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Research on the Classification of Yingde Tea Plantations Based on Time Series Sentinel-2 Images |
CHEN Pan-pan, REN Yan-min*, ZHAO Chun-jiang, LI Cun-jun, LIU Yu* |
Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
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Abstract Tea is a high value-added economic crop with extremely high economic value. It is the main starting point for rural revitalization in mountainous areas of China. However, due to destructive behaviors such as deforestation and planting tea, forest resources are destroyed, and ecological and environmental problems such as soil erosion are caused. Acquiring the spatial distribution of tea plantations quickly and accurately is very important for government supervision and the planning and development of the tea industry. However, due to the rainy weather in the study area and the scattered distribution of tea plantations, which are close to the spectrum of vegetation such as forests, the extraction based on satellite imagery has become a problem. Tea plantations are challenging. In order to find out the spatial distribution of tea plantations in Yingde, this paper systematically analyzes the application potential of medium and high-resolution multispectral Sentinel-2 image data combined with multi-time-series and multi-feature information in tea garden extraction. Taking the whole territory of Yingde as the research area, this paper selects 9 phases of Sentinel-2 image data from 2019 to 2021 to analyze the phenological characteristics of tea tree growth in detail and further explore the characteristics changes of tea plantations and other land types in multiple time series, using the Relief algorithm to sort the importance of all features. According to the result of feature sorting, the feature factors weighted by 90% of the feature weight value are selected, namely 7 vegetation index features and 2 texture features, and 9 kinds of tea garden classification scenes are constructed through different combination rankings, and the RF algorithm is used to evaluate the accuracy of all classification scenes. To select the best classification scene and further discuss the feasibility of the RF classification algorithm and SVM classification algorithm for tea garden extraction. The results show that: (1) When extracting tea plantations in Yingde, February and October are the best combinations to construct multiple characteristics of tea plantations using multi-temporal phases. (2) Compared with the SVM classification method, the RF classification method has high accuracy. Its overall accuracy reaches 91.56%, the Kappa coefficient is 0.89, and the producer accuracy and user accuracy are 80.22% and 84.56%, respectively. This study provides an efficient method for quickly and efficiently obtaining the spatial distribution information of tea plantations in Yingde and provides data support for the government to plan and manage the tea industry.
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Received: 2022-09-02
Accepted: 2023-02-20
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Corresponding Authors:
REN Yan-min, LIU Yu
E-mail: renym@nercita.org.cn; liuyu@nercita.org.cn
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[1] Su S, Chen W, Jing L, et al. Land Use Policy, 2017, 66: 183.
[2] ZHOU Shu-dong, WU Xin-lin(周曙东, 吴新林). Journal of Tea Communication(茶叶通讯),2020, 47(3): 6.
[3] Liu S, Yin Y, Liu X, et al. Ecological Modelling, 2017, 353: 129.
[4] Su S,Zhou X,Wan C,et al. Land Use Policy,2016,50: 379.
[5] Zhu J, Pan Z, Wang H, et al. Sensors, 2019, 19(9): 2087.
[6] Chuang Y C M, Shiu Y S. Sensors, 2016, 16(5): 594.
[7] Dihkan M, Guneroglu N, Karsli F, et al. International Journal of Remote Sensing, 2013, 34(23): 8549.
[8] XU Wei-yan, SUN Rui, JIN Zhi-feng(徐伟燕, 孙 睿, 金志凤). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(S1): 161.
[9] MA Chao, YANG Fei, WANG Xue-cheng(马 超, 杨 飞, 王学成). Remote Sensing for Land & Resources(国土资源遥感), 2019, 31(1): 141.
[10] Das A C. Remote Sensing, 2020, 12(24): 25.
[11] YANG Yan-kui, CHEN Yun-zhi, WU Bo, et al(杨艳魁, 陈芸芝, 吴 波, 等). Jiangsu Agricultural Sciences(江苏农业科学), 2019, 47(2): 210.
[12] TIAN Ying, CHEN Zhuo-qi, HUI Feng-ming et al(田 颖, 陈卓奇, 惠凤鸣, 等). Journal of Beijing Normal University(Natural Science)[北京师范大学学报(自然科学版)], 2019, 55(1): 57.
[13] Kim H O, Yeom J M. International Journal of Remote Sensing, 2014, 35(19-20): 7046.
[14] Rim M, Makram A. International Journal of Remote Sensing, 2020, 41(23): 8986.
[15] Markus I, Francesco V, Clement A. Remote Sensing, 2016, 8(3): 166.
[16] Jesús Soriano-Gonzalez, Eduard Angelats, Maite Martínez-Eixarch, et al. Field Crops Research, 2022, 281: 108507.
[17] Yu Shen, Xiaoyang Zhang, Zhengwei Yang. ISPRS Journal of Photogrammetry and Remote Sensing,2022, 186:55.
[18] Jie Wang, Xiangming Xiao, Luo Liu, et al. Remote Sensing of Environment, 2020, 247:111951.
[19] LI Long-wei, LI Nan, LU Deng-sheng(李龙伟, 李 楠, 陆灯盛). Journal of Zhejiang A&F University(浙江农林大学学报), 2019, 36(5): 841.
[20] ZHAO Xiao-qing, WANG Ping, JING Lin-hai, et al(赵晓晴, 王 萍, 荆林海, 等). Science of Surveying and Mapping(测绘科学), 2020, 45(6): 80.
[21] LI Dan-xia, FANG Wei, LIANG Jun-fen, et al(李丹霞, 方 伟, 梁俊芬, 等). Tropical Agricultural Engineering(热带农业工程), 2019, 43(6): 73.
[22] CHEN Hui-ying, CAO Jun-xi, SUN Shi-li, et al(陈慧英, 操君喜, 孙世利, 等). Guangdong Agricultural Sciences(广东农业科学), 2020, 47(11): 209.
[23] Xia Z, Wang X, Cao G, et al. Journal of Agricultural Science, 2017, 9(4): 116.
[24] Fern R R, Foxley E A, Bruno A, et al. Ecological Indicators, 2018, 94: 16.
[25] WU Jing, LÜ Yu-na, LI Chun-bin, et al(吴 静, 吕玉娜, 李纯斌, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50(9): 194.
[26] ZHOU Jing-ping,LI Cun-jun,SHI Lei-gang,et al(周静平, 李存军, 史磊刚, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报),2016, 47(9): 318.
[27] LIN Tao, ZHANG Da, WANG Jian-jun(林 涛, 张 达, 王建君). Computer Simulation(计算机仿真), 2021, 38(9): 414.
[28] WANG Geng-ze, JIN Hai-liang, GU Xiao-he, et al(王庚泽, 靳海亮, 顾晓鹤, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报),2021, 52(2): 199.
[29] Zhang F, Yang M J. Remote Sensing of Environment, 2020, 251: 112105.
[30] Musbah H, Aly H H, Little T A. Electric Power Systems Research, 2021, 199: 107436.
[31] WANG Bin, HE Bing-hui, LIN Na, et al(王 斌, 何丙辉, 林 娜, 等). Journal of Jilin University (Engineering and Technology Edition)[吉林大学学报(工学版)], 2022, 52(7): 1719.
[32] BAI Jia, SUN Rui, ZHANG He-lin, et al(柏 佳, 孙 睿, 张赫林, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2021, 37(14): 179.
[33] Chen Panpan, Li Cunjun, Chen Shilin, et al. Remote Sensing,2022, 14, 2412.
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