干旱区生态环境知识资源中心(Arid): Desertification simulation using wavelet and box-jenkins time series analysis based on TGSI and albedo remote sensing indices

Arid

DOI	10.1016/j.jaridenv.2023.105069
	Desertification simulation using wavelet and box-jenkins time series analysis based on TGSI and albedo remote sensing indices
	Geloogerdi, Sareh Hashem; Vali, Abbasali; Sharifi, Mohammad Reza
通讯作者	Vali, A
来源期刊	JOURNAL OF ARID ENVIRONMENTS
ISSN	0140-1963
EISSN	1095-922X
出版年	2023
卷号	219
英文摘要	Desertification has been listed as one of the most critical global environmental issues, posing a significant threat to life, particularly in arid and semiarid regions. Therefore, gaining a comprehensive understanding of the present and future desertification trends becomes imperative. This study employs a feature space model, which effectively captures land surface changes related to desertification, enabling the extraction of pertinent information. Subsequently, time series models are used to determine the most accurate desertification simulation. Twenty-one ETM + sensor images were utilized to calculate the Topsoil Grain Size (TGSI) and Albedo remotely sensed indexes. Constructing the Albedo-TGSI feature space, the Desertification Degree Index (DDI) was extracted for each year. Different levels of desertification were identified by applying a natural break classifi-cation on the DDI values, and corresponding break values were obtained. The representative desertification degree for each year was determined by calculating the average of the minimum and maximum break values, resulting in the generation of five distinct time series for five desertification degrees. Different ARIMA models and wavelet transforms were selected to simulate the various desertification degrees based on the analysis of autocorrelation and partial autocorrelation functions and trial and error, respectively. The most suitable ARIMA models with the lowest errors were identified as follows: ARIMA (1,0,7) for severe desertification, ARIMA (0,1,6) for high desertification, ARIMA (0,0,7) for moderate desertification, and ARIMA (3,0,6) for non-desertification degrees. Among the various wavelet transform families tested, the Symlet family proved to be the most effective, except for the low desertification degree. The following wavelet transforms yielded the best results for each degree of desertification: Symlet3 for severe desertification, Symlet7 for high desertification, Symlet7 for moderate desertification, Daubechies 5 (db5) for low desertification, and Symlet7 for non-desertification degree simulations, all exhibiting the minimum error rates.
英文关键词	Desertification Time series ARIMA Wavelet transform
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001101329400001
WOS关键词	ARIMA ; LAND ; NDVI
WOS类目	Ecology ; Environmental Sciences
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/2XILL650/397166
推荐引用方式 GB/T 7714	Geloogerdi, Sareh Hashem,Vali, Abbasali,Sharifi, Mohammad Reza. Desertification simulation using wavelet and box-jenkins time series analysis based on TGSI and albedo remote sensing indices[J],2023,219.
APA	Geloogerdi, Sareh Hashem,Vali, Abbasali,&Sharifi, Mohammad Reza.(2023).Desertification simulation using wavelet and box-jenkins time series analysis based on TGSI and albedo remote sensing indices.JOURNAL OF ARID ENVIRONMENTS,219.
MLA	Geloogerdi, Sareh Hashem,et al."Desertification simulation using wavelet and box-jenkins time series analysis based on TGSI and albedo remote sensing indices".JOURNAL OF ARID ENVIRONMENTS 219(2023).