Forest remote sensing at the Institute of Geomatics focuses on classifying and assessing quantitative measures with remote sensing data of various special, temporal and spectral resolutions. Propose of our research is to provide products on different scales: reaching from tree species maps on singletree level, to regional growing stock maps, to large-scale maps; e.g. the fractional coverage of spruce in Bavaria, Germany. Further, we combine different methods for monitoring forest health/vitality and forest disturbances, e.g. detection of windthrow or bark beetle infestation.

The applied remote sensing data includes different, mainly optical sensors reaching from satellite borne data (MODIS, Landsat, Sentinel-2, RapidEye, Pléiades, WorldView-2,...), airborne data (multispectral cameras, hyperspectral sensors, laser scanner) to UAV-born systems (multispectral and thermal cameras).

Besides using single data sets, we try to utilize the potential of multi-view (3D data, BRDF), multi-temporal (time series) and multi-sensor data. For data analyses we increasingly use machine learning algorithms (Neuronal Networks, Support Vector Machines, Random Forests) besides of well-established methods. In addition to (semi-)empirical models we apply physically-based models (Radiative Transfer Model).

Our research is carried out in close cooperation with national and international partners in research- and industry. Among those are universities, research institutions, public authorities, forest enterprises and engineering offices.

Following courses held at the institute cover the topic forest remote sensing: ‘Introduction to Remote Sensing in Forestry‘, ‘Selected Topics of Geo-data Management‘‚ ‘Remote Sensing and GIS in Natural Resource Management‘, ‘Applied Photogrammetry‘, 'Remote sensing time series analysis' and 'Remote sensing and image processing'. We constantly offer different topics for master theses in forest remote sensing. If you are interested, please contact us.

Current projects

Current publications

Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data

Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data

Fractional cover mapping of spruce and pine at 1ha resolution combining very high and medium spatial resolution satellite imagery

Using canopy heights from digital aerial photogrammetry to enable spatial transfer of forest attribute models: a case study in central Europe

Estimating stand density, biomass and tree species from very high resolution stereo-imagery – towards an all-in-one sensor for forestry applications?

First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

Windthrow Detection in European Forests with Very High-Resolution Optical Data

Use of WorldView-2 stereo imagery and National Forest Inventory data for wall-to-wall mapping of growing stock

Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data

Evaluation of semi-empirical BRDF models inverted against multi-angle data from a digital airborne frame camera for enhancing forest type classification

Early Detection of Bark Beetle Infestation in Norway Spruce (Picea abies, L.) using WorldView-2 Data

Method Analysis for Collecting and Processing in-situ Hyperspectral Needle Reflectance Data for Monitoring Norway Spruce

Contact person

Markus Immitzer, Dipl.-Ing. MSc. Dr.

H85700 Institute of Geomatics

+43 1 47654-85732
+43 1 47654-85709
Office hours
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