Arctic tundra plant phenology and greenness across space and time

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

Prototypical composition ontology for rule-based languages

This paper presents an ontology for the composition of rule-based ontology languages. Since typing compositions is an important issue, the ontology consists of components: two upperlevel ontologies, the metamodel of the ontology language, and a metamodel of reuse constructs that play an important role in composition. With the interplay of these components, type-safe composition of ontology components can be achieved.peer-reviewe

‘LandsatTS': an R package to facilitate retrieval, cleaning, cross‐calibration, and phenological modeling of Landsat time series data

The Landsat satellites provide decades of near‐global surface reflectance measurements that are increasingly used to assess interannual changes in terrestrial ecosystem function. These assessments often rely on spectral indices related to vegetation greenness and productivity (e.g. Normalized Difference Vegetation Index, NDVI). Nevertheless, multiple factors impede multi‐decadal assessments of spectral indices using Landsat satellite data, including ease of data access and cleaning, as well as lingering issues with cross‐sensor calibration and challenges with irregular timing of cloud‐free acquisitions. To help address these problems, we developed the ‘LandsatTS' package for R. This software package facilitates sample‐based time series analysis of surface reflectance and spectral indices derived from Landsat sensors. The package includes functions that enable the extraction of the full Landsat 5, 7, and 8 records from Collection 2 for point sample locations or small study regions using Google Earth Engine accessed directly from R. Moreover, the package includes functions for 1) rigorous data cleaning, 2) cross‐sensor calibration, 3) phenological modeling, and 4) time series analysis. For an example application, we show how ‘LandsatTS' can be used to assess changes in annual maximum vegetation greenness from 2000 to 2022 across the Noatak National Preserve in northern Alaska, USA. Overall, this software provides a suite of functions to enable broader use of Landsat satellite data for assessing and monitoring terrestrial ecosystem function during recent decades across local to global geographic extents

Vegetation structure from LiDAR explains the local richness of birds across Denmark

Classic ecological research into the determinants of biodiversity patterns emphasised the important role of three‐dimensional (3D) vegetation heterogeneity. Yet, measuring vegetation structure across large areas has historically been difficult. A growing focus on large‐scale research questions has caused local vegetation heterogeneity to be overlooked compared with more readily accessible habitat metrics from, for example, land cover maps. Using newly available 3D vegetation data, we investigated the relative importance of habitat and vegetation heterogeneity for explaining patterns of bird species richness and composition across Denmark (42,394 km$^{2}$). We used standardised, repeated point counts of birds conducted by volunteers across Denmark alongside metrics of habitat availability from land‐cover maps and vegetation structure from rasterised LiDAR data (10 m resolution). We used random forest models to relate species richness to environmental features and considered trait‐specific responses by grouping species by nesting behaviour, habitat preference and primary lifestyle. Finally, we evaluated the role of habitat and vegetation heterogeneity metrics in explaining local bird assemblage composition. Overall, vegetation structure was equally as important as habitat availability for explaining bird richness patterns. However, we did not find a consistent positive relationship between species richness and habitat or vegetation heterogeneity; instead, functional groups displayed individual responses to habitat features. Meanwhile, habitat availability had the strongest correlation with the patterns of bird assemblage composition. Our results show how LiDAR and land cover data complement one another to provide insights into different facets of biodiversity patterns and demonstrate the potential of combining remote sensing and structured citizen science programmes for biodiversity research. With the growing coverage of LiDAR surveys, we are witnessing a revolution of highly detailed 3D data that will allow us to integrate vegetation heterogeneity into studies at large spatial extents and advance our understanding of species' physical niches

Aboveground biomass corresponds strongly with drone-derived canopy height but weakly with greenness (NDVI) in a shrub tundra landscape

