iFOS News
Open-Access Paper on Carbon Accounting in Forestry Published
We are proud to announce the publication of a new scientific article developed within the CO₂ForIT project, now available in the European Journal of Forest Research. The paper, titled “Digital technologies for precise carbon balancing in timber procurement”, was authored by Martin Hoppen, Simon Baier, Lennart Schinke, Martin Ziesak, Lukas J. Schreiber, Arthur Wahl, Jiahang Chen, Anil Riza Bektas, Frank Heinze, Michael Schluse, and Jürgen Roßmann.
The publication explores how digital technologies can enable accurate, transparent carbon accounting along the timber value chain — supporting climate-smart forest operations and sustainable resource use.
Accepted: April 23, 2025
Open Access
Read the full paper here: https://doi.org/10.1007/s10342-025-01794-1
SmaSiKaFE: First Project Workshop
Calamities have unfortunately become part of everyday life in modern forestry. Climate change will continue to cause drought-related damage to forest stands in the future. Harvesting trees that have been damaged in this way carries significant risks, especially during motor-manual operations. The SmaSiKaFE project explores ways to safely and efficiently harvest trees affected by drought or insect infestation using chainsaws and additional tools.
The project focuses on motor-manual timber harvesting in stands where, for example, small clusters of damaged trees (so-called calamity nests) need to be removed and the use of a harvester would be too costly or impractical. To this end, the project investigates methods for detecting damaged trees using LiDAR or photo-optical techniques. A digital work order is created, and additional information is provided to forest workers via a mobile app. This app supports tree identification on site and offers recommendations on the use of supplementary tools.
On April 28 and 29, the project team met at the Forestry Training Center (FBZ) in Arnsberg-Neheim to compile initial results and coordinate the next phases of the project. A presentation of the project is planned for a KWH4.0 Academy event in autumn 2025.
Award-Winning Paper on Forest Road Classification Published
We are delighted to share that a new scientific paper developed as part of the Intelliway project has been published and received a Best Paper Award at the 2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor).
The paper, titled “ML-Based Forest Road Classification Based on Car Attached Ultrasonic Sensors”, demonstrates how the iFOS measuring lance and machine learning can be used to assess forest road conditions in a scalable and efficient way — paving the way for improved infrastructure monitoring in forestry operations.
It was authored by Gesa Götte, Simon Baier, Ina Ehrhardt, Andreas Herzog, and Martin Ziesak. We sincerely thank all co-authors and project contributors for their outstanding work and collaboration.
Conference: MetroAgriFor 2024 (29–31 October 2024)
Published in IEEE Xplore: April 11, 2025
DOI: 10.1109/MetroAgriFor63043.2024.10948810
iFOS contributes forestry expertise to the SoilTribes project
We are excited to support the EU-funded SoilTribes project, which aims to promote sustainable soil management by making soil biodiversity more visible and relevant in land-use decisions. The project translates soil science into actionable narratives—fostering awareness, inspiring behavioral change, and informing policy-making.
SoilTribes is a Horizon Europe project under the call “Mission Soil – Soil Deal for Europe”, funded by the European Research Executive Agency (REA). It runs for 36 months, starting in January 2025.
As forestry specialists, iFOS brings practical knowledge of forest ecosystems and management. We contribute to identifying forest-related soil indicators and ensuring that forest soils and their biodiversity are better considered in future planning and practice.
R&D project completed: Intelliway – Forest Road Condition Monitoring
On November 14 the Intelliway Consortium came together at the Forest Research Center Gotha in Thuringia. The project partners presented the results of the research project Intelliway: “Condition Monitoring and Predictive Maintenance for Forest Roads”. It was a pleasure to see how far we’ve advanced the state of the art in forest road management together. Many interesting results were presented:
- the KWF’s new standard for road condition classification in Germany (see fact sheet in German)
- various algorithms to predict future road maintenance needs based on timber transport models, weather, et cetera
- INTEND’s WIS 2.0 road information system for forestries
- an app from eEntwicklung.net to support manual road condition classification
- the complex CONTURA prototype which measures the road surface with laser scanning
- our new Road Scanner 3.0 that strikes the perfect balance between operating cost and road classification power
- and more
Over the course of the Intelliway project we at iFOS put a lot of work and ingenuity into the development of our new and improved Road Scanner 3.0. We overhauled the entire hardware and software and added two more ultrasound sensors to improve detection of wheel ruts. As always, foresters can simply mount the Road Scanner 3.0 on the trailer hitch of their car and let it monitor the road conditions while they are on the move checking on forest health and forestry operations.
