Extracting Tea Plantations from Multitemporal Sentinel-2 Images Based on Deep Learning Networks

Zhongxi Yao, Xiaochen Zhu, Yan Zeng () and Xinfa Qiu
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Zhongxi Yao: School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Xiaochen Zhu: School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yan Zeng: Key Laboratory of Transportation Meteorology, China Meteorological Administration, Nanjing 210041, China
Xinfa Qiu: School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

Agriculture, 2022, vol. 13, issue 1, 1-17

Abstract: Tea is a special economic crop that is widely distributed in tropical and subtropical areas. Timely and accurate access to the distribution of tea plantation areas is crucial for effective tea plantation supervision and sustainable agricultural development. Traditional methods for tea plantation extraction are highly dependent on feature engineering, which requires expensive human and material resources, and it is sometimes even difficult to achieve the expected results in terms of accuracy and robustness. To alleviate such problems, we took Xinchang County as the study area and proposed a method to extract tea plantations based on deep learning networks. Convolutional neural network (CNN) and recurrent neural network (RNN) modules were combined to build an R-CNN model that can automatically obtain both spatial and temporal information from multitemporal Sentinel-2 remote sensing images of tea plantations, and then the spatial distribution of tea plantations was predicted. To confirm the effectiveness of our method, support vector machine (SVM), random forest (RF), CNN, and RNN methods were used for comparative experiments. The results show that the R-CNN method has great potential in the tea plantation extraction task, with an F1 score and IoU of 0.885 and 0.793 on the test dataset, respectively. The overall classification accuracy and kappa coefficient for the whole region are 0.953 and 0.904, respectively, indicating that this method possesses higher extraction accuracy than the other four methods. In addition, we found that the distribution index of tea plantations in mountainous areas with gentle slopes is the highest in Xinchang County. This study can provide a reference basis for the fine mapping of tea plantation distributions.

Keywords: tea plantation extraction; deep learning; remote sensing images; CNN; RNN (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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