Remote sensing and GIS in biodiversity modelling

Research themes

Remote sensing (RS) produces valuable information for biodiversity mapping and monitoring. Due to high spatial and temporal coverage, RS data have clear advantages compared to any other source of information. This research aims to develop novel approaches to integrate RS and geographic information (GI) data into biodiversity assessments. Our ultimate goal is to provide information which is directly applicable in defining biodiversity assessment practises that better meet the requirements of the sustainable use of natural resources and land use planning.

The main objectives:

• The potential of remote sensing in biodiversity studies: examination of the suitability of spectral values and habitat maps derived from satellite images in biodiversity assessments
• Spatial scaling and performance of RS-GI based biodiversity assessments

The effect of statistical techniques and complexity of the models on the performance of RS-GI based models

Staff

Postdoctoral researcher PhD Miia Parviainen (University of Oulu, Department of geography)
Professor PhD Miska Luoto (University of Helsinki, Department of Geography)
Senior researcher PhD Risto K. Heikkinen (Finnish Environment Institute)
Senior researcher PhD Wilfried Thuiller (Laboratoire d’Ecologie Alpine, Université Joseph Fourier)

Research projects

Project: Remote sensing and GIS in evaluating biodiversity, funded by the Finnish Academy (2007-2011)
Contact Person: Miska Luoto

Main publications

• Parviainen, M., Luoto, M. & Heikkinen, R.K. (2010). NDVI-based productivity and heterogeneity as indicators of species richness in boreal landscapes. Boreal Environment Research 15, 301-318.
• Parviainen, M., Luoto, M. & R.K. Heikkinen (2009). The role of local and landscape level productivity in modelling of boreal plant species richness. Ecological Modelling 220, 2690–2701
• Parviainen, M., Marmion, M., Luoto, M., Thuiller, W. & Heikkinen, R.K. (2009). Using summed individual species models and state-of-the-art modelling techniques to identify threatened plant species hotspots. Biological Conservation 142, 2501–2509
• Marmion, M., Parviainen, M. & Luoto, M., Thuiller, W. & R.K. Heikkinen (2009). Evaluation of consensus methods in predictive species distribution modeling. Diversity and Distributions 15, 59–69.
• Marmion, M., Luoto, M., Heikkinen, R. K. & Thuiller, W. (2009). The performance of state-of-the-art modelling techniques depends on geographical distribution of species. Ecological Modelling 220, 3512-3520.
• Parviainen, M., Luoto M, Ryttäri T & R.K. Heikkinen (2008). Modelling the occurrence of threatened plant species in taiga landscapes: methodological and ecological perspectives. Journal of Biogeography 35, 1888-1905.