Biodiv Sci ›› 2021, Vol. 29 ›› Issue (5): 647-660.  DOI: 10.17520/biods.2021013

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UAV-based hyperspectral images and monitoring of canopy tree diversity

Yan Xu1, Congling Zhang1, Ruijiao Jiang1, Zifei Wang1, Mengchen Zhu1, Guochun Shen1,*()   

  1. Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241
  • Received:2021-01-12 Accepted:2021-03-16 Online:2021-05-20 Published:2021-04-22
  • Contact: Guochun Shen


Background & Aims: The species diversity of canopy trees is important for the function and service of natural forest ecosystems. To be able to formulate reasonable biodiversity conservation strategies, it is important to understand patterns of forest canopy diversity through time. However, the development of high-precision forest canopy diversity monitoring at a regional scale is slow due to a limitation in diversity information collection methods. A lot of biodiversity patterns may substantially change due to climate change and human disturbance. However, updating these changes in biodiversity cannot be done in a timely manner. In recent years, the development of canopy hyperspectral image collection based on unmanned aerial vehicle (UAV) and analysis technology has provided an opportunity for the development of new tools for canopy diversity monitoring.
Progresses: Here, we propose using the hyperspectral image of forest canopy for biodiversity monitoring and conservation, development of UAV aerial photography and spatial positioning technology, and the development of hyperspectral image processing technology with deep learning. We use the existing literature to discuss the research status, feasibility, advantages, and disadvantages of using UAV hyperspectral imaging for monitoring of species diversity of forest canopy trees. We believe that canopy hyperspectral images provide indispensable and abundant information for forest biodiversity monitoring. The combination of UAVs and hyperspectral cameras makes it possible to automate the collection of canopy diversity information with both high frequency (e.g., weekly) and high precision (e.g., decimeter- or centimeter-level) at the regional scale. At the same time, the leap in image processing technology made possible through deep learning enables the extraction of individual and species information from canopy hyperspectral images with extremely high precision.
Prospects: Hyperspectral images have rich spectral and spatial information, which greatly improves the identification accuracy of plant species. The combination of UAVs and hyperspectral cameras greatly reduces the difficulty and cost for acquisition of this data. Applying deep learning methods to hyperspectral image processing can effectively collect species diversity information contained in hyperspectral images, and accelerate the research on forest canopy diversity monitoring on a large-scale. However, due to an insufficient sample size of hyperspectral data for species and a limitation in common deep learning models not being fully optimized for hyperspectral images remains a challenge. Future challenges for research include: how to build the hyperspectral species database, how to combine the characteristics of hyperspectral data, and how to optimize the automatic species recognition algorithm.

Key words: canopy diversity, diversity monitoring, hyperspectral image, UAV remote sensing, deep learning