Mapping tea plantations using multitemporal spectral features by harmonised Sentinel-2 and Landsat images in Yingde, China
文献类型: 外文期刊
作者: Qi, Ning 1 ; Yang, Hao 2 ; Shao, Guowen 2 ; Chen, Riqiang 1 ; Wu, Baoguo 1 ; Xu, Bo 2 ; Feng, Haikuan 2 ; Yang, Guijun 2 ; Zhao, Chunjiang 1 ;
作者机构: 1.Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
3.Univ Sci & Technol Beijing, Sch Chem & Bioengn, Beijing 100083, Peoples R China
4.Changan Univ, Coll Geol Engn & Geomat, Xian 710054, Peoples R China
关键词: Tea plantation mapping; Multitemporal spectral features; Remote sensing
期刊名称:COMPUTERS AND ELECTRONICS IN AGRICULTURE ( 影响因子:8.3; 五年影响因子:8.3 )
ISSN: 0168-1699
年卷期: 2023 年 212 卷
页码:
收录情况: SCI
摘要: Tea is a highly demanded and valuable commodity in both domestic and international markets, with China being the world's largest producer and exporter, resulting in the expansion of tea tree cultivation in China over the past few decades. Accurate mapping of tea plantations is vital for tea markets, food security, poverty alleviation, and ecosystem value assessment. However, research on vegetation plantation monitoring has focused primarily on food crops, and the spatial information for the perennial tea tree is often neglected. Moreover, monitoring tea plantations poses significant challenges in terms of the diverse cultivation management measures, the heterogeneity of tea garden landscapes, and the limited availability of clear imagery due to cloud cover. In this study, we constructed a novel tea plantation mapping algorithm based on multitemporal spectral features. In addition, a Tea Plantation Phenological Feature Index (TPI) is proposed to enable easy and reliable monitoring of tea plantations. The algorithm harmonized the Landsat7/8 and Sentinel-2 satellite images on the Google Earth Engine (GEE) platform, utilizing a semi-automatic decision tree model to identify tea gardens in Yingde City, Guangdong Province. Our results show that the tea plantation phenological feature index (TPI), green band in October (Green(Oct)) and cumulative band in April (CBApr) are the most effective multitemporal spectral features for identifying tea gardens. The F1-score, OA, and kappa coefficients (Kappa) of the 2020 tea plantation map generated by the tea plantation mapping algorithm based on multitemporal spectral features are 90.75%, 92.71%, and 0.844, respectively. Furthermore, the algorithm is time-reusable, as evidenced by the F1-score, OA, and kappa coefficients of the generated 2021 tea plantation map, which are 85.99%, 88.86%, and 0.7675, respectively. Our results underscore the exceptional ability of the multitemporal spectral features to separate tea gardens from evergreen forests. The tea plantation mapping algorithm based on multitemporal spectral features is a robust, user-friendly, and effective tool suitable for regional or national annual tea plantation mapping, enabling sustainable production and implementing.
- 相关文献
作者其他论文 更多>>
-
Recognition of wheat rusts in a field environment based on improved DenseNet
作者:Chang, Shenglong;Cheng, Jinpeng;Fan, Zehua;Ma, Xinming;Li, Yong;Zhao, Chunjiang;Chang, Shenglong;Yang, Guijun;Cheng, Jinpeng;Fan, Zehua;Yang, Xiaodong;Zhao, Chunjiang
关键词:Plant disease; Wheat rust; Image processing; Deep learning; Computer vision (CV); DenseNet
-
Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images
作者:Wang, Gengze;Chen, Riqiang;Yang, Guijun;Feng, Haikuan;Wang, Gengze;Chen, Riqiang;Yang, Guijun;Feng, Haikuan;Meng, Di;Jin, Hailiang;Ge, Xiaosan;Wang, Laigang;Feng, Haikuan
关键词:early-season rice mapping; spectral index (SI); synthetic aperture radar (SAR); Simple Non-Iterative Clustering (SNIC); time series filtering; K-Means; Jeffries-Matusita (JM) distance
-
GCVC: Graph Convolution Vector Distribution Calibration for Fish Group Activity Recognition
作者:Zhao, Zhenxi;Zhao, Chunjiang;Zhao, Zhenxi;Yang, Xinting;Zhou, Chao;Zhao, Chunjiang;Zhao, Zhenxi;Yang, Xinting;Zhou, Chao;Zhao, Chunjiang;Zhao, Zhenxi;Yang, Xinting;Zhou, Chao;Zhao, Chunjiang;Liu, Jintao
关键词:Fish; Feature extraction; Activity recognition; Calibration; Adhesives; Training; Convolution; Graph convolution vector calibration; fish group activity; activity feature vector calibration; fish activity dataset
-
Adaptive precision cutting method for rootstock grafting of melons: modeling, analysis, and validation
作者:Chen, Shan;Zhao, Chunjiang;Chen, Shan;Jiang, Kai;Zheng, Wengang;Jia, Dongdong;Zhao, Chunjiang;Jiang, Kai;Zheng, Wengang;Jia, Dongdong;Zhao, Chunjiang
关键词:Melon; Grafting robot; Adaptive cutting; Rootstock pith cavity; Machine vision
-
Long-range infrared absorption spectroscopy and fast mass spectrometry for rapid online measurements of volatile organic compounds from black tea fermentation
作者:Yang, Chongshan;Li, Guanglin;Zhao, Chunjiang;Fu, Xinglan;Yang, Chongshan;Jiao, Leizi;Wen, Xuelin;Lin, Peng;Duan, Dandan;Zhao, Chunjiang;Dong, Daming;Yang, Chongshan;Jiao, Leizi;Wen, Xuelin;Lin, Peng;Duan, Dandan;Dong, Daming;Dong, Chunwang
关键词:Black tea fermentation; Volatile organic compounds; Proton transfer reaction mass spectrometry; Fourier transform infrared spectroscopy; Principal component analysis; Extreme learning machine
-
Comparison of three models for winter wheat yield prediction based on UAV hyperspectral images
作者:Xu, Xiaobin;Teng, Cong;Zhu, Hongchun;Li, Zhenhai;Teng, Cong;Feng, Haikuan;Zhao, Yu
关键词:hyperspectral imagery; unmanned aerial vehicle; winter wheat; yield prediction model; remote sensing
-
Navigation line extraction algorithm for corn spraying robot based on YOLOv8s-CornNet
作者:Guo, Peiliang;Diao, Zhihua;Ma, Shushuai;He, Zhendong;Zhao, Suna;Zhao, Chunjiang;Li, Jiangbo;Zhang, Ruirui;Yang, Ranbing;Zhang, Baohua
关键词:agricultural robotics; computer vision; deep learning; navigation line extraction; network lightweight