The Arctic is warming at twice the rate of the rest of the planet with dramatic
consequences for Northern ecosystems. The rapid warming is predicted to cause
shifts in plant phenology and increases in tundra vegetation productivity. Changes in
phenology and productivity can have knock-on effects on key ecosystem functions.
They directly influence plant-herbivore and plant-pollinator interactions creating the
potential for mismatches and changes in food web structure, and they alter carbon
and nutrient cycling, which in turn influence feedback mechanisms that couple the
tundra biome with the global climate system. Improving our understanding of changes
in tundra phenology and productivity is therefore critical to projecting not only the
future state of Arctic ecosystems, but also the magnitude of potential feedbacks to
global climate change. In this thesis, I combine observations from ground-based
ecological monitoring, satellites and drones (also known as unmanned aerial vehicles
or remotely piloted aircraft systems) to investigate how tundra plant phenology and
productivity are changing across space and time, and to test how observational scales
influences our ability to detect these changes.
Spring plant phenology is tightly linked to temperatures, and advances in spring
phenology are one of the most well documented effects of climate change on global
biological systems. With rapid and near-ubiquitous Arctic warming, the absence of
consistent trends in tundra spring phenology among sites suggests that additional
environmental factors may exert important controls on tundra plant phenology.
Indeed, further to temperature, snowmelt and sea-ice have been reported to strongly
influence tundra phenology. Yet, the relative influence of these three factors has yet
to be evaluated in a single cross-site analysis. In Chapter 2, I tested the importance
of local average spring temperatures, local snowmelt and the timing of the drop in
regional spring sea-ice extent as controls on variation in spring leaf out and flowering
of 14 plant species from long-term records at four coastal sites in Arctic Alaska,
Canada and Greenland. I found that spring phenology was best explained by
snowmelt and spring temperature. In contrast to previous studies, sea-ice did not
predict spring plant phenology at these study sites. This contrasting finding is likely
explained by differences in the scale of the sea-ice measures employed. While many
previous studies used descriptors of circum-polar sea-ice conditions that serve as
aggregate measures for global weather conditions, I tested for the indirect effects of
sea-ice conditions at a regional scale. My findings (re)emphasize the importance of
snowmelt timing for tundra spring plant phenology and therefore highlight the
localised nature of some of the key drivers of tundra vegetation change.
Discrepancies between conventional scales of observation and underlying ecological
processes could limit our ability to explain variation in tundra plant phenology and
vegetation productivity. In the remote biome, ground-based monitoring is logistically
challenging and restricted to comparably few sites and small plot sizes. Multispectral
satellite observations cover the whole biome but are coarse in scale (tens of meters
to kilometres) and uncertainties persist in how trends in vegetation indices like the
Normalised Differential Vegetation Index (NDVI) relate to in situ ecological processes.
Recent advances in drone technologies allow for the collection of multispectral fine-grain
imagery at landscape level and have the potential to bridge the gap in
observational scales. However, collecting high-quality multispectral drone imagery
that is comparable across sensors, space and time remains challenging particularly
when operating in extreme environments such as the tundra. In Chapter 3 of this
thesis, I discuss the key error sources associated with solar angle, weather
conditions, geolocation and radiometric calibration and estimate their relative
contributions to the uncertainty of landscape level NDVI measurements at Qikiqtaruk
in the Yukon Territory of Canada. My findings show that these errors can lead to
uncertainties of greater than ± 10% in peak season NDVI, but also demonstrate they
can be accounted for by improved flight planning, meta-data collection, ground control
point deployment, use of reflectance targets and quality control.
Satellite data suggest that vegetation productivity in the Arctic tundra has been
increasing in recent decades: the tundra is greening. However, the observed trends
show a lot of variation: although many parts of the tundra are greening, others show
reductions in vegetation productivity (sometimes known as browning), and the
satellite-based trends do not always match in situ records of change. Our ability to
explain this variation has been limited by the coarse grain sizes of the satellite
observations. In Chapter 4, I combined time-series of multispectral drone and satellite
imagery (Sentinel 2 and MODIS) of coastal tundra plots at my focal study site
Qikiqtaruk to quantify the correspondence among satellite and drone observations of
vegetation productivity change across spatial scales. My findings show that NDVI
estimates of tundra productivity collected with both platform types correspond well at
landscape scales (10 m – 100 m) but demonstrate that the majority of spatial variation
in NDVI at the study sites occurs at distances below 10 m and is therefore not
captured by the latest generation of publicly available satellite products, like those of
the Sentinel 2 satellites. I observed strong differences in mean estimates and variation
of vegetation productivity between the dominant vegetation types at the field site.
When comparing greening observations over two years, I detected differences in the
amount of variation amongst years and a within-season decline in variation towards
peak growing season for both years. These results suggest that not only the timing,
but also the heterogeneity of tundra landscape phenology can vary within and among
years, and if lowered by warming could alter trophic interactions between species.
The findings presented in this thesis highlight the importance of the localised
processes that influence large-scale patterns and trends in tundra vegetation
phenology and productivity. Localised snowmelt timing best explained variation in
tundra plant phenology and drone imagery revealed meter-scale heterogeneity in
tundra productivity. Research that identifies the most relevant scales at which key
biological processes occur is therefore critical to improving our forecasts of ecosystem
change in the tundra and resulting feedbacks on the global climate system
