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An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery

文献类型：期刊论文

作者Zhu, Jun1,2; Pan, Ziwu1,2; Wang, Hang1,2,3; Huang, Peijie4; Sun, Jiulin5; Qin, Fen1,2,6; Liu, Zhenzhen1,2
刊名SENSORS
出版日期2019-05-01
卷号19期号:9页码:16
关键词remote sensing Sentinel-2 tea plantation identification Random Forest algorithm feature selection China
ISSN号1424-8220
DOI10.3390/s19092087
通讯作者Qin, Fen(qinfun@126.com)
英文摘要As tea is an important economic crop in many regions, efficient and accurate methods for remotely identifying tea plantations are essential for the implementation of sustainable tea practices and for periodic monitoring. In this study, we developed and tested a method for tea plantation identification based on multi-temporal Sentinel-2 images and a multi-feature Random Forest (RF) algorithm. We used phenological patterns of tea cultivation in China's Shihe District (such as the multiple annual growing, harvest, and pruning stages) to extracted multi-temporal Sentinel-2 MSI bands, their derived first spectral derivative, NDVI and textures, and topographic features. We then assessed feature importance using RF analysis; the optimal combination of features was used as the input variable for RF classification to extract tea plantations in the study area. A comparison of our results with those achieved using the Support Vector Machine method and statistical data from local government departments showed that our method had a higher producer's accuracy (96.57%) and user's accuracy (96.02%). These results demonstrate that: (1) multi-temporal and multi-feature classification can improve the accuracy of tea plantation recognition, (2) RF classification feature importance analysis can effectively reduce feature dimensions and improve classification efficiency, and (3) the combination of multi-temporal Sentinel-2 images and the RF algorithm improves our ability to identify and monitor tea plantations.
WOS关键词CLASSIFIER
资助项目National Science and Technology Platform Construction Project of China[2005DKA32300] ; Major Projects of the Ministry of Education Base[16JJD770019]
WOS研究方向Chemistry ; Electrochemistry ; Instruments & Instrumentation
语种英语
WOS记录号WOS:000469766800131
出版者MDPI
资助机构National Science and Technology Platform Construction Project of China ; Major Projects of the Ministry of Education Base
源URL[http://ir.igsnrr.ac.cn/handle/311030/58970]  
专题中国科学院地理科学与资源研究所
通讯作者Qin, Fen
作者单位1.Henan Univ, Coll Environm & Planning, Kaifeng 475004, Peoples R China
2.Henan Univ, Minist Educ, Lab Geospatial Technol Middle & Lower Yellow Rive, Kaifeng 475004, Peoples R China
3.Hanshan Normal Univ, Dept Geog, Chaozhou 521041, Peoples R China
4.Yellow River Engn Consulting Co Ltd, Zhengzhou 450003, Henan, Peoples R China
5.Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
6.Henan Univ, Henan Ind Technol Acad Spatiotemporal Big Data, Kaifeng 475004, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Jun,Pan, Ziwu,Wang, Hang,et al. An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery[J]. SENSORS,2019,19(9):16.
APA Zhu, Jun.,Pan, Ziwu.,Wang, Hang.,Huang, Peijie.,Sun, Jiulin.,...&Liu, Zhenzhen.(2019).An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery.SENSORS,19(9),16.
MLA Zhu, Jun,et al."An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery".SENSORS 19.9(2019):16.

入库方式： OAI收割

来源：地理科学与资源研究所

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