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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, 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
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	2192	11	1027	13	833	15	834	8	97	15	4983	6
	fir	1936	9	564	14	356	15	210	40	13	23	3079	7
	pine	199	42	58	37	15	49	141	12	4	51	417	21
	larch	5	43	91	56	9	55	157	12	81	17	344	16
	Arolla pine	0	.	0	.	0	.	27	17	0	100	27	17
	other conifers	19	40	41	55	4	42	7	27	0	100	72	34
	beech	75	7	67	7	56	9	37	11	36	14	271	4
	maple	11	16	9	17	16	15	16	15	4	34	56	8
	ash	7	22	17	16	10	23	9	20	5	28	47	9
	oak	12	22	14	16	2	39	5	29	8	28	41	11
	sweet chestnut	0	.	0	0	0	.	1	45	42	15	44	14
	other broadleaves	7	16	11	17	5	21	16	14	27	13	67	7
	indeterminable	0	72	0	0	0	100	0	0	0	0	0	68
	total	4463	7	1900	9	1306	11	1460	8	318	7	9448	4
classes 1 and 2	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3402	9	7592	7	14128	5	23598	4	3442	12	52163	3
	fir	2813	10	3565	10	5076	8	1960	12	440	26	13853	5
	pine	742	19	616	18	230	56	2550	12	111	46	4248	9
	larch	31	42	268	26	68	50	4852	8	2057	11	7276	6
	Arolla pine	0	.	0	.	0	.	1095	15	8	91	1103	15
	other conifers	122	39	209	26	33	36	175	26	9	92	548	16
	beech	5760	6	4899	6	4465	8	2842	10	2800	13	20766	4
	maple	1751	9	1615	11	1743	12	1611	11	319	24	7039	5
	ash	926	17	1530	11	1230	17	1172	15	542	25	5400	7
	oak	538	24	630	18	67	36	229	26	471	21	1936	11
	sweet chestnut	0	.	0	0	0	.	77	56	2904	12	2981	12
	other broadleaves	880	13	1517	10	965	14	2316	9	3237	10	8915	5
	indeterminable	8	48	6	87	5	47	27	55	20	78	67	34
	total	16974	4	22446	3	28009	3	42505	3	16359	4	126293	1
classes 3 and 4	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	9027	7	10014	7	21504	5	29925	4	5094	14	75564	3
	fir	7608	8	6685	9	9746	7	3925	11	853	26	28817	4
	pine	794	16	1369	15	447	52	1908	14	97	51	4616	9
	larch	82	45	331	24	61	57	6351	8	2200	11	9024	6
	Arolla pine	0	.	0	.	0	.	919	15	0	0	919	15
	other conifers	211	70	516	35	12	100	7	74	49	100	794	30
	beech	10445	5	9687	6	8103	8	3786	11	2081	11	34102	3
	maple	1427	12	1038	13	1333	12	1034	13	99	36	4930	6
	ash	1004	17	2845	12	1052	18	700	20	193	38	5795	8
	oak	749	20	1430	13	100	38	182	38	369	27	2832	10
	sweet chestnut	0	.	0	0	0	.	48	39	1141	15	1190	15
	other broadleaves	531	28	1036	17	207	23	717	21	919	15	3410	9
	indeterminable	11	100	0	0	0	0	41	58	0	0	51	50
	total	31888	3	34951	3	42566	3	49542	2	13095	6	172042	1
classes 5 and 6	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2746	14	2963	12	8035	7	11137	6	2575	17	27456	4
	fir	2132	16	2696	12	4280	10	2153	16	466	30	11727	6
	pine	89	51	294	29	42	61	267	26	7	100	699	17
	larch	5	100	71	37	38	63	2170	11	897	21	3181	10
	Arolla pine	0	.	0	.	0	.	358	22	0	0	358	22
	other conifers	113	82	507	50	28	100	0	0	18	100	666	41
	beech	2581	12	5269	10	2297	15	1171	17	471	19	11790	6
	maple	188	31	297	34	106	31	107	32	0	0	698	18
	ash	204	32	645	22	268	41	148	38	52	78	1316	15
	oak	106	47	1121	29	32	81	41	49	35	58	1335	25
	sweet chestnut	0	.	0	0	0	.	45	62	870	28	915	26
	other broadleaves	83	45	456	38	22	60	304	60	33	45	897	29
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	8247	7	14320	6	15149	5	17901	5	5423	10	61039	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17366	6	21597	6	44500	4	65494	3	11209	12	160166	2
	fir	14489	7	13510	8	19458	6	8248	11	1772	24	57477	4
	pine	1824	16	2337	14	734	41	4866	11	220	43	9981	8
	larch	123	36	761	20	176	40	13531	7	5234	10	19824	6
	Arolla pine	0	.	0	.	0	.	2398	13	8	92	2407	13
	other conifers	466	60	1273	35	77	55	189	26	75	99	2079	26
	beech	18861	5	19922	5	14921	7	7836	9	5388	11	66928	3
	maple	3377	8	2959	10	3198	10	2768	10	422	24	12723	5
	ash	2141	14	5037	11	2560	16	2028	14	792	25	12558	7
	oak	1405	18	3195	15	202	31	457	26	883	20	6143	9
	sweet chestnut	0	.	0	0	0	.	172	47	4958	12	5129	11
	other broadleaves	1501	16	3020	12	1199	13	3354	12	4215	10	13289	6
	indeterminable	19	61	6	87	5	47	68	42	20	78	118	29
	total	61572	2	73617	2	87030	3	111408	2	35195	4	368822	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. 1197780. Birmensdorf, Swiss Federal Research Institute WSL

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