Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Petri Varvia"'
Autor:
Mikko Kukkonen, Mari Myllymäki, Janne Räty, Petri Varvia, Matti Maltamo, Lauri Korhonen, Petteri Packalen
Publikováno v:
Annals of Forest Science, Vol 81, Iss 1, Pp 1-13 (2024)
Abstract Key message Data acquisition of remote sensing products is an essential component of modern forest inventories. The quality and properties of optical remote sensing data are further emphasised in tree species-specific inventories, where the
Externí odkaz:
https://doaj.org/article/719169f0f1a0404d9acd82629be48372
Publikováno v:
SoftwareX, Vol 24, Iss , Pp 101563- (2023)
Gaussian process regression (GPR) is a non-parametric kernel-based machine learning method. GPR is based on Bayesian formalism, which enables the estimation of prediction uncertainty of the response variables. We propose an R package that provides an
Externí odkaz:
https://doaj.org/article/62e9eaa72fe648a681b579ea54a44e88
Autor:
Svetlana Saarela, Petri Varvia, Lauri Korhonen, Zhiqiang Yang, Paul L. Patterson, Terje Gobakken, Erik Næsset, Sean P. Healey, Göran Ståhl
Publikováno v:
MethodsX, Vol 11, Iss , Pp 102321- (2023)
Global commitments to mitigating climate change and halting biodiversity loss require reliable information about Earth's ecosystems. Increasingly, such information is obtained from multiple sources of remotely sensed data combined with data acquired
Externí odkaz:
https://doaj.org/article/3ebce9aaf32e499f9a5fd22f49b64ff9
Autor:
Matti Maltamo, Janne Räty, Paula Soares, Jacob L. Strunk, Margarida Tomé, Diogo Nepomuceno Cosenza, Petri Varvia, Lauri Korhonen, Petteri Packalen
Publikováno v:
Canadian Journal of Forest Research. 52:385-395
Semi- and nonparametric models are popular in the area-based approach (ABA) using airborne laser scanning. It is unclear, however, how many predictors and training plots are needed to provide accurate predictions without overfitting. This work aims t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d093418ac9091546d67f53f000aceb0f
https://doi.org/10.1139/cjfr-2021-0192
https://doi.org/10.1139/cjfr-2021-0192
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-14
Single-photon airborne light detection and ranging (LiDAR) systems provide high-density data from high flight altitudes. We compared single-photon and linear-mode airborne LiDAR for the prediction of species-specific volumes in boreal coniferous-domi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::367f625e8a00dda4a0830e93427de511
https://doi.org/10.1109/tgrs.2021.3060670
https://doi.org/10.1109/tgrs.2021.3060670
Publikováno v:
JOURNAL OF QUANTITATIVE SPECTROSCOPY AND RADIATIVE TRANSFER. 208:19-28
In this paper, Bayesian inversion of a physically-based forest reflectance model is investigated to estimate of boreal forest canopy leaf area index (LAI) from EO-1 Hyperion hyperspectral data. The data consist of multiple forest stands with differen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54ac88f82d1b1c0d9d9c05fe9d22cbfe
http://juuli.fi/Record/0287440718
http://juuli.fi/Record/0287440718
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 55:1671-1681
In this paper, we propose an approach to quantify the plot-level uncertainty in species-specific growing stock volume estimated from airborne laser scanning data and aerial imagery. This is accomplished by adopting the framework of Bayesian inference
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d5c8ead299c2eeaef2f62733053feb8
https://doi.org/10.1109/tgrs.2016.2628960
https://doi.org/10.1109/tgrs.2016.2628960
Publikováno v:
Journal of Quantitative Spectroscopy and Radiative Transfer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=r3c4b2081b22::b7293b247cee95c69671e2f6ba267528
While the analysis of airborne laser scanning (ALS) data often provides reliable estimates for certain forest stand attributes -- such as total volume or basal area -- there is still room for improvement, especially in estimating species-specific att
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2248c24891d208d0ad0e0537b5240e88
Hyperspectral remote sensing data carry information on the leaf area index (LAI) of forests, and thus in principle, LAI can be estimated based on the data by inverting a forest reflectance model. However, LAI is usually not the only unknown in a refl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8721e1b5574be5dbbf20f1455bc468a7
https://erepo.uef.fi/handle/123456789/3698
https://erepo.uef.fi/handle/123456789/3698