While the Intelliway project comes to a close, participants of the final discussion agreed that this line of development needs to continue since systematic road condition monitoring is essential for maintaining forest roads cost-effectively. Representatives of various state forestries expressed an interest in integrating the Road Scanner 3 into their regular operations.
Hence, we’re not done with road condition monitoring at all. In the R&D project CO₂For-IT we’re developing a Road Scan Box that can be mounted on logging trucks. We’re also in negotiations with several stakeholders to continue with large scale trials of the Road Scanner 3 in state forestries. Over the next years we plan to bring objective road condition monitoring and the associated cost-savings to forest road networks across Germany and beyond.
Proud Partner in the SmaSiKaFE Project Team
The aim of SmaSiKaFE (Smart and Safe Calamity Logging) is to make the harvesting of damaged trees safer, more ecological, and more economical through comprehensive digital support.
The project focuses on motor-manual harvesting in calamity-affected forest areas, where traditional mechanized solutions are often not viable. At its core is the concept of the Smart Forest Worker—a digitally supported forestry professional equipped with tools like a mobile app to assess risks and select the safest and most effective felling method.
To enable this, the project combines terrestrial laser scanning (TLS) and photo-optical methods to collect detailed data on damaged forest areas. These data are analyzed on a single-tree basis to provide recommendations for safe felling techniques—such as winch-assisted or wedge-supported cutting—as well as for optimal log bucking strategies. A digital tool supports forest workers in making sorting decisions directly on site, aiming to maximize wood value while ensuring safety and efficiency.
SmaSiKaFE delivers a prototype solution for safe and efficient motor-manual harvesting, tailored also for smaller forest owners and dispersed damage events such as bark beetle nests.
The project is led by RIF e.V., in collaboration with RWTH Aachen University, Forstliches Bildungszentrum (FBZ NRW), Scientes Mondium UG, palos DE GmbH and iFOS GmbH. The Social Insurance for Agriculture, Forestry and Horticulture (SVLFG) is an associated partner.
SmaSiKaFE is funded by the German Federal Ministry of Food and Agriculture (BMEL) through the Fachagentur für Nachwachsende Rohstoffe (FNR) (project code: 2224NR113 A-F). The project runs from December 2024 to November 2027.
Introducing two new Masters of Science
We are excited to announce that our former employees Ram Sai Chigurupati and Jakub Wołosz have both defended their master’s theses with top grades in October. They worked together on developing artificial intelligence (AI) systems in the wood harvesting context: AI to power our Smart Chainsaws and AI for analyzing drone images of harvesting operations. While the two devoted a lot of brain power to their outstanding research, field work in the forest was also an essential part of it.
Mr. Chigurupati earned his MSc from TU Dresden with his thesis “Activity Recognition and Stem Segment Identification Using Smart Chainsaws”. In this work he developed and evaluated the machine learning models that are needed for the Smart Chainsaw to “feel” how much wood is harvested.
Mr. Wołosz defended his thesis titled “Utilizing a U-NET Convolutional Network to Characterize Felled Tree Stem Sections on UAV-Orthomosaics” at Warsaw University of Life Sciences. Here, he applied several machine learning models that were pre-trained for characterizing windthrown trees to identify and measure newly harvested stem sections.
It was a great pleasure to see our mentees grow their skillset and teach them about data science in forestry. With this experience and their impressive talents, these two young men are well-equipped for contributing substantially to the workforce. We wish them success with all their future endeavors!
Smart Forestry Research Published
We are proud to announce the publication of a scientific paper developed as part of the Smart Forestry project. The article, titled “Smart forestry – a forestry 4.0 approach to intelligent and fully integrated timber harvesting”, has been published in the International Journal of Forest Engineering (Volume 35, Issue 2, 2024), as part of a special issue on the digitalization of forest operations.
