The National Forest Inventory
Updated: 30 August 2022
Next update: 30 August 2023
| 2021 | Change, per cent | |||
|---|---|---|---|---|
| 1 000 cubic meter | Share | 2020 - 2021 | 2012 - 2021 | |
| Growing stock | ||||
| Total | 995 414 | 100 | 0.8 | 11.3 |
| Spruce | 436 679 | 44 | 0.9 | 12.6 |
| Pine | 307 202 | 31 | 0.9 | 10.3 |
| Broad-leaved | 251 533 | 25 | 0.6 | 10.4 |
| Annual increment | ||||
| Total | 24 049 | 100 | 0.0 | -4.8 |
| Spruce | 12 827 | 53 | 0.5 | -3.3 |
| Pine | 5 418 | 23 | -0.2 | -10.8 |
| Broad-leaved | 5 804 | 24 | -1.1 | -2.3 |
Selected tables and figures from this statistics
- Growing stock inside bark and annual increment inside bark. 1 000 m³Download table as ...Growing stock inside bark and annual increment inside bark. 1 000 m³
Growing stock Annual increment Total Spruce Pine Broad-leaved Total Spruce Pine Broad-leaved 1999 685 682 304 081 229 874 151 727 23 076 11 684 6 163 5 229 2000 697 998 308 614 233 949 155 436 23 488 11 858 6 273 5 357 2002 685 885 306 527 228 492 150 866 23 197 12 135 5 952 5 109 2003 704 487 313 979 235 030 155 478 23 997 12 627 6 064 5 306 2004 720 789 323 213 238 137 159 439 25 540 13 709 6 151 5 680 2005 735 610 331 236 241 730 162 644 25 674 13 868 6 092 5 714 2006 747 945 336 201 244 622 167 122 25 526 13 746 6 010 5 769 2007 764 952 343 720 249 201 172 031 25 262 13 644 5 944 5 674 2008 783 982 352 558 254 554 176 870 24 897 13 482 5 857 5 559 2009 822 569 360 501 260 521 201 547 24 839 12 969 5 748 6 122 2010 842 419 369 747 265 439 207 233 24 606 12 908 5 783 5 915 2011 877 731 379 823 273 905 224 003 24 942 13 030 5 978 5 935 2012 894 133 387 844 278 516 227 773 25 274 13 261 6 071 5 942 2007-2011 911 711 396 891 282 789 232 031 25 598 13 529 6 117 5 952 2008-2012 929 393 407 099 286 489 235 804 25 912 13 821 6 115 5 975 2009-2013 911 712 396 891 282 790 232 031 25 598 13 529 6 117 5 952 2010-2014 929 393 407 100 286 490 235 804 25 911 13 821 6 115 5 975 2011-2015 941 659 412 984 289 685 238 989 26 120 13 928 6 098 6 095 2012-2016 952 103 417 955 292 031 242 117 25 819 13 747 5 920 6 152 2013-2017 964 915 424 433 296 255 244 229 25 421 13 635 5 719 6 068 2014-2018 974 453 427 798 299 754 246 901 24 906 13 332 5 572 6 003 2015-2019 978 479 428 329 301 858 248 290 24 186 12 845 5 450 5 889 2017-2021 995 414 436 679 307 202 251 533 24 049 12 827 5 418 5 804 Explanation of symbolsDownload table as ... - Total area surveyed, by type of vegetation and regions. Km²Download table as ...Total area surveyed, by type of vegetation and regions. Km²
2017-2021 Total Productive forest land Unproductive forest Broadleaved bogs and pine bogs Sedge and peat bogs Other area Appraised regions, total 323 783 82 660 48 252 9 103 13 798 169 970 Oslo and Viken 25 132 11 679 2 479 514 744 9 716 Innlandet 51 963 21 549 4 707 1 874 2 429 21 404 Vestfold og Telemark 17 384 6 904 2 916 368 604 6 592 Agder 16 535 6 281 3 299 387 546 6 022 Rogaland, Vestland and Møre og Romsdal 57 451 10 330 7 795 755 1 360 37 211 Trøndelag - Trööndelage 42 110 11 020 7 059 2 330 3 914 17 787 Nordland - Nordlánnda 38 323 6 642 6 057 796 1 249 23 579 Troms og Finnmark - Romsa ja Finnmárku 74 883 8 255 13 939 2 078 2 952 47 659 Explanation of symbolsDownload table as ... - Productive forest area, by development class. 1 000 hectares and per centDownload table as ...Productive forest area, by development class. 1 000 hectares and per cent1
