基于高光谱数据和机器学习模型的互叶女贞叶片功能特征预测

IF 3.7 4区地球科学 Q2 REMOTE SENSING

European Journal of Remote Sensing Pub Date : 2023-12-22 DOI:10.1080/22797254.2023.2294951

Wei Li, Xueyan Zuo, Zhijun Liu, Leichao Nie, Huazhe Li, Junjie Wang, Zhiguo Dou, Yang Cai, Xiajie Zhai, Lijuan Cui

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引用次数: 0

摘要

研究互花叶斯巴达的功能特征有助于了解其入侵机制，并开发一种方法来改善其在沿海地区的管理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Predictions of Spartina alterniflora leaf functional traits based on hyperspectral data and machine learning models

Investigating the functional traits of Spartina alterniflora can provide insights towards understanding its invasion mechanism, and developing a method leaves can improve its management in coastal ...

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来源期刊

European Journal of Remote Sensing REMOTE SENSING-

CiteScore

7.00

自引率

2.50%

发文量

审稿时长

>12 weeks

期刊介绍： European Journal of Remote Sensing publishes research papers and review articles related to the use of remote sensing technologies. The Journal welcomes submissions on all applications related to the use of active or passive remote sensing to terrestrial, oceanic, and atmospheric environments. The most common thematic areas covered by the Journal include: -land use/land cover -geology, earth and geoscience -agriculture and forestry -geography and landscape -ecology and environmental science -support to land management -hydrology and water resources -atmosphere and meteorology -oceanography -new sensor systems, missions and software/algorithms -pre processing/calibration -classifications -time series/change analysis -data integration/merging/fusion -image processing and analysis -modelling European Journal of Remote Sensing is a fully open access journal. This means all submitted articles will, if accepted, be available for anyone to read anywhere, at any time, immediately on publication. There are no charges for submission to this journal.