干旱区生态环境知识资源中心(Arid): A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

Arid

DOI	10.1002/ldr.4391
	A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area
	Abolhasani, Azam; Zehtabian, Gholamreza; Khosravi, Hassan; Rahmati, Omid; Alamdarloo, Esmail Heydari; D'Odorico, Paolo
通讯作者	Khosravi, H
来源期刊	LAND DEGRADATION & DEVELOPMENT
ISSN	1085-3278
EISSN	1099-145X
出版年	2022
卷号	33 期号:17 页码:3358-3374
英文摘要	Although land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001-2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km(2). An inventory map of LD was generated based on the LD study sites. The locations were randomly split-sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo-environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine-learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.
英文关键词	GIS land degradation remote sensing spatial modelling sustainability
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000837491100001
WOS关键词	LANDSLIDE SUSCEPTIBILITY ; DESERTIFICATION ; CLASSIFICATION ; EROSION ; AID
WOS类目	Environmental Sciences ; Soil Science
WOS研究方向	Environmental Sciences & Ecology ; Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393727
推荐引用方式 GB/T 7714	Abolhasani, Azam,Zehtabian, Gholamreza,Khosravi, Hassan,et al. A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area[J],2022,33(17):3358-3374.
APA	Abolhasani, Azam,Zehtabian, Gholamreza,Khosravi, Hassan,Rahmati, Omid,Alamdarloo, Esmail Heydari,&D'Odorico, Paolo.(2022).A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area.LAND DEGRADATION & DEVELOPMENT,33(17),3358-3374.
MLA	Abolhasani, Azam,et al."A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area".LAND DEGRADATION & DEVELOPMENT 33.17(2022):3358-3374.