Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites
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 monitorin...
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Online Access: | https://doi.org/10.1088/1748-9326/abbf7d https://doaj.org/article/c265eacfe3174156a78632d3ed8981c1 |
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ftdoajarticles:oai:doaj.org/article:c265eacfe3174156a78632d3ed8981c1 2023-09-05T13:17:22+02:00 Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites Jakob J Assmann Isla H Myers-Smith Jeffrey T Kerby Andrew M Cunliffe Gergana N Daskalova 2020-01-01T00:00:00Z https://doi.org/10.1088/1748-9326/abbf7d https://doaj.org/article/c265eacfe3174156a78632d3ed8981c1 EN eng IOP Publishing https://doi.org/10.1088/1748-9326/abbf7d https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/abbf7d 1748-9326 https://doaj.org/article/c265eacfe3174156a78632d3ed8981c1 Environmental Research Letters, Vol 15, Iss 12, p 125002 (2020) Arctic tundra vegetation monitoring landscape phenology satellite drones UAV and RPAS NDVI Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 article 2020 ftdoajarticles https://doi.org/10.1088/1748-9326/abbf7d 2023-08-13T00:37:16Z 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 sensors allow us to study ecological systems at previously inaccessible scales and fill gaps in our understanding of tundra ecosystem processes. Capturing fine-scale variation across tundra landscapes will improve predictions of the ecological impacts and climate feedbacks of ... Article in Journal/Newspaper Arctic Herschel Herschel Island Tundra Yukon Directory of Open Access Journals: DOAJ Articles Arctic Canada Herschel Island ENVELOPE(-139.089,-139.089,69.583,69.583) Yukon Environmental Research Letters 15 12 125002 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic tundra vegetation monitoring landscape phenology satellite drones UAV and RPAS NDVI Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 |
spellingShingle |
Arctic tundra vegetation monitoring landscape phenology satellite drones UAV and RPAS NDVI Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 Jakob J Assmann Isla H Myers-Smith Jeffrey T Kerby Andrew M Cunliffe Gergana N Daskalova 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 Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 |
description |
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 sensors allow us to study ecological systems at previously inaccessible scales and fill gaps in our understanding of tundra ecosystem processes. Capturing fine-scale variation across tundra landscapes will improve predictions of the ecological impacts and climate feedbacks of ... |
format |
Article in Journal/Newspaper |
author |
Jakob J Assmann Isla H Myers-Smith Jeffrey T Kerby Andrew M Cunliffe Gergana N Daskalova |
author_facet |
Jakob J Assmann Isla H Myers-Smith Jeffrey T Kerby Andrew M Cunliffe Gergana N Daskalova |
author_sort |
Jakob J Assmann |
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 |
https://doi.org/10.1088/1748-9326/abbf7d https://doaj.org/article/c265eacfe3174156a78632d3ed8981c1 |
long_lat |
ENVELOPE(-139.089,-139.089,69.583,69.583) |
geographic |
Arctic Canada Herschel Island Yukon |
geographic_facet |
Arctic Canada Herschel Island Yukon |
genre |
Arctic Herschel Herschel Island Tundra Yukon |
genre_facet |
Arctic Herschel Herschel Island Tundra Yukon |
op_source |
Environmental Research Letters, Vol 15, Iss 12, p 125002 (2020) |
op_relation |
https://doi.org/10.1088/1748-9326/abbf7d https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/abbf7d 1748-9326 https://doaj.org/article/c265eacfe3174156a78632d3ed8981c1 |
op_doi |
https://doi.org/10.1088/1748-9326/abbf7d |
container_title |
Environmental Research Letters |
container_volume |
15 |
container_issue |
12 |
container_start_page |
125002 |
_version_ |
1776198571964497920 |