This open-access paper explores cutting-edge concepts and technologies in the field of forest engineering, showcasing an integrated approach to data-driven, intelligent timber harvesting. It reflects the collaborative efforts of a multidisciplinary team of authors: Martin Hoppen, Jiahang Chen, Julia Kemmerer, Simon Baier, Anil Riza Bektas, Lukas Schreiber, Dorothea Mayer, Alexander Kaulen, Martin Ziesak, and Jürgen Roßmann.
We are excited to contribute to the ongoing digital transformation in forestry and thank all project partners for their valuable input.
Published: March 11, 2024
Read the article here: https://doi.org/10.1080/14942119.2024.2323238
The Future of Timber Harvesting: iFOS is now Part of the CO2For-IT Project
We are excited to announce our participation in the research project “CO2For-IT”. This initiative is dedicated to the development, prototypical implementation, and practical testing of a “Forest Data Space”. Our goal is to revolutionize sustainable and climate-positive timber production. At the heart of “CO2For-IT” lies the “Forest Data Space”. This platform is built on the principles of openness, federation, trustworthiness, and security, as advocated by the European Gaia-X initiative. It will serve as a foundation for comprehensive sustainability monitoring, covering both the biological production in forests and the technical production processes.
The “Forest Data Space” is a game-changer, offering new, extensive incentives for digital and sovereign participation to forestry actors who have traditionally operated in analog or semi-digital modes. This initiative promises to catalyze digital transformation across the entire sector, enabling for the first time the comprehensive optimization of value creation processes and networks, from saplings to wood-based products.
This platform will enable cross-value chain monitoring of CO2 balances. It will provide crucial data for developing strategies to combat climate change, positioning the “Forest Data Space” as an enabling technology for the green digital transformation of forestry value chains through data-driven services.
A significant part of our contribution to the “CO2For-IT” project involves precise carbon accounting during timber harvesting and transportation, based on individual log sections. For this purpose, we are deploying both an advanced version of our smart chainsaw and our iFOS software in harvesters. In this endeavor, the seamless integration of machinery enabled by our iFOS middleware system across the entire timber harvesting process is key to ensuring accurate carbon tracking.
Furthermore, we are focusing on the physical and emotional stress faced by humans during timber harvesting. Finally, we will enable the deployment of a more advanced forest road condition monitoring sensor system “Messlanze”, which will be specifically designed for timber trucks. Optimizing truck routes means less fuel consumption and a smaller carbon footprint, aligning perfectly with our commitment to sustainability and environmental responsibility.
Project Partners:
We are proud to collaborate with a consortium of esteemed partners in this project:
- Materna Information & Communications SE
- RIF Institut für Forschung und Transfer e. V.
- RWTH Aachen University, including the Institute for Man-Machine Interaction (MMI), the Laboratory for Machine Tools and Production Engineering (WZL), and the Institute for Industrial Engineering (IAW)
- Rhenus Forest Logistics GmbH & Co. KG
- HSM Hohenloher Spezial Maschinenbau GmbH & Co. KG
- Forstliches Forschungs- und Kompetenzzentrum Gotha (ThüringenForst – AöR)
- Kuratorium für Waldarbeit und Forsttechnik e. V. (KWF)
- foldAI
Together with these partners, we look forward to advancing sustainable forestry practices and contributing to the fight against climate change through the “CO2For-IT” project.
BSc. / MSc. Thesis [position filled]
The Potential of Smart Chainsaws in Motor-manual Timber Harvesting
Location: remote / Roding (Bavaria)
THIS POSITION HAS BEEN FILLED. DO NOT APPLY!
As part of the “Smart Forestry” project funded by the German Agency for Renewable Resources (FNR), we are developing and integrating digital twins into large forestry machines as well as hand-held equipment such as chainsaws.
Your topic: Determination of the Potential of Smart Chainsaws in Motor-manual Timber Harvesting
In a time study, STIHL’s flagship chainsaw MS500i equipped with advanced sensor technology is tracked during logging. Information about the execution of the work (work phases), but also about the work objects (attributes of the trees to be felled, attributes of the produced stem sections) will be recorded. In the course of your scientific work, you will analyze these data sets in different respects, evaluate them statistically or geostatistically, and visualize them. You can build on previous research activities and on the expertise of iFOS GmbH. You will receive professional support from an in-house mentor at any time.
