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volume of bolewood (HG 2010) #211
Wood volume of the stem without bark or stump ≥7 cm in diameter (limit for merchantable wood) of all trees and shrubs with a diameter at breast height (dbh) ≥12 cm, based on the stem-form functions according to Kaufmann (2001). The definition of the assortments is based on the 2010 edition of the Swiss trading customs for raw timber (HG 2010).

timber assortment classes (HG 2010) #1788
Sorting of roundwood into four diameter classes on the basis of the 2010 and 2021 editions of the Swiss Trading Customs for Raw Timber (HG 2010, Waldwirtschaft Schweiz et al. 2010). Classes 1 and 2, 3 and 4, as well as 5 and 6 of the HG 2010 are each combined into one class.

main tree species #90
Type of trees and shrubs ≥12 cm in diameter at breast height (dbh) with the ten most common species or species groups in Switzerland ('main tree species') categorised, and the classes 'other conifers' and 'other broadleaves' for the remaining species. The main tree species are: spruce (Picea spp.), fir (Abies spp.), pine (Pinus sylvestris, P. nigra, P. strobus, P. mugo subsp. uncinata), larch (Larix spp.), Arolla pine (Pinus cembra), beech (Fagus sylvatica), maple (Acer spp.), ash (Fraxinus spp.), oak (Quercus spp.) and chestnut (Castanea sativa). Reference: Field Survey (MID 50: Baumart)

production region #49
Demarcation of Switzerland into five regions (Jura, Plateau, Pre-Alps, Alps and Southern Alps) with relatively uniform growth and timber production conditions. The production regions were established by the Federal Office of Forestry long before the first National Forest Inventory (NFI1, 1983-1985). With a small exception along Lake Geneva, the boundaries of the production regions still follow the municipal boundaries of the time. Unlike the NFI, the Forestry Statistics of the Federal Statistical Office don't use the production regions as demarcation but rather the forestry zones, whose boundaries are somewhat different.

accessible forest without shrub forest #434
Forest of which less than two-thirds is covered with shrubs that can be accessed on foot.

1.4-km grid #410
NFI's sampling grid with a mesh size of 1.4 km. The 1.4-km grid is the grid size covering all the previous terrestrial Inventories, which is why it is also called the base grid.

Estimates and standard error of the estimates

The results published in the NFI tables are estimates for variables in the Swiss forest (population parameters) whose true values are not known and must therefore be extrapolated (estimated) from the NFI sample data.

The extrapolations carry uncertainties. However, the accuracy of the extrapolations can be reliably estimated from the NFI sample, which is designed to be a random sample. To this end, all NFI tables include a second figure in addition to the estimate itself, namely the standard error of the estimate.

In most tables the percent standard error is reported (±%), but the absolute standard error (±) is also reported occasionally, especially for estimated percentages. The relationship between absolute and percent standard error is as follows:

percent standard error = absolute standard error / estimate × 100

absolute standard error = percent standard error × estimate / 100

Information on the forest road network is based on a full survey of all forest roads. In this case, there is no need to specify a standard error because there is no uncertainty caused by sampling.

Confidence interval of the estimate

With the estimate and the standard error of the estimate, the limits of the so-called confidence interval of the estimate can be calculated as:

lower limit = estimate – tQ × absolute standard error

upper limit = estimate + tQ × absolute standard error

If the simple standard error is used for the calculation (tQ = 1), then the 68% confidence interval is formed. In this case, the true value of the population parameter has a 68% probability of lying within this confidence interval of the estimate. If the double standard error is used (tQ = 2), then there is a 95% probability that the true value lies within this 95% confidence interval.

Significance of the estimate

The confidence interval can be used to statistically test whether the estimated population parameter is larger or smaller than a specific reference or target value, or whether two estimated population parameters truly differ (in the real population). For practical applications, proceed as follows: if a reference value lies outside the confidence interval, the estimated population value can be interpreted as being significantly different from it; if it lies within the confidence interval, the difference between the estimated population parameter and the reference value can be interpreted as random or not significant. When two population parameters are compared, they can be interpreted as differing significantly if their confidence intervals do not overlap.

