A Novel Strategy for Constructing Ecological Index of Tea Plantations Integrating Remote Sensing and Environmental Data

文献类型： 外文期刊

第一作者： Mao, Yilin

作者： Mao, Yilin;Li, He;Shen, Jiazhi;Ding, Zhaotang;Sun, Litao;Wang, Yu;Xu, Yang;Fan, Kai;Han, Xiao;Ma, Qingping;Shi, Hongtao;Bi, Caihong;Feng, Yunlai

作者机构：

关键词： Plantations; Monitoring; Remote sensing; Temperature sensors; Temperature measurement; Ecosystems; Humidity; Convolutional neural networks gate recurrent unit (CNN-GRU); ecological tea plantation; environmental parameters; multisource remote sensing; plant community; UAV

期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （ 影响因子：4.7； 五年影响因子：5.0 ）

ISSN： 1939-1404

年卷期： 2024 年 17 卷

页码：

收录情况： SCI

摘要： The structure of plant communities and their response to temperature variations are an essential basis for evaluating the ecological structure and function of tea plantations. However, field surveys and quantitative evaluation of plant communities and ecotea plantations remain challenging. In this study, a novel strategy was proposed for rapid surveillance of plant community structure and its response to changes in weather conditions in tea plantations. This strategy aims to construct the normalized tea plantation ecological index (NTEI) by synergizing environmental parameters with multisource remote sensing data; establish the fitting and inversion model of NTEI by cascading the Fourier function with the convolutional neural networks gate recurrent unit (CNN-GRU) network; and evaluate the variability of the plant community in tea plantations by analyzing the variation characteristics of the NTEI and the measured temperature. The study revealed the following: First, the NTEI can objectively characterize the plant communities of tea plantations, and its variation characteristics were consistent with the changes in vegetation phenology and temperature; second, the Fourier function has the potential to quantify NTEI, and it is fitting R-2 for the NTEI of nine plant communities ranged from 0.840 to 0.921; third, the CNN-GRU has the most advantage in establishing the prediction model of NTEI, and its prediction accuracy was Rp(2) = 0.955 and RMSEP = 0.314; and fourth, the plant communities with high species richness increased regional ecological stability, had a strong buffering capacity against temperature changes, and had less variability in NTEI. The results provide significant guidance for building plant community structures and improving the ecological benefits of tea plantations.

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