Mapping groundwater-dependent ecosystems by means of multi-layer supervised classification

Identifying groundwater-dependent ecosystems is the first step towards their protection. This paper presents a machine learning approach that maps groundwater-dependent ecosystems by extrapolating from the characteristics of a small sample of known wetland and non-wetland areas to find other areas with similar geological, hydrological and biotic markers. Explanatory variables for wetland occurrence include topographic elevation, lithology, vegetation vigor, and slope-related variables, among others. Supervised classification algorithms are trained based on the ground truth sample, and their outcomes are checked against an official inventory of groundwater-dependent ecosystems for calibration. This method is illustrated through its application to a UNESCO Biosphere Reserve in central Spain. Support vector machines, tree-based classifiers, logistic regression and k-neighbors classification predicted the presence of groundwater-dependent ecosystems adequately (>96% test and AUC scores). The ensemble mean of the best five classifiers rendered a 90% success rate when computed per surface area. This method can optimize fieldwork during the characterization stage of groundwater-dependent ecosystems, thus contributing to integrate wetland protection in land use planning.

Automated summary

This paper introduces a machine learning approach to map groundwater-dependent ecosystems by extrapolating from known wetland and non-wetland areas characteristics. The method, illustrated in a UNESCO Biosphere Reserve in central Spain, uses supervised classification algorithms trained on ground truth samples and checked against an official inventory of groundwater-dependent ecosystems for calibration. The results show a high success rate in predicting the presence of groundwater-dependent ecosystems, demonstrating the potential of this method in optimizing fieldwork for wetland protection in land use planning.