Dealing with missing values

When calculating a results table, data are not always available for all combinations of characteristics^* of the classification variables and regional demarcation applied. In most cases, this indicates that the attribute estimated with the respective target variable does not occur or occurs only very rarely. Usually, the value 0 is used (imputed). However, as this value is not based on direct measurements, the associated standard error is represented with a full stop [.]. If the imputed value of 0 is referred to in the calculation, e.g. in the case of percentages or certain change estimates, no value can be entered. In this case, the estimated value and standard error are both represented with a full stop [.].

For example, no Arolla pines have been found and measured in the Swiss Plateau to date (growing stock of Arolla pines by production region). The values can be assumed to be missing because Arolla pine does not occur in the Swiss Plateau and therefore the growing stock there must be 0.

^* e.g. ‘Arolla pine’ characteristic of the classification variable ‘tree species’ and ‘Swiss plateau’ characteristic of the ‘regional demarcation’

Changes

There are two types of changes in the NFI:

The first type involves specific target variables relating to components of change, such as increment, fellings, mortality and losses. These target variables are only available for two consecutive inventory cycles, e.g. NFI3–NFI4. In the evaluation of components of change, the classification variable characteristic of the second inventory cycle is assigned to the first inventory cycle. These evaluations therefore do not take into account potential changes in a classification variable characteristic from the earlier to the later inventory (e.g. from private to public ownership).

With the second type of change, the difference in target variables, such as number of stems, growing stock or forest area, is used to assess the change between two inventory cycles. Target variables are generally used to represent states, such as that in NFI4, but can also indicate the net change between any two inventory cycles, e.g. NFI1–NFI4. In evaluations of change using these target variables, the change in a classification variable characteristic is considered in the analysis. Consequently, one can detect, for example, that the forest area without shrub forest increased between the two inventory cycles. This only has an effect on classification variables whose characteristic (class) can actually change, e.g. the affiliation with a forest area or the tree condition.

