Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies
The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in the use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relativ...
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ftmdpi:oai:mdpi.com:/2072-4292/13/8/1588/ 2023-08-20T04:05:03+02:00 Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies Annie S. Guillaume Kevin Leempoel Estelle Rochat Aude Rogivue Michel Kasser Felix Gugerli Christian Parisod Stéphane Joost agris 2021-04-20 application/pdf https://doi.org/10.3390/rs13081588 EN eng Multidisciplinary Digital Publishing Institute Environmental Remote Sensing https://dx.doi.org/10.3390/rs13081588 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 8; Pages: 1588 alpine ecology Arabis alpina digital elevation models (DEMs) light detection and ranging (LiDAR) multiscale photogrammetry spatial scale species distribution models (SDM) terrain attributes very high resolution Text 2021 ftmdpi https://doi.org/10.3390/rs13081588 2023-08-01T01:32:26Z The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in the use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1 m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aim of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant, Arabis alpina, in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs, up to a spatial resolution of at least 1 m, rivalled the accuracy of LiDAR DEMs, largely owing to the customizability of PHOTO DEMs to the study sites compared to commercially available LiDAR DEMs. We obtained DEMs at spatial resolutions of 6.25 cm–8 m for PHOTO and 50 cm–32 m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32 m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50 cm in such studies. Text Arctic Climate change MDPI Open Access Publishing Arctic Remote Sensing 13 8 1588 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
alpine ecology Arabis alpina digital elevation models (DEMs) light detection and ranging (LiDAR) multiscale photogrammetry spatial scale species distribution models (SDM) terrain attributes very high resolution |
spellingShingle |
alpine ecology Arabis alpina digital elevation models (DEMs) light detection and ranging (LiDAR) multiscale photogrammetry spatial scale species distribution models (SDM) terrain attributes very high resolution Annie S. Guillaume Kevin Leempoel Estelle Rochat Aude Rogivue Michel Kasser Felix Gugerli Christian Parisod Stéphane Joost Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies |
topic_facet |
alpine ecology Arabis alpina digital elevation models (DEMs) light detection and ranging (LiDAR) multiscale photogrammetry spatial scale species distribution models (SDM) terrain attributes very high resolution |
description |
The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in the use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1 m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aim of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant, Arabis alpina, in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs, up to a spatial resolution of at least 1 m, rivalled the accuracy of LiDAR DEMs, largely owing to the customizability of PHOTO DEMs to the study sites compared to commercially available LiDAR DEMs. We obtained DEMs at spatial resolutions of 6.25 cm–8 m for PHOTO and 50 cm–32 m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32 m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50 cm in such studies. |
format |
Text |
author |
Annie S. Guillaume Kevin Leempoel Estelle Rochat Aude Rogivue Michel Kasser Felix Gugerli Christian Parisod Stéphane Joost |
author_facet |
Annie S. Guillaume Kevin Leempoel Estelle Rochat Aude Rogivue Michel Kasser Felix Gugerli Christian Parisod Stéphane Joost |
author_sort |
Annie S. Guillaume |
title |
Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies |
title_short |
Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies |
title_full |
Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies |
title_fullStr |
Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies |
title_full_unstemmed |
Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies |
title_sort |
multiscale very high resolution topographic models in alpine ecology: pros and cons of airborne lidar and drone-based stereo-photogrammetry technologies |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13081588 |
op_coverage |
agris |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change |
genre_facet |
Arctic Climate change |
op_source |
Remote Sensing; Volume 13; Issue 8; Pages: 1588 |
op_relation |
Environmental Remote Sensing https://dx.doi.org/10.3390/rs13081588 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13081588 |
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Remote Sensing |
container_volume |
13 |
container_issue |
8 |
container_start_page |
1588 |
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