Description: The Kansas Applied Remote Sensing (KARS) Program has continued the development of the Potential Wetland Areas (PWA) database and mapped PWA in more than 300 12-digit Hydrologic Unit Codes (HUCs) in Kansas using the Topographic Wetland Identification Process (TWIP) developed by the Kansas Water Office (KWO). The TWIP ArcGIS toolbox runs on individual 12-digit HUCs. LiDAR-derived digital elevation models (DEMs) (2-m) were the primary data source to map all depressions or sinks in the landscape and to derive terrain analysis data layers (i.e., slope, flow direction and flow accumulation). Using flow accumulation and slope, the Topographic Wetness Index (TWI) was calculated with depressions having a relatively high flow accumulation and low slope to have an increased likelihood of being actual wetlands. A map of potential wetland areas was derived by extracting depressions or sinks with a high TWI. Using Depth To Flood (DTF) values generated by the Floodplain (FLDPLN) model was used to map “mudflat wetlands” that are present in stream reaches flowing into reservoirs and large lakes. Additional landscape context information, in the form of geodatabase attributes, were generated using a set of ancillary GIS data layers. The ancillary GIS data layers include a 10-meter buffered road network, a 1-meter buffered drainage network and water body data set (extracted from the National Hydrography Dataset (NHD), land cover 2005 Kansas Land Cover Patterns database and wetness maps derived from Landsat TM imagery. Each contextual layer was intersected with the PWA layer and attributes indicate if an individual PWA polygon intersects with the contextual features. For PWA identified as wet from the Landsat TM wetness map, the Probability attribute will change from “potential” to “likely”. An accuracy assessment was performed using existing data and roadside surveys collected in 2013. Funding for this project was provided by the Kansas GIS Policy Board, Data Development Contract 2013:2135.
Copyright Text: Dana Peterson, Mike Houts, Ryan Callihan, Jude Kastens, Maureen Connolly, Britini Jacobs