Automated individual tree crown detection and delineation (ITCD) using remotely sensed
data plays an increasingly significant role in efficiently, accurately, and completely …

The identification of individual tree species is crucial for forest inventory, carbon stocks
assessment as well as for habitat and ecosystem change studies. Previous studies have …

Tree species identification constitutes a bottleneck in remote sensing-based forest inventory.
In passive images the differentiating features overlap and bidirectional reflectance hampers …

Remote sensing (RS) studies aiming at mapping forest biomass typically rely on ground-
based estimates of biomass for calibration or validation. Whatever the biophysical variable …

Remote sensing offers the potential to study forest ecosystems by providing spatially and
temporally distributed information on key biophysical and biochemical variables. The …

National forest inventories (NFIs) have a long history, although their current major features
date only to the early years of the twentieth century. Recent issues such as concern over the …

Forest inventories based on single-tree interpretation of airborne laser scanning (ALS) data
often rely on an allometric estimation chain in which inaccuracies in the estimates of the …

A harvester enables detailed roundwood data to be collected during harvesting operations
by means of the measurement apparatus integrated into its felling head. These data can be …

This study looked to investigate the basic properties and species classification performance
of waveform (WF) features in LiDAR data for the main tree species in Finland. We conducted …

Automated tree species classification using high density airborne light detection and ranging
(LiDAR) data will support more precise forest inventory but further research is required to …