1 000 hectares Per cent Total Development class I Development class II Development class III Development class IV Development class V Total Development class I Development class II Development class III Development class IV Development class V 2004-2008 3 391 91 789 748 679 1 084 100 3 23 22 20 32 2005-2009 3 519 95 784 759 730 1 151 100 3 22 21 21 33 2006-2010 3 513 92 779 762 714 1 166 100 3 22 22 20 33 2007-2011 3 510 77 767 783 702 1 180 100 2 22 22 20 34 2008-2012 3 507 74 762 787 711 1 174 100 2 22 22 20 34 2009-2013 3 500 69 745 785 714 1 187 100 2 21 22 20 34 2010-2014 3 499 59 740 786 718 1 196 100 2 21 22 21 34 2011-2015 3 483 61 725 786 718 1 193 100 2 21 23 21 34 2012-2016 3 488 66 711 789 718 1 203 100 2 20 23 21 35 2013-2017 3 483 70 687 792 733 1 201 100 2 20 23 21 35 2014-2018 3 473 70 685 780 737 1 202 100 2 20 22 21 35 2015-2019 3 461 80 673 774 739 1 195 100 2 19 22 21 35 2016-2020 3 450 84 666 775 740 1 186 100 2 19 22 21 34 2017-2021 3 444 85 672 751 750 1 186 100 2 20 22 22 34 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. Explanation of symbolsDownload table as ... - Growing stock, by type of land, tree species and regions. 1 000 m³ under barkDownload table as ...Growing stock, by type of land, tree species and regions. 1 000 m³ under bark
2017-2021 Total Productive forest land Other type of land Total Spruce Pine Broad-leaved Total Spruce Pine Broad-leaved Total 995 414 878 603 408 535 259 695 210 373 116 811 28 144 47 507 41 160 Oslo and Viken 182 885 165 784 85 954 53 308 26 523 17 101 6 810 6 608 3 682 Innlandet 247 972 229 161 122 345 75 642 31 175 18 811 6 921 6 431 5 458 Vestfold og Telemark 104 160 90 654 42 879 26 216 21 559 13 506 4 450 5 625 3 430 Agder 96 599 83 798 25 792 38 335 19 670 12 801 1 923 8 107 2 771 Rogaland, Vestland and Møre og Romsdal 142 349 124 359 40 619 39 865 43 875 17 990 906 7 767 9 316 Trøndelag - Trööndelage 127 552 109 046 68 374 18 311 22 360 18 506 5 194 8 738 4 574 Nordland - Nordlánnda 53 360 44 019 19 944 3 151 20 924 9 341 1 939 2 038 5 364 Troms og Finnmark - Romsa ja Finnmárku 40 538 31 781 2 628 4 867 24 286 8 757 0 2 192 6 564 Explanation of symbolsDownload table as ... - Annual increment under bark , by type of land, tree species and surveyed regions. 1 000 m3Download table as ...Annual increment under bark , by type of land, tree species and surveyed regions. 1 000 m3
2017-2021 Total Productive forest area Other type of land Total Spruce Pine Broad-leaved Total Spruce Pine Broad-leaved Total 24 049 21 933 12 301 4 718 4 914 2 116 526 700 890 Oslo and Viken 4 408 4 141 2 389 997 755 267 118 78 71 Innlandet 6 155 5 815 3 524 1 561 730 340 131 101 107 Vestfold og Telemark 2 602 2 360 1 264 534 562 242 85 83 74 Agder 2 012 1 785 809 623 353 227 43 132 52 Rogaland, Vestland and Møre og Romsdal 3 347 2 951 1 448 504 1 000 396 35 108 254 Trøndelag - Trööndelage 3 328 3 018 2 060 314 643 310 79 130 102 Nordland - Nordlánnda 1 419 1 217 681 76 460 202 35 33 134 Troms og Finnmark - Romsa ja Finnmárku 778 647 126 109 412 131 0 35 97 Explanation of symbolsDownload table as ... - Registered incidence of different habitats in productive forest, by region. Per centDownload table as ...Registered incidence of different habitats in productive forest, by region. Per cent1
