Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites

• Published in: Environmental research letters 2020-12, Vol.15 (12), p.125002
• Main Authors: Assmann, Jakob J, Myers-Smith, Isla H, Kerby, Jeffrey T, Cunliffe, Andrew M, Daskalova, Gergana N
• Format: Article
• Language: English
• Subjects: Arctic tundra; Climate change; drones, UAV and RPAS; Ecological monitoring; Environmental changes; Grain size; Ground-based observation; Heterogeneity; Landsat; Landsat satellites; Landscape; landscape phenology; NDVI; Normalized difference vegetative index; Pixels; Plant growth; Polar environments; Remote sensing; satellite; Satellite imagery; Satellite observation; Satellites; scale; Seasonal variations; Spatial heterogeneity; Spatial variations; Taiga & tundra; Tundra; Vegetation; Vegetation index; vegetation monitoring
• ISSN: 1748-9326; 1748-9326
• DOI: 10.1088/1748-9326/abbf7d

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 R2 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 environmental change in the Arctic.