Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification
Research output: Contribution to journal › Article › peer-review
Standard Standard
Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification. / Adnan, Syed; Maltamo, Matti; Mehtätalo, Lauri et al.
In: Remote Sensing of Environment, Vol. 260, 07.2021, p. 112464.
In: Remote Sensing of Environment, Vol. 260, 07.2021, p. 112464.
Research output: Contribution to journal › Article › peer-review
HarvardHarvard
Adnan, S, Maltamo, M, Mehtätalo, L, Ammaturo, RNL, Packalen, P & Valbuena, R 2021, 'Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification', Remote Sensing of Environment, vol. 260, pp. 112464. https://doi.org/10.1016/j.rse.2021.112464
APA
Adnan, S., Maltamo, M., Mehtätalo, L., Ammaturo, R. N. L., Packalen, P., & Valbuena, R. (2021). Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification. Remote Sensing of Environment, 260, 112464. https://doi.org/10.1016/j.rse.2021.112464
CBE
Adnan S, Maltamo M, Mehtätalo L, Ammaturo RNL, Packalen P, Valbuena R. 2021. Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification. Remote Sensing of Environment. 260:112464. https://doi.org/10.1016/j.rse.2021.112464
MLA
Adnan, Syed et al. "Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification". Remote Sensing of Environment. 2021, 260. 112464. https://doi.org/10.1016/j.rse.2021.112464
VancouverVancouver
Adnan S, Maltamo M, Mehtätalo L, Ammaturo RNL, Packalen P, Valbuena R. Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification. Remote Sensing of Environment. 2021 Jul;260:112464. Epub 2021 Apr 23. doi: 10.1016/j.rse.2021.112464
Author
RIS
TY - JOUR
T1 - Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification
AU - Adnan, Syed
AU - Maltamo, Matti
AU - Mehtätalo, Lauri
AU - Ammaturo, Rhei N.L.
AU - Packalen, Petteri
AU - Valbuena, Rubén
PY - 2021/7
Y1 - 2021/7
KW - Forest structure
KW - Forest aboveground biomass
KW - Gini coefficient
KW - L-moments
KW - Airborne laser scanning
U2 - 10.1016/j.rse.2021.112464
DO - 10.1016/j.rse.2021.112464
M3 - Article
VL - 260
SP - 112464
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
SN - 0034-4257
ER -