Identification and Classification of Degradation-Indicator Grass Species in a Desertified Steppe Based on HSI-UAV

IF 0.8 4区 化学 Q4 SPECTROSCOPY Spectroscopy Pub Date : 2023-11-01 DOI:10.56530/spectroscopy.dr5881c1
Xinchao Gao, Fei Hao, W. Pi, Xiangbing Zhu, Tao Zhang, Yuge Bi, Yanbin Zhang
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Abstract

The emergence and number of grassland degradation-indicator grass species are important in evaluating the extent of grassland degradation. Plant populations in desertified steppe are distributed randomly and at low density. Specifically, degradation-indicator grass species mainly exist as individuals, making spectrum-based identification difficult. Here, a low-altitude unmanned aerial vehicle (UAV) hyperspectral remote-sensing system was constructed to identify the typical degradation-indicator grass species of a desertified steppe in China. The ASI index (Artemisia frigida Willd. and Stipa breviflora Grisb. index) and classification rules were proposed and applied. We implemented a comprehensive application of amplified differences in spectral characteristics between vegetation communities and assigned plant senescence reflectance-index bands, using the characteristics of the plant populations under observation and UAV hyperspectral remote-sensing data, to solve the problems resulting from high similarity while identifying ground objects. Our results lay a solid foundation for monitoring and evaluating desertified steppe degradation-indicator grass species based on remote sensing.
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基于 HSI-UAV 的荒漠化草原退化指示草种的识别与分类
草原退化指示草种的出现和数量对于评估草原退化程度非常重要。荒漠化草原上的植物种群随机分布,密度较低。具体而言,退化指示草种主要以个体形式存在,因此难以进行光谱识别。本文构建了一个低空无人飞行器(UAV)高光谱遥感系统,以识别中国荒漠化草原上典型的退化指示草种。提出并应用了ASI指数(Artemisia frigida Willd.和Stipa breviflora Grisb.指数)和分类规则。我们综合应用了植被群落间光谱特征的放大差异,利用观测到的植物种群特征和无人机高光谱遥感数据,分配了植物衰老反射率指数波段,解决了在识别地面目标时因相似度高而产生的问题。我们的研究成果为基于遥感技术监测和评估荒漠化草原退化指示草种奠定了坚实的基础。
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来源期刊
Spectroscopy
Spectroscopy 物理-光谱学
CiteScore
1.10
自引率
0.00%
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0
审稿时长
3 months
期刊介绍: Spectroscopy welcomes manuscripts that describe techniques and applications of all forms of spectroscopy and that are of immediate interest to users in industry, academia, and government.
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