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Land degradation modeling of dust storm sources using MODIS and meteorological time series data
Journal of Arid Environments ( IF 2.6 ) Pub Date : 2021-04-30 , DOI: 10.1016/j.jaridenv.2021.104507
Mohsen Bakhtiari , Ali Darvishi Boloorani , Ataollah Abdollahi Kakroodi , Kazem Rangzan , Alijafar Mousivand

Land degradation affects environmental integrity and threatens sustainable development worldwide by contributing to a number of social, economic, and ecological problems. This study uses time series of MODIS products, meteorological data, and ground truth map from dust storm sources to assess the spatial-temporal behavior of land degradation through a Random Forest algorithm in the southwest of Iran. Spatial-temporal variations in land surface biophysical properties of dust sources and surrounding areas were modeled using different time-series data on a 16-days, monthly, seasonal, annual, and 20-year basis from 2000 to 2019. Land degradation was estimated using the Random Forest algorithm. Dust storm sources were used to evaluate land degradation estimates. Furthermore, sensitivity analysis was carried out to identify the most influential biophysical variables. The results revealed that (i) 16-day time series data performs best in assessing land degradation; (ii) Apparent Thermal Inertia (ATI) was identified as the most influential variable for land degradation assessment; and (iii) there is a marked tendency towards land degradation in areas with severe land cover changes, mainly change from wetland to other classes.



中文翻译:

利用MODIS和气象时间序列数据建立沙尘暴源土地退化模型。

土地退化通过引发许多社会,经济和生态问题,影响环境完整性并威胁全球可持续发展。这项研究使用MODIS产品的时间序列,气象数据和沙尘暴来源的地面真相图,通过伊朗西南部的“随机森林”算法评估土地退化的时空行为。利用不同的时间序列数据,从2000年至2019年的16天,每月,季节性,年度和20年的基础上,模拟了粉尘源和周围地区土地表面生物物理特性的时空变化。随机森林算法。沙尘暴源用于评估土地退化估计。此外,进行了敏感性分析,以确定最有影响力的生物物理变量。结果显示(i)16天时间序列数据在评估土地退化方面表现最佳;(ii)表观热惯性(ATI)被认为是土地退化评估中最具影响力的变量;(iii)在土地覆被发生严重变化的地区,主要是从湿地向其他类别的变化,土地退化的趋势明显。

更新日期:2021-04-30
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