Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites
This is the final version. Available on open access from IOP Publishing via the DOI in this record Data availability statement The data and code that support the findings of this study are openly available at the following URL: (https://github.com/jakobjassmann/qhi_phen_ts). Data across scales are r...
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ftunivexeter:oai:ore.exeter.ac.uk:10871/125351 2024-09-15T18:10:52+00:00 Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites Assmann, JJ Myers-Smith, IH Kerby, JT Cunliffe, AM Daskalova, GN 2020 http://hdl.handle.net/10871/125351 https://doi.org/10.1088/1748-9326/abbf7d en eng IOP Publishing https://github.com/jakobjassmann/qhi_phen_ts Vol. 15 (120, article 125002 doi:10.1088/1748-9326/abbf7d NE/M016323/1 NE/L002558/1 CP-061R-17 754513 http://hdl.handle.net/10871/125351 1748-9318 Environmental Research Letters © 2020 The Author(s). Published by IOP Publishing Ltd. Open access. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. https://creativecommons.org/licenses/by/4.0/ Arctic tundra vegetation monitoring landscape phenology satellite drones UAV and RPAS NDVI scale Article 2020 ftunivexeter https://doi.org/10.1088/1748-9326/abbf7d 2024-07-29T03:24:13Z This is the final version. Available on open access from IOP Publishing via the DOI in this record Data availability statement The data and code that support the findings of this study are openly available at the following URL: (https://github.com/jakobjassmann/qhi_phen_ts). Data across scales are required to monitor ecosystem responses to rapid warming in the Arctic and to interpret tundra greening trends. Here, we tested the correspondence among satellite- and drone-derived seasonal change in tundra greenness to identify optimal spatial scales for vegetation monitoring on Qikiqtaruk - Herschel Island in the Yukon Territory, Canada. We combined time-series of the Normalised Difference Vegetation Index (NDVI) from multispectral drone imagery and satellite data (Sentinel-2, Landsat 8 and MODIS) with ground-based observations for two growing seasons (2016 and 2017). We found high cross-season correspondence in plot mean greenness (drone-satellite Spearman's ρ 0.67-0.87) and pixel-by-pixel greenness (drone-satellite R 2 0.58-0.69) for eight one-hectare plots, with drones capturing lower NDVI values relative to the satellites. We identified a plateau in the spatial variation of tundra greenness at distances of around half a metre in the plots, suggesting that these grain sizes are optimal for monitoring such variation in the two most common vegetation types on the island. We further observed a notable loss of seasonal variation in the spatial heterogeneity of landscape greenness (46.2%-63.9%) when aggregating from ultra-fine-grain drone pixels (approx. 0.05 m) to the size of medium-grain satellite pixels (10-30 m). Finally, seasonal changes in drone-derived greenness were highly correlated with measurements of leaf-growth in the ground-validation plots (mean Spearman's ρ 0.70). These findings indicate that multispectral drone measurements can capture temporal plant growth dynamics across tundra landscapes. Overall, our results demonstrate that novel technologies such as drone platforms and compact multispectral ... Article in Journal/Newspaper Herschel Herschel Island Tundra Yukon University of Exeter: Open Research Exeter (ORE) Environmental Research Letters 15 12 125002 |
institution |
Open Polar |
collection |
University of Exeter: Open Research Exeter (ORE) |
op_collection_id |
ftunivexeter |
language |
English |
topic |
Arctic tundra vegetation monitoring landscape phenology satellite drones UAV and RPAS NDVI scale |
spellingShingle |
Arctic tundra vegetation monitoring landscape phenology satellite drones UAV and RPAS NDVI scale Assmann, JJ Myers-Smith, IH Kerby, JT Cunliffe, AM Daskalova, GN Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites |
topic_facet |
Arctic tundra vegetation monitoring landscape phenology satellite drones UAV and RPAS NDVI scale |
description |
This is the final version. Available on open access from IOP Publishing via the DOI in this record Data availability statement The data and code that support the findings of this study are openly available at the following URL: (https://github.com/jakobjassmann/qhi_phen_ts). Data across scales are required to monitor ecosystem responses to rapid warming in the Arctic and to interpret tundra greening trends. Here, we tested the correspondence among satellite- and drone-derived seasonal change in tundra greenness to identify optimal spatial scales for vegetation monitoring on Qikiqtaruk - Herschel Island in the Yukon Territory, Canada. We combined time-series of the Normalised Difference Vegetation Index (NDVI) from multispectral drone imagery and satellite data (Sentinel-2, Landsat 8 and MODIS) with ground-based observations for two growing seasons (2016 and 2017). We found high cross-season correspondence in plot mean greenness (drone-satellite Spearman's ρ 0.67-0.87) and pixel-by-pixel greenness (drone-satellite R 2 0.58-0.69) for eight one-hectare plots, with drones capturing lower NDVI values relative to the satellites. We identified a plateau in the spatial variation of tundra greenness at distances of around half a metre in the plots, suggesting that these grain sizes are optimal for monitoring such variation in the two most common vegetation types on the island. We further observed a notable loss of seasonal variation in the spatial heterogeneity of landscape greenness (46.2%-63.9%) when aggregating from ultra-fine-grain drone pixels (approx. 0.05 m) to the size of medium-grain satellite pixels (10-30 m). Finally, seasonal changes in drone-derived greenness were highly correlated with measurements of leaf-growth in the ground-validation plots (mean Spearman's ρ 0.70). These findings indicate that multispectral drone measurements can capture temporal plant growth dynamics across tundra landscapes. Overall, our results demonstrate that novel technologies such as drone platforms and compact multispectral ... |
format |
Article in Journal/Newspaper |
author |
Assmann, JJ Myers-Smith, IH Kerby, JT Cunliffe, AM Daskalova, GN |
author_facet |
Assmann, JJ Myers-Smith, IH Kerby, JT Cunliffe, AM Daskalova, GN |
author_sort |
Assmann, JJ |
title |
Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites |
title_short |
Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites |
title_full |
Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites |
title_fullStr |
Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites |
title_full_unstemmed |
Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites |
title_sort |
drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites |
publisher |
IOP Publishing |
publishDate |
2020 |
url |
http://hdl.handle.net/10871/125351 https://doi.org/10.1088/1748-9326/abbf7d |
genre |
Herschel Herschel Island Tundra Yukon |
genre_facet |
Herschel Herschel Island Tundra Yukon |
op_relation |
https://github.com/jakobjassmann/qhi_phen_ts Vol. 15 (120, article 125002 doi:10.1088/1748-9326/abbf7d NE/M016323/1 NE/L002558/1 CP-061R-17 754513 http://hdl.handle.net/10871/125351 1748-9318 Environmental Research Letters |
op_rights |
© 2020 The Author(s). Published by IOP Publishing Ltd. Open access. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1088/1748-9326/abbf7d |
container_title |
Environmental Research Letters |
container_volume |
15 |
container_issue |
12 |
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125002 |
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1810448455503446016 |