Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data
Fractional cover of green vegetation (FCover) is a key variable when observing Arctic vegetation under a changing climate. Vegetation changes over large areas are traditionally monitored by linking plot-scale measurements to satellite data. However, integrating field and satellite data is not straig...
Published in: | Remote Sensing of Environment |
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2019
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Online Access: | http://hdl.handle.net/10138/299367 |
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ftunivhelsihelda:oai:helda.helsinki.fi:10138/299367 2024-01-07T09:41:21+01:00 Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data Riihimäki, Henri Luoto, Miska Heiskanen, Janne Department of Geosciences and Geography BioGeoClimate Modelling Lab Doctoral Programme in Geosciences Helsinki Institute of Sustainability Science (HELSUS) Institute for Atmospheric and Earth System Research (INAR) Earth Change Observation Laboratory (ECHOLAB) Global Atmosphere-Earth surface feedbacks 2019-02-22T13:36:01Z 14 application/pdf http://hdl.handle.net/10138/299367 eng eng EXCERPTA MEDICA INC-ELSEVIER SCIENCE INC 10.1016/j.rse.2019.01.030 Riihimäki , H , Luoto , M & Heiskanen , J 2019 , ' Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data ' , Remote Sensing of Environment , vol. 224 , pp. 119-132 . https://doi.org/10.1016/j.rse.2019.01.030 RIS: urn:CE9FADF6CE0E893F696008BCB4D533F4 ORCID: /0000-0001-6203-5143/work/54485693 85061308148 430a739b-d734-44de-8bd1-6cb6c242c5ce http://hdl.handle.net/10138/299367 000462421200009 cc_by openAccess info:eu-repo/semantics/openAccess Unmanned aerial vehicles UAV Drones Modifiable Area Unit Problem (MAUP) Upscaling Resolution Arctic High-latitude Monitoring ARCTIC TUNDRA LAND-COVER SPATIAL-RESOLUTION ACCURACY CLIMATE INDEX NDVI AREA PHOTOGRAMMETRY CLASSIFICATION 1172 Environmental sciences Article publishedVersion 2019 ftunivhelsihelda 2023-12-14T00:06:55Z Fractional cover of green vegetation (FCover) is a key variable when observing Arctic vegetation under a changing climate. Vegetation changes over large areas are traditionally monitored by linking plot-scale measurements to satellite data. However, integrating field and satellite data is not straightforward. Typically, the satellite data are at a much coarser scale in comparison to field measurements. Here, we studied how Unmanned Aerial Systems (UASs) can be used to bridge this gap. We covered three 250 m x 250 m sites in Fennoscandian tundra with varying productivity ana FCover, ranging from barren vegetation to shrub tundra. The UAS sites were then used to train satellite data-based FCover models. First, we created a binary vegetation classification (absent, present) by using UAS-derived RGB-orthomosaics and logistic regression. Secondly, we used the classification to calculate FCover to Planet CubeSat (3 m), Sentinel-2A MSI (10 m, 20 m), and Landsat 8 OLI (30 m) grids, and examined how well FCover is explained by various spectral vegetation indices (VI) derived from satellite data. The overall classification accuracies for the UAS sites were >= 90%. The UAS-FCover were strongly related to the tested VIs (D-2 89% at best). The explained deviance was generally higher for coarser resolution data, indicating that the effect of data resolution should be taken into account when comparing results from different sensors. VIs based on red-edge (at 740 nm, 783 nm), or near-infrared and shortwave infrared (SWIR) had the highest performance. We recommend wider inspection of red-edge and SWIR bands for future Arctic vegetation research. Our results demonstrate that UASs can be used for observing FCover at multiple scales. Individual UAS sites can serve as focus areas, which provide information at the finest resolution (e.g. individual plants), whereas a sample of several UAS sites can be used to train satellite data and examine vegetation over larger extents. Peer reviewed Article in Journal/Newspaper Arctic Fennoscandian Tundra HELDA – University of Helsinki Open Repository Arctic Remote Sensing of Environment 224 119 132 |
institution |
Open Polar |
collection |
HELDA – University of Helsinki Open Repository |
op_collection_id |
ftunivhelsihelda |
language |
English |
topic |
Unmanned aerial vehicles UAV Drones Modifiable Area Unit Problem (MAUP) Upscaling Resolution Arctic High-latitude Monitoring ARCTIC TUNDRA LAND-COVER SPATIAL-RESOLUTION ACCURACY CLIMATE INDEX NDVI AREA PHOTOGRAMMETRY CLASSIFICATION 1172 Environmental sciences |
