干旱区生态环境知识资源中心(Arid): Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making

Arid

DOI	10.1016/j.heliyon.2024.e25731
	Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making
	Alqadhi, Saeed; Bindajam, Ahmed Ali; Mallick, Javed; Talukdar, Swapan; Rahman, Atiqur
通讯作者	Talukdar, S
来源期刊	HELIYON
EISSN	2405-8440
出版年	2024
卷号	10 期号:4
英文摘要	This study aims to quantitatively and qualitatively assess the impact of urbanisation on the urban ecosystem in the city of Abha, Saudi Arabia, by analysing land use changes, urbanisation processes and their ecological impacts. Using a multidisciplinary approach, a novel remote sensingbased urban ecological condition index (RSUSEI) will be developed and applied to assess the ecological status of urban surfaces. Therefore, the identification and quantification of urbanisation is important. To do so, we used hyper-tuned artificial neural network (ANN) as well as Land Cover Change Rate (LCCR), Land Cover Index (LCI) and Landscape Expansion Index (LEI). For the development of (RSUSEI), we have used four advanced models such as fuzzy Logic, Principle Component Analysis (PCA), Analytical Hierarchy Process (AHP) and fuzzy Analytical Hierarchy Process (FAHP) to integrate various ecological parameters. In order to obtain more information for better decision making in urban planning, sensitivity and uncertainty analyses based on a deep neural network (DNN) were also used. The results of the study show a multi-layered pattern of urbanisation in Saudi Arabian cities reflected in the LCCR, indicating rapid urban expansion, especially in the built-up areas with an LCCR of 0.112 over the 30-year period, corresponding to a more than four-fold increase in urban land cover. At the same time, the LCI shows a remarkable increase in 'built-up' areas from 3.217% to 13.982%, reflecting the substantial conversion of other land cover types to urban uses. Furthermore, the LEI emphasises the complexity of urban growth. Outward expansion (118.98 km2), Edge-Expansion (95.22 km2) and Infilling (5.00 km2) together paint a picture of a city expanding outwards while filling gaps in the existing urban fabric. The RSUSEI model shows that the zone of extremely poor ecological condition covers an area of 157-250 km2, while the natural zone covers 91-410 km2. The DNN based sensitivity analysis is useful to determine the optimal model, while the integrated models have lower input parameter uncertainty than other models. The results of the study have significant implications for the management of urban ecosystems in arid areas and the protection of natural habitats while improving the quality of life of urban residents. The RSUSEI model can be used effectively to assess urban surface ecology and inform urban management techniques.
英文关键词	Urban ecological condition Artificial neural network Fuzzy logic FAHP Sensitivity analysis Deep learning Remote sensing
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:001200304000001
WOS关键词	HEAT-ISLAND ; LAND-COVER ; INDEX ; INDICATORS ; HEALTH ; NDVI ; URBANIZATION ; STRATEGIES ; CITIES ; AREA
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404012
推荐引用方式 GB/T 7714	Alqadhi, Saeed,Bindajam, Ahmed Ali,Mallick, Javed,et al. Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making[J],2024,10(4).
APA	Alqadhi, Saeed,Bindajam, Ahmed Ali,Mallick, Javed,Talukdar, Swapan,&Rahman, Atiqur.(2024).Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making.HELIYON,10(4).
MLA	Alqadhi, Saeed,et al."Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making".HELIYON 10.4(2024).