Agriculture, forestry, hunting and fishing
lst, The National Forest Inventory, timber, logs, productive forest area, growing stock, growth, quality class, felling class, spruce, pine, broad-leaved treesForestry , Agriculture, forestry, hunting and fishing

The National Forest Inventory


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Key figures


million cubic metres growing stock in Norwegian forests

Growing stock and annual increment. Volume inside bark
1 000 cubic meterShareChange, per centChange, per cent
2016 - 20172008 - 2017
Growing stock
Total964 9151001.323.1
Spruce424 432441.520.4
Pine296 255311.416.4
Broad-leaved244 228250.938.1
Annual increment
Total25 421100-1.52.1
Spruce13 63554-0.81.1
Pine5 71922-3.4-2.4
Broad-leaved6 06824-1.39.2

See selected tables from this statistics

Table 1 
Growing stock inside bark and annual increment inside bark. 1 000 m³

Growing stock inside bark and annual increment inside bark. 1 000 m³
Growing stockAnnual increment
1991588 476273 333191 540123 60320 48510 7035 3104 473
1992599 243276 788194 806127 64920 92110 8925 4114 618
1993609 399279 968197 904131 52621 33711 0705 4984 769
1998651 688292 018218 305141 36421 94511 2195 8554 871
1999685 682304 081229 874151 72723 07611 6846 1635 229
2000697 998308 614233 949155 43623 48811 8586 2735 357
2002685 885306 527228 492150 86623 19712 1355 9525 109
2003704 487313 979235 030155 47823 99712 6276 0645 306
2004720 789323 213238 137159 43925 54013 7096 1515 680
2005735 610331 236241 730162 64425 67413 8686 0925 714
2006747 945336 201244 622167 12225 52613 7466 0105 769
2007764 952343 720249 201172 03125 26213 6445 9445 674
2008783 982352 558254 554176 87024 89713 4825 8575 559
2005-2009822 569360 501260 521201 54724 83912 9695 7486 122
2006-2010842 419369 747265 439207 23324 60612 9085 7835 915
2007-2011877 731379 822273 904224 00224 94113 0315 9795 934
2008-2012894 131387 843278 516227 77225 27413 2626 0715 943
2009-2013911 712396 891282 790232 03125 59813 5296 1175 952
2010-2014929 393407 100286 490235 80425 91113 8216 1155 975
2011-2015941 659412 984289 685238 98926 12013 9286 0986 095
2012-2016952 103417 955292 031242 11725 81913 7475 9206 152
2013-2017964 915424 433296 255244 22925 42113 6355 7196 068

Table 2 
Total area surveyed, by type of vegetation and regions. Km²

Total area surveyed, by type of vegetation and regions. Km²
2013-2017TotalProductive forest land1Unproductive forestBroadleaved bogs and pine bogsSedge and peat bogsOther area2
1Areas abrove the coniferous forest line are also included
2Including freshwater
Appraised regions, total323 78283 08447 9179 05613 475170 250
Østfold, Akershus, Oslo and Hedmark37 03219 6162 8131 5311 34811 724
Oppland, Buskerud and Vestfold42 25415 2144 2899021 75120 098
Telemark, Aust-Agder and Vest-Agder31 73011 9166 1627561 05611 840
Rogaland, Hordaland, Sogn og Fjordane and Møre og Romsdal58 41410 5837 7568431 41737 815
Sør-Trøndelag and Nord- Trøndelag41 13910 8806 8462 3073 71917 387
Nordland and Troms64 59811 3259 2441 2351 92840 866
Finnmark48 6163 55110 8041 4842 25730 520

Table 3 
Productive forest area, by development class. 1 000 hectares and per cent

Productive forest area, by development class. 1 000 hectares and per cent1
1 000 hectares
TotalDevelopment class IDevelopment class IIDevelopment class IIIDevelopment class IVDevelopment class V
1Refer to the counties Østfold, Akershus, Oslo, Hedmark, Oppland, Buskerud og Vestfold.These are the only counties that are included in all the inventory cycles presented. As from the inventory cycle 2006-2010, areas above the coniferous forest line are also included.
2000-20043 3371118027226631 039
2001-20053 362948037276851 053
2002-20063 368948027426731 056
2003-20073 378937937416791 072
2004-20083 391917897486791 084
2005-20093 519957847597301 151
2006-20103 513927797627141 166
2007-20113 510777677837021 180
2008-20123 507747627877111 174
2009-20133 500697457857141 187
2010-20143 499597407867181 196
2011-20153 483617257867181 193
2012-20163 488667117897181 203
2013-20173 483706877927331 201
Per cent

Table 4 
Growing stock, by type of land, tree species and regions. 1 000 m³ under bark