spellingShingle |
Unmanned aerial vehicles UAV Drones Modifiable Area Unit Problem (MAUP) Upscaling Resolution Arctic High-latitude Monitoring ARCTIC TUNDRA LAND-COVER SPATIAL-RESOLUTION ACCURACY CLIMATE INDEX NDVI AREA PHOTOGRAMMETRY CLASSIFICATION 1172 Environmental sciences Riihimäki, Henri Luoto, Miska Heiskanen, Janne Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data |
topic_facet |
Unmanned aerial vehicles UAV Drones Modifiable Area Unit Problem (MAUP) Upscaling Resolution Arctic High-latitude Monitoring ARCTIC TUNDRA LAND-COVER SPATIAL-RESOLUTION ACCURACY CLIMATE INDEX NDVI AREA PHOTOGRAMMETRY CLASSIFICATION 1172 Environmental sciences |
description |
Fractional cover of green vegetation (FCover) is a key variable when observing Arctic vegetation under a changing climate. Vegetation changes over large areas are traditionally monitored by linking plot-scale measurements to satellite data. However, integrating field and satellite data is not straightforward. Typically, the satellite data are at a much coarser scale in comparison to field measurements. Here, we studied how Unmanned Aerial Systems (UASs) can be used to bridge this gap. We covered three 250 m x 250 m sites in Fennoscandian tundra with varying productivity ana FCover, ranging from barren vegetation to shrub tundra. The UAS sites were then used to train satellite data-based FCover models. First, we created a binary vegetation classification (absent, present) by using UAS-derived RGB-orthomosaics and logistic regression. Secondly, we used the classification to calculate FCover to Planet CubeSat (3 m), Sentinel-2A MSI (10 m, 20 m), and Landsat 8 OLI (30 m) grids, and examined how well FCover is explained by various spectral vegetation indices (VI) derived from satellite data. The overall classification accuracies for the UAS sites were >= 90%. The UAS-FCover were strongly related to the tested VIs (D-2 89% at best). The explained deviance was generally higher for coarser resolution data, indicating that the effect of data resolution should be taken into account when comparing results from different sensors. VIs based on red-edge (at 740 nm, 783 nm), or near-infrared and shortwave infrared (SWIR) had the highest performance. We recommend wider inspection of red-edge and SWIR bands for future Arctic vegetation research. Our results demonstrate that UASs can be used for observing FCover at multiple scales. Individual UAS sites can serve as focus areas, which provide information at the finest resolution (e.g. individual plants), whereas a sample of several UAS sites can be used to train satellite data and examine vegetation over larger extents. Peer reviewed |
author2 |
Department of Geosciences and Geography BioGeoClimate Modelling Lab Doctoral Programme in Geosciences Helsinki Institute of Sustainability Science (HELSUS) Institute for Atmospheric and Earth System Research (INAR) Earth Change Observation Laboratory (ECHOLAB) Global Atmosphere-Earth surface feedbacks |
format |
Article in Journal/Newspaper |
author |
Riihimäki, Henri Luoto, Miska Heiskanen, Janne |
author_facet |
Riihimäki, Henri Luoto, Miska Heiskanen, Janne |
author_sort |
Riihimäki, Henri |
title |
Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data |
title_short |
Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data |
title_full |
Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data |
title_fullStr |
Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data |
title_full_unstemmed |
Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data |
title_sort |
estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data |
publisher |
EXCERPTA MEDICA INC-ELSEVIER SCIENCE INC |
publishDate |
2019 |
url |
http://hdl.handle.net/10138/299367 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Fennoscandian Tundra |
genre_facet |
Arctic Fennoscandian Tundra |
op_relation |
10.1016/j.rse.2019.01.030 Riihimäki , H , Luoto , M & Heiskanen , J 2019 , ' Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data ' , Remote Sensing of Environment , vol. 224 , pp. 119-132 . https://doi.org/10.1016/j.rse.2019.01.030 RIS: urn:CE9FADF6CE0E893F696008BCB4D533F4 ORCID: /0000-0001-6203-5143/work/54485693 85061308148 430a739b-d734-44de-8bd1-6cb6c242c5ce http://hdl.handle.net/10138/299367 000462421200009 |
op_rights |
cc_by openAccess info:eu-repo/semantics/openAccess |
container_title |
Remote Sensing of Environment |
container_volume |
224 |
container_start_page |
119 |
op_container_end_page |
132 |
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1787422167850287104 |