Remote sensing and forest inventories in Nordic countries – roadmap for the future

Figures & data

Table 1. Development of NFIs in Nordic countries.

	Pre-sampling era	Field sampling era	RS era
Denmark	National Forest Census was carried out roughly every 10 years from 1881 to 2000 based on questionnaires sent to forest owners.	The first national sample-based forest inventory was initiated in 2002.	Satellite images were used for a reference to the questionnaire surveys between 1990 and 2011. Lidar data used for mapping the forest resources 2006–2007.
Finland	A visual assessment was carried out in Finland in the nineteenth century (von Berg 1859, (republished 1995)).	The first forest inventory based on strip sampling was carried out 1921–1924 (Ilvessalo 1927). Cluster sampling has been used since the 5th NFI 1964–1970 (Kuusela and Salminen 1969).	Aerial photographs used in a two-phase sampling procedure in Lapland (Poso and Kujala 1971). Satellite images used for multi-source forest inventory for mapping and for municipality level results since the 1980s (Tomppo 1990).
Norway	The first nationwide subjective assessment was started in 1739 (Fryjordet 1968). The intention was to conduct a full NFI, but the survey became a more subjective description of the state of the resources (Nissen 1937).	An NFI following modern sampling principles (strip survey) was started in 1919.	Satellite images for mapping the forest resources (Gjertsen 2007). Lidar and/or photogrammetric data used for mapping and estimation (Breidenbach and Astrup 2010; Rahlf et al. 2014).
Sweden	In 1735 the Swedish county governors were ordered for the first time to report to the government on the state of the forests in their counties (Anon. 1914).	The first NFI based on strip survey 1923–1929 (Thorell and Östlin 1931; SOU 1932). Cluster sampling has been used since the 3rd NFI 1953–1962 (Ranneby et al. 1987).	Satellite images for mapping the forest resources 2000–2015. Lidar and/or photogrammetric data used for mapping since 2016 (Nilsson et al. 2017).

Table 2. Current NFI design in the Nordic countries.

	Method used	Number of strata	Cycle	N of plots per year	Use of differential GNSS	Proportion of permanent clusters	Minimum reporting unit
Denmark	Cluster sampling	–	5 years	8600		33%	Region (NUTS3)
Finland	Stratified cluster sampling	6	Panel system of 5 years	15,000	Since 2014 100%	60%	District (landskap)
Norway	Stratified systematic sampling	4	Panel system of 5 years	4400	Currently 50%. Measurement campaign ongoing.	100%	County (fylke)
Sweden	Stratified cluster sampling	5	Panel system of 5 years	10,000	From 2017 onwards	65%	County (län)

Table 3. The main features of the Nordic countries.

	Denmark	Finland	Norway	Sweden
Total land area (km^2)	43,098	303,900	323,782	407,339
of that, proportion of  productive forest (%)	15	67	27	58
of that, proportion of  private forests (%)	74	61	88	51
Total volume (mio m^3)	132	2400	1109	3273
Annual gross increment (mio m^3)	7	108	26	123

Table 4. Current FMI organization in the Nordic countries.

	Method used	Who pays	Subsidies available	Coordination
Denmark	Visual assessment of aerial photos in combination with field measurements	Forest owner	No	No
Finland	Area-based laser scanning	Government (computer-suggested treatments) Forest owner (forest plan)	Data collection fully paid by public funds	Yes, centralized data collection
Norway	Visual assessment of aerial photos or area-based laser scanning	Forest owner	Yes (currently about 57%)	Yes (by the region forester)
Sweden	Visual assessment of aerial photos	Forest owner	No	No

Table 5. Current map products.

	Name of the product	RS material used	Publishing times	Coverage	Resolution	Field data	Availability for public
Denmark	Land use/land cover map	Landsat	1990, 2000, 2011	100%	30 m × 30 m	None	No
	Forest resource map	ALS data	2006–2007	100%	25 m × 25 m	NFI plots	No (Yes for research purposes)
	Forest resource map	ALS data	2014–2015	100%	25 m × 25 m	NFI plots	Yes
Finland	MS-NFI map	Landsat (Resourcesat 1 LISS-III, Spot)	2015, 2013, 2011	100%	16 m × 16 m	NFI plots	Yes (older maps exist but not published)
	FMI forest resources map	ALS data	Parts of country yearly	Full coverage 2020	16 m × 16 m	Special set of plots	No (legislation to open it under work)
Norway	SR16 Forest resources map	ALS/image matching data	2017	Trøndelag region available (ca. 50,000 km^2)	16 m × 16 m	NFI plots	Yes
	SATSKOG	Landsat	2007	100%	30 m× 30 m	NFI plots	Yes
Sweden	SLU forest map	Landsat	2000, 2005, 2010	100%	25 m × 25 m	NFI plots	Yes
	Skogliga grunddata	ALS data	2016	100% (Except alpine areas)	12.5 m × 12.5 m	NFI plots	Yes
	National Corine	Spot	2012	100%	10.0 × 10.0–25.0 × 25.0	NFI plots	Yes

Table 6. The drawbacks and benefits of different forest information sources from the point of view of given decision problems.

