The majority of the new dust sources are due to environmental degradation caused by human activities. To prevent further dust source generation, the first step is to identify the new dust sources and predict the dust source occurrences in the vulnerable areas. We developed a novel approach that combines machine learning algorithms and remote sensing techniques for dust source detection and susceptibility in Iran's arid northeastern part.
We identified over 60 dust sources in the study area using MODIS satellite images during the 2005–2016 period. We found that slope, geomorphology, and land use had a major influence in generating dust sources in this region. The results revealed that the developed method has the ability to predict dust source occurrences in the arid-semiarid area of the Middle East. This research's findings could assist decision-makers and land managers in properly managing the lands susceptible to dust generation and preventing consequent dust storms.
Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping (Ecological Informatics)
Photo by Darya Tryfanava on Unsplash