Biodiversity and forest entropy - a Canada, Germany and Sweden collaboration

生物多样性和森林熵——加拿大、德国和瑞典的合作

• 批准号: 575747-2022
• 负责人: SanchezAzofeifa, GerardoArturoGA
• 金额: $ 1.82万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750883
• 关键词: Biodiversity; forest; entropy; Canada; Germany

In recent years there has been an increase in the proposition that remote sensing can identify biodiversity from space. The scientific literature has suggested two main processes to deal with this critical topic: optical hyperspectral remote sensing and Light Detection and Ranging (LiDAR). Most of these proposals look at forest ecosystems without considering the effects of secondary growth, defined as the forest re-emerging after land abandonment. Recent studies suggest that secondary growth is becoming dominant in forest ecosystems worldwide. Emergent research indicates that machine learning approaches can be used to analyze optical hyperspectral remote sensing and LiDAR technology to characterize secondary forest ecosystems and their entropy. Entropy is defined here as the self-organization of the energy used by an ecosystem, and that changes as forest structure and composition change. The main goal of this proposal is to develop a partnership with researchers in Germany and Sweden. They are currently working on issues of biodiversity and forest detection from space. The proposal will see collaborations in Canada and Europe via the training of three graduate students at top European laboratories. In addition, the proposal will seek the participation of Dr. Sanchez-Azofeifa in the writing of a funding proposal to be submitted to the Bavarian Research Funding Agency. This Alliance Catalyst will benefit Canada by examining emerging remote sensing technologies that estimate biodiversity and forest entropy in Boreal, temperate, and tropical ecosystems.

近年来，越来越多的人提出，遥感可以从太空中识别生物多样性。科学文献提出了处理这一关键问题的两个主要过程：光学高光谱遥感和激光探测和测距(LiDAR)。这些建议大多着眼于森林生态系统，而没有考虑二次生长的影响，二次生长的定义是土地废弃后重新出现的森林。最近的研究表明，二次生长正在成为全球森林生态系统的主导。新兴的研究表明，机器学习方法可以用于分析光学高光谱遥感和LiDAR技术，以表征次生林生态系统及其熵。在这里，熵被定义为生态系统所使用的能量的自组织，并且随着森林结构和组成的变化而变化。这项提议的主要目标是与德国和瑞典的研究人员发展伙伴关系。他们目前正在研究生物多样性和从太空探测森林的问题。该提案将通过在欧洲顶级实验室培训三名研究生，在加拿大和欧洲开展合作。此外，该提案将寻求Sanchez-Azofeifa博士参与编写一份供资提案，然后提交给巴伐利亚研究资助机构。该联盟催化剂将通过检查新兴的遥感技术来使加拿大受益，这些技术估计了北方、温带和热带生态系统中的生物多样性和森林熵。