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total number of stems #3
Number of stems of all living and dead trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm. The total number of stems is the sum of the number of stems and the number of deadwood stems.

diameter at breast height (10 classes) #1317
Diameter at breast height (dbh) of the trees and shrubs ≥12 cm dbh – in ten classes. Reference: Field Survey (MID 60: Brusthöhendurchmesser, MID 62: Umfang)

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
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: production region
unit: 1000 n
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
diameter at breast height (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7029	11	10618	10	16255	9	32818	5	4154	17	70873	4
	fir	7403	10	4880	12	7197	11	2776	14	663	29	22920	6
	pine	277	31	255	41	383	57	5569	18	81	100	6564	16
	larch	74	80	791	54	341	50	3812	12	1554	23	6571	11
	Arolla pine	0	.	0	.	0	.	1629	27	32	100	1662	27
	other conifers	490	38	572	39	167	41	1127	32	20	100	2376	20
	beech	10487	8	9012	10	6986	10	5637	12	6150	14	38272	5
	maple	3984	14	3185	14	2485	16	3524	13	792	31	13970	7
	ash	1965	18	2093	16	1606	17	1918	18	1517	24	9100	8
	oak	1321	38	847	28	54	58	946	32	1152	38	4318	18
	sweet chestnut	0	.	0	.	0	.	55	75	7888	15	7942	15
	other broadleaves	3395	11	5050	12	3376	13	10284	10	10307	12	32413	6
	indeterminable	141	44	50	58	191	30	252	29	59	57	692	17
	total	36565	5	37353	5	39039	5	70347	4	34370	6	217675	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3640	11	6303	10	10737	8	19476	6	2985	15	43140	4
	fir	4315	10	2620	14	4218	10	1608	14	320	33	13080	6
	pine	598	28	348	37	278	79	3596	14	52	58	4871	12
	larch	17	100	344	39	67	78	2698	13	1839	18	4965	10
	Arolla pine	0	.	0	.	0	.	1372	19	16	100	1388	19
	other conifers	137	43	178	31	53	58	69	50	0	.	437	21
	beech	5999	9	4390	9	4932	10	3164	13	3751	17	22234	5
	maple	2006	12	1881	16	2018	13	1828	16	257	31	7990	7
	ash	863	19	922	16	1428	18	1019	24	650	34	4881	10
	oak	750	30	696	24	140	49	339	33	750	24	2674	13
	sweet chestnut	0	.	0	.	0	.	34	71	4404	14	4438	14
	other broadleaves	972	18	1812	14	1276	23	3370	13	4311	13	11742	7
	indeterminable	54	58	17	100	89	45	109	48	18	100	287	27
	total	19352	4	19509	5	25234	5	38682	4	19351	6	122129	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3020	11	3276	10	7882	7	12939	5	2129	15	29246	4
	fir	2166	11	1547	14	2584	11	1153	16	269	33	7720	6
	pine	819	22	329	27	201	53	1722	15	147	51	3218	11
	larch	14	71	97	44	9	100	2481	12	1062	18	3663	10
	Arolla pine	0	.	0	.	0	.	785	20	0	.	785	20
	other conifers	31	81	107	44	0	.	33	71	0	.	170	34
	beech	4597	8	2933	9	3265	9	1794	12	1698	15	14287	5
	maple	925	16	783	14	1177	15	752	18	104	56	3741	8
	ash	533	25	1127	16	787	25	641	22	144	35	3233	10
	oak	390	23	381	25	45	55	105	42	288	28	1208	13
	sweet chestnut	0	.	0	.	0	.	49	79	1637	18	1686	18
	other broadleaves	340	24	639	19	258	29	455	18	1558	18	3249	10
	indeterminable	34	71	17	100	42	41	105	38	0	.	198	26
	total	12870	5	11234	5	16249	5	23015	4	9035	7	72404	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2139	9	2295	9	4932	7	8542	5	1465	14	19373	3
	fir	1652	10	1251	10	1694	9	842	15	184	29	5624	5
	pine	366	19	367	18	134	58	727	17	34	60	1629	11
	larch	21	58	97	36	7	100	1931	11	739	16	2795	9
	Arolla pine	0	.	0	.	0	.	338	20	0	.	338	20
	other conifers	28	61	54	47	0	.	0	.	8	100	90	35
	beech	2783	7	2036	8	2017	10	1038	13	658	16	8532	4
	maple	424	15	209	21	337	17	356	18	44	47	1369	8
	ash	283	21	675	15	315	21	171	27	66	44	1509	10
	oak	304	24	364	18	53	53	66	42	151	32	938	12
	sweet chestnut	0	.	0	.	0	.	21	58	721	18	742	18
	other broadleaves	232	33	296	20	51	38	184	26	456	20	1219	12
	indeterminable	15	71	0	.	0	.	71	37	0	.	86	33
	total	8247	4	7644	4	9540	4	14288	3	4525	6	44244	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1270	10	1194	11	3147	7	4904	5	997	21	11511	4
	fir	1001	11	950	12	1559	10	599	16	190	29	4299	6
	pine	136	25	249	22	66	50	299	23	14	100	765	13
	larch	14	70	84	31	14	100	1415	11	470	16	1998	9
	Arolla pine	0	.	0	.	0	.	191	26	0	.	191	26
	other conifers	42	62	74	49	0	.	0	.	8	100	124	36
	beech	1272	10	1535	8	969	13	503	18	309	20	4588	5
	maple	150	22	116	27	170	24	113	28	0	.	549	13
	ash	74	35	444	17	83	38	98	28	33	72	733	13
