COAT Tools is a method development project, consisting of several PhD projects. Its main objective is the “Methodological advancement of Climate-ecological Observatory for Arctic Tundra”. Four interdisciplinary PhD projects (projects 1-4) are funded by one of the Thematic Commitments of UiT The Arctic University of Norway within the theme “Energy, Climate, Society and Environment”. Additional three PhD projects in ecology (projects 5-7) are funded by the Tromsø Research Foundation.
1) Remote sensing of vegetation parameters in the forest-tundra ecotone
The first project is a collaboration between the Department of Physics and Technology and NINA, and aims at developing methods based on new satellite sensors that can be used to infer vegetation state and its changes, particularly at the ecotone between forest and tundra. Phd-student: Jørgen Andreas Agersborg.
2) Platform for distributed observatories in Arctic environments
The second project, is a collaboration between the Departments of Computer Science and Arctic and Marine Biology. Its aims are to make data collection through networks of sensors (such as camera traps or audio recordings) more efficient, and to develop high performance but robust processors at the sensor units to allow direct communication. Phd-student: Mike Murphy.
3) Efficient Bayesian analysis of dynamics and changes in ecosystem models
The third project, is a collaboration between the Departments of Mathematics and Statistics and Arctic and Marine Biology. Data collected by the different COAT modules aim at assessing the relative effects of climate, management and endogenous dynamics on ecosystem changes. This project will develop Bayesian statistical methods to implement dynamic models of these effects and their interactions. Phd-student: Pedro Da Silva Nicolau.
4) Research-based activities in natural science education.
The fourth project, is a collaboration between the Departments of Education and Arctic and Marine Biology. While research-based activities in schools are increasingly popular, both as “outreach” and as “citizen science”, we need to investigate systematically how the impacts of both aspects can be jointly maximized. This project will assess how new technologies can help at the interface between natural science research and education. Phd-student: Ingrid Jensvoll.
5) Ground-based acoustic sensor system
Digital acoustic sensors represent a new and promising methodology in the context of research and monitoring of terrestrial arctic biodiversity. Compared to conventional census methods (i.e. manual recordings made by people in the field), automatic acoustic sensors can alleviate a number of the logistical, technical and environmental challenges. However, while acoustic sensors have a great potential to improve on the quantification of state variable that are central to COAT science modules, there are several tasks that need to be completed before this technology can be fully operational. This project will convert raw acoustic sensor data to operational ecological state variables, Calibrate automatic sensor-based state variables against manually recorded variables, and develop protocols for observation systems based on the acoustic sensors. Phd-student: Marita Anti Strømeng.
6) Drone-based imagery system
Aerial photography with drones (unmanned aerial vehicles [UAV]) gives unique possibilities for measurements of several of COAT’s state variables that are more efficient/area-extensive, less environmentally intrusive than manual measurements on ground, and more precise/high-resolution than images from manned aircrafts and satellites. Drone technology holds promise to fill many of the needs of landscape-scale research on tundra ecosystems. However, yet there are no routine protocols for acquiring, processing and analyzing drone areal imagery data for this purpose. This project will convert aerial drone photography to operational ecological state variables, calibrate drone-based state variables against ground-based measurements and remote sensing, and develop protocols for observation systems based drones. Phd-student: Isabell Eischeid.
7) Ground-based optical system
We have recently developed a new camera trap that is able to continuously record the activity of small mammals (shrews, lemmings, voles, weasels, ermine) in tundra ecosystems throughout the year; including winter under the snow. However, several remaining tasks need to be completed before the sensors can fulfil their potential. This project will convert raw optical sensor data to operational ecological state variables, calibrate automatic sensor-based state variables against manually recorded variables, and develop protocols for observation systems based on ground-based optical sensors. PhD-student: Eivind Flittie Kleiven.