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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 NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was accessible on foot.

1.4 km grid, subgrids 1-5 #1746
Sub-grids 1, 2, 3, 4 and 5 of the field surveys on the sampling grid with a mesh size of 1.4 km (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.

NFI5
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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	2133	11	1026	13	791	16	738	9	84	17	4772	6
	fir	1899	9	562	14	352	15	208	41	12	24	3034	7
	pine	199	42	58	37	15	49	125	13	3	59	399	22
	larch	5	43	91	56	9	55	109	14	59	22	273	20
	Arolla pine	0	.	0	.	0	.	20	19	0	100	20	19
	other conifers	19	40	41	55	3	46	4	30	0	100	68	35
	beech	75	7	65	7	54	9	33	11	34	14	260	4
	maple	10	16	9	17	15	15	14	16	3	39	51	8
	ash	7	22	15	15	10	23	7	22	4	31	43	10
	oak	12	22	13	16	2	43	4	31	7	29	39	11
	sweet chestnut	0	.	0	0	0	.	1	50	42	15	43	15
	other broadleaves	7	16	10	17	4	23	13	16	24	14	58	8
	indeterminable	0	72	0	0	0	100	0	0	0	0	0	68
	total	4366	7	1891	10	1255	11	1275	9	272	8	9059	4
classes 1 and 2	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3295	10	7495	7	12593	6	20646	4	2997	13	47026	3
	fir	2792	10	3495	10	5007	8	1848	12	423	27	13565	5
	pine	742	19	616	18	230	56	2363	13	104	49	4054	9
	larch	31	42	268	26	68	50	3511	8	1403	14	5281	7
	Arolla pine	0	.	0	.	0	.	823	16	7	100	830	16
	other conifers	122	39	209	26	33	36	111	29	9	92	484	17
	beech	5701	7	4804	6	4363	8	2603	11	2614	14	20085	4
	maple	1694	9	1587	11	1593	12	1384	12	211	29	6468	5
	ash	923	17	1489	11	1196	17	911	17	493	27	5012	7
	oak	538	24	618	19	57	39	184	30	436	23	1832	11
	sweet chestnut	0	.	0	0	0	.	42	61	2869	12	2911	12
	other broadleaves	797	12	1436	11	776	16	1812	10	2737	11	7559	6
	indeterminable	8	48	6	87	5	47	22	66	4	58	46	36
	total	16645	4	22023	3	25920	4	36259	3	14309	5	115155	2
classes 3 and 4	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8715	7	9740	8	19245	5	27427	4	4804	15	69930	3
	fir	7505	8	6574	9	9546	7	3694	12	835	26	28155	4
	pine	794	16	1369	15	447	52	1770	14	83	57	4464	9
	larch	82	45	331	24	61	57	5645	9	1833	13	7952	7
	Arolla pine	0	.	0	.	0	.	761	17	0	0	761	17
	other conifers	211	70	516	35	12	100	5	100	49	100	791	30
	beech	10304	5	9432	6	7740	8	3505	11	1999	12	32980	3
	maple	1393	12	1025	13	1295	12	914	14	84	40	4712	6
	ash	934	18	2779	12	1052	18	654	21	173	41	5592	8
	oak	749	20	1355	14	91	41	174	39	350	28	2720	10
	sweet chestnut	0	.	0	0	0	.	39	42	1133	15	1172	15
	other broadleaves	493	30	966	17	203	23	577	25	795	16	3034	10
	indeterminable	11	100	0	0	0	0	13	100	0	0	23	71
	total	31190	3	34087	3	39692	3	45178	3	12138	7	162285	1
classes 5 and 6	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2683	14	2894	12	7202	8	10120	6	2306	19	25205	4
	fir	1981	16	2632	12	4185	10	2046	16	461	31	11305	6
	pine	89	51	294	29	42	61	262	27	0	0	688	18
	larch	5	100	71	37	38	63	1979	11	705	24	2797	10
	Arolla pine	0	.	0	.	0	.	302	23	0	0	302	23
	other conifers	113	82	507	50	28	100	0	0	18	100	666	41
	beech	2351	12	4961	9	2224	15	1140	18	463	20	11138	6
	maple	164	32	297	34	96	32	68	37	0	0	626	19
	ash	204	32	645	22	268	41	124	41	38	100	1279	16
	oak	106	47	1047	31	32	81	41	49	35	58	1261	26
	sweet chestnut	0	.	0	0	0	.	23	74	848	28	871	28
	other broadleaves	78	47	230	32	22	60	240	74	28	50	598	33
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	7773	7	13580	6	14137	6	16346	5	4900	11	56736	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16826	6	21155	6	39831	5	58931	4	10190	13	146933	2
	fir	14177	7	13264	8	19090	6	7796	11	1732	24	56058	4
	pine	1824	16	2337	14	734	41	4520	11	190	48	9605	8
	larch	123	36	761	20	176	40	11243	8	4000	12	16303	6
	Arolla pine	0	.	0	.	0	.	1905	15	8	100	1913	15
	other conifers	466	60	1273	35	76	56	120	29	75	99	2010	26
	beech	18431	5	19261	5	14381	7	7281	10	5110	11	64463	3
	maple	3261	9	2919	10	2999	10	2379	11	298	29	11856	5
	ash	2068	15	4929	11	2526	16	1696	16	708	28	11926	7
	oak	1405	18	3034	15	182	34	403	28	828	21	5852	10
	sweet chestnut	0	.	0	0	0	.	105	44	4892	12	4997	11
	other broadleaves	1375	17	2642	12	1006	14	2643	15	3584	11	11250	6
	indeterminable	19	61	6	87	5	47	35	56	4	58	70	34
	total	59974	3	71580	3	81004	3	99058	2	31619	5	343236	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. 1385233. Birmensdorf, Swiss Federal Research Institute WSL

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