Hans Ole Ørka

TL;DR: In this article, the authors present the case for using Lidar sampling as a means to enable timely and robust large-area characterizations, and discuss the potential of using lidar in an integrated sampling framework for large area ecosystem characterization and monitoring.

TL;DR: Evaluating the potential of two high spectral and spatial resolution hyperspectral sensors, operating at different wavelengths, for tree species classification of boreal forests showed that the HySpex VNIR 1600 sensor is effective in borealTree species classification with kappa accuracies over 0.8.

TL;DR: The results revealed that the use of UAS imagery can provide relatively accurate and timely forest inventory information at a local scale, and highlights the practical advantages of U AS-assisted forest inventories, including adaptive planning, high project customization, and rapid implementation, even under challenging weather conditions.

TL;DR: In this paper, a study was conducted on 197 Norway spruce and 180 birch trees (leaves on conditions) in a boreal forest reserve in Norway using an airborne laser scanner (ALS) data.

TL;DR: In this article, the authors examined the single-trees-level response of two LiDAR sensors in over 13, 000 forest trees in southern Finland and achieved an accuracy of 88−90% in the classification of Scots pine, Norway spruce, and birch.