Light detection and ranging (lidar) data acquired from airborne or spaceborne platforms
have revolutionized measurement and mapping of forest attributes. Airborne data are often …

Data processing for forestry applications is challenged by the increasing availability of multi-
source and multi-temporal data. The advancements of Deep Learning (DL) algorithms have …

Forest inventory attributes are an important source of information for a variety of strategic
and tactical forest management purposes. However, it is not possible or feasible for field …

The use of light detection and ranging (LiDAR) techniques for recording and analyzing tree
and forest structural variables shows strong promise for improving established hyperspectral …

In this study, for five sites around the world, we look at the effects of different model types
and variable selection approaches on forest yield modelling performances in an area-based …

Machine learning combines inductive and automated techniques for recognizing patterns.
These techniques can be used with remote sensing datasets to map aboveground biomass …

Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means
of remote sensing and are increasingly applied in the field of forest ecology and …

Abstract Airborne Light Detection and Ranging (LiDAR) information alone is insufficient for
species-specific prediction of forest stand attributes, and therefore auxiliary optical image …

In a landscape consisting primarily of intensive forestry interspersed with some protected
areas, multifunctional forestry with retention trees can play a crucial role in nature …

As concerns rise over potential effects of greenhouse gas related climate change on
terrestrial ecosystems, forest managers require growth and yield modeling capabilities …