2017-2021 Productive forest area below the coniferous forest line Standing dead trees Dead wood lying Trees with nutrient-rich bark Trees with pendant lichens Late succsessions of deciduous Old trees Rich ground vegetation The whole country 100.0 2.6 18.1 0.2 2.9 1.6 2.5 3.2 Oslo and Viken 100.0 2.7 16.8 0.0 2.9 2.0 3.1 3.7 Innlandet 100.0 1.4 12.8 0.0 5.3 0.8 3.3 1.9 Vestfold og Telemark 100.0 3.1 26.3 0.8 1.4 3.5 5.0 6.4 Agder 100.0 4.8 19.5 0.3 0.0 5.6 0.4 1.7 Rogaland, Vestland and Møre og Romsdal 100.0 3.4 18.0 0.5 1.0 0.8 1.3 3.3 Trøndelag - Trööndelage 100.0 2.0 20.7 0.4 5.1 1.8 3.5 2.4 Nordland - Nordlánnda 100.0 1.7 24.1 0.4 2.7 0.6 1.2 5.6 Troms og Finnmark - Romsa ja Finnmárku 100.0 3.7 17.6 0.1 0.0 0.0 0.5 3.6 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. Explanation of symbolsDownload table as ...
More figures from this statistics
- 06289: Growing stock under bark and annual increment under bark, by species of tree (1 000 m³)
- 06288: Productive forest area, except area under regeneration, by species of tree and surveyed regions (km²)
- 06286: Productive forest area, by development class
- 06290: Growing stock under bark, by type of land, species of tree and surveyed regions
- 10605: Registered incidence of different habitats in productive forest, by region (per cent)
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.
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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 gross 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).
Name: The National Forest Inventory
Topic: Agriculture, forestry, hunting and fishing
Division for Housing, Property, Spatial and Agricultural Statistics
The results are mainly published at region level.
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 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.
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 10: 2010-2014 11: 2015-2019 12: 2020-2024
As from the inventory cycle 2007-2011 Finnmark county was surveyed for the first time.
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.
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.
No external users have access to statistics before they are released at 8 a.m. on ssb.no after at least three months’ advance notice in the release calendar. This is one of the most important principles in Statistics Norway for ensuring the equal treatment of users.
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.
The statistics are developed, produced and disseminated pursuant to Act no. 32 of 21 June 2019 relating to official statistics and Statistics Norway (the Statistics Act).
Not relevant
As from the inventory cycle 2007-2011, the statistics include all counties. Finnnmark was surveyed for the first time during the five-year cycle 2007-2011. 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.
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.
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.
Not relevant
Employees of Statistics Norway have a duty of confidentiality.
Statistics Norway does not publish figures if there is a risk of the respondent’s contribution being identified. This means that, as a general rule, figures are not published if fewer than three units form the basis of a cell in a table or if the contribution of one or two respondents constitutes a very large part of the cell total.
Statistics Norway can make exceptions to the general rule if deemed necessary to meet the requirements of the EEA agreement, if the respondent is a public authority, if the respondent has consented to this, or when the information disclosed is openly accessible to the public.
More information can be found on Statistics Norway’s website under Methods in official statistics, in the ‘Confidentiality’ section.
Figures on property level are not published.
The National Forest Inventory carried out the first assessment at county level in 1919.
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
