Groundwater Dependent Ecosystem (GDE) protection is increasingly being recognized as essential for the sustainable management and allocation of water resources. GDE services are crucial for human well-being and for a variety of flora and fauna. However, the conservation of GDEs is only possible if knowledge about their location and extent is available. Several studies have focused on the identification of GDEs at specific locations using ground-based measurements. However, recent progress in remote sensing technologies and their integration with Geographic Information Systems (GIS) has provided alternative ways to map GDEs at a much larger spatial extent. This paper presents a review of the geospatial methods that have been used to map and delineate GDEs at spatial different extents. Additionally, a summary of the satellite sensors useful for identification of GDEs and the integration of remote sensing data with ground-based measurements in the process of mapping GDEs is presented.

Groundwater Dependent Ecosystems (GDEs) are increasingly threatened by humans' rising demand for water resources. Consequently, it is imperative to identify the location of GDEs to protect them. This paper develops a methodology to identify the probability of an ecosystem to be groundwater dependent. Probabilities are obtained by modeling the relationship between the known locations of GDEs and factors influencing groundwater dependence, namely water table depth and climatic aridity index. Probabilities are derived for the state of Nevada, USA, using modeled water table depth and aridity index values obtained from the Global Aridity database. The model selected results from the performance comparison of classification trees (CT) and random forests (RF). Based on a threshold-independent accuracy measure, RF has a better ability to generate probability estimates. Considering a threshold that minimizes the misclassification rate for each model, RF also proves to be more accurate. Regarding training accuracy, performance measures such as accuracy, sensitivity, and specificity are higher for RF. For the test set, higher values of accuracy and kappa for CT highlight the fact that these measures are greatly affected by low prevalence. As shown for RF, the choice of the cutoff probability value has important consequences on model accuracy and the overall proportion of locations where GDEs are found.

Anthropogenic actions such as groundwater pumping, agricultural practices, industrialization, and waste disposal can greatly affect groundwater resources which would eventually drive changes in vulnerable ecosystems. Therefore, it is clear that there is a need to identify the locations of groundwater dependent ecosystems (GDEs) to enable the development of policies that adequately address their protection. The purpose of this study is to propose a method based on geospatial data sets and random forest algorithm to map the distribution of GDEs in the United States at 1 km spatial resolution. This paper presents the results in Nevada. The method is based on the principle that ecosystems will use water in proportion to its availability and the dependence on that resource will be expected to increase with higher aridity of the environment. Results show that random forest is a promising technique for the identification and characterization of GDEs using geospatial data sets as predictor variables.