气候变化领域集成服务门户(CCPortal): Predicting land cover driven ecosystem service value using artificial neural network model

CCPortal

DOI	10.1016/j.rsase.2024.101180
	Predicting land cover driven ecosystem service value using artificial neural network model
	Hossain, Niamat Ullah Ibne; Fattah, Md. Abdul; Morshed, Syed Riad; Jaradat, Raed
发表日期	2024
ISSN	2352-9385
起始页码	34
卷号	34
英文摘要	Understanding the synergies and trade-offs of major cities' ecosystem services is vital to mitigating regional ecological and environmental risks and enhancing human well-being in this era of rapid urbanization and global climate change. This study aimed to assess and predict the land use- and land cover (LULC)-driven ecosystem service value (ESV) dynamics in Arkansas's capital city, Little Rock. Historical LULC data were derived by applying support vector machine learning algorithms to Landsat satellite imagery. The benefit transfer method was utilized to identify nine types of ecosystem services and their corresponding economic values. A cellular automata artificial neural network model was used to simulate future potential LULC and ESV patterns. Vegetation accounted for more than 94% of total ESV over the past two decades. However, a 38.40% expansion of built-up areas resulted in a 45.28% decrease in vegetated areas, which reduced total ESV from $3619.73 x 106 to $2563.81 x 106 during 2003-2023. By 2033, the city's urban area will expand to 72.75% of the total area and will witness further declines of 30.35 km2 in vegetation, 19.30 km2 in barren soil, and 1.69 km2 in waterbody areas. Consequently, the ESVs of these natural landscapes will decline by $708.58 x 106, $44.87 x 106, and $15.69 x 106, respectively. Provisioning services will be most affected, followed by supporting, regulating, and cultural services. The study findings provide reference information to policymakers and the local government for use in adopting sustainable land management policies, thereby promoting the ecological value of Little Rock.
英文关键词	Ecosystem services; Ecosystem service valuation; Cellular automata artificial neural network model; Support vector machine algorithm
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing
WOS类目	Environmental Sciences ; Remote Sensing
WOS记录号	WOS:001221923500001
来源期刊	REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/301093
作者单位	Arkansas State University; State University System of Florida; Florida State University; Khulna University of Engineering & Technology (KUET); Khalifa University of Science & Technology
推荐引用方式 GB/T 7714	Hossain, Niamat Ullah Ibne,Fattah, Md. Abdul,Morshed, Syed Riad,et al. Predicting land cover driven ecosystem service value using artificial neural network model[J],2024,34.
APA	Hossain, Niamat Ullah Ibne,Fattah, Md. Abdul,Morshed, Syed Riad,&Jaradat, Raed.(2024).Predicting land cover driven ecosystem service value using artificial neural network model.REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT,34.
MLA	Hossain, Niamat Ullah Ibne,et al."Predicting land cover driven ecosystem service value using artificial neural network model".REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 34(2024).