Citizen scientists map global forests
Data from various sources including that from citizen scientists from Austria have been combined to produce two global forest maps — both at one-km resolution — that will provide a more accurate view of global forests.
New maps of global forest cover from the International Institute for Applied System Analysis (IIASA) — Austria-based non-profit organisation — are freely available for exploration and download on the Geo-Wiki web site.
“The new maps rely on a combination of recent multi-sensory remote sensing data, statistics and crowdsourcing,” said Dmitry Schepaschenko, the lead author of the study.
“By combining different data sources and incorporating the input of trained citizen scientists, we were able to produce new maps that are more accurate than any existing data source,” he noted in a paper that appeared in the journal Remote Sensing of the Environment.
The first map known as “best-guess” map uses eight different data sources and relied on a network of citizen scientists to check or validate the classification of land cover, by looking at high-resolution satellite imagery of different locations.
The second map was further calibrated using regional and country-level forest statistics from the Food and Agriculture Organisation (FAO).
A comparison of the two maps highlights the countries where there are discrepancies and hence raises questions about reporting. The new maps were produced for the year 2000 as a base year for modelling. The team also plans to update them with data for 2010 in the near future.
The new maps will be useful not only for research, but also for policymakers who rely on forest data for planning and decision-making purposes. While there are many existing sources of data about forests, including satellite imagery, there is broad disagreement between the data products.
Knowing the location and extent of forests is vital information for ecology, climate change, and economic modelling. The maps will also help researchers looking for the best reference information to estimate deforestation and forest degradation.