干旱区生态环境知识资源中心(Arid): Dynamic monitoring of desertification in Naiman Banner based on feature space models with typical surface parameters derived from LANDSAT images

Arid

DOI	10.1002/ldr.3533
	Dynamic monitoring of desertification in Naiman Banner based on feature space models with typical surface parameters derived from LANDSAT images
	Guo, Bing 1,4,5,6,8; Zang, Wenqian 3; Han, Baomin 1; Yang, Fei 2; Luo, Wei 7; He, Tianli 1; Fan, Yewen 4; Yang, Xiao 1; Chen, Shuting 1
通讯作者	Zang, Wenqian
来源期刊	LAND DEGRADATION & DEVELOPMENT
ISSN	1085-3278
EISSN	1099-145X
出版年	2020
卷号	31 期号:12 页码:1573-1592
英文摘要	Naiman Banner is one of the most typical semi-arid vulnerable ecological zones that is characterized by vegetation degradation and severe desertification. Previous studies mostly utilized a single feature space or only the linear correlation model to monitor desertification. In this study, the optimal monitoring model that fully considers the multiple feature spaces and the nonlinear relationship between surface parameters in Naiman Banner was determined, and then the spatial-temporal evolution patterns of the desertification from 1989 to 2017 were analyzed. The results showed that: (a) The albedo-normalized difference vegetation index point-to-point model was applicable to areas with high-vegetation coverage, whereas the albedo-modified soil-adjusted vegetation index (MSAVI) linear model had better applicability in areas with relatively low vegetation coverage. Considering the surface cover, the desertification monitoring index based on albedo-MSAVI linear model had the best applicability. (b) During 1989-2017, the total desertification area percentage of Naiman County decreased from 90.85% in 1989 to 63.35% in 2017, showing a significant downward trend. (c) During the past 30 years, the desertification process was significantly influenced by human activities, such as 'Three-North' Shelterbelt, Returning Farmland to Forestry, and Returning Farmland to Grassland (d) The desertification levels of unused land and grassland with low coverage was relatively high, whereas the desertification degree of cultivated and forest lands was low. These results can be used as an effective reference for the remote sensing monitoring and evaluation of desertification and can provide data and decision support for the ecological restoration of the region.
英文关键词	albedo-NDVI point-to-point model desertification feature space Naiman Banner spatial pattern
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000514619100001
WOS类目	Environmental Sciences ; Soil Science
WOS研究方向	Environmental Sciences & Ecology ; Agriculture
来源机构	中国科学院地理科学与资源研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315126
作者单位	1.Shandong Univ Technol, Sch Civil Architectural Engn, Zibo, Shandong, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China; 3.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100101, Peoples R China; 4.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China; 5.East China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai, Peoples R China; 6.Key Lab Geomat & Digital Technol Shandong Prov, Qingdao, Peoples R China; 7.North China Inst Aerosp Engn, Collaborat Innovat Ctr Aerosp Remote Sensing Info, Langfang, Peoples R China; 8.Chinese Acad Sci, Key Lab Digital Earth Sci Inst Remote Sensing & D, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Guo, Bing,Zang, Wenqian,Han, Baomin,et al. Dynamic monitoring of desertification in Naiman Banner based on feature space models with typical surface parameters derived from LANDSAT images[J]. 中国科学院地理科学与资源研究所,2020,31(12):1573-1592.
APA	Guo, Bing.,Zang, Wenqian.,Han, Baomin.,Yang, Fei.,Luo, Wei.,...&Chen, Shuting.(2020).Dynamic monitoring of desertification in Naiman Banner based on feature space models with typical surface parameters derived from LANDSAT images.LAND DEGRADATION & DEVELOPMENT,31(12),1573-1592.
MLA	Guo, Bing,et al."Dynamic monitoring of desertification in Naiman Banner based on feature space models with typical surface parameters derived from LANDSAT images".LAND DEGRADATION & DEVELOPMENT 31.12(2020):1573-1592.