A review of UAS-based estimation of forest traits and characteristics in landscape ecology

Context: Forest traits and characteristics are challenging to measure across ecosystems with traditional field methods. There is a ripe opportunity for unoccupied aerial systems (UAS) to contribute to landscape ecology through mapping forest traits and characteristics and linking scales between ground surveys and airborne/spaceborne remote sensing. Objectives: We consider the unique perspective of UAS in forests and the considerations that come with working with an emerging technology. Methods: We performed a literature review of which forest traits and characteristics have been derived from UAS and dive into a case example of how researchers derive a particular trait, aboveground carbon stock, from UAS-based data. Results: UAS are most useful and cost-effective in contexts where high resolution data are required across a limited spatial extent. Due to the high spatial resolution and ability to fly close to top-of-canopy, UAS excel at measuring morphological and physiological characteristics, like canopy structure and foliar chemical traits. Combining spectral and structural information can be done particularly easily with UAS data and enhances aboveground carbon estimation from UAS. UAS-based lidar is best for measuring forest structural attributes, but RGB imagery with post-processing is an acceptable alternative for a tight budget. Conclusions: UAS contribute to landscape ecology through measuring forest traits and characteristics in novel ways. We need better metadata and validation reporting and method standardization to improve reproducibility and comparison across UAS forest studies. This review is written for ecologists interested in measuring forests at a landscape scale, and particularly for researchers interested in adding UAS to their toolkit.