Growing stock, by type of land, tree species and regions. 1 000 m³ under bark
2013-2017TotalProductive forest landOther type of land
Total964 915859 044400 192253 413205 440105 87124 24142 84238 789
Østfold, Akershus, Oslo and Hedmark252 347237 337123 00783 48130 84815 0104 1897 0473 774
Oppland, Buskerud and Vestfold191 222173 05196 18245 28731 58218 1717 8194 7795 573
Telemark, Aust-Agder and Vest-Agder173 933151 98156 55760 95434 47021 9524 89312 0535 006
Rogaland, Hordaland, Sogn og Fjordane and Møre og Romsdal141 711124 82242 16939 22843 42516 8898717 3928 625
Sør-Trøndelag and Nord- Trøndelag117 064100 30662 31417 00320 98916 7584 9247 7594 075
Nordland and Troms73 42562 31519 9625 40936 94411 1101 5442 4427 124
Finnmark15 2159 23302 0507 1835 98201 3704 612

Table 5 
Annual increment under bark , by type of land, tree species and surveyed regions. 1 000 m3

Annual increment under bark , by type of land, tree species and surveyed regions. 1 000 m3
TotalProductive forest areaOther type of land
Total25 42123 37913 1735 0125 1952 042462707873
Østfold, Akershus, Oslo and Hedmark7 0956 8274 0821 8728732687611874
Oppland, Buskerud and Vestfold5 1704 8393 03588991533114580106
Telemark, Aust-Agder and Vest-Agder4 2653 8401 8251 210805425101212112
Rogaland, Hordaland, Sogn og Fjordane and Møre og Romsdal3 5693 1841 5575781 04938526122236
Sør-Trøndelag and Nord- Trøndelag3 1082 8351 8852816682738311079
Nordland and Troms1 8821 6577891137552253137157

Table 6 
Registered incidence of different habitats in productive forest, by region. Per cent

Registered incidence of different habitats in productive forest, by region. Per cent1
2013-2017Productive forest area below the coniferous forest lineStanding dead treesDead wood lyingTrees with nutrient-rich barkTrees with pendant lichensLate succsessions of deciduousOld treesRich ground vegetation
1Corresponds to the registration of habitats for vulnerable and endangered species (red listed species) in ordinaryforest management planning. Two of more habitats may be registered within the same area.
Østfold, Akershus, Oslo and Hedmark100.
Oppland, Buskerud and Vestfold100.02.414.
Telemark, Aust-Agder and Vest-Agder100.
Rogaland, Hordaland, Sogn og Fjordane and Møre og Romsdal100.03.513.
Sør-Trøndelag and Nord- Trøndelag100.
Nordland, Troms and Finnmark100.

About the statistics

The statistics provide information on the condition and development of Norway’s forest resources. They give figures on growing stock, annual increments, forest area, age distribution, type of land and tree species.


Definitions of the main concepts and variables

Growing stock

Total volume of the standing forest under bark. Comprises trees with a diamter of at least 5 cm at breast height (1.3 metre above ground level).

Annual increment, forest

Annual increment in volume in standing forest inside bark.

Development class

Describes the forest's development class from non- regenerated forest to old forest.

Site quality

An expression of the area's capacity to produce wood when stocked with a tree species suitable for the local growing conditions. The site quality of the H40-system is based upon the top height (the average height of the hundred trees per hectare with the largest diameter) of the trees at the age of 40 years at breast height (1.3 m above ground level).

Standard classifications

Classification of productive forest area by development class

Classification of productive forest area by site quality (H40)

Administrative information

Name and topic

Name: The National Forest Inventory
Topic: Agriculture, forestry, hunting and fishing

Next release

Responsible division

Division for Housing, Property, Spatial and Agricultural Statistics

Regional level

The results are mainly published at region level.

Frequency and timeliness

The results are published yearly. The National Forest Inventory has an inventory cycle of five years. From 1994 the assessment is running. A new result based on the registrations from the last 5 years can always be estimated for the regions and for the country

International reporting

International reporting of results from The National Forest Inventory are reported by the The Norwegian Institute of Bioeconomy Reasearch.


Microdata are stored by The National Forest Inventory.


Background and purpose

The National Forest Inventory is a sample plot inventory aimed at providing data on natural resources and the environment for forest land in Norway. The Inventory is conducted by the Norwegian Forest and Landscape Institute. Inventory work was started in 1919, with the different inventory cycles taking place in the following years:

1: 1919-30 2: 1937-56 3: 1957-64 4: 1964-76 5: 1980-86 6: 1986-93 7: 1994-98 8: 2000-04 9: 2005-09

The entire country (except Finnmark county) was surveyed during the most recent period. Each inventory cycle covers the most important forest districts, while inventories in western and northern Norway have been carried out less frequently and are sometimes incomplete.

Users and applications

The most central users of the results from the National Forest Inventory are public administration at national and county level. The results serve as important input for the formation of forestry policies and control the effects of it.

In recent years, the demand for national forestry statistics has increased, and the National Forest Inventory is a central data source. Data from the inventories are used for example in research to develop descriptive models of forest dynamics.

