Mapping Wetland Probability Across Massachusetts with Machine Learning and Multiscale Predictors

Abstract. Wetlands perform a vital array of ecosystem functions, but up to 50 % of global wetlands have been lost and those that remain are under ongoing threat from development pressures. Accurate and comprehensive maps are critical for the management and protection of wetland resources. Conventional methods for wetland mapping are time consuming and resource intensive, and the common mapping methods that rely on the inspection of aerial imagery often miss forested and other wetland types that do not have a distinctive visual signature, i.e. cryptic wetlands. The use of machine learning and spatial data to map wetlands is a growing field that promises a fast and efficient complement to conventional methods and improved detection of forested and other cryptic wetlands. In this paper we demonstrate the use of a random forest model to generate a large-scale, state-wide map of wetland probabilities in the Commonwealth of Massachusetts, using widely available open source software and publicly accessible data. Through this model we also test the efficacy of multi-scale predictors, including not only terrain derivatives used in previous research but also multi-scale implementations of soil, vegetation, and spectral data. The random forest was trained on the official Massachusetts wetland inventory, and achieved an overall accuracy rate of 92 % relative to that dataset. The model showed particular promise in detecting cryptic wetlands by identifying an additional 40 % of probable wetland area statewide, and an additional 46 % of forested wetland specifically. The use of diverse multi-scale predictors was supported by model performance, variable importance measures, and the feature selection process. This strategy for improving detection of cryptic wetlands and creating better estimates of wetland extent, using non-proprietary software and data, will be a vital adjunct to conventional methods for wetland mapping and monitoring.