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.