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

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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.