Effects of LiDAR DEM Smoothing and Conditioning Techniques on a Topography‐Based Wetland Identification Model

Abstract

AbstractAccurate and widely available wetland inventories are needed for wetland conservation and environmental planning. We propose an open source, automated wetland identification model that relies primarily on light detection and ranging (LiDAR) digital elevation models (DEMs). LiDAR DEMs are increasingly available and provide the resolution needed to map detailed topographic metrics and areas of likely soil saturation, but the choice of smoothing and conditioning techniques can significantly impact the accuracy of hydrologic parameter extraction. So far, the effect of these preprocessing steps on wetland delineation has not been thoroughly analyzed. We test the response of a Random Forest wetland classifier, using topographic wetness index, curvature, and cartographic depth‐to‐water index as input variables, to combinations of smoothing techniques (none, mean, median, Gaussian, and Perona‐Malik) and conditioning techniques (Fill, Impact Reduction Approach, and A* least‐cost path analysis) for four sites in Virginia, USA. The Random Forest model was configured to account for imbalanced data sets, and manually surveyed wetlands were used for verification. Applying Perona‐Malik smoothing and A* conditioning yielded the highest accuracy across all sites and considerably reduced model runtime. We found that models could be further improved by individualizing the smoothing method and scale to each input variable. Using only topographic information, the wetland identification model could accurately detect wetlands in all sites (81‐91% recall). Model overprediction varied across sites, represented by precision scores ranging from 22 to 69%. In its current form, the wetland model shows strong potential to support wetland field surveying by identifying likely wetland areas.