ABSTRACT

Wind and soil confrontation under specific atmospheric conditions leads to the horrendous phenomenon of dust storms, particularly in arid lands. The negative impact of storms on the health and life of living beings is evident, resulting in many losses in the economic, social, and environmental sectors. Dust storm detection is the most important topic in dust studies, where many algorithms and indices have already been proposed using satellite imageries and remote sensing techniques. Despite the advantage of dust detection, these algorithms present drawbacks. This includes the need to determine different thresholds for different regional events or simply better respond to oceanic dust storms. The objective of this study is to develop a new Dust Storm Detection Index, called DSDI, without the need to determine different thresholds for each event. To establish the new index, it was necessary to distinguish the properties of dust, clouds, and land surfaces in the images of the MODIS sensors. These properties have been characterized by analyzing their spectral and thermal profiles at different wavelengths during dust storms from 2004 to 2009 in Yazd province, Central Iran, which is constantly coping with severe storms. Spectral and thermal ranges of 0.46, 0.56, 3.9, 1.4, 11, 12, and 13.6 μm were the most suitable discriminating bands of dust from the cloud and land surfaces. The brightness temperature difference of the thermal bands and the spectral ratio of reflectance bands, i.e. DSDI, has developed an appropriate relationship for separating land surfaces and clouds of dust particles. There was a significant correlation between DSDI and horizontal visibility (P-value= 0.05 & 0.01). It confirmed the success of this algorithm to detect storms in the study area, as an arid land.

摘要

特定大气条件下的风和土壤对抗会导致可怕的沙尘暴现象，特别是在干旱地区。风暴对生物健康和生命的负面影响是显而易见的，在经济、社会和环境领域造成许多损失。沙尘暴检测是沙尘研究中最重要的课题，已经使用卫星图像和遥感技术提出了许多算法和指数。尽管灰尘检测具有优势，但这些算法存在缺陷。这包括需要为不同的区域事件确定不同的阈值，或者只是更好地应对海洋沙尘暴。本研究的目的是开发一种新的沙尘暴检测指数，称为 DSDI，无需为每个事件确定不同的阈值。为了建立新的指数，需要区分 MODIS 传感器图像中的灰尘、云和地表的特性。通过分析 2004 年至 2009 年在伊朗中部亚兹德省发生沙尘暴期间不同波长的光谱和热分布，这些特性已被表征，该省一直在应对严重的风暴。0.46, 0.56, 3.9, 1.4, 11, 12, 和 13.6 μm 的光谱和热范围是最合适的云和地表尘埃识别带。热波段的亮温差和反射波段的光谱比，即 DSDI，已经形成了分离地表和尘埃颗粒云的适当关系。DSDI 与水平能见度之间存在显着相关性（P 值 = 0.05 & 0.01）。