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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 NFI4/NFI5 #2282
Forest that was less than two-thirds covered with shrubs in both NFI4 (2009-2017) and NFI5 (2018-2026) and is 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 NFI4/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	7155	11	10635	10	17985	8	37629	5	5766	15	79170	4
	fir	7498	10	4880	12	7264	11	2898	13	698	27	23238	6
	pine	277	31	255	41	383	57	6474	17	181	60	7569	15
	larch	74	80	791	54	341	50	5569	10	3281	17	10055	9
	Arolla pine	0	.	0	.	0	.	2497	21	49	74	2546	20
	other conifers	490	38	572	39	184	38	1502	27	20	100	2768	18
	beech	10539	8	9196	10	7004	10	5940	12	6441	14	39119	5
	maple	4026	13	3260	14	2813	16	4516	13	1353	28	15967	7
	ash	1984	17	2263	16	1653	17	2718	16	1660	22	10279	8
	oak	1321	38	847	28	54	58	997	31	1184	37	4403	17
	sweet chestnut	0	.	0	.	0	.	149	69	7956	15	8105	15
	other broadleaves	3612	11	5359	11	4562	13	13129	10	12384	11	39046	5
	indeterminable	141	44	50	58	208	29	362	24	110	41	871	15
	total	37116	5	38107	5	42450	5	84380	3	41082	6	243136	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3714	11	6342	10	12221	8	22557	5	3623	13	48458	4
	fir	4389	10	2620	14	4218	10	1644	14	338	32	13208	6
	pine	598	28	348	37	278	79	3843	14	52	58	5118	12
	larch	17	100	344	39	67	78	3937	12	2796	15	7161	9
	Arolla pine	0	.	0	.	0	.	1898	17	16	100	1914	17
	other conifers	137	43	178	31	53	58	157	51	0	.	525	22
	beech	6041	9	4431	9	5010	10	3422	13	3994	16	22899	5
	maple	2095	11	1936	16	2176	13	2112	14	306	31	8625	7
	ash	863	19	982	16	1523	18	1331	21	710	31	5408	9
	oak	750	30	712	24	184	44	375	31	872	22	2894	13
	sweet chestnut	0	.	0	.	0	.	53	58	4496	14	4549	14
	other broadleaves	1107	19	1890	13	1560	20	4159	12	5085	12	13801	7
	indeterminable	54	58	17	100	89	45	126	44	18	100	305	26
	total	19766	4	19799	5	27379	5	45615	4	22304	5	134863	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3098	11	3310	10	8558	7	14440	5	2284	14	31690	3
	fir	2181	11	1547	14	2633	11	1185	16	283	31	7830	6
	pine	819	22	329	27	201	53	1816	14	147	51	3311	11
	larch	14	71	97	44	9	100	3006	10	1622	15	4748	8
	Arolla pine	0	.	0	.	0	.	1000	19	0	.	1000	19
	other conifers	31	81	107	44	0	.	40	61	0	.	177	33
	beech	4623	8	2951	9	3329	9	1870	12	1762	15	14535	4
	maple	951	16	783	14	1207	14	897	18	187	42	4024	8
	ash	533	25	1135	16	787	25	739	20	169	32	3364	10
	oak	390	23	381	25	45	55	137	37	313	27	1266	13
	sweet chestnut	0	.	0	.	0	.	87	62	1646	18	1733	17
	other broadleaves	376	24	669	18	258	29	521	17	1823	16	3648	10
	indeterminable	34	71	17	100	42	41	112	36	19	100	224	25
	total	13051	5	11324	5	17068	4	25849	3	10256	6	77548	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2206	9	2302	9	5506	6	9306	5	1575	13	20895	3
	fir	1667	10	1251	10	1711	8	885	15	184	29	5699	5
	pine	366	19	367	18	134	58	789	16	41	52	1697	10
	larch	21	58	97	36	7	100	2313	10	912	14	3350	8
	Arolla pine	0	.	0	.	0	.	402	18	0	.	402	18
	other conifers	28	61	54	47	0	.	0	.	8	100	90	35
	beech	2790	7	2036	8	2123	10	1088	13	679	15	8716	4
	maple	438	14	209	21	343	17	381	17	44	47	1414	8
	ash	302	20	692	15	315	21	171	27	66	44	1544	9
	oak	304	24	381	18	61	48	66	42	173	29	985	12
	sweet chestnut	0	.	0	.	0	.	29	50	721	18	750	18
	other broadleaves	232	33	310	19	51	38	245	25	524	18	1362	11
	indeterminable	15	71	0	.	0	.	91	33	0	.	105	30
	total	8369	4	7698	4	10251	4	15766	3	4926	6	47010	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1307	10	1219	10	3436	6	5191	5	1025	20	12178	4
	fir	1016	11	966	12	1581	10	623	15	197	28	4383	6
	pine	136	25	249	22	66	50	334	22	21	74	808	13
	larch	14	70	84	31	14	100	1548	10	580	14	2240	8
	Arolla pine	0	.	0	.	0	.	212	24	0	.	212	24
	other conifers	42	62	74	49	0	.	0	.	8	100	124	36
	beech	1331	10	1535	8	1006	12	528	17	317	20	4717	5
