干旱区生态环境知识资源中心(Arid): Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms

Arid

DOI	10.1016/j.gecco.2020.e00971
	Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms
	Ge, Genbatu 1,2,3; Shi, Zhongjie 1; Zhu, Yuanjun 1; Yang, Xiaohui 1,3; Hao, Yuguang 2,3
通讯作者	Yang, Xiaohui ; Hao, Yuguang
来源期刊	GLOBAL ECOLOGY AND CONSERVATION
ISSN	2351-9894
出版年	2020
卷号	22
英文摘要	The importance of land use and cover change (LUCC) has gradually attracted more attention due to its influence on the climate and ecosystem. Consequently, the necessity of accurate LUCC mapping has become increasingly apparent. Over the past decades, although a large number of machine learning algorithms have been developed to improve the accuracy and reliability of remote sensing image classification, especially for LUCC classification, there is a lack of studies that assess the performance of machine learning algorithms in arid desert-oasis mosaic landscapes. In this study, the main objective is to provide a reference for the extraction of LUCC information in dryland regions with oasis-desert mosaic landscapes by comparing the performances of the k-nearest neighbor (KNN), random forest (RF), support vector machine (SVM) and artificial neural network (ANN) for the LUCC classification of the Dengkou Oasis, China. Landsat-8 Operational Land Imager (OLI) image data were used with spectral indices and auxiliary variables that were derived from a digital terrain model to classify 7 different land cover categories. The highest overall accuracy was produced by the ANN (97.16%), which was closely followed by the RF (96.92%), SVM (96.20%), and finally KNN (93.98%); statistically similar accuracies were obtained for the ANN, SVM and RF. The RF algorithm performed well across several aspects, such as stability, ease of use and processing time during the parameter tuning. Overall, the random forest algorithm is a good first choice method for land-cover classification in this study area, and the elevation and some spectral indices, such as the NDVI, MSAVI2 and MNDWI, should be used as variables to improve the overall accuracy. (C) 2020 The Authors. Published by Elsevier B.V.
英文关键词	Land use/cover change Machine learning algorithms Arid area Desert-oasis mosaic landscape
类型	Article
语种	英语
国家	Peoples R China
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000539265300007
WOS关键词	COVER CLASSIFICATION ; RANDOM FOREST ; TRAINING DATA ; LIDAR DATA ; VEGETATION ; DYNAMICS ; OPTIMIZATION ; NETWORK ; SOIL ; ACCURACY
WOS类目	Biodiversity Conservation ; Ecology
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/319237
作者单位	1.Chinese Acad Forestry, Inst Desertificat Studies, Beijing 100091, Peoples R China; 2.Chinese Acad Forestry, Expt Ctr Desert Forestry, Dengkou 015200, Inner Mongolia, Peoples R China; 3.State Forestry Adm, Dengkou Desert Ecosyst Res Stn, Dengkou 015200, Peoples R China
推荐引用方式 GB/T 7714	Ge, Genbatu,Shi, Zhongjie,Zhu, Yuanjun,et al. Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms[J],2020,22.
APA	Ge, Genbatu,Shi, Zhongjie,Zhu, Yuanjun,Yang, Xiaohui,&Hao, Yuguang.(2020).Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms.GLOBAL ECOLOGY AND CONSERVATION,22.
MLA	Ge, Genbatu,et al."Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms".GLOBAL ECOLOGY AND CONSERVATION 22(2020).