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Environments 2016, 3(2), 9; doi:10.3390/environments3020009

Estimating the Probability of Vegetation to Be Groundwater Dependent Based on the Evaluation of Tree Models

National Oceanic and Atmospheric Administration-Cooperative Remote Sensing Science and Technology (NOAA-CREST) Center, The City College of New York, New York, NY 10031, USA
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Academic Editors: Luigi Berardi and Daniele Laucelli
Received: 28 January 2016 / Revised: 10 March 2016 / Accepted: 22 March 2016 / Published: 2 April 2016
(This article belongs to the Special Issue Data-Modelling Applications in Water System Management)
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Abstract

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. View Full-Text
Keywords: groundwater dependent ecosystems; decision trees; random forest; GIS; remote sensing; phreatophytes groundwater dependent ecosystems; decision trees; random forest; GIS; remote sensing; phreatophytes
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pérez Hoyos, I.C.; Krakauer, N.Y.; Khanbilvardi, R. Estimating the Probability of Vegetation to Be Groundwater Dependent Based on the Evaluation of Tree Models. Environments 2016, 3, 9.

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