Covering 40% of the terrestrial surface, dryland ecosystems characteristically have distinct vegetation structures that are strongly linked to their function. Existing survey approaches cannot provide sufficiently fine-resolution data at landscape-level extents to quantify this structure appropriately. Using a small, unpiloted aerial system (UAS) to acquire aerial photographs and processing theses using structure-from-motion (SfM) photogrammetry, three-dimensional models were produced describing the vegetation structure of semi-arid ecosystems at seven sites across a grass–to shrub transition zone. This approach yielded ultra-fine (<1cm2) spatial resolution canopy height models over landscape-levels (10ha), which resolved individual grass tussocks just a few cm3 in volume. Canopy height cumulative distributions for each site illustrated ecologically-significant differences in ecosystem structure. Strong coefficients of determination (r2 from 0.64 to 0.95) supported prediction of above-ground biomass from canopy volume. Canopy volumes, above-ground biomass and carbon stocks were shown to be sensitive to spatial changes in the structure of vegetation communities. The grain of data produced and sensitivity of this approach is invaluable to capture even subtle differences in the structure (and therefore function) of these heterogeneous ecosystems subject to rapid environmental change. The results demonstrate how products from inexpensive UAS coupled with SfM photogrammetry can produce ultra-fine grain biophysical data products, which have the potential to revolutionise scientific understanding of ecology in ecosystems with either spatially or temporally discontinuous canopy cover

Lightweight drones have emerged recently as a remote sensing survey tool of choice for ecologists, conservation practitioners and environmental scientists. In published work, there are plentiful details on the parameters and settings used for successful data capture, but in contrast there is a dearth of information describing the operational complexity of drone deployment. Information about the practices of flying in the field, whilst currently lacking, would be useful for others embarking on new drone‐based investigations. As a group of drone‐piloting scientists, we have operated lightweight drones for research in over 25 projects, in over 10 countries, and in polar, desert, coastal and tropical ecosystems, with many hundreds of hours of flying experience between us. The purpose of this paper was to document the lesser‐reported methodological pitfalls of drone deployments so that other scientists can understand the spectrum of considerations that need to be accounted for prior to, and during drone survey flights. Herein, we describe the most common challenges encountered, alongside mitigation and remediation actions that increase the chances of safe and successful data capture. Challenges are grouped into the following categories: (i) pre‐flight planning, (ii) flight operations, (iii) weather, (iv) redundancy, (v) data quality, (vi) batteries. We also discuss the importance of scientists undertaking ethical assessment of their drone practices, to identify and mitigate potential conflicts associated with drone use in particular areas. By sharing our experience, our intention is that the paper will assist those embarking on new drone deployments, increasing the efficacy of acquiring high‐quality data from this new proximal aerial viewpoint.

Beavers are the archetypal keystone species, which can profoundly alter ecosystem structure and function through their ecosystem engineering activity, most notably the building of dams. This can have a major impact upon water resource management, flow regimes and water quality. Previous research has predominantly focused on the activities of North American beaver (Castor canadensis) located in very different environments, to the intensive lowland agricultural landscapes of the United Kingdom and elsewhere in Europe. Two Eurasian beavers (Castor fiber) were introduced to a wooded site, situated on a first order tributary, draining from intensively managed grassland. The site was monitored to understand impacts upon water storage, flow regimes and water quality. Results indicated that beaver activity, primarily via the creation of 13 dams, has increased water storage within the site (holding ca. 1000m3 in beaver ponds) and beavers were likely to have had a significant flow attenuation impact, as determined from peak discharges (mean 30±19% reduction), total discharges (mean 34±9% reduction) and peak rainfall to peak discharge lag times (mean 29±21% increase) during storm events. Event monitoring of water entering and leaving the site showed lower concentrations of suspended sediment, nitrogen and phosphate leaving the site (e.g. for suspended sediment; average entering site: 112±72mgl-1, average leaving site: 39±37mgl-1). Combined with attenuated flows, this resulted in lower diffuse pollutant loads in water downstream. Conversely, dissolved organic carbon concentrations and loads downstream were higher. These observed changes are argued to be directly attributable to beaver activity at the site which has created a diverse wetland environment, reducing downstream hydrological connectivity. Results have important implications for beaver reintroduction programs which may provide nature based solutions to the catchment-scale water resource management issues that are faced in agricultural landscapes.

The Arctic tundra is warming rapidly, yet the exact mechanisms linking warming and observed ecological changes are often unclear. Understanding mechanisms of change requires long‐term monitoring of multiple ecological parameters. Here, we present the findings of a collaboration between government scientists, local people, park rangers, and academic researchers that provide insights into changes in plant composition, phenology, and growth over 18 yr on Qikiqtaruk‐Herschel Island, Canada. Qikiqtaruk is an important focal research site located at the latitudinal tall shrub line in the western Arctic. This unique ecological monitoring program indicates the following findings: (1) nine days per decade advance of spring phenology, (2) a doubling of average plant canopy height per decade, but no directional change in shrub radial growth, and (3) a doubling of shrub and graminoid abundance and a decrease by one‐half in bare ground cover per decade. Ecological changes are concurrent with satellite‐observed greening and, when integrated, suggest that indirect warming from increased growing season length and active layer depths, rather than warming summer air temperatures alone, could be important drivers of the observed tundra vegetation change. Our results highlight the vital role that long‐term and multi‐parameter ecological monitoring plays in both the detection and attribution of global change.

Rapid technological advances have dramatically increased affordability and accessibility of unmanned aerial vehicles (UAVs) and associated sensors. Compact multispectral drone sensors capture high-resolution imagery in visible and near-infrared parts of the electromagnetic spectrum, allowing for the calculation of vegetation indices, such as the normalised difference vegetation index (NDVI) for productivity estimates and vegetation classification. Despite the technological advances, challenges remain in capturing high-quality data, highlighting the need for standardized workflows. Here, we discuss challenges, technical aspects, and practical considerations of vegetation monitoring using multispectral drone sensors and propose a workflow based on remote sensing principles and our field experience in high-latitude environments, using the Parrot Sequoia (Pairs, France) sensor as an example. We focus on the key error sources associated with solar angle, weather conditions, geolocation, and radiometric calibration and estimate their relative contributions that can lead to uncertainty of more than ±10% in peak season NDVI estimates of our tundra field site. Our findings show that these errors can be accounted for by improved flight planning, metadata collection, ground control point deployment, use of reflectance targets, and quality control. With standardized best practice, multispectral sensors can provide meaningful spatial data that is reproducible and comparable across space and time.