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湿地植被地上生物量遥感估算方法研究进展

赵天舸1,于瑞宏1*,张志磊2,白雪松1,曾庆奥1
  

  1. (1内蒙古大学环境与资源学院, 呼和浩特 010021; 2包头市环境保护局, 内蒙古包头 014060)
  • 出版日期:2016-07-10 发布日期:2016-07-10

Estimation of wetland vegetation aboveground biomass based on remote sensing data: A review.

ZHAO Tian-ge1, YU Rui-hong1*, ZHANG Zhi-lei2, BAI Xue-song1, ZENG Qing-ao1#br#   

  1. (1 College of Environment and Resources, Inner Mongolia University, Hohhot 010021, China; 2Baotou City Environmental Protection Bureau, Baotou 014060, Inner Mongolia, China).
  • Online:2016-07-10 Published:2016-07-10

摘要: 湿地植被生物量是衡量湿地生态系统健康状况的重要指标,其估算方法研究一直是湿地领域的研究热点。传统的植被地上生物量测算主要依靠样方调查,对于复杂湿地生态系统存在一定的局限,而随着遥感估算方法的发展,湿地植被生物量的研究实现了长期、动态且大尺度的监测。本文在查阅和分析国内外相关文献的基础上,以遥感数据为主要数据源,阐述了基于光学、合成孔径雷达(SAR)、激光雷达(Lidar)以及多源协同遥感数据反演湿地植物地上生物量的理论基础及计算原理,总结了其研究进展,分析了其适用性,继而从湿地植被生物量监测类型的拓展、多源遥感数据的融合、遥感数据的同化以及遥感机理模型发展等方面出发,对植物生物量研究的发展趋势进行了深入探讨。

关键词: 根际CO2浓度, 番茄, 光合生理

Abstract: Wetland vegetation biomass is a key factor to measure the fitness of wetland ecosystem. How to estimate biomass is one of the most hotspot topics in the wetland research. The general methods of estimating the vegetation aboveground biomass mainly depended on quadrat, which is limited when dealing with complicated wetland ecosystem. With the progress of remote sensing, the observation of wetland vegetation biomass has turned into a long-lasting, dynamic and largescale manner. After analyzing a large amount of related references, we illustrated theoretical basis and calculation principle of the estimation of wetland vegetation aboveground biomass, based on optical remote sensing image, SAR data, Lidar data and multisource, using remote sensing data as data source. We summarized the research progress, analyzed the applicability and limitation of the methods. Then, we described the developing trend in study on wetland vegetation biomass in several aspects, such as expanding the monitoring type of wetland vegetation biomass, integrating the multisource remote sensing data, assimilating the remote sensing data and developing the remote sensing mechanism models.

Key words: root-zone CO2 concentration, tomato, photosynthetic physiology.