	maple	150	22	116	27	183	23	122	27	0	.	571	12
	ash	74	35	456	16	83	38	98	28	40	63	751	12
	oak	93	31	318	18	0	.	41	59	57	41	509	14
	sweet chestnut	0	.	0	.	0	.	15	72	337	24	352	23
	other broadleaves	51	47	165	29	45	47	129	34	57	42	447	17
	indeterminable	0	.	0	.	19	58	14	71	0	.	34	45
	total	4215	5	5183	4	6433	4	8856	4	2639	10	27325	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	671	13	588	13	1658	9	2127	7	423	16	5466	5
	fir	480	15	543	14	740	12	247	21	37	45	2047	7
	pine	15	71	61	37	24	58	72	34	7	100	179	21
	larch	0	.	34	45	0	.	822	13	222	21	1078	11
	Arolla pine	0	.	0	.	0	.	36	45	0	.	36	45
	other conifers	14	100	47	51	0	.	0	.	8	100	69	42
	beech	374	16	606	12	264	23	176	24	151	28	1570	8
	maple	15	71	48	43	34	65	43	46	0	.	140	27
	ash	53	43	119	25	35	53	29	59	10	100	247	19
	oak	26	60	116	31	7	100	6	100	15	71	170	25
	sweet chestnut	0	.	0	.	0	.	16	71	98	27	114	25
	other broadleaves	14	100	43	41	16	71	70	44	15	71	158	26
	indeterminable	0	.	0	.	0	.	7	100	0	.	7	100
	total	1661	8	2205	6	2777	7	3651	5	987	9	11281	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	239	20	237	20	610	12	1031	10	217	25	2335	7
	fir	211	19	286	17	327	16	226	24	30	49	1080	9
	pine	7	100	27	50	0	.	14	71	0	.	48	38
	larch	7	100	0	.	15	71	319	18	93	27	433	15
	Arolla pine	0	.	0	.	0	.	50	38	0	.	50	38
	other conifers	0	.	41	58	0	.	0	.	0	.	41	58
	beech	80	33	234	20	138	28	64	39	44	41	560	13
	maple	16	71	7	100	6	100	7	100	0	.	36	45
	ash	15	71	16	72	15	71	12	70	6	100	65	34
	oak	18	70	51	38	0	.	7	100	0	.	76	32
	sweet chestnut	0	.	0	.	0	.	8	100	104	33	112	31
	other broadleaves	16	71	27	50	0	.	6	100	7	100	56	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	608	12	926	10	1111	9	1745	8	502	14	4892	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	47	47	104	27	167	23	286	17	88	33	691	11
	fir	51	42	132	26	120	29	42	41	7	100	352	16
	pine	0	.	7	100	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	144	27	95	37	239	22
	Arolla pine	0	.	0	.	0	.	43	52	0	.	43	52
	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	334	16	556	13	280	20	1624	7
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	20	58	33	51	145	23	25	59	238	18
	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	.	54	38	7	100	61	36
	Arolla pine	0	.	0	.	0	.	22	58	0	.	22	58
	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	.	7	100	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	44	41	103	27	53	38	256	17	103	32	558	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14	71	14	71	43	40	59	35	18	72	147	22
	fir	15	71	0	.	7	100	0	.	0	.	22	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	15	71	14	100	29	61
	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	.	50	51	50	51
	other broadleaves	0	.	27	61	0	.	0	.	0	.	27	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	28	50	58	44	50	37	87	29	83	39	306	17
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18465	7	24771	7	50216	5	92771	4	15045	11	201267	3
	fir	17537	7	12238	8	18613	7	7766	11	1800	23	57954	4
	pine	2218	18	1642	16	1086	50	13348	12	450	46	18744	10
	larch	148	44	1447	35	452	47	17726	7	9621	11	29394	6
	Arolla pine	0	.	0	.	0	.	6172	15	65	79	6237	15
	other conifers	749	30	1100	25	245	31	1698	26	44	70	3835	15
	beech	25786	5	21041	6	18896	7	13110	10	13424	12	92257	3
	maple	7691	10	6357	10	6761	11	8093	10	1890	25	30791	5
	ash	3830	13	5690	10	4429	14	5099	14	2661	22	21709	6
	oak	2902	24	2864	16	359	30	1640	25	2614	24	10380	11
	sweet chestnut	0	.	0	.	0	.	364	58	15506	12	15870	12
	other broadleaves	5408	11	8505	10	6492	12	18259	9	19895	9	58559	5
	indeterminable	244	32	84	44	358	21	714	17	146	40	1546	11
	total	84978	3	85738	3	107906	3	186761	2	83161	4	548544	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. 1358383. Birmensdorf, Swiss Federal Research Institute WSL

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