Forest Big Data

Forest Big Data

Better data enables more efficient and higher quality planning and operations in the entire wood supply chain. D2I is building the foundations for the next generation forest resource management system in Finland. Connecting a wide network of public and private actors, the system aims to improve the competitiveness of the forest industry and increase the value added of the products. It serves the entire network of actors in wood production and procurement.

Result highlights

High density laser scanning of trees

The main objective was to develop novel methods for deriving single-tree-level attributes from active 3D remote sensing data. The use of high density laser scanning was evaluated in predicting:

Main conclusions:

More information:
V. Kankare (2015). The prediction of single-tree biomass, logging recoveries and quality attributes with laser scanning techniques. Dissertationes Forestales 195, Unversity of Helsinki.

Utilizing data collected by harvester

The main object was to study operational harvester data potential in updating forest resource information and as a reference data for Airborne laser scanning. The pilot was carried out at one forest inventory area of Finnish Forest Centre by Metsäteho and Arbonaut. Research data, consisting of 255 stands and 335 000 cut stems, was collected with six harvesters.

Main conclusions:

Sensor technology of forest machines is developing rapidly and that has been in focus of Aalto University's research in Forest Big Data. The aim is to support the automation of forest machines and develop operator support systems through advanced sensor technology. On the other hand, the same sensors will have in the future essential role in collection of big data during operations.

As an example of results is presented integration of low-cost IMU and two 2D laser scanners into a 3D Lidar to enable mobile 3D scanning in forest. Pose is estimated using inertial aided visual (Lidar) odometry. Ground echoes are also separated from other Lidar measurements and visualized in different color. Point cloud visualization with the help of Point Cloud Library.

Lassie in mobile
Forest Big Data platform

Numerous methods provide information on forests each with their own time cycles, granularities, accuracies, costs, and viewpoints. Effective utilization of available forest resources is thus not only based on short-cycled, increasingly accurate, even cost-effective data inventory methods. Instead, by providing easy access to best available up-to-date information on forests is expected to generate new applications and businesses and bring together varying users, thus enhancing the utilization of forest resources. The goal of the research task was to specify and demonstrate a platform providing data inquiry services for users and applications to easily access available forest data sources.

The aim of Forest Big Data platform is to provide uniform view to heterogeneous forest data sources by specifying a common data inquiry interface and a data structure for representing data and required metadata, in particular, the uncertainty. To provide easy access to the data sources, the platform offers basic services for updating data with growth prediction models and for combining several up-to-date data estimates by means of Bayesian data fusion. The platform was demonstrated with a simple real forest data case for testing the data structure, and suitable data updating and data fusion services.

Season map layer for timber harvesting

Seasonal fluctuation is a big challenge for wood procurement and causes remarkable extra cost for forestry. Main reason for that is varying soil bearing capacity that depends on soil properties and weather conditions. Climate change will make matters worse through even shorter winters and longer thaw periods. A new product concept was developed and tested targeting more precise harvesting planning. The tool produces a static spatial estimate for optimal timber harvesting season for large areas.

Input data is:

Estimation method is logistic regression and rule based classification. The output is a map layer that can be integrated with current harvesting planning systems.

Season map layer for timber harvesting

More results

Forest Big Datan tulosseminaari 8.3.2016 Heurekassa (in Finnish)

Kohti puuhuollon digitalisaatiota -tuloskalvosarja (in Finnish)


International peer-reviewed articles

E. Ahokas, J. Hyyppä, X. Yu, X. Liang, L. Matikainen, K. Karila, P. Litkey, A. Kukko, A. Jaakkola, H. Kaartinen, M. Holopainen & M. Vastaranta (2016). Towards Automatic single-sensor mapping by multispectral airborne laser scanning. ISPRS Congress 2016.

V. Kankare, J. Vauhkonen, T. Tanhuanpää, M. Holopainen, M. Vastaranta, M. Joensuu, A. Krooks, J. Hyyppä, H, Hyyppä, P. Alho & R. Viitala (2014). Accuracy in estimation of timber assortments and stem distribution – A comparison of airborne and terrestrial laser scanning techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 97:89-97.

M. Holopainen, M. Vastaranta, & J. Hyyppä (2014). Outlook for the next generation's precision forestry in Finland. Forests 2014, 5(7), 1682-1694.

Z. Hou, Q. Xu, J. Vauhkonen, M. Maltamo & T. Tokola (2015). Species-specific combination and calibration between area-based and tree-based diameter distributions using airborne laser scanning. Canadian Journal of Forest Research.

J. Hyyppä, M. Karjalainen, X. Liang, A. Jaakkola, K. Karila, H. Kaartinen, A. Kukko, J. C. White, M. A. Wulder, M. Vastaranta, M. Holopainen, V. Kankare, H. Hyyppä, M. Vaaja, M. Hollaus & M. Katoh (2015). Remote Sensing of Forests from Lidar and Radar. Invited book chapter in Remote Sensing Handbook (Editor P. Thenkabail), Boca Raton: CRC Press, 2015, 397-428.

