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Mapping groundwater-dependent ecosystems by means of multi-layer supervised classification.
- Source :
-
Journal of Hydrology . Dec2021:Part A, Vol. 603, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
Abstract
- • Groundwater-dependent wetlands rank among the world's most endangered ecosystems. • A novel machine learning approach to map groundwater-dependent ecosystems is presented. • Multi-parametric supervised classification rendered test scores over 96%. • Support vector machines, tree-based classifiers and logistic regression proved suitable. • Results could be used to inform wetland management and land use policy. 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00221694
- Volume :
- 603
- Database :
- Academic Search Index
- Journal :
- Journal of Hydrology
- Publication Type :
- Academic Journal
- Accession number :
- 153526600
- Full Text :
- https://doi.org/10.1016/j.jhydrol.2021.126873