干旱区生态环境知识资源中心(Arid): Monitoring and forecasting desertification and land degradation using remote sensing and machine learning techniques in Sistan plain, Iran

Arid

DOI	10.1016/j.jafrearsci.2024.105375
	Monitoring and forecasting desertification and land degradation using remote sensing and machine learning techniques in Sistan plain, Iran
	Hashemi, Zohreh; Sodaeizadeh, Hamid; Mokhtari, Mohammad Hossien; Ardakani, Mohammad Ali Hakimzadeh; Aliabadi, Kazem Kamali
通讯作者	Sodaeizadeh, H
来源期刊	JOURNAL OF AFRICAN EARTH SCIENCES
ISSN	1464-343X
EISSN	1879-1956
出版年	2024
卷号	218
英文摘要	Monitoring and predicting desertification in arid regions are crucial for addressing environmental and societal challenges. Remote sensing is vital for tracking land surfaces and ecosystems changes. The study aims to use remote sensing-based data to monitor and predict desertification in the Sistan Plain through a data screening approach. The study's satellite data consisted of Landsat 5 and 8 images taken in June each year over 10 years (1990-2020). Remote sensing-based indices, including land use and land cover (LULC) map, normalized differential vegetation index (NDVI), improved vegetation index (EVI), vegetation condition index (VCI), surface temperature condition index (TCI), modified normalized differential water level index (MNDWI) and salinity index (SI) were used in the study. In addition to satellite data, environmental indices, including standardized precipitation index (SPI) and streamflow drought index (SDI), were used. The study employed the random forest (RF) method and the mixed model of automated cells and Markov chain (CA-Markov) to monitor desertification and quantitatively predict its condition in 2030. Root-mean-square error (RMSE) and mean-square error (MSE) indicators were used to evaluate the error. Based on the findings, the RF correlation coefficient (R2) and RMSE were obtained about 0.97 and 0.08, respectively. High coefficient values and low RMSE values indicate that the random forest model is highly efficient in assessing desertification for the study period from 1990 to 2020. The change detection method revealed that desertification increased from 1990 to 2010 but decreased from 2010 to 2020. The decreasing trend is expected to continue until 2030. The Kappa coefficient for the prediction of desertification in 2030 was found to be 0.94, which indicates a correct classification based on the collected samples. In addition, the study identified the SI and SDI as effective indices in the desertification process in the study area. Overall, this study provides valuable insights into monitoring and predicting desertification, which could help develop appropriate strategies for managing and controlling desertification in the Sistan Plain through remote sensing and machine learning techniques.
英文关键词	Desertification Remote sensing Machine learning Sistan plain
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001295694000001
WOS关键词	DIFFERENCE WATER INDEX ; VEGETATION ; NDWI
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404319
推荐引用方式 GB/T 7714	Hashemi, Zohreh,Sodaeizadeh, Hamid,Mokhtari, Mohammad Hossien,et al. Monitoring and forecasting desertification and land degradation using remote sensing and machine learning techniques in Sistan plain, Iran[J],2024,218.
APA	Hashemi, Zohreh,Sodaeizadeh, Hamid,Mokhtari, Mohammad Hossien,Ardakani, Mohammad Ali Hakimzadeh,&Aliabadi, Kazem Kamali.(2024).Monitoring and forecasting desertification and land degradation using remote sensing and machine learning techniques in Sistan plain, Iran.JOURNAL OF AFRICAN EARTH SCIENCES,218.
MLA	Hashemi, Zohreh,et al."Monitoring and forecasting desertification and land degradation using remote sensing and machine learning techniques in Sistan plain, Iran".JOURNAL OF AFRICAN EARTH SCIENCES 218(2024).