Mapping above-ground biomass in old-growth deciduous forests using open-access satellite data, field plots, and machine learning algorithms - Laya Zeinali Yadegari; Hormoz Sohrabi; Elia Quirós; Markus Immitzer (Advances in Space Research, 2025)

A scalable, annual aboveground biomass product for monitoring carbon impacts of ecosystem restoration projects - Clement Atzberger; Markus Immitzer; Kyle S. Hemes; Mathias Kästenbauer; Josué López; Talita Terra; Clara Rajadel-Lambistos; Saulo Franco de Souza; Kleber Trabaquini; Nathan Wolff (Remote Sensing of Environment, 2025)

Sentinel-2 vs. PlanetScope: Comparison and combination for tree species classification in two central European forest ecosystems - Ardalan Daryaei; Michael Lechner; Anna Iglseder; Lars T. Waser; Markus Immitzer (Remote Sensing Applications: Society and Environment, 2025)

Sugarcane yield estimation in Thailand at multiple scales using the integration of UAV and Sentinel-2 imagery - Jaturong Som-ard; Markus Immitzer; Francesco Vuolo; Clement Atzberger (Precision Agriculture, 2024)
Timely and accurate estimates of sugarcane yield provide valuable information for food management, bio-energy production, (inter)national trade, industry planning and government policy. Remote sensing and machine learning approaches can improve sugarcane yield estimation. Previous attempts have however often suffered from too few training samples due to the fact that field data collection is expensive and time-consuming. Our study demonstrates that unmanned aerial vehicle (UAV) data can be used to generate field-level yield data using only a limited number of field measurements. Plant height obtained from RGB UAV-images was used to train a model to derive intra-field yield maps based on 41 field sample plots spread over 20 sugarcane fields in the Udon Thani Province, Thailand. The yield maps were subsequently used as reference data to train another model to estimate yield from multi-spectral Sentinel-2 (S2) imagery. The integrated UAV yield and S2 data was found efficient with RMSE of 6.88 t/ha (per 10 m × 10 m pixel), for average yields of about 58 t/ha. The expansion of the sugarcane yield mapping across the entire region of 11,730 km 2 was in line with the official statistical yield data and highlighted the high spatial variability of yields, both between and within fields. The presented method is a cost-effective and high-quality yield mapping approach which provides useful information for sustainable sugarcane yield management and decision-making.

Effects of interaction between forest structure and precipitation event characteristics on fuel moisture conditions - Gergő Diószegi; Markus Immitzer; Mortimer M. Müller; Harald Vacik (Agricultural and Forest Meteorology, 2023)

Tree Species Mapping in Mangrove Ecosystems Using UAV-RGB Imagery and Object-Based Image Classification - Mojdeh Miraki; Hormoz Sohrabi; Markus Immitzer (Journal of the Indian Society of Remote Sensing, 2023)

Effects of habitat quality and hiking trails on the occurrence of Black Grouse (Tetrao tetrix L.) at the northern fringe of alpine distribution in Austria - Markus Immitzer; Ursula Nopp-Mayr; Margit Zohmann (Journal of Ornithology, 2014)