Connecting the dots; Improving value of an Arctic Observation System through machine learning

Connecting the dots; Improving value of an Arctic Observation System through machine learning.

Human activities are resulting in loss of biodiversity and natural habitats. In the Arctic, the impact of climate change is especially strong as the pace of warming is three times that of global average. It is essential to monitor ecosystem health and convey information to decision makers to mitigate consequences for society. With the rapid development in sensor technology and remote sensing the amount of environmental data from the Arctic has increased exponentially. We are now at a stage where data availability is not the bottle neck for knowledge but rather the integration of data from various sources into relevant synoptic products. The Greenland Ecosystem Monitoring Program has collected data for more than 25 years to quantify the rate of climate change and impacts on ecosystems. The time series and expertise build up in the program are globally unique. The aim of this project is to improve the value of this unique data collection program by involving data scientists to improve data processing and analysis and to develop novel synoptic ecosystem indicators. Developments will be readily applicable to environmental monitoring systems globally.