NFI4
volume of bolewood (HG 2010)
timber assortment classes (HG 2010) · main tree species
regional demarcation: production region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
timber assortment classes (HG 2010)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
smallwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1906	9	1153	9	1045	9	857	6	82	11	5043	4
	fir	1477	7	729	11	684	10	84	11	14	21	2987	5
	pine	118	33	67	20	25	45	145	10	5	39	360	12
	larch	6	45	33	22	17	48	200	14	53	11	309	10
	Arolla pine	0	.	0	.	0	90	47	20	0	96	48	19
	other conifers	13	31	64	47	4	35	20	33	0	72	100	31
	beech	90	5	61	5	55	7	29	9	34	10	269	3
	maple	11	12	10	13	15	11	14	11	3	25	52	6
	ash	9	13	22	9	8	16	6	18	6	27	50	7
	oak	10	15	15	12	2	41	4	26	7	23	38	8
	sweet chestnut	0	.	0	84	0	.	1	45	35	10	37	10
	other broadleaves	9	13	9	11	5	17	21	12	27	10	71	6
	indeterminable	0	50	0	57	0	100	0	51	0	100	1	31
	total	3649	6	2164	7	1858	8	1428	4	265	4	9364	3
classes 1 and 2	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4671	6	8633	6	14095	4	23779	3	3103	8	54281	2
	fir	3237	7	3294	7	4740	6	1896	9	418	20	13584	3
	pine	854	15	863	13	229	34	2713	9	168	43	4827	6
	larch	94	34	355	19	64	30	4623	6	1818	8	6952	4
	Arolla pine	0	.	0	.	34	79	1056	11	6	60	1095	11
	other conifers	154	31	277	24	41	29	138	22	5	64	615	14
	beech	6298	5	5211	5	4302	6	2649	8	2628	10	21089	3
	maple	1533	7	1659	9	1592	9	1419	10	301	18	6504	4
	ash	1054	11	2026	8	1207	12	1014	13	743	16	6044	5
	oak	669	17	709	12	71	26	219	21	439	17	2106	8
	sweet chestnut	0	.	25	86	0	.	59	55	2677	9	2761	9
	other broadleaves	1015	12	1398	8	791	11	2347	7	2967	7	8519	4
	indeterminable	29	38	8	83	9	44	39	34	25	44	110	20
	total	19608	3	24457	3	27176	2	41949	2	15298	3	128488	1
classes 3 and 4	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	9706	5	11425	5	22542	4	29225	3	4185	8	77083	2
	fir	7943	7	6681	7	9919	5	3212	9	714	19	28469	3
	pine	1056	14	1490	12	386	37	1997	11	167	39	5096	7
	larch	104	36	484	19	187	36	6225	6	1970	8	8970	5
	Arolla pine	0	.	0	.	19	80	977	11	5	71	1002	11
	other conifers	167	51	358	28	8	100	2	100	17	100	552	24
	beech	11607	4	9917	5	7850	6	3279	9	1957	10	34610	3
	maple	1209	9	951	11	899	10	819	11	135	34	4013	5
	ash	1147	12	2985	9	971	12	514	17	214	24	5832	6
	oak	811	15	1650	11	97	26	215	27	288	22	3061	8
	sweet chestnut	0	.	21	93	0	.	28	37	1057	11	1106	10
	other broadleaves	632	23	896	14	214	21	682	19	813	11	3237	8
	indeterminable	11	72	12	72	0	0	31	46	0	0	54	34
	total	34393	2	36870	2	43093	2	47205	2	11523	3	173084	1
classes 5 and 6	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2515	10	2684	9	7715	6	10773	5	2162	12	25848	3
	fir	1746	12	1916	11	4081	8	1603	13	453	27	9799	5
	pine	132	32	246	23	47	48	297	21	11	60	733	13
	larch	7	100	71	41	54	58	1894	9	796	15	2822	8
	Arolla pine	0	.	0	.	5	100	342	17	3	100	351	17
	other conifers	41	71	292	42	17	100	0	0	4	100	353	36
	beech	2769	10	4820	8	2368	11	928	15	545	24	11429	5
	maple	105	33	215	30	55	33	120	37	11	72	506	17
	ash	155	25	582	16	171	30	65	37	31	57	1004	12
	oak	324	27	961	19	91	41	27	43	21	64	1423	14
	sweet chestnut	0	.	3	100	0	.	30	66	1061	23	1094	22
	other broadleaves	226	45	419	52	24	90	168	37	69	43	906	28
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	8018	5	12209	5	14628	4	16246	4	5166	8	56268	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18798	4	23895	4	45397	3	64633	3	9532	7	162254	2
	fir	14403	6	12620	6	19424	5	6794	8	1599	18	54840	3
	pine	2160	12	2665	11	687	30	5152	8	351	38	11015	6
	larch	210	33	943	16	321	32	12942	5	4636	7	19053	4
	Arolla pine	0	.	0	.	59	75	2422	10	14	58	2495	10
	other conifers	375	38	991	25	69	41	160	23	26	91	1620	18
	beech	20764	4	20009	4	14575	6	6885	8	5163	9	67397	2
	maple	2858	7	2835	8	2561	7	2372	9	450	19	11076	4
	ash	2364	10	5615	7	2358	10	1599	12	994	16	12930	5
	oak	1814	13	3335	10	261	24	464	21	754	15	6628	7
	sweet chestnut	0	.	50	90	0	.	117	49	4830	9	4997	9
	other broadleaves	1881	14	2723	12	1035	11	3218	9	3877	7	12734	5
	indeterminable	40	41	20	56	9	44	70	30	25	44	165	19
	total	65668	2	75700	2	86755	2	106828	2	32252	3	367203	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1197741. Birmensdorf, Swiss Federal Research Institute WSL

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