Biodiv Sci ›› 2018, Vol. 26 ›› Issue (8): 862-877.  DOI: 10.17520/biods.2018143

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Applications of remote sensing technology in avian ecology

Qian Lei1,2, Jinya Li1, Keming Ma1,*()   

  1. 1 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085
    2 University of Chinese Academy of Sciences, Beijing 100049
  • Received:2018-05-15 Accepted:2018-08-14 Online:2018-08-20 Published:2018-09-27
  • Contact: Ma Keming
  • About author:# Co-first authors


Avian ecological studies tend to center on birds and their habitats. According to the literature, studies in avian ecology have shifted from focusing on behavior and habitat selection to focusing on human disturbance, habitat suitability and habitat structure, which has been made possible partially due to remote sensing (RS) technology. Characteristics and applications of RS data are varied. Here, we assessed various RS methods, considering the current state of avian ecology. Light remote sensing is most commonly used. Infrared trigger cameras and video complement field work to monitor brooding, defensive and other behaviors, while the infrared images contain massive amounts of data. Multi-spectral images are used most frequently for mapping habitat and can directly track species when captured at a high spatial resolution. Hyperspectral data has great potential in classifying objects with similar spectral characteristics. LiDAR data mainly contributes to studies of habitat structure. Researchers have used Radar to monitor flying birds over extended periods of time, where the microwave images with multi-polarization may promote the precision of mapping complex habitats. In practice, we recognize that data scale may affect study results and that some RS inversion model parameters lack ecological significance. Multi-source data could enhance mapping accuracy and provide context for the intersection of spatial and temporal resolutions of images. In the future, RS technology development should pay more attention to provide specific spectral information, more convenient interpretation methods, and more rational multi-source data combinaions, for a better use of them.

Key words: microwave remote sensing, infrared, LiDAR, multi-spectral, hyperspectral, habitat parameter inversion, object identification