干旱区生态环境知识资源中心(Arid): An Ensemble Learning Approach for Land Use/Land Cover Classification of Arid Regions for Climate Simulation: A Case Study of Xinjiang, Northwest China

Arid

DOI	10.1109/JSTARS.2023.3247624
	An Ensemble Learning Approach for Land Use/Land Cover Classification of Arid Regions for Climate Simulation: A Case Study of Xinjiang, Northwest China
	Du, Haoyang; Li, Manchun; Xu, Yunyun; Zhou, Chen
通讯作者	Zhou, C
来源期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN	1939-1404
EISSN	2151-1535
出版年	2023
卷号	16 页码:2413-2426
英文摘要	Accurate classifications of land use/land cover (LULC) in arid regions are vital for analyzing changes in climate. We propose an ensemble learning approach for improving LULC classification accuracy in Xinjiang, northwest China. First, multisource geographical datasets were applied, and the study area was divided into Northern Xinjiang, Tianshan, and Southern Xinjiang. Second, five machine learning algorithms-k-nearest neighbor, support vector machine (SVM), random forest (RF), artificial neural network (ANN), and C4.5-were chosen to develop different ensemble learning strategies according to the climatic and topographic characteristics of each subregion. Third, stratified random sampling was used to obtain training samples and optimal parameters for each machine learning algorithm. Lastly, each derived approach was applied across Xinjiang, and subregion performance was evaluated. The results showed that the LULC classification accuracy achieved across Xinjiang via the proposed ensemble learning approach was improved by >= 6.85% compared with individual machine learning algorithms. By specific subregion, the accuracies for Northern Xinjiang, Tianshan, and Southern Xinjiang increased by >= 6.70%, 5.87%, and 6.86%, respectively. Moreover, the ensemble learning strategy combining four machine learning algorithms (i.e., SVM, RF, ANN, and C4.5) was superior across Xinjiang and Tianshan; whereas, the three-algorithm (i.e., SVM, RF, and ANN) strategy worked best for the Northern and Southern Xinjiang. The innovation of this study is to develop a novel ensemble learning approach to divide Xinjiang into different subregions, accurately classify land cover, and generate a new land cover product for simulating climate change in Xinjiang.
英文关键词	Ensemble learning Classification algorithms Remote sensing Support vector machines Machine learning algorithms Climate change Radio frequency Arid areas China ensemble learning land use land cover classification machine learning
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000952892000003
WOS关键词	SENSING IMAGE CLASSIFICATION ; IGBP DISCOVER ; SYSTEM ; ACCURACY ; DATABASE ; MODIS ; RASTERIZATION ; INFORMATION ; ALGORITHMS ; CONVERSION
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396906
推荐引用方式 GB/T 7714	Du, Haoyang,Li, Manchun,Xu, Yunyun,et al. An Ensemble Learning Approach for Land Use/Land Cover Classification of Arid Regions for Climate Simulation: A Case Study of Xinjiang, Northwest China[J],2023,16:2413-2426.
APA	Du, Haoyang,Li, Manchun,Xu, Yunyun,&Zhou, Chen.(2023).An Ensemble Learning Approach for Land Use/Land Cover Classification of Arid Regions for Climate Simulation: A Case Study of Xinjiang, Northwest China.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,16,2413-2426.
MLA	Du, Haoyang,et al."An Ensemble Learning Approach for Land Use/Land Cover Classification of Arid Regions for Climate Simulation: A Case Study of Xinjiang, Northwest China".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 16(2023):2413-2426.