SARGDV: Efficient identification of groundwater-dependent vegetation using synthetic aperture radar

TL;DR

This paper presents SARGDV, a cost-effective, high-accuracy machine learning method using Sentinel-1 SAR data to identify groundwater-dependent vegetation at large scales, aiding ecological protection and sustainable groundwater management.

Contribution

The paper introduces SARGDV, a novel binary classification model utilizing XGBoost and SAR data for large-scale groundwater-dependent vegetation identification.

Findings

Achieved 77% precision in classifying GDVs

Demonstrated 96% overall accuracy

Proved effectiveness over variable regions and climates

Abstract

Groundwater depletion impacts the sustainability of numerous groundwater-dependent vegetation (GDV) globally, placing significant stress on their capacity to provide environmental and ecological support for flora, fauna, and anthropic benefits. Industries such as mining, agriculture, and plantations are heavily reliant on groundwater, the over-exploitation of which risks impacting groundwater regimes, quality, and accessibility for nearby GDVs. Cost effective methods of GDV identification will enable strategic protection of these critical ecological systems, through improved and sustainable groundwater management by communities and industry. Recent application of synthetic aperture radar (SAR) earth observation data in Australia has demonstrated the utility of radar for identifying terrestrial groundwater-dependent ecosystems at scale. We propose a robust classification method to advance identification of GDVs at scale using processed SAR data products adapted from a recent previous method. The method includes the development of SARGDV, a binary classification model, which uses the extreme gradient boosting (XGBoost) algorithm in conjunction with three data cubes composed of Sentinel-1 SAR interferometric wide images. The images were collected as a one-year time series over Mount Gambier, a region in South Australia, known to support GDVs. The SARGDV model demonstrated high performance for classifying GDVs with 77% precision, 76% true positive rate and 96% accuracy. This method may be used to support the protection of GDV communities globally by providing a long term, cost-effective solution to identify GDVs over variable regions and climates, via the use of freely available, high-resolution, globally available Sentinel-1 SAR data sets.