Do airborne laser scanning biomass prediction models benefit from Landsat time series, hyperspectral data or forest classification in tropical mosaic landscapes?
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%0 Journal Article
%1 journals/aeog/HeiskanenAPPP19
%A Heiskanen, Janne
%A Adhikari, Hari
%A Piiroinen, Rami
%A Packalen, Petteri
%A Pellikka, Petri K. E.
%D 2019
%J Int. J. Appl. Earth Obs. Geoinformation
%K dblp
%P 176-185
%T Do airborne laser scanning biomass prediction models benefit from Landsat time series, hyperspectral data or forest classification in tropical mosaic landscapes?
%U http://dblp.uni-trier.de/db/journals/aeog/aeog81.html#HeiskanenAPPP19
%V 81
@article{journals/aeog/HeiskanenAPPP19,
added-at = {2020-02-20T00:00:00.000+0100},
author = {Heiskanen, Janne and Adhikari, Hari and Piiroinen, Rami and Packalen, Petteri and Pellikka, Petri K. E.},
biburl = {https://www.bibsonomy.org/bibtex/2543f82f2e817056245e6963cdb11c9fc/dblp},
ee = {https://doi.org/10.1016/j.jag.2019.05.017},
interhash = {8213a3c215a2d87c13bf1a3df20c0465},
intrahash = {543f82f2e817056245e6963cdb11c9fc},
journal = {Int. J. Appl. Earth Obs. Geoinformation},
keywords = {dblp},
pages = {176-185},
timestamp = {2020-02-21T12:44:44.000+0100},
title = {Do airborne laser scanning biomass prediction models benefit from Landsat time series, hyperspectral data or forest classification in tropical mosaic landscapes?},
url = {http://dblp.uni-trier.de/db/journals/aeog/aeog81.html#HeiskanenAPPP19},
volume = 81,
year = 2019
}
