干旱区生态环境知识资源中心(Arid): Desertification prediction with an integrated 3D convolutional neural network and cellular automata in Al-Muthanna, Iraq

Arid

DOI	10.1007/s10661-022-10379-z
	Desertification prediction with an integrated 3D convolutional neural network and cellular automata in Al-Muthanna, Iraq
	Aldabbagh, Yasir Abdulameer Nayyef; Shafri, Helmi Zulhaidi Mohd; Mansor, Shattri; Ismail, Mohd Hasmadi
通讯作者	Shafri, HZM
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN	0167-6369
EISSN	1573-2959
出版年	2022
卷号	194 期号:10
英文摘要	Desertification is a major environmental issue all over the world, and Al-Khidhir district, Al-Muthanna, in the south of Iraq is no exception. In mapping, assessing, and predicting desertification, remote sensing and geospatial solutions (spatial analysis, machine learning) are crucial. During 1998-2018, this study employed satellite images from Landsat TM, ETM +, and OLI to map and predict desertification in the Al-Khidhir district. The year 2028 was chosen as the target date. Prediction models were constructed using a 3D convolutional neural network (3D CNN) and cellular automata (CA) techniques. In addition to the historical land cover maps, the model incorporated desertification indicators identified as important in the study, including geology, soil type, distance from waterways, elevation, population density, and Normalized Difference Vegetation Index (NDVI). Several accuracy metrics were used to evaluate the models, including overall accuracy (OA), average accuracy (AA), and the Kappa index (K). The simulated and actual land cover maps from 1998 and 2008 were used to evaluate the desertification prediction models. The 3D CNN model outperforms the typical 2D CNN for both the 2008 and 2018 images, according to the results. For the 2008 image, the 3D CNN model achieved 89.675 OA, 69.946 AA, and 0.781 K, while the 2018 image achieved 91.494 OA, 75.138 AA, and 0.770 K. The 2D CNN model performed a little worse than the 3D CNN model. The results of the change assessment showed that between 1998 and 2008, agricultural land was the dominant class (39%, 47.4%, respectively). The bare land, however, was the most dominant class in 2018, accounting for 46.6% of the total, compared to 26.2% for agricultural land. The spatial distribution characteristics of desertification in the Al-Khidhir, in the year 1998, were prevalent in the area's south (25.9%). In the following 10 years, desertification has spread to the surrounding territories. In the year 2008, desertification increased in the north of the study area (50.8%). Unless the local administration of Al-Khidhir district establishes desertification control strategies, this study suggests that the extent of bare land could expand in 2028 (54.1%).
英文关键词	Desertification prediction Convolutional neural networks Cellular automata Al-Muthanna
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000849356000001
WOS关键词	LAND-COVER CLASSIFICATION ; REGION ; SOUTH ; MODEL
WOS类目	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/2XILL650/392480
推荐引用方式 GB/T 7714	Aldabbagh, Yasir Abdulameer Nayyef,Shafri, Helmi Zulhaidi Mohd,Mansor, Shattri,et al. Desertification prediction with an integrated 3D convolutional neural network and cellular automata in Al-Muthanna, Iraq[J],2022,194(10).
APA	Aldabbagh, Yasir Abdulameer Nayyef,Shafri, Helmi Zulhaidi Mohd,Mansor, Shattri,&Ismail, Mohd Hasmadi.(2022).Desertification prediction with an integrated 3D convolutional neural network and cellular automata in Al-Muthanna, Iraq.ENVIRONMENTAL MONITORING AND ASSESSMENT,194(10).
MLA	Aldabbagh, Yasir Abdulameer Nayyef,et al."Desertification prediction with an integrated 3D convolutional neural network and cellular automata in Al-Muthanna, Iraq".ENVIRONMENTAL MONITORING AND ASSESSMENT 194.10(2022).