Desertification Monitoring in Northern China by Combining a Novel 3-D Desertification Index With a Gaussian Mixture Model

As desertification is one of the most severe ecological and environmental issues worldwide, monitoring desertification and studying its evolution patterns are highly important for its governance and prevention. In this study, a novel desertification monitoring method is developed that combines the three-dimensional desertification index (TDDI) and Gaussian mixture model (GMM). The results of applying this method to desertification monitoring, which is based on historical Google Earth images, in northern China from 2000 to 2020 indicate that the accuracy of desertification classification using the TDDI and GMM algorithms exceeds 82%. Compared with the national desertification survey statistics, the accuracy of classifying areas with different degrees of desertification exceeds 93.4%. In terms of the stability of the monitoring results under different data source and spatial region conditions, TDDI<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MODIS</sub> shows a strong correlation and high consistency with TDDI<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Landsat</sub> and TDDI<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sentinel-2</sub>. The overall accuracies are greater than 55%. Additionally, the TDDI comprehensively considers the soil moisture level, vegetation coverage, and surface conditions and reflects the complexity of the desertification process more accurately than the NDVI and DDI.