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||<15%> {{http://venda.uef.fi/moin_static197/common/inverse/pubs/PetriVarvia2019.png|Publication|height="80",width="80"}}||P. Varvia, T. Lähivaara, M. Maltamo, P. Packalen, A. Seppänen<<BR>>[[https://doi.org/10.1109/TGRS.2018.2883495|Gaussian Process Regression for Forest Attribute Estimation From Airborne Laser Scanning Data]]<<BR>>IEEE Transactions on Geoscience and Remote Sensing '''57''' (6): 3361-3369, 2019.||

Remote sensing of forest

Global and local environmental changes are modifying forest ecosystems rapidly. The consequences of ecosystem changes on biodiversity, functioning of forests, and provision of ecosystem services are largely unclear. The analysis of ecosystem changes in different spatial scales is scientifically challenging. Reliable data of forest characteristics is a fundamental requirement for analysing environmental changes and their impacts. During the last years airborne laser scanning (ALS) has evolved to most accurate source for remote sensing data in forest environment applications. ALS is a remote-sensing technique that provides with three-dimensional (3D) high-precision measurements of targets in the form of a point cloud, based on laser-ranging measurements. The principle of ALS is illustrated in the figure on the right hand side. We develop Bayesian inversion methods for ALS based remote sensing of forest.

Remote sensing

Remote sensing

Both area-based and individual tree detection -based approaches are considered. In the area based estimation, the plot-level forest attributes (such as mean tree height and volume, biomass, etc.) are predicted directly from the ALS data, typically using regression -type methods. In individual tree detection, by contrast, the aim is to derive the plot-level statistics from the individual tree information inferred from the ALS data. In our approach, a set of canopy height models (CHM) are fitted to the ALS data. The animated figure on the left shows an example of the ALS point cloud (the point color illustrates the altitude of the reflection point), and the CHMs (green shapes) corresponding to field measurements (indicated with text 'measurement') and ALS data ('estimate'). In this example case, most of the trees were successfully detected based on the ALS data.

Contact

Past and present collaborators

Publications

Publication

K. Kansanen, J. Vauhkonen, T. Lähivaara, A. Seppänen, M. Maltamo, L. Mehtätalo
Estimating forest stand density and structure using Bayesian individual tree detection, stochastic geometry, and distribution matching
ISPRS Journal of Photogrammetry and Remote Sensing 152: 66-78, 2019.

Publication

P. Varvia, T. Lähivaara, M. Maltamo, P. Packalen, A. Seppänen
Gaussian Process Regression for Forest Attribute Estimation From Airborne Laser Scanning Data
IEEE Transactions on Geoscience and Remote Sensing 57 (6): 3361-3369, 2019.

Publication

P. Varvia, M. Rautiainen, A. Seppänen
Bayesian estimation of seasonal course of canopy leaf area index from hyperspectral satellite data
Journal of Quantitative Spectroscopy and Radiative Transfer, 208: 19-28, 2018

Publication

P. Varvia, T. Lähivaara, M. Maltamo, P. Packalén, T. Tokola, A. Seppänen
Uncertainty quantification in ALS-based species-specific forest inventory
IEEE Transactions on Geoscience and Remote Sensing, 55: 1671 – 1681, 2017

Publication

P. Varvia, M. Rautiainen, A. Seppänen
Modeling uncertainties in estimation of canopy LAI from hyperspectral remote sensing data – A Bayesian approach
Journal of Quantitative Spectroscopy and Radiative Transfer, 191: 19-29, 2017

Publication

K. Kansanen, J. Vauhkonen, T. Lähivaara, L. Mehtätalo
Stand density estimators based on individual tree detection and stochastic geometry
Canadian Journal of Forest Research, 46: 1359-1366, 2016.

Publication

T. Lähivaara, A. Seppänen, J.P. Kaipio, J. Vauhkonen, L. Korhonen, T. Tokola, M. Maltamo
Bayesian Approach to Tree Detection Based on Airborne Laser Scanning Data
IEEE Transactions on Geoscience and Remote Sensing, 52: 2690 – 2699, doi: 10.1109/TGRS.2013.2264548, 2014.

Publication

M. Vauhkonen, T. Tarvainen, T. Lähivaara
Inverse problems
A chapter for Mathematical Modelling
Editor: S. Pohjolainen
Springer, 2016.
ISBN: 978-3-319-27834-6 (Print) 978-3-319-27836-0 (Online)

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