Disturbance Mapping in Arctic Tundra Improved by a Planning Workflow for Drone Studies: Advancing Tools for Future Ecosystem Monitoring

• Published in: Remote Sensing
• Main Authors: Isabell Eischeid, Eeva M. Soininen, Jakob J. Assmann, Rolf A. Ims, Jesper Madsen, Åshild Ø. Pedersen, Francesco Pirotti, Nigel G. Yoccoz, Virve T. Ravolainen
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: classifier; disturbance; drone; ecological monitoring; GLCM; herbivore; Science; Q; Arctic; Svalbard; Climate change; Tundra
• Online Access: https://doi.org/10.3390/rs13214466; https://doaj.org/article/0a4bff786604402896281db7f08f5ca9

The Arctic is under great pressure due to climate change. Drones are increasingly used as a tool in ecology and may be especially valuable in rapidly changing and remote landscapes, as can be found in the Arctic. For effective applications of drones, decisions of both ecological and technical character are needed. Here, we provide our method planning workflow for generating ground-cover maps with drones for ecological monitoring purposes. The workflow includes the selection of variables, layer resolutions, ground-cover classes and the development and validation of models. We implemented this workflow in a case study of the Arctic tundra to develop vegetation maps, including disturbed vegetation, at three study sites in Svalbard. For each site, we generated a high-resolution map of tundra vegetation using supervised random forest (RF) classifiers based on four spectral bands, the normalized difference vegetation index (NDVI) and three types of terrain variables—all derived from drone imagery. Our classifiers distinguished up to 15 different ground-cover classes, including two classes that identify vegetation state changes due to disturbance caused by herbivory (i.e., goose grubbing) and winter damage (i.e., ‘rain-on-snow’ and thaw-freeze). Areas classified as goose grubbing or winter damage had lower NDVI values than their undisturbed counterparts. The predictive ability of site-specific RF models was good (macro-F1 scores between 83% and 85%), but the area of the grubbing class was overestimated in parts of the moss tundra. A direct transfer of the models between study sites was not possible (macro-F1 scores under 50%). We show that drone image analysis can be an asset for studying future vegetation state changes on local scales in Arctic tundra ecosystems and encourage ecologists to use our tailored workflow to integrate drone mapping into long-term monitoring programs.