The forest industry is an important user of the data. Among others thing, they need the data for strategic planning in the sawmill and pulp industry. The data are also used by educational institutions and by professionals in agriculture, forestry and environmental protection.

Equal treatment of users

No external users have access to the statistics and analyses before they are published and accessible simultaneously for all users on ssb.no at 8 am. Prior to this, a minimum of three months' advance notice is given inthe Statistics Release Calendar. Read more about principles for equal treatement of all users on ssb.no.

Coherence with other statistics

Statistics Norway has estimated the productive forest area in The Sample Surveys of Agriculture and Forestry 2004 and 2008, The Census of Agriculture and Forestry 1979 and 1989. The Farm Register of the Norwegian Agricultural Authority also contains information about productive forest area at property level. Total productive forest area based on the Farm Register is published in the annual structural statsitics of forestry.

Legal authority

Not relevant

EEA reference

Not relevant



The statistics include all counties except Finnmark, however Finnmark will also be surveyed during the present five-year cycle. As from the inventory cycle 2005-2009, areas above the coniferous forest line are also included. Protected or other closed-off areas of productive forest are not included.

The figures are published annually.

Data sources and sampling

The only data source is the National Forest Inventory's database. One of the main tasks of the National Forest Inventory is the assessment of timber resources. Data are collected so that the volume can be computed for different tree species, diameters and quality classes. Numbers of trees and annual increments are also calculated.

The National Forest Inventory's data collection is based on data from permanent sample plots. For the entire country except Finnmark, a systematic sample plot inventory in a bond by 3 x 3 kilometres is established. In the present inventory cycle, sample plots for Finnmark are also established. The plots are visited every five years and the survey forms the basis for statistics for the whole of Norway. In order to publish data by county, temporary plots are established in the counties when each county is appraised. Each county is appraised every fifteen years. An extensive number of attributes concerning forest conditions are recorded on the plots, some of which describe the area. Parameters that characterise level of development and species composition of the vegetation, certain aspects of biodiversity, utilisation and yield capacity of the land, forest treatment, conditions surrounding forest operations, etc., are measured or estimated. Inside a 250 square metre circle, every tree with a diameter of more than five centimetres in breast height (1.3 metres above ground level) is callipered.

The sampling design has changed considerably over the years. The first two cycles were carried out as strip sampling inventories. A system of parallel strips was established throughout the area of interest, and measurements were taken within these strips. In the middle of the 1950s, the strip sampling was replaced by a systematic sample plot inventory, a method which has also been used subsequently. However, minor alterations concerning sampling design have been made several times.

An important difference between the period 1986-1993 and the previous inventory cycles was the introduction of permanent sample plots. A sub-sample of the established plots was marked in order to be able to re-measure the exact same area in future inventories. This was to provide greater possibilities for detecting changes in forest conditions. The permanent plots were re-measured during the period 1994-1998, according to a specific pattern. The inventory of one single year will provide representative results for the whole country.

Collection of data, editing and estimations

Highly conspicuous markings are avoided in order to prevent the location of the plots from being too obvious to passers-by. The permanent plots should represent a random sample of the forests in Norway, and should not be treated any different than the rest of the forests. A total of approximately 16 000 permanent sample plots have been established, of which about 10 500 are located on productive forest and other wooded land below the coniferous forest limit. On average, the sampled area comprises about 3 x 10 -5 of the surveyable area.

Before each field season, training is held for the field crew. During the field season, the office staff visit the field workers at least once and some controls are carried out. In most cases, a control of the assessment is done. About 5 per cent of the sample plots are surveyed once more.

Corrections of the field instructions are made before every field season. A main revision is carried out every five years.

In order to estimate figures, for instance for a county, the area factor must be known. In a 3 x 3 kilometre net the area factor will be close to nine square kilometres or 900 hectares. Each sample plot will represent 900 hectares. For each tree measured, a volume with and without bark and the increment are estimated. Multiplying this with the area factor will establish how much each tree represents in this area. The volume for the growing stock in a county for instance can be found by summarising the volume of each measured tree in the county multiplied with the area factor.

Seasonal adjustment

Not relevant


Figures on property level are not published.

Comparability over time and space

The National Forest Inventory carried out the first assessment at county level in 1919.

Accuracy and reliability

Sources of error and uncertainty

Systematic errors are caused by errors or uncertainties in measurement, estimation and recording in the field, which are one-sided. Efforts are being made to reduce these errors as far as possible by training the field crews and checking their measurements. An example of errors of this type is the possibility of apparent area changes for productive forest land, which are really caused by different methods of judging the coniferous forest limit. The magnitude of systematic errors cannot normally be calculated.

Random errors of the results are caused by the limited sample of the forest area and wood resources measured by the inventory, in addition to random errors of measurement. A measure for the random error is the so-called standard error, which is possible to calculate. The root mean square error (RMS error) depends on the number of sample plots and the variance of the parameter of interest, for instance volume of growing stock. If the observations are divided into more groups, the magnitude of the RMS error will be higher within each group.


Not relevant