RT Journal Article
T1 Mapping groundwater-dependent ecosystems by means of multi-layer supervised classification
A1 Martínez Santos, Pedro
A1 Díaz Alcaide, Silvia
A1 Gómez-Escalonilla Canales, Víctor
A1 Hera Portillo, África de la
AB 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 groundwaterdependent ecosystems, thus contributing to integrate wetland protection in land use planning.
PB Elsevier
SN 0022-1694
YR 2021
FD 2021-12
LK https://hdl.handle.net/20.500.14352/4671
UL https://hdl.handle.net/20.500.14352/4671
LA eng
NO Ministerio de Ciencia, Innovación y Universidades (España)
NO Ministerio de Educación, Formación Profesional y Deportes (España)
DS Docta Complutense
RD 12 abr 2025