Abstract
Tea tree is an economically important crop. The rapid and efficient mapping of the distribution and dynamic changes in tea plantations informs decision making for government departments. It also plays an important role in rational tea planting and environmental governance. This study used the 10 m Sentinel-2 image data to map the spatial distribution of tea plantations in Yingde City, China. In this article, we analyzed the differences in vegetation and texture characteristics between the new and mature tea plantations. We found that the texture features of the new and mature tea plantations were significantly different in contrast, which can be used as an index for tea plantation extraction. Moreover, we selected machine learning classifiers, including support vector machine (SVM) and random forests (RF) method, which were utilized to extract the preliminary classification to complete spatial distribution mapping of tea plantations. The overall accuracy of SVM and RF was 90.79% and 89.42%, respectively, and the kappa coefficient was 0.88 and 0.86, respectively. SVM had the highest overall accuracy in terms of tea plantation distribution at the regional scale. These results demonstrate that: (1) separating tea plantations into mature and new tea plantations, taking into account vegetation and texture features such as soil brightness index and contrast, will help improve the accuracy of tea plantation classification and (2) using multi-period images combined with machine learning classification methods can improve the efficiency and accuracy of tea plantation identification.
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Data availability
The datasets generated during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
We are grateful to Yingde City Agriculture and Rural Bureau for the statistical data support. We also greatly appreciated suggestions from anonymous reviewers and editor staff for the improvement of our manuscript.
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This work was supported by Guangdong Province Key Field R&D Program Project (Project No. 2018B020241001).
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All authors prepared the methodological concept of this study, the original draft, as well as the editing of the manuscript. All authors have read and agreed to the published version of the manuscript. All authors have read and agreed to the published version of the manuscript.
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Chen, P., Zhao, C., Duan, D. et al. Extracting tea plantations in complex landscapes using Sentinel-2 imagery and machine learning algorithms. COMMUNITY ECOLOGY 23, 163–172 (2022). https://doi.org/10.1007/s42974-022-00077-8
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DOI: https://doi.org/10.1007/s42974-022-00077-8