Abstract:

Emergent ecosystem properties, such as population and trait distributions, biodiversity and energy and water fluxes, occur because of the dynamic interactions of individuals in their environment. Remote sensing, where image data is collected over large areas, can provide information about individual organisms that reveals important ecosystem patterns and processes that are critical for macrosystems scale biology. In this review, we summarize the primary challenges of conducting organismal-scale remote sensing, such as the detection, delineation and characterization of organisms including trees, birds and mammals. For each, we highlight existing and emerging solutions that directly address these challenges. Algorithmic advancements in the realm of deep learning are one solution to addressing the challenges of limited field data, particularly for applications that require models to generalize across ecosystems and transfer to new environments and sensors. Ecological knowledge can be integrated into novel data processing pipelines such as characterizing organisms from a different perspective, translating ecological rules to mathematical expressions and casting uncertainty. To realize the potential of organismal remote sensing requires deliberate interdisciplinary collaboration with the shared goal of developing methods to produce useful ecological data products.

Links:

Citation:

S. J. Graves, R. Chowdhry, M. Zhou, I. Harmon, B. Weinstein, S. K. M. Ernest, A. Zare, E. P. White, and S. A. Bohlman, “Facilitating macrosystem biology with organismal-scale airborne remote sensing: Challenges and opportunities,” Funct. Ecol., 2025.

@article{graves2025facilitating,
title={Facilitating macrosystem biology with organismal-scale airborne remote sensing: Challenges and opportunities},
author={Graves, Sarah J and Chowdhry, Ritesh and Zhou, Meilun and Harmon, Ira and Weinstein, Ben and Ernest, SK Morgan and Zare, Alina and White, Ethan P and Bohlman, Stephanie A},
journal={Functional Ecology},
year={2025},
publisher={Wiley Online Library}
}