干旱区生态环境知识资源中心(Arid): Soil erosion prediction using Markov and CA-Markov chains methods and remote sensing drought indicators

Arid

DOI	10.1016/j.ecoinf.2023.102386
	Soil erosion prediction using Markov and CA-Markov chains methods and remote sensing drought indicators
	Mokarram, Marzieh; Zarei, Abdol Rassoul
通讯作者	Zarei, AR
来源期刊	ECOLOGICAL INFORMATICS
ISSN	1574-9541
EISSN	1878-0512
出版年	2023
卷号	78
英文摘要	Climate change and reduced rainfall in the past decade have resulted in an alarming increase in soil erosion within arid and semi-arid regions, including southern Iran. Given the significance of this issue, our research aimed to predict soil erosion in the southwest of Fars province, Iran, by utilizing climatic indicators and remote sensing. To determine the key indicators of drought influencing soil erosion, the principal component analysis (PCA) method was employed. Subsequently, Markov chains and Cellular Automata Markov chain (CA-Markov) were utilized to forecast soil erosion and drought conditions in 2040. The findings revealed a steady rise in drought frequency within the region from 2000 to 2020. Notably, meteorological indices and meteorological drought exhibited strong correlations with the Standard Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI) (RSPEI = 0.98 and RSPI = 0.97). The correlation analysis between soil erodibility indices and drought identified Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) as significantly linked to erosion rates. The soil erosion predictions using Markov and CAMarkov chains in 2040 indicated that approximately 38% of the region faces erosion risk based on the NDVI index, while around 47% face risk based on the EVI index. Moreover, the southern and eastern regions are projected to experience increased erosion due to the combination of low rainfall and high drought in the upcoming years. These results underscore the profound impact of drought on critical soil properties, including soil moisture, texture, and the adhesion of soil particles. Consequently, drought-induced soil erosion may lead to greater wastage and ecological repercussions. By leveraging remote sensing drought indicators and their influence on soil erosion, effective land management practices can be implemented to prevent soil erosion and mitigate its adverse effects on the ecological potential of watersheds.
英文关键词	Soil erosion Drought Sustainable land management remote sensing Markov and CA-Markov chain methods PAC method
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001130023200001
WOS关键词	VEGETATION ; INDEXES ; SPEI ; SPI
WOS类目	Ecology
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395999
推荐引用方式 GB/T 7714	Mokarram, Marzieh,Zarei, Abdol Rassoul. Soil erosion prediction using Markov and CA-Markov chains methods and remote sensing drought indicators[J],2023,78.
APA	Mokarram, Marzieh,&Zarei, Abdol Rassoul.(2023).Soil erosion prediction using Markov and CA-Markov chains methods and remote sensing drought indicators.ECOLOGICAL INFORMATICS,78.
MLA	Mokarram, Marzieh,et al."Soil erosion prediction using Markov and CA-Markov chains methods and remote sensing drought indicators".ECOLOGICAL INFORMATICS 78(2023).