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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.

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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

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Adnan, Syed ; Maltamo, Matti ; Mehtätalo, Lauri et al. / Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification. In: Remote Sensing of Environment. 2021 ; Vol. 260. pp. 112464.

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 -