生物多样性 ›› 2021, Vol. 29 ›› Issue (5): 647-660.DOI: 10.17520/biods.2021013

• 综述 • 上一篇    下一篇

无人机高光谱影像与冠层树种多样性监测

徐岩1, 张聪伶1, 降瑞娇1, 王子斐1, 朱梦晨1, 沈国春1,*()   

  1. 华东师范大学生态与环境科学学院, 浙江天童森林生态系统国家野外科学观测研究站, 上海 200241
  • 收稿日期:2021-01-12 接受日期:2021-03-16 出版日期:2021-05-20 发布日期:2021-04-22
  • 通讯作者: 沈国春
  • 作者简介:* E-mail: gcshen@des.ecnu.edu.cn
    第一联系人:#
  • 基金资助:
    国家自然科学基金(31870404)

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

摘要:

冠层树种多样性是自然森林生态系统功能和服务的重要基础。及时掌握冠层多样性的现状及变化趋势, 是探讨诸多重要生态学问题的前提, 更是制定合理生物多样性保护策略的基础。但受制于传统的多样性信息采集方法, 区域尺度的高精度冠层多样性监测发展较为缓慢; 许多在气候变化和人类干扰下的生物多样性分布信息得不到及时更新。近年来基于无人机的冠层高光谱影像收集与分析技术的发展, 使得冠层多样性监测迎来了新的发展契机。本文从森林冠层高光谱影像出发, 介绍了与多样性监测相关的无人机航拍和基于深度学习的图像处理技术, 并结合已有文献, 探讨了无人机高光谱应用于森林冠层树种多样性监测的研究现状、可行性、优势及缺陷等。我们认为冠层高光谱影像为多样性监测提供了不可或缺且丰富的原始信息; 而无人机与高光谱相机的结合, 使得区域化高频率(如每周)、高精度(如分米乃至厘米级)的冠层多样性信息自动化收集成为可能。然而高光谱影像数据量大、数据维度高与数据结构非线性的特点为影像处理带来了挑战, 而深度学习技术的飞跃, 使得从冠层高光谱影像中提取个体及物种信息达到了极高精度。恰当地使用这些技术将大大提升冠层树种多样性的自动化监测水平, 由此也将帮助我们在当前剧变环境下及时掌握森林冠层多样性的现状与变化, 为生物多样性研究与保护提供可靠的数据支撑。

关键词: 冠层多样性, 多样性监测, 高光谱影像, 无人机遥感, 深度学习

Abstract

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