Disturbance mapping in arctic tundra improved by a planning workflow for drone studies: Advancing tools for future ecosystem monitoring
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 charac...
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Online Access: | https://hdl.handle.net/10037/23541 https://doi.org/10.3390/rs13214466 |
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ftunivtroemsoe:oai:munin.uit.no:10037/23541 2023-05-15T14:27:17+02:00 Disturbance mapping in arctic tundra improved by a planning workflow for drone studies: Advancing tools for future ecosystem monitoring Eischeid, Isabell Soininen, Eeva M Assmann, Jakob J. Ims, Rolf Anker Madsen, Jesper Pedersen, Åshild Ø. Pirotti, Francesco Yoccoz, Nigel Ravolainen, Virve T. 2021-11-06 https://hdl.handle.net/10037/23541 https://doi.org/10.3390/rs13214466 eng eng MDPI Eischeid, I. (2022). Tundra vegetation ecology from the sky - Aerial images and photogrammetry as tools to monitor landscape change. (Doctoral thesis). https://hdl.handle.net/10037/25016 . Remote Sensing Andre: Tromsø forskningsstiftelse Eischeid I, Soininen EM, Assmann, Ims, Madsen, Pedersen, Pirotti, Yoccoz, Ravolainen. Disturbance mapping in arctic tundra improved by a planning workflow for drone studies: Advancing tools for future ecosystem monitoring. Remote Sensing. 2021;13(21) FRIDAID 1959365 doi:10.3390/rs13214466 2072-4292 https://hdl.handle.net/10037/23541 openAccess Copyright 2021 The Author(s) VDP::Matematikk og naturvitenskap: 400::Zoologiske og botaniske fag: 480::Økologi: 488 VDP::Mathematics and natural scienses: 400::Zoology and botany: 480::Ecology: 488 Klimaendringer / Climate change Vegetasjon / Vegetation Økosystem / Ecosystem Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2021 ftunivtroemsoe https://doi.org/10.3390/rs13214466 2022-05-11T22:58:43Z 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. Article in Journal/Newspaper Arctic Arctic Climate change Svalbard Tundra University of Tromsø: Munin Open Research Archive Arctic Svalbard Remote Sensing 13 21 4466 |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
VDP::Matematikk og naturvitenskap: 400::Zoologiske og botaniske fag: 480::Økologi: 488 VDP::Mathematics and natural scienses: 400::Zoology and botany: 480::Ecology: 488 Klimaendringer / Climate change Vegetasjon / Vegetation Økosystem / Ecosystem |
spellingShingle |
VDP::Matematikk og naturvitenskap: 400::Zoologiske og botaniske fag: 480::Økologi: 488 VDP::Mathematics and natural scienses: 400::Zoology and botany: 480::Ecology: 488 Klimaendringer / Climate change Vegetasjon / Vegetation Økosystem / Ecosystem Eischeid, Isabell Soininen, Eeva M Assmann, Jakob J. Ims, Rolf Anker Madsen, Jesper Pedersen, Åshild Ø. Pirotti, Francesco Yoccoz, Nigel Ravolainen, Virve T. Disturbance mapping in arctic tundra improved by a planning workflow for drone studies: Advancing tools for future ecosystem monitoring |
topic_facet |
VDP::Matematikk og naturvitenskap: 400::Zoologiske og botaniske fag: 480::Økologi: 488 VDP::Mathematics and natural scienses: 400::Zoology and botany: 480::Ecology: 488 Klimaendringer / Climate change Vegetasjon / Vegetation Økosystem / Ecosystem |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Eischeid, Isabell Soininen, Eeva M Assmann, Jakob J. Ims, Rolf Anker Madsen, Jesper Pedersen, Åshild Ø. Pirotti, Francesco Yoccoz, Nigel Ravolainen, Virve T. |
author_facet |
Eischeid, Isabell Soininen, Eeva M Assmann, Jakob J. Ims, Rolf Anker Madsen, Jesper Pedersen, Åshild Ø. Pirotti, Francesco Yoccoz, Nigel Ravolainen, Virve T. |
author_sort |
Eischeid, Isabell |
title |
Disturbance mapping in arctic tundra improved by a planning workflow for drone studies: Advancing tools for future ecosystem monitoring |
title_short |
Disturbance mapping in arctic tundra improved by a planning workflow for drone studies: Advancing tools for future ecosystem monitoring |
title_full |
Disturbance mapping in arctic tundra improved by a planning workflow for drone studies: Advancing tools for future ecosystem monitoring |
title_fullStr |
Disturbance mapping in arctic tundra improved by a planning workflow for drone studies: Advancing tools for future ecosystem monitoring |
title_full_unstemmed |
Disturbance mapping in arctic tundra improved by a planning workflow for drone studies: Advancing tools for future ecosystem monitoring |
title_sort |
disturbance mapping in arctic tundra improved by a planning workflow for drone studies: advancing tools for future ecosystem monitoring |
publisher |
MDPI |
publishDate |
2021 |
url |
https://hdl.handle.net/10037/23541 https://doi.org/10.3390/rs13214466 |
geographic |
Arctic Svalbard |
geographic_facet |
Arctic Svalbard |
genre |
Arctic Arctic Climate change Svalbard Tundra |
genre_facet |
Arctic Arctic Climate change Svalbard Tundra |
op_relation |
Eischeid, I. (2022). Tundra vegetation ecology from the sky - Aerial images and photogrammetry as tools to monitor landscape change. (Doctoral thesis). https://hdl.handle.net/10037/25016 . Remote Sensing Andre: Tromsø forskningsstiftelse Eischeid I, Soininen EM, Assmann, Ims, Madsen, Pedersen, Pirotti, Yoccoz, Ravolainen. Disturbance mapping in arctic tundra improved by a planning workflow for drone studies: Advancing tools for future ecosystem monitoring. Remote Sensing. 2021;13(21) FRIDAID 1959365 doi:10.3390/rs13214466 2072-4292 https://hdl.handle.net/10037/23541 |
op_rights |
openAccess Copyright 2021 The Author(s) |
op_doi |
https://doi.org/10.3390/rs13214466 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
21 |
container_start_page |
4466 |
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1766300934939344896 |