Dust source mapping using satellite imagery and machine learning models

Journal Title: Arid Regions Geographic Studies - Year 2022, Vol 13, Issue 47

Abstract

Predicting dust sources area and determining the affecting factors is necessary in order to prioritize management and practice deal with desertification due to wind erosion in arid areas. Therefore, this study aimed to evaluate the application of three machine learning models (including generalized linear model, artificial neural network, random forest) to predict the vulnerability of dust centers during the years 2005 to 2018 in the Central Desert of Iran. For this purpose, the dust source areas were extracted in the study area using MODIS satellite images using four indicators including BTD3132, BTD2931, NDDI and variable D, and finally 135 hotspots were identified and used in modeling. In this study, conditional factors affecting dust were considered for modeling including land use, soil science, geology, distance from waterway, normalized vegetation difference index (NDVI), land slope and climate. The results showed that among the applied algorithms, random forest with 63.5% accuracy was the most accurate model and followed by artificial neural network with 43.4% accuracy and generalized linear model with 43.2% accuracy. In addition, among factors, land use and soil were identified as the most effective factors on dust source area. The results of this study can provide valuable information for regional managers and policy makers and help them to make useful decisions in management.

Authors and Affiliations

Mahdi Boroughani; Fahimeh Mirchooli; Maziar Mohammadi

Keywords

Related Articles

Flood zoning of Shahrchai River in Urmia using HEC-RAS model

Aim: This research was conducted to simulate flood flow in the Shahrchai River in Urmia County, Iran. The Shahrchai River, which passes through the city of Urmia, experiences a flooding state during the spring season, ca...

Revealing the Relationship between Temporal and Spatial Changes in the Vegetation Cover of Sistan and Baluchistan Province with Climatic Elements

Aim: Changes in climate parameters, including precipitation and temperature, either alone or together, cause fluctuations in vegetation. The purpose of this research is to determine the relationship between climatic para...

Analyzing the influencing factors on the hydropolitical relations of the political-spatial units of the Gavakhuni watershed

Aim: The most important purpose of this research is to identify the key factors affecting the hydropolitical relations of political-spatial units in the Gavakhuni catchment area. Material & Method: The current re...

Explanation the effects of establishment and loading of industries on the economic Sustainability of rural areas (Case study: Mobarake County)

Aim: The aim of the present research is to identify and evaluate industries' loading effects on economic sustainability and unsustainability in rural settlements in Mobarakeh County. Material & Method: This researc...

Explaining the pattern of sustainable regeneration of inefficient urban neighborhoods with emphasis on governance and the role of local activists

The purpose of this research is to evaluate and explain the role and position of local activists in the process of sustainable regeneration of inefficient and worn-out tissues of Mashhad.In this research, an attempt ha...

Download PDF file
  • EP ID EP729533
  • DOI -
  • Views 106
  • Downloads 0

How To Cite

Mahdi Boroughani; Fahimeh Mirchooli; Maziar Mohammadi (2022). Dust source mapping using satellite imagery and machine learning models. Arid Regions Geographic Studies, 13(47), -. https://europub.co.uk/articles/-A-729533