Volume 12, Issue 1 (2024)
ECOPERSIA 2024, 12(1): 25-37 |
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Afshari M, Vali A. Application of Maximum Entropy Model and Remote Sensing Technique to predict susceptible areas to dust storms in Isfahan Province, Iran. ECOPERSIA 2024; 12 (1) :25-37
URL: http://ecopersia.modares.ac.ir/article-24-73093-en.html
URL: http://ecopersia.modares.ac.ir/article-24-73093-en.html
1- PhD student , Department of Arid Land Management, Faculty of Natural Resources and Earth Sciences,University of Kashan, Kashan, Iran
2- Associate Professor, Department of Arid Lands Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran.(vali@kashanu.ac.ir) , vali@kashanu.ac.ir
2- Associate Professor, Department of Arid Lands Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran.(vali@kashanu.ac.ir) , vali@kashanu.ac.ir
Abstract: (65 Views)
Aims: This study modeled sensitive areas to dust storms in Isfahan province, which is sensitive to successive droughts, and dust storms because of its climatic condition, and proximity to the desert, using meteorological codes related to dust, AOD values, and Maximum Entropy model (MaxEnt).
Materials & methods: 200 occurrence points of dust were determined using dust meteorological codes and AOD values of MODIS sensor, Terra satellite, (2011-2022). Ten parameters including temperature, rainfall, albedo, altitude, slope, land use, enhanced vegetation index (EVI), normalized difference moisture index (NDMI), normalized difference salinity index (NDSI), and frequency percentage of erosive wind seed were considered dust-predictive factors. Finally, the MaxEnt model was utilized for modeling dust susceptibility. The performance of the model was specified using the AUC value and the importance of each influential factor was identified utilizing the Jackknife test.
Findings: The findings indicated that areas susceptible to dust are mainly bare lands, salt lands, and poor rangeland located mostly in the north, northeast to parts of the east and southeast of the Province, and also the central parts towards the southwest of Isfahan Province. According to the results, the MaxEnt model, with AUC=0.72, had a good efficiency in modeling susceptible areas to dust storms in Isfahan Province.
Conclusion: The major conclusion of this study is that the MaxEnt model had good performance in mapping susceptible areas to dust in Isfahan Province. The results of this research can be useful for decision-makers in identifying the areas prone to dust storms.
Materials & methods: 200 occurrence points of dust were determined using dust meteorological codes and AOD values of MODIS sensor, Terra satellite, (2011-2022). Ten parameters including temperature, rainfall, albedo, altitude, slope, land use, enhanced vegetation index (EVI), normalized difference moisture index (NDMI), normalized difference salinity index (NDSI), and frequency percentage of erosive wind seed were considered dust-predictive factors. Finally, the MaxEnt model was utilized for modeling dust susceptibility. The performance of the model was specified using the AUC value and the importance of each influential factor was identified utilizing the Jackknife test.
Findings: The findings indicated that areas susceptible to dust are mainly bare lands, salt lands, and poor rangeland located mostly in the north, northeast to parts of the east and southeast of the Province, and also the central parts towards the southwest of Isfahan Province. According to the results, the MaxEnt model, with AUC=0.72, had a good efficiency in modeling susceptible areas to dust storms in Isfahan Province.
Conclusion: The major conclusion of this study is that the MaxEnt model had good performance in mapping susceptible areas to dust in Isfahan Province. The results of this research can be useful for decision-makers in identifying the areas prone to dust storms.
Article Type: Original Research |
Subject:
Desert Ecosystems
Received: 2023/12/24 | Accepted: 2024/02/7 | Published: 2024/04/22
Received: 2023/12/24 | Accepted: 2024/02/7 | Published: 2024/04/22
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