Data acquisition methods
Decision problem and specific examples	Critical properties of data for the task	Sampling-based field inventory (NFI)	Satellite images − Finland (MS-NFI) − Sweden (k-nn map) − Sweden (satellite data change detection)	3D point cloud from aerial photos − Norway (NFI)	ALS − Finland (separate management inventory) − Norway (separate management inventory) − Sweden (NFI)	Traditional FMI (visual assessment, aerial images) − Sweden − Norway − Finland (where ALS not yet available)
(I) Inspection of forestry operations (for forest law) − detection of clear cuttings − monitoring the regeneration after clearcutting − detection of thinnings − monitoring if the thinning regimes are followed − monitoring of removal of wind-thrown trees after storms	− Timeliness is the most critical property − Non-detection is the biggest source of uncertainty − Over-estimation of changes leads to inefficiency	+ Produces regional information (with reliability estimate) on how well forest law is followed − No stand-level data	+ Possibility for annual monitoring − Data is often outdated (1–2 years) due to data availability	− The whole country cannot be covered in a short time − It takes more than a year to produce the map	− The whole country cannot be covered in a short time − It takes more than year to produce the map	+ Detection of thinnings and wind throws easy visually − Costly − Timeliness a serious problem
(II) Formulation of forest policies and monitoring of sustainability − can we increase cuttings? − is forestry sustainable? − is silviculture and forest management at good level? − are the environmental values considered in forestry operations − are cuttings a threat for important habitats/forest types − change monitoring	− Unbiased − Known reliability − Timeliness − Relevant information content	+ Possibility for unbiased estimates + Possible to estimate reliability + Possible to get up-to-date data for large areas + Possible to react to information needs quickly + Possibility for accurate in situ observations + Time series exist − Costly − Some parameters (e.g. forest fragmentation) impossible or impractical to assess − Estimates of changes in rare classes not reliable	+ Produces information on landscape patterns − Most relevant parameters cannot be estimated or data reliability too poor for most information needs − Change estimates for small areas possible but unreliable − Local and regional estimates synthetic, and not necessarily unbiased	+ Produces information on landscape patterns + Change estimates for small areas possible − Some relevant parameters cannot be estimated or data reliability too poor for most information needs − Local and regional estimates synthetic, and not necessarily unbiased	+ Produces information on landscape patterns + Change estimates for small areas possible − Some relevant parameters cannot be estimated or data reliability too poor for most information needs − Local and regional estimates synthetic, and not necessarily unbiased	+ Produces information on landscape patterns − Some relevant parameters cannot be estimated or data reliability too poor for most information needs − Estimates are likely to be at least somewhat biased at all spatial scales − Estimation of reliability of the forest resources map not straightforward
(III) Harvest scheduling − timing of harvests	− Reliable data at stand level − Accuracy of the characteristics needed for decision-making − Critical information contents (in Sweden tree species-level map not produced)	− Does not produce data at stand level	− Reliability at stand-level poor	+ Estimates of growing stock are reliable at stand level + Possibility to assess spatial arrangement − Species identification, estimation of age, increment have high uncertainty	+ Estimates of growing stock are reliable at stand level + Possibility to assess spatial arrangement − Species identification, estimation of age, increment have high uncertainty	+ Estimates of growing stock are fairly reliable at stand level + Species identification, estimation of age, increment − Costly
(IV) Promoting good practices in forest management − encouraging to tending of seedling stands − pre-commercial thinnings	− Reliable data at stand level − Timeliness	− Does not produce data at stand level	− Reliability at stand-level poor	− Species identification, estimation of age, increment have high uncertainty − Estimates of young/seedling stands have high uncertainty	− Species identification, estimation of age, increment have high uncertainty − Estimates of young/seedling stands have high uncertainty	+ Estimates of growing stock are fairly reliable at stand level + Species identification, estimation of age, increment − Costly
(V) Timber procurement planning (by forest industry) − buying timber from forest owners − fulfilling demand of mills	− Location of forest stands with immediate cutting potential − Suitable timber assortments − Timeliness, location	− Does not produce data at stand level	+ Timeliness − Reliability at stand-level not very good	+ Good reliability − Difficult and costly to achieve timeliness − Reliability of species-level data	+ Good reliability − Costly to achieve timeliness − Reliability of species-level data	+ Fairly good reliability at stand level + Species identification, estimation of age, increment − Costly
(VI) Operational planning of harvesting − harvest season selection: winter or summer − clustering harvests − selection of the mill to transport − optimal bucking − end use of the timber	− Accurate information on growing stock and it’s quality − Specific site information needed at stand level, also in surroundings − Data need to be accurate at the time of harvesting: no need of accurate data in other time points	− Does not produce data at stand level	− Reliability at stand-level poor	+ Growing stock information sufficiently good for most purposes − Diameter distribution shape in different type of stands poorly distinguished − Estimates of timber quality currently not included	+ Growing stock data good − Diameter distribution shape in different type of stands poorly distinguished − Estimates of timber quality currently not included	+ Estimates of growing stock are fairly reliable at stand level + Species identification, estimation of age, increment − Costly
(VII) Investment planning − where to locate a mill? − sufficiency of timber resources for a planned mill	− Reliable long-term scenarios − Location of resources and potential − Timber assortments	+ Long-term forecasting of sample plot data is feasible − Spatial resolution can be too low for regional analyses	+ Full cover data + Reliable enough for resource estimation − Reliability for scenarios	+ Possibilities for full cover data exist − Timeliness can be an issue	+ Possibilities for full cover data exist − Timeliness may be an issue	− Regional estimates of unknown quality − Biased estimates

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