Requirements
- BSc. or MSc. student in the field of forest science / forest engineering or comparable
- knowledge in the field of motor-manual harvesting
- interest in time studies
- knowledge in the field of statistics
- knowledge in the field of GIS
If you are…
- reliable,
- can work independently and in a structured manner,
- like to venture into new and unknown territory without hesitation,
…we offer:
- working in a young, dynamic company,
- personal freedom and flexible working hours,
- space for creativity in solving technical issues,
- the possibility of subsequent employment in interesting projects with impact in the forestry and environmental sector.
iFOS and Smart Forestry
iFOS GmbH is an internationally operating industry-related research, engineering, and advisory company in the field of forestry. We cooperate closely with renowned partners in German and European forestry such as equipment manufacturers, universities, state forests, and wood manufacturers.
You will contribute to the Smart Forestry project funded by the German Agency for Renewable Resources (FNR). The goal of this research project is to develop cross-cluster approaches for intelligent, fully-integrated forest harvesting based on Forestry 4.0 principles. We cross-link all actors along the wood harvesting network with digital twins. These freely configurable networks enable all actors to communicate more effectively and on equal terms. The aim is to improve control, valuation, and optimization of the forest harvesting process for everyone involved and enables integration with related processes.
Are you excited to transform forestry?
- Then prepare your CV and point out how you match the requirements listed above,
- e-mail your application documents to lukas.schreiber[[at]]ifos-gmbh.de
- and we will get back to you within a week to schedule an interview.
We are looking forward to hear from you!
THIS POSITION HAS BEEN FILLED. DO NOT APPLY!
Master’s Thesis or Internship [position filled]
Smart Chainsaw and Machine Learning
Location: remote / Roding (Bavaria)
THIS POSITION HAS BEEN FILLED. DO NOT APPLY!
You are excited about applying cutting-edge research in a practical context?
You want to help make the forest industry fit for the digital age?
Then this is the opportunity for you!
Your Topic: Machine Learning in a Chainsaw’s Digital Twin
Your internship or thesis revolves around STIHL’s flagship chainsaw MS500i, equipped with additional sensors to measure engine parameters and its motion in 3D-space. In addition, this chainsaw has Bluetooth connections to interface with other actors in the harvesting chain in near-real-time.
You will develop machine learning models to infer actionable information from the sensor data. This will involve the following steps:
- prepare chainsaw sensor data from a real wood harvesting operation for analysis;
- train machine learning models to infer the workers’ actions and the locations of cut timber sections from the sensor data;
- validate your models using UAV and other data;
- provide guidance on how to deploy these machine learning models to resource-limited devices in the forest.
You can rely on our expertise and experience in forestry, geographic information systems, machine learning, and so on. A mentor will be ready to support you anytime.
If you …
- are enrolled in a scientific or technical master’s degree program;
- are fluent in at least one programming language and keen to work with Python;
- have taken a course in machine learning;
- know the basics of coordinate transformations in three dimensions;
- have applied numerical analysis techniques to solve problems during your studies;
- are a self-starter and excited to learn;
… we offer you:
- work in a young, dynamic company;
- personal freedom and self-determined working hours;
- space for creativity in solving technical issues;
- opportunities to get to know project partners such as STIHL, RWTH Aachen’s Institute for Man-Machine Interaction, the Bavarian State Forests, and others;
- the chance to continue with a permanent job with impact in the forestry and environmental sector.
iFOS and Smart Forestry
iFOS GmbH is an internationally operating industry-related research, engineering and advisory company in the field of forestry. We cooperate closely with renowned partners in German and European forestry such as equipment manufacturers, universities, state forests and wood manufacturers.
You will contribute to the Smart Forestry project funded by the German Agency for Renewable Resources (FNR). The goal of this research project is to develop cross-cluster approaches for intelligent, fully-integrated forest harvesting based on Forestry 4.0 principles. We cross-link all actors along the wood harvesting network with digital twins. These freely configurable networks enable all actors to communicate more effectively and on equal terms. The aim is to improve control, valuation and optimization of the forest harvesting process for everyone involved and enables integration with related processes.