H. Hyyti & A. Visala (2015). A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs. International Journal of Navigation and Observation, vol. 2015, Article ID 503814, 18 pages, 2015. doi:10.1155/2015/503814

H. Kaartinen, J. Hyyppä, M. Vastaranta, A. Kukko, A. Jaakkola, Y. Xiaowei, J. Pyörälä, L. Xinlian, L. Jingbin, W. Yungschen, R. Kaijaluoto, T. Melkas, M. Holopainen & H. Hyyppä (2015). Accuracy of Kinematic Positioning Using Global Satellite Navigation Systems under Forest Canopies. Forests 2015, 6, 3218-3236.

V. Kankare, X. Liang, M. Vastaranta, X. Yu, M. Holopainen & J. Hyyppä (2015). Diameter distribution estimation with laser scanning based multisource single tree inventory. ISPRS Journal of Photogrammetry and Remote Sensing 2015, 108: 161-171.

V. Kankare, J. Vauhkonen, M. Holopainen, M. Vastaranta, J. Hyyppä, H. Hyyppä & P. Alho (2015). Sparse Density, Leaf-off Airborne Laser Scanning Data in Aboveground Biomass Component prediction. Forests 2015, 6, 1839-1857.

V. Kankare, M. Joensuu, J. Vauhkonen, M. Holopainen, T. Tanhuanpää, M. Vastaranta, J. Hyyppä, H. Hyyppä, P. Alho, J. Rikala & M. Sipi (2014). Estimation of timber quality of Scots pine with terrestrial laser scanning. Forests 2014, 5: 1879–1895.

V. Kankare, M. Holopainen, M. Vastaranta, X. Liang, X. Yu, H. Kaartinen, A. Kukko & J. Hyyppä (2016). Outlook for the single-tree-level forest inventory in Nordic countries. Proceedings of GISOV 2016.

X. Liang, V. Kankare, X. Yu, J. Hyyppä & M. Holopainen (2014). Automatic stem curve measurement using terrestrial laser scanning. IEEE Transactions on Geoscience and Remote Sensing (TGRS), 52(3):1739-1748.

X. Liang, A. Kukko, H. Kaartinen, J. Hyyppä, W. Yu, A. Jaakkola & Y. Wang (2014). Possibilities of a Personal Laser Scanning System for Forest Mapping and Ecosystem Services. Sensors 2014, 14(1), 1228-1248.

X. Liang, Y. Wang, A. Jaakkola, A. Kukko, H. Kaartinen, J. Hyyppa, E. Honkavaara & J. Liu (2015). Forest Data Collection Using Terrestrial Image-Based Point Clouds From a Handheld Camera Compared to Terrestrial and Personal Laser Scanning. IEEE Transactions on Geoscience and Remote Sensing. 53 (9): 5117-5132.

J. Räty, J. Vauhkonen, M. Maltamo & T. Tokola (2015). On the potential to predetermine dominant tree species based on airborne laser scanning data for improving subsequent predictions of species-specific timber volumes. Forest Ecosystems.

X. Liang, V. Kankare, J. Hyyppä, Y. Wang, A. Kukko, H. Haggrén, X. Yu, H. Kaartinen, A. Jaakkola, F. Guan, M. Holopainen & M. Vastaranta (2016). Terrestrial laser scanning in forest inventories. Review. ISPRS Journal of Photogrammetry and Remote Sensing 2016, 115, 63-77.

J. Pyörälä, V. Kankare, M. Vastaranta, M. Holopainen, J. Rikala, M. Sipi & J. Hyyppä (2016). Pinus sylvestris L. branch detection and branch diameter measurements and estimation of wood quality of standing trees in Terrestrial Laser Scanning point clouds. Submitted manuscript: Journal: Forestry. 2016.

N. Saarinen, V. Kankare, M. Vastaranta, V. Luoma, J. Pyörälä, T. Tanhuanpää, X. Liang, H. Kaartinen, A. Kukko, A. Jaakkola, X. Yu, M. Holpainen & J. Hyyppä (2016). Feasibility of terrestrial laser scanning for collecting stem volume information from single trees. Manuscript, will be submitted to ISPRS Journal of Photogrammetry and Remote Sensing, 2016.

J. Siipilehto, H. Lindeman, M. Vastaranta, X. Yu, J. Uusitalo (2016). Reliability of the predicted stand structure for clear-cut stands using optional methods: airborne laser scanning-based methods, smartphone-based forest inventory application Trestima and pre-harvest measurement tool EMO. Silva Fennica 50(3) 24 p. article id 1568.

