干旱区生态环境知识资源中心(Arid): Improvement of mapping vegetation cover for arid and semiarid areas using a local nonlinear modelling method and landsat images

Arid

DOI	10.1071/RJ19081
	Improvement of mapping vegetation cover for arid and semiarid areas using a local nonlinear modelling method and landsat images
	Sun, H.; Wang, Q.; Wang, G. X.; Luo, P.; Jiang, F. G.
通讯作者	Wang, GX
来源期刊	RANGELAND JOURNAL
ISSN	1036-9872
EISSN	1834-7541
出版年	2020
卷号	42 期号:3 页码:161-169
英文摘要	Accurately estimating and mapping vegetation cover for monitoring land degradation and desertification of arid and semiarid areas using remotely sensed images is promising but challenging in remote, sparsely vegetated and large areas. In this study, a novel method - geographically weighted logistic regression (GWLR - integrating geographically weighted regression (GWR) and a logistic model) was proposed to improve vegetation cover mapping of Kangbao County, Hebei of China using Landsat 8 image and field data. Additionally, a new method to determine the bandwidth of GWLR is presented. Using cross-validation, GWLR was compared with a globally linear stepwise regression (LSR), a local linear modelling method GWR and a nonparametric method, k-nearest neighbours (kNN) with varying numbers of nearest plots. Results demonstrated (1) the red and near infrared relevant band ratios and vegetation indices significantly improved mapping; (2) the GWLR, GWR and kNN methods led to more accurate predictions than LSR; (3) GWLR reduced overestimations and underestimations compared with LSR, kNN and GWR, and also eliminated negative and very large estimates caused by GWR and LSR; and (4) The maximum distance of spatial autocorrelation could be used to determine the bandwidth for GWLR. Overall, GWLR proved more promising for mapping vegetation cover of arid and semiarid areas.
英文关键词	accurate estimation desertification geographically weighted logistic regression Kangbao County land degradation northern China remote sensing spatial variability vegetation cover
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000582762700001
WOS关键词	DESERTIFICATION ; REGRESSION ; RANGELAND ; INDEX
WOS类目	Ecology
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/2XILL650/327158
作者单位	[Sun, H.; Jiang, F. G.] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China; [Sun, H.; Jiang, F. G.] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Hunan, Peoples R China; [Sun, H.; Jiang, F. G.] Key Lab Natl Forestry & Grassland Adm Forest Reso, Changsha 410004, Peoples R China; [Wang, Q.; Wang, G. X.] Southern Illinois Univ, Dept Geog & Environm Resources, Carbondale, IL 62901 USA; [Luo, P.] Chinese Acad Forestry, Res Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China
推荐引用方式 GB/T 7714	Sun, H.,Wang, Q.,Wang, G. X.,et al. Improvement of mapping vegetation cover for arid and semiarid areas using a local nonlinear modelling method and landsat images[J],2020,42(3):161-169.
APA	Sun, H.,Wang, Q.,Wang, G. X.,Luo, P.,&Jiang, F. G..(2020).Improvement of mapping vegetation cover for arid and semiarid areas using a local nonlinear modelling method and landsat images.RANGELAND JOURNAL,42(3),161-169.
MLA	Sun, H.,et al."Improvement of mapping vegetation cover for arid and semiarid areas using a local nonlinear modelling method and landsat images".RANGELAND JOURNAL 42.3(2020):161-169.