Dust storm detection using random forests and phys

Authors: Amir Hossein Souri Sanaz Vajedian

Publish Date: 2015/07/09

Volume: 124, Issue: 5, Pages: 1127-1141

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Abstract

Dust storms are important phenomena over large regions of the arid and semiarid areas of the Middle East Due to the influences of dust aerosols on climate and human daily activities dust detection plays a crucial role in environmental and climatic studies Detection of dust storms is critical to accurately understand dust their properties and distribution Currently remotely sensed data such as MODIS Moderate Resolution Imaging Spectroradiometer with appropriate temporal and spectral resolutions have been widely used for this purpose This paper investigates the capability of two physicalbased methods and random forests RF classifier for the first time to detect dust storms using MODIS imagery Since the physicalbased approaches are empirical they suffer from certain drawbacks such as high variability of thresholds depending on the underlying surface Therefore classificationbased approaches could be deployed as an alternative In this paper the most relevant bands are chosen based on the physical effects of the major classes particularly dust cloud and snow on both emissive infrared and reflective bands In order to verify the capability of the methods OMAERUV AAOD aerosol absorption optical depth product from OMI Ozone Monitoring Instrument sensor is exploited In addition some small regions are selected manually to be considered as ground truth for measuring the probability of false detection POFD and probability of missing detection POMD The dust class generated by RF is consistent qualitatively with the location and extent of dust observed in OMERAUV and MODIS true colour images Quantitatively the dust classes generated for eight dust outbreaks in the Middle East are found to be accurate from 7 and 6 of POFD and POMD respectively Moreover results demonstrate the sound capability of RF in classifying dust plumes over both water and land simultaneously The performance of the physicalbased approaches is found weaker than RF due to empirical thresholds that are not always true

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