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UAS4Ecology Lab

What we do

The laboratory combines the latest drone and sensor technology with field-based ecological methods. We have a full suite of UAS sensors (eg LiDAR, hyperspectral, multispectral, thermal) and operate a variety of rotor-based and fixed-wing drones. In addition, the laboratory has the necessary high-precision GNSS equipment and hand-held hyperspectral sensors for 'ground control' and 'ground truthing'. The laboratory's staff have often worked under challenging conditions in remote areas all over the world e.g., on mountainsides in Greenland and have therefore optimized the equipment for data collection in extreme environments. We also have hardware and software for efficient processing and analysis of UAS-based data

Below you can see more about some of the projects in which UAS4Ecology has participated with drones and sensors

Shrubification in the arctic

Examining Shrubification in the arctic with drones and remote sensing

August 2015 field measurements were performed at three different areas in Greenland along the arctic circle close to Kangerlussuaq, Sisimiut and Tasiilaq by scientists from Aarhus University. In total 10 study sites were chosen.

The main purpose of the study was to investigate dynamics behind shrub cover in the Arctic by using a drone-based remote sensing perspective. Increased shrub cover, or shrubification, caused by climate change is a concern for arctic biodiversity.

Drone-based remote sensing can be a helpful tool in exploring the obvious gap between small scale plot-based studies and large-scale satellite-based studies. Drone-based remote sensing studies can be of ultrahigh spatial resolution, which allows one to describe relatively local microenvironments and still being able to cover reasonably large areas to be linked on a regional scale.

Drone used: Mikrokopter Quadro XL, i.e. a four rotor UAS. Electronic equipment was charged by portable solar panels installed at the campsite.

The drone was able to fly autonomously by creating a predestined route. Waypoints for the drone flight were created from a field laptop with Mikrokopter firmware and by targeting a 66% x 70% overlap for the VIS and NIR images it resulted in flying transects with about 5 meters in between.


  • Canon EOS 100D, with a CMOS APS-C sensor of 5184 × 3456 pixels (18 Megapixels, MP) provided images within the visible wavelengths (VIS)
  • A modified Canon EOS 100D with a Near-infrared (NIR) filter captured images within the NIR wavelengths (830-1200nm)
  • Micasense RedEdge Multispectral sensor (MUL) measuring 5 relatively narrow spectral bands

Photos: Urs Treier

The Mols Laboratory

Plant diversity within the world’s grasslands is changing during the Anthropocene

As part of a project to understand grassland vegetation dynamics better, the UAS LiDAR campaigns in Mols Bjerge first started in October 2017 across a 6.7 ha area inside the fences of the rewilding experiment, with total size around 150 ha.

The field site, a semi-natural grassland area in National park Mols Bjerge, Denmark, hosts a large-scale, so-called rewilding project. The overall purpose of the experiment was (is) to study and monitor the effects in a near-natural environment from large herbivores allowed to freely roam the area.

Flights above the same area have been repeated each year in April since then. Furthermore, several rounds of differential GPS measurements were performed within the time period, to measure exact locations of shrub species in the area. Additional, biomass samples of Cytisus scoparius were harvested in relation to the 2018 UAS flights in April.

With recent development of close-range remote sensing platforms and sensors it is now possible to enlarge the coverage of observations while maintaining fine-scale resolution. Furthermore, the data contains information from multiple aspects of plant diversity aligned in time and space to provide an enhanced understanding of grassland dynamics.

LiDAR sensor mounted on UAS can be used to identify shrub species in a seminatural grassland. The high accuracy in shrub classification approved to calculate fine-scale biomass from a single species throughout a landscape scale extent and close-range remote sensing leads to new opportunities to study and understand grassland plant diversity dynamics at unprecedented spatial and temporal scales. 

Further reading: “Detecting shrub encroachment in seminatural grasslands using UAS LiDAR” 

Mapping vegetation at Molslaboratoriet med eBee X From YouTube - UAS4Ecology Lab

Detecting Arctic heterogeneity

Disentangling fine-scale plant-soil relationships across a changing Arctic environment

Hyperspectral field measurements were gathered across a network of plots, as part of a field campaign on Qeqertarsuaq (Disko Island), Greenland. 
Across a two-month period, from June – August 2019, more than a thousand spectral soil and leaf measurements were taken from a range of vegetation and soil types using an ASD Fieldspec and plant contact probe. The aim was to see whether plants (which often obscure soil) could provide an insight into underlying soil characteristics. The study area was characterised by the presence of dwarf shrubs but showed large variation in plant community structure across small scales and, much like other parts of the Arctic, is warming rapidly due to climate change.
Hyperspectral field sensors facilitate measurements at a very high spectral resolution, providing detailed information about a measured target from the visible to the shortwave infrared (350 – 2500 nm) parts of the electromagnetic spectrum. Patterns of reflected light captured by a contact probe can therefore be used to detect key environmental characteristics, such as nitrogen or water in leaves, and soil organic matter content. 
By combining field spectroscopy with field and laboratory measurements using statistical models, estimates of soil texture, pH and nutrient status were made, with plant-soil relationships then quantified across space. The use of field spectroscopy facilitated the quantification of soil characteristics across a large number of soil and plant samples, which would have been infeasible with traditional laboratory methods, and provided an important reference point for current conditions in the Arctic.

Photos: Oliver Baines


Urs Treier

Lab manager Department of Biology - Ecoinformatics and Biodiversity