Are you excited to transform forestry?
- Then prepare your CV and point out how you match the requirements listed above,
- e-mail your application documents to lukas.schreiber[[at]]ifos-gmbh.de
- and we will get back to you within a week to schedule an interview.
We are looking forward to hear from you!
THIS POSITION HAS BEEN FILLED. DO NOT APPLY!
Insights from Drone Data for the Chainsaw Field Study
The utilization of processed photogrammetric drone data significantly contributes to unlocking valuable insights in our field study. We collected a comprehensive and detailed dataset of drone images. Proper planning and execution enabled us to cover a vast area in little time. We processed these images through the EDEO photogrammetry processing pipeline to generate highly accurate three-dimensional models of the study area.
By leveraging the georeferenced 3D models, we gain a deeper understanding of the spatial distribution of the forest and many required tree attributes such as canopy cover and vegetation density, but also tree heights and stem section measurements. These insights provide essential context for interpreting the data stream of the smart STIHL chainsaw in different areas within the study site.
Unveiling a Treasure Chest of Data
After the initial days of operation, the preliminary examination of the raw data reveals a promising and unique treasure chest which the iFOS team will evaluate in the upcoming weeks.
The sensors integrated in the chainsaw delivered valuable data, which was streamed directly to laptops for further analysis. This data collection process will provide comprehensive insights that enable us to examine critical parameters such as spatial positioning of the stem sections, wood dimensions and working efficiency.
The cooperation between our research team, the STIHL development team, the forest management of BaySF, and the dedicated forest workers played a vital role in the success of this very first field test. Their expertise and support greatly contributed to the smooth execution of the study and the acquisition of high-quality data.
The analysis process will involve precise examination of the recorded data, extracting patterns, trends, and correlations to derive meaningful insights and potential optimizations. We will use this information to develop software for deducing harvesting data from the raw data in real-time. We are eager to share our findings and contribute to the continuous evolution of smart forestry practices. Stay tuned for forthcoming updates as we embark on the evaluation phase.
Drone-Assisted Reference Data Collection for Smart Chainsaw Field Study
In the pursuit of enhancing the Smart Chainsaw field study, the integration of drone flights will further improve our data collection. With careful planning, we conducted drone flights over two study plots, amassing over 12 000 overlapping photos in the last two days. These images will be utilized to create accurate georeferenced photogrammetric models of the field plots. The drone-acquired reference data will be crucial in assessing the spatial positioning of the felled stem sections, as well as their dimensions.
With this valuable dataset at our disposal, we are excited to analyze and synthesize the information, further advancing the development and optimization of smart forest practices.
Field Study: Smart Chainsaw at BaySF’s Forstbetrieb Roding
The ongoing field study featuring the smart chainsaw is progressing well. Our current focus is testing the chainsaw in collaboration with “Forstbetrieb Roding” of BaySF, specifically in mixed forest stands with strong tree diameters. The recorded work steps include cutting a felling notch, making a felling cut, delimbing, and performing separation cuts. Raw data captured by the saw’s integrated sensor box is streamed to laptops and recorded for analysis.
The cooperation between our team and the forest management and workers of BaySF has been excellent. In the event of any issues with the prototypes, the STIHL developers are readily available by phone to provide quick assistance.
After the initial days of operation, a preliminary examination of the raw data reveals a promising and unique treasure chest which the iFOS team will evaluate in the upcoming weeks.
Smart STIHL Chainsaw – Upcoming Field Study
Our first field study in the Smart Forestry project is about to begin in the coming days. In this globally unique experiment, we will test a smart chainsaw from STIHL equipped with advanced capabilities. This innovative MS500i prototype chainsaw can detect its position and motion in space and simultaneously record important parameters from the powertrain, including engine speed.
The main objective of our study is to derive production parameters for the harvested wood. Similar to a harvester head, the chainsaw will accurately record the location and assortment of every stem section. This information will be invaluable within the context of Industry 4.0, as it can be shared instantly with other participants in the harvesting chain, such as skidders, forwarders and so on. This enables the seamless transfer of data regarding the dimension and position of the log sections.
We are excited to collaborate closely with esteemed partners including STIHL, BaySF, and RWTH Aachen University in the upcoming days for this field study.
