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

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 identificati...

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Autores principales: Zhu, Jun, Pan, Ziwu, Wang, Hang, Huang, Peijie, Sun, Jiulin, Qin, Fen, Liu, Zhenzhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540259/
https://www.ncbi.nlm.nih.gov/pubmed/31060327
http://dx.doi.org/10.3390/s19092087
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author Zhu, Jun
Pan, Ziwu
Wang, Hang
Huang, Peijie
Sun, Jiulin
Qin, Fen
Liu, Zhenzhen
author_facet Zhu, Jun
Pan, Ziwu
Wang, Hang
Huang, Peijie
Sun, Jiulin
Qin, Fen
Liu, Zhenzhen
author_sort Zhu, Jun
collection PubMed
description 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.
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spelling pubmed-65402592019-06-04 An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery Zhu, Jun Pan, Ziwu Wang, Hang Huang, Peijie Sun, Jiulin Qin, Fen Liu, Zhenzhen Sensors (Basel) Article 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. MDPI 2019-05-05 /pmc/articles/PMC6540259/ /pubmed/31060327 http://dx.doi.org/10.3390/s19092087 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhu, Jun
Pan, Ziwu
Wang, Hang
Huang, Peijie
Sun, Jiulin
Qin, Fen
Liu, Zhenzhen
An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery
title An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery
title_full An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery
title_fullStr An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery
title_full_unstemmed An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery
title_short An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery
title_sort improved multi-temporal and multi-feature tea plantation identification method using sentinel-2 imagery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540259/
https://www.ncbi.nlm.nih.gov/pubmed/31060327
http://dx.doi.org/10.3390/s19092087
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