Research progress and development trend of remote sensing information extraction methods of vegetation
HUANG Pei1(), PU Junwei1, ZHAO Qiaoqiao1, LI Zhongjie2, SONG Haokun3, ZHAO Xiaoqing3()
1. Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China 2. Simao Jinlancang High-Yield Plantation Ltd., Pu’er 665699, China 3. School of Earth Science, Yunnan University, Kunming 650500, China
The remote sensing information extraction of vegetation is the basis and key link for remote sensing investigation and dynamic monitoring of vegetation coverage, which is of great significance for regional ecological environment protection and sustainable development. For this purpose, the research progress on the remote sensing information extraction methods of vegetation was reviewed from prior knowledge, expert knowledge and related auxiliary information, extraction of vegetation phenological features, the fusion of multi-source remote sensing data, machine learning, and other methods. Then, the main problems and challenges existing at the present stage were pointed out, and the future development trend was put forward. The research shows that there are many methods to extract remote sensing information about vegetation, and different methods have their own advantages and disadvantages in the application. However, the research on remote sensing information extraction methods of vegetation is currently facing many challenges, such as the lack of openness of high-resolution remote sensing data, the poor stability of parameter settings in vegetation information extraction models, the prominent phenomenon of same objects with different spectra and different objects with the same spectrum, the difficulties in automatic extraction of vegetation remote sensing information based on an expert knowledge base, and the need in further research on the multiple-method fusion. Therefore, making more breakthroughs in integrating multi-source data, multiple methods and new features of multi-temporal remote sensing images will be necessary to promote the refined, automated, and intelligent development of remote sensing information extraction of vegetation.
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2022, Vol. 34 
), 普军伟1, 赵巧巧1, 李忠杰2, 宋浩昆3, 赵筱青3(
