{ "currentVersion": 10.91, "id": 19, "name": "Wetland Probability", "type": "Feature Layer", "description": "Herrera developed a GIS-based model that overlays spatial datasets known to correlate with wetland presence to calculate the relative likelihood of wetlands on the ground. Six metrics were identified to be included in this analysis:1. Topographic Wetness Index (TWI): TWI is a measure of wetness based on LiDAR data. It is function of slope and contributing areas and assumes that concave, low gradient areas gather water while steep, convex areas shed water. TWI is highly correlated with soil moisture content and is frequently used as a metric in wetland identification analyses.Herrera calculated TWI for the City using 2016 LiDAR data with a 3-foot cell size. Values range from 0.18 to 27.4. Herrera considered anything greater than 6 as an area of high potential wetness.2. Topographic Position Index (TPI): TWI is a measure of the elevation change between a given grid cell and the average elevation of its immediate neighbors. Areas of topographic depression have been shown to be correlated with wetland presence because they are conducive to water pooling. Herrera calculated TPI using 2016 LiDAR data with a 3-foot cell size.3. Hydric Soil Status: Hydric soils are formed under conditions of saturation, flooding or ponding during the growing season such that anaerobic conditions develop. Hydric soil polygons were obtained from the Natural Resources Conservation Service (NRCS) and are classified as Not Hydric, Partially Hydric, and All Hydric.4. Landsat Dynamic Surface Water Extent (DSWE):The U.S. Geological Survey collects Landsat satellite data on a reoccurring basis that collects data on the existence and condition of surface water. This data product is a six-band raster dataset called the Dynamic Surface Water Extent (DSWE) with 30-meter pixels with each pixel being classified as either wet or not. Herrera obtained DSWE datasets collected in the rainy season (March) for 5 dates between 2010 and 2018 and calculated the number of times each pixel was wet. If a pixel is shown as wet multiple times, it is reasonable to assume a higher level of confidence of wetness at that location.5. Mapped Springs:The City of Mercer Island has mapped the locations of springs. Shallow groundwater has been observed at these locations which is a strong predictor of wetland presence. Herrera applied a 15-foot buffer to each mapped spring point or polyline for use in this analysis.After each metric was calculated, the following scoring matrix was applied at a 3-foot grid cell to dataset. The datasets were then overlaid together to obtain a cumulative wetland model score for each grid cell:GIS-Based Wetland Model Metrics and ScoringConveyance TypeMetricAttributeScoringTopographic Wetness IndexArea of high potential wetness (TWI > 6) 3Topographic Position IndexArea mapped as a depression3Hydric SoilsNot HydricPartially HydricAll Hydric1 2 3SpringArea located within 15 feet of a mapped spring2Landsat DSWEPixel not mapped as wet on any datePixel mapped as wet on 1-2 datesPixel mapped as wet on 3-4 datesPixel mapped as wet on all 5 dates0123", "geometryType": "esriGeometryPolygon", "sourceSpatialReference": { "wkid": 2926, "latestWkid": 2926 }, "copyrightText": "Mercer Island, Herrera Environmental Consultants", "parentLayer": { "id": 16, "name": "Wetland" }, "subLayers": [], "minScale": 6000, "maxScale": 0, "drawingInfo": { "renderer": { "type": "uniqueValue", "field1": "CLASS", "field2": null, "field3": null, "defaultSymbol": null, "defaultLabel": null, 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