Jari Vauhkonen
Professori
Metsäsuunnittelun professori
jari.vauhkonen@uef.fi | 050 470 7627
Julkaisut
56 kappaletta-
Comments on Soimakallio et al. (2022) “Closing an open balance: The impact of increased tree harvest on forest carbon”
Vauhkonen, Jari. 2023. Global change biology bioenergy. 15: 536-537 -
Future browsing damage in seedling stands according to projected forest resources and moose population density
Vauhkonen, Jari; Matala, Juho; Nikula, Ari. 2023. Silva fennica. 57: . 23012 -
Stochastic multicriteria acceptability analysis as a forest management priority mapping approach based on airborne laser scanning and field inventory data
Rana, Parvez; Vauhkonen, Jari. 2023. Landscape and urban planning. 230: -
Stochastic multicriteria acceptability analysis as a forest management priority mapping approach based on airborne laser scanning and field inventory data
Rana, Parvez; Vauhkonen, Jari. 2023. Landscape and urban planning. 230: -
Using a digital elevation model to place overland flow fields and uncleaned ditch sections for water protection in peatland forest management
Niemi, Mikko T; Ojanen, Paavo; Sarkkola, Sakari; Vasander, Harri; Minkkinen, Kari; Vauhkonen, Jari. 2023. Ecological engineering. 190: . 106945 -
Assessing the provisioning potential of ecosystem services in a Scandinavian boreal forest : suitability and tradeoff analyses on grid-based wall-to-wall forest inventory data
Vauhkonen J, Ruotsalainen R. 2017. Forest ecology and management. 389: 272-284 -
Large tree diameter distribution modelling using sparse airborne laser scanning data in a subtropical forest in Nepal
Rana Parvez, Vauhkonen Jari, Junttila Virpi, Hou Zhengyang, Gautam Basanta, Cawkwell Fiona, Tokola Timo. 2017. Isprs journal of photogrammetry and remote sensing. 134: 86-95 -
Reconstructing forest canopy from the 3D triangulations of airborne laser scanning point data for the visualization and planning of forested landscapes
Vauhkonen J, Ruotsalainen R. 2017. Annals of forest science. 74: 1-13 -
Extracting canopy surface texture from airborne laser scanning data for the supervised and unsupervised prediction of area-based forest characteristics
Niemi MIkko, Vauhkonen Jari. 2016. Remote sensing. 8: 582 -
On the potential to predetermine dominant tree species based on sparse-density airborne laser scanning data for improving subsequent predictions of species-specific timber volumes
Räty Janne, Vauhkonen Jari, Maltamo Matti, Tokola Timo. 2016. Forest ecosystems. 3: 1-17