J. Tang, Y. Chen, J. Hyyppä, A. Jaakkola, H. Kaartinen, A. Kukko, E. Khoramshahi, T. Hakala & M. Holopainen (2015). SLAM aided Stem Mapping for Forest Inventory with Small-footprint LiDAR. Forests 2015, 6(12), 4588-4606.

M. Vastaranta, N. Saarinen, V. Kankare, M. Holopainen, H. Kaartinen, J. Hyyppä & H. Hyyppä (2014). Multisource single-tree inventory in the prediction of tree quality variables and logging recoveries. Remote Sensing, 2014, 6, 3475-3491.

M. Vastaranta, E. G. Latorre, V. Luoma, N. Saarinen, M. Holopainen & J. Hyyppä (2015). Evaluation of a Smartphone 
App for Forest Sample Plot Measurements. Forests. 2015; 6(4):1179-1194.

J.C. White, N.C. Coops, M.A. Wulder, M. Vastaranta, T. Hilker & P. Tompalski (2016). Remote sensing for enhancing 
forest inventories: A review. Canadian Journal of Remote Sensing, in press. (February 29, 2016; Manuscript ID: CJRS-15-0146.R2; 
Accepted March 8, 2016).

X. Yu, P. Litkey, J. Hyyppä, M. Holopainen & M. Vastaranta (2014). Assessment of Low Density Full-Waveform 
Airborne Laser Scanning for Individual Tree Detection and Tree Species Classification. Forests 5(5), 1011-1031.

X. Yu, J. Hyyppä, M. Karjalainen, K. Nurminen, K. Karila, M. Vastaranta, V. Kankare, H. Kaartinen, M. Holopainen, E. Honkavaara, A. Kukko, A. Jaakkola, X. Liang, Y. Wang, H. Hyyppä & M. Katoh (2015). Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes. Remote Sens. 2015, 7, 15933-15954.

Other publications

M. Holopainen, M. Vastaranta & J. Hyyppä (2014). Outlook for the next generation's precision forestry in Finland. Forests 2014, 5(7), 1682-1694.

M. Holopainen, M. Vastaranta & J. Hyyppä (2014). Yksityiskohtaisen metsävaratiedon tuottaminen – kohti täsmämetsätaloutta? Metsätieteen aikakauskirja 4/2014.

H. Hyyti & A. Visala (2015). Low-cost 3D LIDAR for an Autonomous Forest Machine. SHERPA Summer School 2015 Workshop 1.-5.6.2015, University of Oulu.

J. Hämäläinen, M. Holopainen, J. Hynynen, J. Jyrkilä, P.T. Rajala, R. Ritala, T. Räsänen & A. Visala (2014). Perusteita seuraavan sukupolven metsävarajärjestelmälle – "Forest Big Data" -hanke. Metsätieteen aikakauskirja 4/2014.

J. Kauppinen, K. Väätäinen, S. Tauriainen, K. Einola, J. Malinen & M. Sirén (2016). Monilähdetietoa hyödyntävien karttaopasteiden tarve puunkorjuussa: Haastattelututkimus hakkuukoneenkuljettajille. Luonnonvara- ja biotalouden tutkimus 15/2016.

T. Melkas, M. Salmi & J. Hämäläinen (2014). Satelliittipaikannuksen tarkkuus hakkuukoneessa (Abstract in English). Metsätehon raportti 231.

T. Melkas & J. Hämäläinen (2015). Hakkuukoneella kerätyn puustotiedon hyödyntäminen. Menetelmäkuvaus referenssitiedon keräämiseksi kaukokartoitukseen ja metsävaratietojen päivitykseen. Abstract in English. Metsätehon raportti 237.

T. Melkas, J. Peuhkurinen, S. Santaranta, L. Sirro, A. Poikela, J-A. Sorsa & J. Hämäläinen (2016). Hakkuukoneella kerätyn mittaustiedon soveltuvuus referenssiaineistoksi ja metsävaratietojen päivitykseen. Metsätehon raportti xxx (Manuscript).

J. Siipilehto & A. Kangas (2015). Näslundin pituuskäyrä ja siihen perustuvia malleja läpimitan ja pituuden välisestä riippuvuudesta suomalaisissa talousmetsissä. Metsätieeen aikakauskirja 4/2015: 215-236.

T. Räsänen, A. Usenius, A. Heikkilä, P. Holmila & T. Usenius (2016). Tukkiröntgendata sahapuun ohjauksessa. Metsätehon raportti xxx (Manuscript).

P. Venäläinen, T. Räsänen & J. Hämäläinen (2015). Potential Business Models for Forest Big Data. Metsäteho's Report 235.

P. Venäläinen, T. Räsänen & J. Hämäläinen (2016). Tiestö- ja kuljetusdatan nykytila, visio ja toimenpideohjelma. Metsätehon raportti xxx (Manuscript).