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...
Published in: | Remote Sensing |
---|---|
Main Authors: | , , , , , , , , |
Other Authors: | , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
MDPI
2021
|
Subjects: | |
Online Access: | http://hdl.handle.net/11577/3410107 https://doi.org/10.3390/rs13214466 |
id |
ftunivpadovairis:oai:www.research.unipd.it:11577/3410107 |
---|---|
record_format |
openpolar |
spelling |
ftunivpadovairis:oai:www.research.unipd.it:11577/3410107 2024-02-27T08:37:26+00:00 Disturbance mapping in arctic tundra improved by a planning workflow for drone studies: Advancing tools for future ecosystem monitoring Eischeid I. Soininen E. M. Assmann J. J. Ims R. A. Madsen J. Pedersen A. O. Pirotti F. Yoccoz N. G. Ravolainen V. T. Eischeid, I. Soininen, E. M. Assmann, J. J. Ims, R. A. Madsen, J. Pedersen, A. O. Pirotti, F. Yoccoz, N. G. Ravolainen, V. T. 2021 http://hdl.handle.net/11577/3410107 https://doi.org/10.3390/rs13214466 eng eng MDPI info:eu-repo/semantics/altIdentifier/wos/WOS:000719309000001 volume:13 issue:21 firstpage:4466 journal:REMOTE SENSING http://hdl.handle.net/11577/3410107 doi:10.3390/rs13214466 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85119157870 info:eu-repo/semantics/openAccess Classifier Disturbance Drone Ecological monitoring GLCM Grubbing Herbivore Random forest Svalbard Winter climate effect info:eu-repo/semantics/article 2021 ftunivpadovairis https://doi.org/10.3390/rs13214466 2024-01-31T18:04:01Z 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 Climate change Svalbard Tundra Padua Research Archive (IRIS - Università degli Studi di Padova) Arctic Svalbard Remote Sensing 13 21 4466 |
institution |
Open Polar |
collection |
Padua Research Archive (IRIS - Università degli Studi di Padova) |
op_collection_id |
ftunivpadovairis |
language |
English |
topic |
Classifier Disturbance Drone Ecological monitoring GLCM Grubbing Herbivore Random forest Svalbard Winter climate effect |
spellingShingle |
Classifier Disturbance Drone Ecological monitoring GLCM Grubbing Herbivore Random forest Svalbard Winter climate effect Eischeid I. Soininen E. M. Assmann J. J. Ims R. A. Madsen J. Pedersen A. O. Pirotti F. Yoccoz N. G. Ravolainen V. T. Disturbance mapping in arctic tundra improved by a planning workflow for drone studies: Advancing tools for future ecosystem monitoring |
topic_facet |
Classifier Disturbance Drone Ecological monitoring GLCM Grubbing Herbivore Random forest Svalbard Winter climate effect |
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. |
author2 |
Eischeid, I. Soininen, E. M. Assmann, J. J. Ims, R. A. Madsen, J. Pedersen, A. O. Pirotti, F. Yoccoz, N. G. Ravolainen, V. T. |
format |
Article in Journal/Newspaper |
author |
Eischeid I. Soininen E. M. Assmann J. J. Ims R. A. Madsen J. Pedersen A. O. Pirotti F. Yoccoz N. G. Ravolainen V. T. |
author_facet |
Eischeid I. Soininen E. M. Assmann J. J. Ims R. A. Madsen J. Pedersen A. O. Pirotti F. Yoccoz N. G. Ravolainen V. T. |
author_sort |
Eischeid I. |
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 |
http://hdl.handle.net/11577/3410107 https://doi.org/10.3390/rs13214466 |
geographic |
Arctic Svalbard |
geographic_facet |
Arctic Svalbard |
genre |
Arctic Climate change Svalbard Tundra |
genre_facet |
Arctic Climate change Svalbard Tundra |
op_relation |
info:eu-repo/semantics/altIdentifier/wos/WOS:000719309000001 volume:13 issue:21 firstpage:4466 journal:REMOTE SENSING http://hdl.handle.net/11577/3410107 doi:10.3390/rs13214466 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85119157870 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.3390/rs13214466 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
21 |
container_start_page |
4466 |
_version_ |
1792044444049997824 |