Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

In this study, the potential of multispectral airborne laser scanner (ALS) data to model and predict some forest characteristics was explored. Four complementary characteristics were considered, namely, aboveground biomass per hectare, Gini coefficient of the diameters at breast height, Shannon dive...

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Bibliographic Details
Main Authors: Michele Dalponte, Liviu Theodor Ene, Terje Gobakken, Erik Næsset, Damiano Gianelle
Format: Article
Language:English
Published: MDPI AG 2018-04-01
Series:Remote Sensing
Subjects:
LiDAR
Shannon index
Gini coefficient
aboveground biomass
Online Access:http://www.mdpi.com/2072-4292/10/4/586

Internet

http://www.mdpi.com/2072-4292/10/4/586

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