SLOPE Project – intelligent forest management

Evaluation of a forest stand with TLS

Evaluation of a forest stand with TLS

Integrated proceSsing and controL systems fOr sustainable forest Production in mountain arEas

The mission of SLOPE is to develop an integrated system for the optimization of the forest production in mountain areas. Its aim is to fill the gap between the more expensive and less flexible forestry operations in mountain areas and optimized cut-to-length systems in flatland forests.

Therefore the purpose is, to combine spatial information and multi-sensor data from remote sensing. Data occurs via UAVs (Unmanned Aerial Vehicles) and TLS (Terrestrial Laser Scanner) surveying systems to a model for Sustainable Forest Management. With these utilities optimal planning and logistics management can be achieved in forest operations. In the long run, the geo- referenced, combined and harmonized data can be used to repeat measurements and monitor forest health, growth rate, brush cutting, damage, harvest and reforestation.

In SLOPE the aerial platform ‘‘eBee Ag sensefly’’, a fully autonomous drone was tested. It is capable of acquiring high resolution multi-spectral images. Users simply select the area they want to map, launch the drone by throwing it into the air, and the eBee flies, acquires images, and lands all by itself. Later, vegetation indices obtained from UAV images are compared with data obtained from satellite rapidEye images analysis. In a next step, TLS will be used for digital in-field surveying. This provides foresters with detailed information about the vegetation’s structure before and after harvesting. About 40 million reflection points can be collected in one 360° scan, collectively referred to as a point cloud.

Amazingly, the scanner used in this project collected the data in 3min 40sec. TLS is very accurate, the representation starts by a virtual 3D forest and ends by the demonstration of the trunk shape and cutting options for a single tree on the SLOPE platform.

SLOPE will add value to the mountain forest production. Novel sensing technologies allow reliable stumpage price evaluation and quality control of the harvested material. The methodological basis for a semi-automated and real-time grading system for the mountainous forest production will be developed. Thus log and biomass segregation is improved, forest inventories can be updated easily and stand growth and yield models can be refine steadily. But how is this done? The integration of intelligent systems in the cable crane/processor head/truck allows measuring a set of different data, which is used to assess the assortment variety. Different systems will be used to trace the material from the site throughout the whole supply chain.

Integration of information about the forest (topographic and geomorphological, cadastral data, road network, environmental, climatic data, etc.), harvesting data (extraction distance and direction, stand density, silviculture management, utilization amount etc.) and material origin, quality and availability in a unique system, accessible online and available in real time to a series of operators (e.g. logistic operators, brokers, forest owners, sawmills) will be addressed by the project. This will enable a series of services, ranging from stumpage price evaluation, pre-selling, forest harvest planning, logistic management, real-time information stream, timber and biomass purchasing on web platform.

The Slope Project is funded by the European Union Seventh Framework Programme. The consortium consists of renowned international partners like GRAPHITECH, CNR-IVALSA, COMPOLAB, COASTWAY, MHG-SYSTEMS, BOKU, FLYBY, GREIFENBERG, TREEMETRICS and ITENE. For further information please have a look at

Blogpost and photos submitted by Maximilian Kastner (Institute of Forest Engineering, University of Natural Resources and Life Sciences, Vienna, Austria) – maximilian.kastner(at)

The content, structure and grammar is at the discretion of the author only.

This post is published as entry #11 of our #Forests2015 blog competition. It is submitted in the “Open” category.

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