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祁连山中部金露梅种群生长过程中的植被光谱特征

乔雨1,赵传燕2*,戎占磊2,解欢欢1,高婵婵1,高云飞1,王方圆1#br#   

  1. 1兰州大学生命科学学院, 草地农业生态系统国家重点实验室, 兰州 730000; 2兰州大学草地农业科学与技术学院, 草地农业生态系统国家重点实验室, 兰州 730000)
  • 出版日期:2017-01-10 发布日期:2017-01-10

Potentilla froticosa population| physiological and ecological characteristics| vegetation spectral| vegetation index| dynamic change.

QIAO Yu1, ZHAO Chuan-yan2*, RONG Zhan-lei2, XIE Huan-huan1, GAO Chan-chan1, GAO Yun-fei1, WANG Fang-yuan1#br#   

  1. (1State Key Laboratory of Grassland and AgroEcosystem, School of Life Science, Lanzhou University, Lanzhou 730000, China; 2State Key Laboratory of Grassland and AgroEcosystem, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China).
  • Online:2017-01-10 Published:2017-01-10

摘要: 金露梅灌丛是祁连山最具代表性的高寒落叶灌丛,其生长过程对生态系统服务功能有重要影响。2015年生长季对其叶功能性状进行了观测,并利用地物光谱仪(ASD)对金露梅灌丛不同物候期的高光谱反射率进行了测定。结果表明:金露梅灌丛的叶面积、叶面积指数(leaf area index, LAI)、绿色叶面积指数、叶片叶绿素含量(以SPAD值表示)从生长初期开始呈现先增大,到生长后期开始下降的规律;不同物候期金露梅灌丛的反射光谱波形曲线变化规律基本相似;植被指数NDVI、EVI、CIred edge与LAI、叶片SPAD值均达到了显著(P<0.05)或极显著相关(P<0.001);EVILAI的相关性最好,NDVI与叶片SPAD值的相关性在整个生长阶段最为稳定。根据相关性分析,建立了不同物候期金露梅灌丛LAI、SPAD预测模型,为金露梅植被生长过程的遥感监测提供了方法。

Abstract: Potentilla froticosa is a most representative alpine deciduous shrub in Qilian Mountains. The growing process of the shrub plays an important role in ecosystem services. In the growing season of 2015, we observed leaf traits of P. froticosa and measured its hyperspectral reflectance by ASD in different phenological phases. The results showed that leaf area, leaf area index (LAI), green leaf area index, chlorophyll content (SPAD values) increased from the beginning of the growing season, reaching their peak in the first half of August, and then declined in the end of the growing season. The change trends of reflection spectrum waveform curve of P. fruticosa shrub in different phenological phases were similar. The correlations between the vegetation indexes (NDVI, EVI, and CIred edge) and LAI, leaf SPAD value were significant (P<0.05) or extremely significant (P<0.001). The relationship between EVI and LAI was the best, and the relationship between NDVI and leaf SPAD value was the most stable. According to the regression analysis, statistic models were built to estimate the LAI and leaf SPAD value considering vegetation indexes in different phenological phases. It is concluded that the LAI and leaf SPAD value can be indicators of plant growth state, and we can monitor the growing process of P. froticosa by the EVI and NDVI derived from remote sensing data at regional scale.