At the moment I am modelling and mapping different forest variables, ecosystem services and risks based on field data (e.g. national forest inventory data) and different remote sensing data (such as airborne laser scanning data and aerial- and satellite images). These models can be used for example to make wall-to-wall maps over forest landscape or implemented to forest planning system to support decision making.
I am also participating to teaching in the courses related to forest mensuration, inventory and remote sensing in SLU. I am also a guest lecture in Forest inventory and modelling course in University of Eastern Finland.
I have a master and doctoral degree in Forest Sciences from the University of Eastern Finland, Joensuu, Finland. In my Phd-studies I was developing airborne laser scanning based forest inventory to support decission making in forest management. I have done my postdoc studies in SLU, where I was e.g. quantifying post-fire fallen trees using remote sensing and improving predicition and mapping berry yields in forest landscape.
Bohlin, I., Maltamo, M., Hedenås, H., Dahlgren, J. and Mehtätalo, L. 2021. Predicting Bilberry and Cowberry yields using remote sensing and NFI field plots in Sweden. Manuscript.
Miina, J., Bohlin, I., Lind, T., Dahlgren, J., Härkönen, K., Packalen, T. and Tolvanen, A. 2021. Estimating and predicting the coverages and yields of bilberry and lingonberry using the national forest inventories in Finland and Sweden. Manuscript.
Bohlin, I., Seielstad, C. and Granström, A. 2021. Massive fire-induced tree felling in a boreal landscape - mechanisms and spatial patterns. Manuscript.
Mononen, L., Auvinen, A.-P., Packalen, P., Virkkala, R., Valbuena, R., Bohlin, I., Valkama, J. and Vihervaara, P. 2018. Usability of citizen science observations together with airborne laser scanning data in determining the habitat preferences of forest birds. Forest Ecology and Management 430: 498-508.
Bohlin, I., Olsson, H., Bohlin, J. and Granström, A. 2017. Quantifying post-fire fallen trees using multi-temporal lidar. International Journal of Applied Earth Observation and Geoinformation 63: 186-195.
Bohlin, J., Bohlin, I., Jonzen, J. and Nilsson, M. 2017. Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory. Silva Fennica 51(2): article id 2021.
Pippuri, I., Suvanto, A., Maltamo, M., Korhonen, K.T., Pitkänen, J. and Packalen, P. 2016. Classification of forest land attributes using multi-source remote sensing. International Journal of Applied Earth Observation and Geoinformation 44:11-22. Online First (25th July 2015)
Vihervaara, P., Mononen, L., Auvinen, A.P., Virkkala, R., Lü Y, Pippuri, I., Packalen, P., Valbuena, R. and Valkama, J. 2015. How to integrate remotely sensed data and biodiversity for ecosystem assessment at landscape scale. Landscape Ecology 30(3): 501-516.
Pippuri, I., Maltamo, M., Packalen, P. and Mäkitalo, J. 2013. Predicting species-specific basal areas in urban forests using airborne laser scanning and existing stand register data. European Journal of Forest Research 132 (5-6):999-1012.
Korhonen, L., Pippuri, I., Packalén, P., Heikkinen, V., Maltamo, M. and Heikkilä, J. 2013. Detection of the need for seedling stands tending using high-resolution remote sensing data. Silva Fennica 47(2): article id 952.
Packalen, P., Vauhkonen, J., Kallio, E., Peuhkurinen, J., Pitkänen, J., Pippuri, I., Strunk, J. and Maltamo, M. 2013. Predicting the spatial pattern of trees by airborne laser scanning. International Journal of Remote Sensing 34(14): 5154-5165.
Pippuri, I., Kallio, E., Maltamo, M., Peltola, H. and Packalén, P. 2012. Exploring horizontal area-based metrics to discriminate the spatial pattern of trees and need for first thinning using airborne laser scanning. Forestry 85(2): 305-314.