Published October 29, 2016 | Version v1
Dataset Open

LiDAR-derived forest structure data and predictions of the locations of old-growth forests for Central Finland.

  • 1. Open Knowledge Finland


This archive contains data and analysis code for the Biodiversity Map -project conducted by Open Knowledge Finland (

The files listed below are all released to the public domain under a CC0 public domain dedication (


Inside the archive is a comma-separated file "background.csv" containing LiDAR-derived forest structure variables for 2/3 of Central Finland. These were derived from 3 raster data sets describing forest canopy maximum height (mh), forest canopy cover (cc) and lidar return intensity (in). The rasters had resolutions of 6 metres, 6 metres and 2 metres, respectfully. An 18 m resolution grid was then used to aggregate the rasters into average, minimum and maximum values + standard deviations of the original variables. The original LiDAR data was made available by the National Land Survey of Finland.

FILE 2: conservation.lambdas
This file contains fitted parameters for the maxent model. For more information, check maxent documentation at

FILE 3: conserved_swd.csv
Forest structure variables at 18 meter resolution for old-growth conservation areas in Central Finland. A subset of background.csv. This file still has a header, the variables are the same as in background.csv

A shell script used to convert LiDAR files to raster maps of forest structure with GRASS 7.

FILE 5: lidar_coverage.png
A map showing the extent of LiDAR data available for Central Finland when we did the analyses.

A shell script used to fit the maximum entropy model to predict the locations of conservation-area-like forests in Central Finland.

FILE 7: projection_product.csv
The results of the maxent model in a comma separated file. The first row has the variable names: x,y,product_fit. x and y are coordinates in the CRS ETRS-TM35FIN (EPSG:3067). product_fit is "the probablility that this 18*18 meter grid cell is old-growth conservation area".

A file with a description of the dataset in human-readable form.

The data in these files was collected to validate the results of the aforementioned maxent model. The data were collected in a hierarchical sampling scheme: six randomly determinded unintersecting 9 km * 9 km landscape windows were chosen for sampling. From each window, three samples were taken. One sample from conservation areas, one sample from the "best" 10 % of forests as determined by the maxent model excluding conservation areas and one random sample. Not all windows contained conservation areas, and not all areas were accessible (islands, for example). In addition a few areas were skipped due to time constraints.

The sampled points are identified by their lanscape window (suuralue), their sample (otos) and their sample number (mittauspiste).

FILE 9: validation_felled.csv
A comma separated list of those points that were not measured because they were felled.

FILE 10: validation_gps_results_2016-09-07.csv
A list of gps coordinates for all the sample points. product_fit is the value of the geographically closest prediction from the maxent model described above.

FILE 11: validation_lying_deadwood_transects_2016-08-30.csv
A comma separated file with data from deadwood transects. From each validation point, three 30 m long transects were made with 120 degree angles between them, and all lying deadwood more than 2 cm in diameter were measured. For some validation points, there were geographical obstructions which prevented the full 90 m of transect being surveyed, this is also recorded in the data. Each row holds measurements from one lying trunk.

FILE 12: validation_relascope_2016-08-30.csv
Relascope measurements from the validation points. Each row is measurements for one species from one validation point. Dead and alive trees are counted separately.


For more in-depth descritions of the files, read the file named README.
For some auxilliary files and information, check our old hackathon repository on github:


This work was supported by a grant from Jenny and Antti Wihuri foundation. Computational resources were provided by JulkICTLab of the Finnish Government..


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