This is the author accepted manuscript. The final version is available from IOP Publishing via the DOI in this recordData accessibility: The data that support the findings of this study are openly available at the following DOI: https://doi.org/10.5285/61C5097B-6717-4692-A8A4-D32CCA0E61A9)Arctic landscapes are changing rapidly in response to warming, but future predictions are hindered by difficulties in scaling ecological relationships from plots to biomes. Unmanned aerial systems (UAS, hereafter 'drones') are increasingly used to observe Arctic ecosystems over broader extents than can be measured using ground-based approaches and facilitate the interpretation of coarse-grained remotely-sensed datasets. However, more information is needed about how drone-acquired remote sensing observations correspond with ecosystem attributes such as aboveground biomass. Working across a willow shrub-dominated alluvial fan at a focal study site in the Canadian Arctic, we conducted peak season drone surveys with a RGB camera and multispectral multi camera array to derive photogrammetric reconstructions of canopy and normalised difference vegetation index (NDVI) maps along with in situ point intercept measurements and biomass harvests from 36, 0.25 m2 plots. We found high correspondence between canopy height measured using in situ point intercept compared to drone-photogrammetry (concordance correlation coefficient = 0.808), although the photogrammetry heights were positively biased by 0.14 m relative to point intercept heights. Canopy height was strongly and linearly related to aboveground biomass, with similar coefficients of determination for point framing (R2 = 0.92) and drone-based methods (R2 = 0.90). NDVI was positively related to aboveground biomass, phytomass and leaf biomass. However, NDVI only explained a small proportion of the variance in biomass (R2 between 0.14 and 0.23 for logged total biomass) and we found moss cover influenced the NDVI-phytomass relationship. Biomass is challenging to infer from drone-derived NDVI, particularly in ecosystems where bryophytes cover a large proportion of the land surface. Our findings suggest caution with broadly attributing change in fine-grained NDVI to biomass differences across biologically and topographically complex tundra landscapes. By comparing structural, spectral and on-the-ground ecological measurements, we can improve understanding of tundra vegetation change as inferred from remote sensing.Natural Environment Research Council (NERC)Dartmouth CollegeAarhus University Research FoundationEuropean Union Horizon 202

A Multi-Level View of the Antecedents and Consequences of Trust in Virtual Leaders

Although trust is widely acknowledged as critical to virtual teams, little is known regarding the causes and consequences of trust in leaders of virtual teams. This paper examines the antecedents and consequences of trust in virtual team leaders. Using survey and archival data from a massively multiplayer online game (MMOG), this study’s findings show that trust in the leader is affected by team members’ use of synchronous communication and breadth of communication with leaders as well as team members’ distance from each other. Furthermore, reasoning that team size and culture create a shared context qualifying team members’ experiences, we found that team size and collectivistic values diminished the benefits of synchronous communication and breadth of communication, respectively. The findings also revealed that trust in leaders had a positive relationship to team performance. Detailed discussion of the findings is provided in the conclusion of the paper

MMOGs as Emerging Opportunities for Research on Virtual Organizations and Teams

Massively Multiplayer Online Games (MMOG) offer new promising opportunities to research virtual organizations and teams. The characteristics of MMOGs allow researchers to obtain objective data from a large and multi-national population. Lasting over months or even years, MMOGs facilitate longitudinal studies and ensure a high involvement of participants. Moreover, collecting data from online surveys and game servers keeps the costs of MMOG studies low. In this paper, we illustrate how research in MMOGs can utilize these opportunities to overcome some limitations of traditional research environments. Further we discuss the diverse information and communication technology (ICT) usage in MMOGs and therefore argue that research in MMOGs can provide a glimpse into the future application of ICT in real life organizations

Illumination Geometry and Flying Height Influence Surface Reflectance and NDVI Derived from Multispectral UAS Imagery

Small unmanned aerial systems (UAS) have allowed the mapping of vegetation at very high spatial resolution, but a lack of standardisation has led to uncertainties regarding data quality. For reflectance measurements and vegetation indices (Vis) to be comparable between sites and over time, careful flight planning and robust radiometric calibration procedures are required. Two sources of uncertainty that have received little attention until recently are illumination geometry and the effect of flying height. This study developed methods to quantify and visualise these effects in imagery from the Parrot Sequoia, a UAV-mounted multispectral sensor. Change in illumination geometry over one day (14 May 2018) had visible effects on both individual images and orthomosaics. Average near-infrared (NIR) reflectance and NDVI in regions of interest were slightly lower around solar noon, and the contrast between shadowed and well-illuminated areas increased over the day in all multispectral bands. Per-pixel differences in NDVI maps were spatially variable, and much larger than average differences in some areas. Results relating to flying height were inconclusive, though small increases in NIR reflectance with height were observed over a black sailcloth tarp. These results underline the need to consider illumination geometry when carrying out UAS vegetation survey