First steps in developing a state wetland inventory

Background

The U.S. Fish and Wildlife Service (USFWS) has a National Wetland Inventory (NWI) and an online Wetlands Mapper that provide a national geospatial dataset of wetlands that were developed from aerial imagery. In some regions of the U.S. NWI data has been found to have considerable omissions and errors of commission as compared with wetland field verification ( Matthews et al. 2016 ).

NWI spatial data is commonly utilized by researchers as a data layer for wetlands in the United States, including Illinois. This can be problematic when analyzing habitat suitability for threatened and endangered species that require wetland habitat for part or all of their lifecycle ( Blake-Bradshaw 2020 ;  Glisson et al. 2017 ;  Munger et al. 1998 ), determination of nitrogen removal models ( Cheng et al. 2020 ;  Hansen et al. 2018 ), predicting trends of wetland gains and losses (Wilen and Bates 1995), estimating wetland carbon storage (Hinson et al. 2017), mapping wetland plant communities (Correll et al. 2018) and wetland modeling for various uses (Mainali et al. 2023; Hondula et al. 2021;  Alizad et al. 2020 ; Horvath et al. 2017). Thus there is a need for updated wetland spatial data.

Some wetlands in Illinois are already inventoried by different federal, state or local entities although no central spatial database of these wetlands currently exist. Click  wetlands  to explore the locations and aerial views of some of these wetlands in Scene Viewer.

Illinois has not developed a state wetland inventory but four counties in NE Illinois have developed county wetland inventories ( DuPage , Kane,  McHenry , and Lake). For instance, Lake County, Illinois, has a County Wetland Inventory (LCWI) and Advanced Identification (ADID) of wetlands. The LCWI initially utilized Soil Conservation Service 1989 wetland inventory data as well as overlays of soil maps, NWI maps and surface water features, and Agricultural Stabilization and Conservation Service 1987 aerial color slides, and infrared photography to develop the LCWI maps ( Dreher et al. (1992) . Dreher et al. (1992) reported that the Lake County Wetland Inventory (LCWI) wetland boundaries were field verified to be more accurate than the NWI in Lake County. The LCWI and ADID data have subsequently been updated and used in local planning, including wetland restoration (IU-ERI, 2022).

The Illinois Natural History Survey (INHS) Wetland Science Program has been field surveying wetlands in Illinois since 1989 utilizing the three-factor definition of having hydric vegetation, hydric soils and specific indicators of hydrology as set forth by the Army Corps of Engineers Wetland Delineation Manual (Environmental Laboratory 1987) and pertinent regional supplements such as the Regional Supplement to the Army Corps of Engineers (ACOE) Wetland Delineation Manual: Midwest Region (USACE 2010). Data has been collected in each of the 102 counties in Illinois. Project corridors evaluated are <1 acre to hundreds of acres.

Methods

I chose to start with analyzing the Des Plaines watershed since INHS has about 1800 wetland points in this watershed and two counties - DuPage and Lake - have existing County Wetland Inventories that partially cover this watershed. Most of the watershed does not have CWI and the methods will describe one angle to obtain information about the existing INHS and CWI data in order create a spatial model to predict where wetlands are in the landscape and eventually create a wetland inventory for this watershed. Note: This map does have one of the results - Topographic Wetness Index, MFD Algorithm as well - a treatment that is explained in this section.

Field verified data collected by the INHS as well as the respective County Wetland Inventory data can be useful in discerning validity of GIS analysis models such as the Topographic Wetness Index (TWI) (aka Compound Topographic Index CTI) that will be utilized in this study. TWI is one way to determine where water may be accumulating in a landscape and can be useful for predicting where wetlands might occur. Factors included in this calculation are flow direction and flow accumulation determined with hydrology spatial analysis tools as well as the DEM raster. The formula for TWI = ln (a/tan b) where is the natural logarithm, ‘a’ is area upslope contributing to flow (aka flow accumulation) and b is the localized slope in radians (determined from the DEM raster).

Three different flow direction algorithms (DINF, MFD and D8) were investigated and for one algorithm there was one additional spatial analysis where one of the conditions (D8) - eliminating the first step of fill - was altered to compare fill versus no fill in the treatment. The 'no fill' treatment was done out of curiosity to see if perhaps some smaller wetlands would get 'filled in' as compared to the other that did have 'fill' conditioning that is the standard protocol. The D8 algorithm specifies flow direction from each cell flows to its steepest downslope neighbor that can be in one of eight directions. DINF algorithm specifies flow direction is the steepest direction on eight triangular facets. MFD algorithm specifies flow direction is fractioned from a cell to all downslope neighbors, partitioning flow according to the local slope.

Results

Below are TWI results from analyses in the Des Plaines watershed region.

The TWI with the MFD Algorithm provided the best results in terms of closeness to a normal distribution of data. Utilizing the 'Extract Multi Values to points' tool developed a statistical relationship between these wetland points and TWI values in the Des Plaines watershed. Skewness=0.046 Kurtosis=3.76

Further investigations

This data, along with data that still needs to be analyzed in relation to the Lake and DuPage County Wetland Inventories, can then be utilized to eliminate areas in the watershed that would unlikely have wetlands. For instance if we solely based selection on the graph above we might eliminate areas in the Des Plaines watershed having TWI values of -5.2 through 3.1. Then we would utilize other spatial analyses to discern high potential of having wetlands in the landscape stepwise toward the goal of a wetland inventory for this watershed as a prototype for a state-wide wetland inventory. Utilization of other tools such as remote sensing, hydrologic modeling of depth to water index, LiDAR ( Halabisky  et al. 2020) and further field verification efforts will be needed to fully realize useful spatial models for this endeavor.