Advancing Forest Inventory: Field Trip to Sailershausen

Advancing Forest Inventory: Field Trip to Sailershausen

March 24, 2025

Today, staff from the Earth Observation Research Cluster conducted a field trip to Sailershausen to visit three fully inventoried forest plots and to discuss examples of remote sensing applications with the forestry administration of the University of Würzburg. The goal of this research is to apply the R package TreeCompR to these plots using Airborne LiDAR data and compare the results with tree-by-tree in-situ measurements.

This comparison aims to calibrate and refine an airborne LiDAR data-based approach and thus improve the application in forest inventory and forest management planning. By improving the accuracy of this method, we can better assess the necessary measures for sustainable forest management.

The work is being carried out within the EO4CAM project, with the University Forest serving as a pilot region.

 

you may also like:

A Glimpse into Our Research: Data on Display in the Foyer

A Glimpse into Our Research: Data on Display in the Foyer

Stepping into the foyer, visitors are now greeted by large, striking images that tell the story of our research through data. Each visual represents a unique scientific perspective – from the Arctic to the cultivated landscapes of Bavaria, and from forest canopies to...

Successful MSc defense by Sonja Maas

Successful MSc defense by Sonja Maas

Big congratulations to Sonja Maas, who successfully defended her Master thesis today on the highly relevant and increasingly pressing topic: LiDAR-Based Acquisition Strategies for Forest Management Planning in a Mature Beech Stand Supervised by Dr. Julian Fäth and...

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Two days ago, our PostDoc Dr. Jakob Schwalb-Willmann visited the Institute for Geoinformatics at University of Münster to give a talk at IFGI’s GI Forum titled “Can animals be used to classify land use? Employing movement-tracked animals as environmental informants using deep learning”.