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An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery
Zhu, Jun1,2; Pan, Ziwu1,2; Wang, Hang1,2,3; Huang, Peijie4; Sun, Jiulin5; Qin, Fen1,2,6; Liu, Zhenzhen1,2
2019-05-01
Source PublicationSENSORS
ISSN1424-8220
Volume19Issue:9Pages:16
Corresponding AuthorQin, Fen(qinfun@126.com)
AbstractAs 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.
Keywordremote sensing Sentinel-2 tea plantation identification Random Forest algorithm feature selection China
DOI10.3390/s19092087
WOS KeywordCLASSIFIER
Indexed BySCI
Language英语
Funding ProjectNational Science and Technology Platform Construction Project of China[2005DKA32300] ; Major Projects of the Ministry of Education Base[16JJD770019]
Funding OrganizationNational Science and Technology Platform Construction Project of China ; Major Projects of the Ministry of Education Base
WOS Research AreaChemistry ; Electrochemistry ; Instruments & Instrumentation
WOS SubjectChemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation
WOS IDWOS:000469766800131
PublisherMDPI
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/58971
Collection中国科学院地理科学与资源研究所
Corresponding AuthorQin, Fen
Affiliation1.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
Recommended Citation
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.
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