changes.
This study shows that a combination of optical and radar data when used with a Random Forest classifier might result in accurate land-cover maps, including wetland classes. This was shown with the combination of the Random Forest classifier with the radar data, which had a higher overall accuracy than the Support Vector Machine method or when radar data was not used. In this study we were able to assess metrics commonly attributed to wetland stress, including land cover, vegetation change, and stream channelization, through the use of remotely sensed and geographic data. All of these metrics were then combined into one Rapidly Assessed Wetland Stress Index (RAWSI), with each wetland area being assigned a stress value. Using a Getis-Ords Gi* statistic, we identified several hot spots of the RAWSI occurring within inland wetlands, especially in Kent and Sussex County in Delaware. Several cold spots of the RAWSI were also identified within coastal areas mostly contained in Kent County. The identification of hot and cold spots on a wetland-specific scale can be applied to understand spatial patterns of wetland stress, possibly giving insight into how current wetland regulations can be improved.
The informative new index RAWSI identified higher levels of stress among forested wetlands than non-forested wetlands and open water. The main factor contributing to higher stress in forested wetlands is land covers, such as urban and agriculture, within the wetland buffer. Even though non-forested wetlands experienced the lowest overall stress, the vegetation loss occurring within these wetlands provided higher amounts of stress than in any other wetland type. Higher stress is also observed in counties dominated by agriculture rather than urban areas. The channelization of streams was the biggest factor leading to higher stress in these agricultural regions. In the future, we recommend using any advancements in remote sensing to capture as many factors impacting a wetland as possible.