	oak	93	31	308	18	0	.	41	59	57	41	499	14
	sweet chestnut	0	.	0	.	0	.	6	100	337	24	343	24
	other broadleaves	36	53	158	30	45	47	106	38	38	52	383	18
	indeterminable	0	.	0	.	19	58	14	71	0	.	34	45
	total	4087	5	5112	4	6073	4	8290	4	2454	11	26016	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	657	13	588	13	1486	10	2008	8	402	17	5141	5
	fir	480	15	535	14	734	13	241	21	37	45	2027	7
	pine	15	71	61	37	24	58	72	34	0	.	171	22
	larch	0	.	34	45	0	.	794	13	193	23	1021	11
	Arolla pine	0	.	0	.	0	.	29	50	0	.	29	50
	other conifers	14	100	47	51	0	.	0	.	8	100	69	42
	beech	347	17	571	12	264	23	176	24	142	29	1500	8
	maple	7	100	48	43	34	65	23	59	0	.	112	30
	ash	53	43	119	25	35	53	22	71	10	100	240	19
	oak	26	60	108	33	7	100	6	100	15	71	163	25
	sweet chestnut	0	.	0	.	0	.	7	100	98	27	105	26
	other broadleaves	14	100	36	45	16	71	52	54	15	71	132	29
	indeterminable	0	.	0	.	0	.	7	100	0	.	7	100
	total	1613	8	2147	6	2599	7	3436	6	921	10	10717	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	231	20	237	20	577	13	949	10	210	26	2204	7
	fir	203	19	274	18	327	16	226	24	30	49	1061	9
	pine	7	100	27	50	0	.	14	71	0	.	48	38
	larch	7	100	0	.	15	71	290	19	86	28	398	15
	Arolla pine	0	.	0	.	0	.	50	38	0	.	50	38
	other conifers	0	.	41	58	0	.	0	.	0	.	41	58
	beech	62	37	234	20	129	29	58	42	44	41	527	13
	maple	16	71	7	100	6	100	0	.	0	.	29	50
	ash	15	71	16	72	15	71	12	70	0	.	59	36
	oak	18	70	41	41	0	.	7	100	0	.	66	33
	sweet chestnut	0	.	0	.	0	.	0	.	104	33	104	33
	other broadleaves	16	71	27	50	0	.	6	100	7	100	56	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	575	12	904	10	1069	9	1612	8	482	15	4641	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	47	47	104	27	145	24	244	18	55	43	594	12
	fir	51	42	132	26	109	30	42	41	7	100	342	16
	pine	0	.	7	100	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	134	28	65	45	199	24
	Arolla pine	0	.	0	.	0	.	29	61	0	.	29	61
	other conifers	7	100	27	62	7	100	0	.	0	.	41	47
	beech	8	100	20	58	16	71	22	57	37	45	102	27
	maple	0	.	0	.	0	.	8	100	0	.	8	100
	ash	7	100	9	100	17	72	0	.	0	.	33	51
	oak	0	.	29	50	8	100	10	100	0	.	47	41
	sweet chestnut	0	.	0	.	0	.	0	.	53	47	53	47
	other broadleaves	0	.	8	100	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	119	28	335	15	302	17	490	13	217	23	1462	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	20	58	15	72	138	24	18	72	206	20
	fir	29	50	14	71	12	71	14	71	25	58	95	28
	pine	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	0	.	0	.	43	41	0	.	43	41
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	32	52	7	100	0	.	0	.	39	46
	maple	0	.	0	.	0	.	6	100	0	.	6	100
	ash	0	.	17	71	0	.	0	.	0	.	17	71
	oak	0	.	14	71	0	.	0	.	0	.	14	71
	sweet chestnut	0	.	0	.	0	.	7	100	45	53	53	48
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	44	41	97	28	35	45	230	18	89	35	494	13
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14	71	14	71	35	44	52	38	18	72	133	24
	fir	15	71	0	.	7	100	0	.	0	.	22	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	7	100	14	100	22	74
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	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	.	16	100	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	44	57	44	57
	other broadleaves	0	.	7	100	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	28	50	38	54	42	41	73	32	76	41	258	19
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18061	7	24648	7	45211	6	82069	4	12434	13	182423	3
	fir	17315	7	12202	8	18442	7	7503	11	1726	24	57188	4
	pine	2218	18	1642	16	1086	50	12006	13	328	58	17280	10
	larch	148	44	1447	35	452	47	13606	8	6022	14	21675	7
	Arolla pine	0	.	0	.	0	.	4451	17	49	100	4500	17
	other conifers	749	30	1100	25	227	33	1229	31	44	70	3349	16
	beech	25556	5	20763	6	18583	7	12390	10	12790	13	90082	3
	maple	7511	10	6229	10	6226	11	6610	11	1197	27	27773	5
	ash	3793	14	5423	10	4287	14	3881	15	2420	23	19805	6
	oak	2902	24	2802	16	307	32	1520	27	2412	26	9943	11
	sweet chestnut	0	.	0	.	0	.	180	56	15330	12	15510	12
	other broadleaves	5004	10	8033	10	5022	13	14458	10	16692	11	49210	5
	indeterminable	244	32	84	44	341	21	560	19	77	50	1305	12
	total	83501	3	84373	3	100183	3	160463	3	71520	5	500040	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. 1358379. Birmensdorf, Swiss Federal Research Institute WSL

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