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number of stems of young forest plants #406
Number of living conifers and conifers ≥10 cm in height and ≤11.9 cm in diameter at breast height (dbh).

size classe (young forest; 5 classes) #2623
Size of young forest plants ≥10 cm in height and ≤11.9 cm in diameter at breast height (dbh) – in five size classes (two height and three dbh classes). Reference: Field Survey (MID 507: Jungwaldklasse)

main tree species in young forest (counting) #2625
Species of young forest plants ≥10 cm in height and ≤11.9 cm in diameter at breast height (dbh) with individual classes for the ten most important species or species groups in Switzerland («main tree species»), as well as the classes «other conifers», «other broadleaves» and «species not assessed in all inventories». 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 1051: Jungwaldpflanzenart - Zählung)

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.

stockable and accessible forest without shrub forest NFI2-NFI5 #2615
Forest that was less than two-thirds covered with shrubs in the four inventories NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026), was accessible on foot and could be stocked, i.e. did not contain forest roads, recreational facilities, streams, avalanche paths, etc.

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
number of stems of young forest plants
size classe (young forest; 5 classes) · main tree species in young forest (counting)
regional demarcation: production region
unit: n/ha
evaluation area: stockable and accessible forest without shrub forest NFI2-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
size classe (young forest; 5 classes)	main tree species in young forest (counting)	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
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	.
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
height 10-39 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	569	29	874	26	997	18	666	15	47	71	681	11
	fir	995	17	1784	13	1341	21	122	32	46	50	839	9
	pine	8	100	7	100	0	.	21	45	0	.	9	38
	larch	0	.	0	.	0	.	13	74	10	100	5	62
	Arolla pine	0	.	0	.	0	.	21	100	21	100	9	78
	other conifers	44	71	44	51	7	100	0	.	0	.	18	40
	beech	5672	16	4068	17	2433	30	1296	35	1066	38	2825	10
	maple	7225	17	7846	17	3174	20	631	21	336	29	3721	10
	ash	4533	16	4221	16	2346	20	2050	35	3522	30	3163	10
	oak	1359	37	814	26	59	39	136	29	390	58	506	20
	sweet chestnut	0	.	0	.	0	.	7	70	111	43	15	38
	other broadleaves	1135	20	914	28	908	17	1652	19	396	29	1121	11
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	21541	11	20571	9	11265	12	6615	15	5945	21	12912	5
height 40-129 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	215	30	625	31	575	16	532	15	37	40	445	12
	fir	244	27	719	18	241	25	40	32	12	74	250	13
	pine	3	100	9	61	0	.	47	53	0	.	17	46
	larch	0	.	3	100	0	.	40	35	4	100	13	33
	Arolla pine	0	.	0	.	0	.	20	33	7	100	7	32
	other conifers	0	.	7	58	0	.	0	.	0	.	1	58
	beech	3762	14	2422	14	1143	21	342	22	227	61	1510	8
	maple	877	28	1751	19	501	29	95	19	48	35	644	13
	ash	326	19	905	15	410	25	175	21	464	46	430	10
	oak	5	71	89	67	3	100	26	34	16	71	30	43
	sweet chestnut	0	.	0	.	0	.	0	.	21	44	2	44
	other broadleaves	461	17	516	15	448	23	407	16	222	38	425	9
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	5893	12	7045	9	3321	11	1724	9	1059	25	3775	5
DBH 0-3.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	80	24	123	16	233	13	229	14	36	38	159	8
	fir	114	38	258	21	83	25	17	29	17	39	96	15
	pine	1	100	2	71	0	.	25	31	7	81	9	28
	larch	1	100	4	60	0	.	27	28	3	70	10	25
	Arolla pine	0	.	0	.	0	.	9	29	0	.	3	29
	other conifers	1	100	6	50	0	.	0	.	1	100	2	42
	beech	2064	11	1533	13	788	18	226	41	91	40	909	8
	maple	253	18	577	17	234	20	46	23	11	55	223	11
	ash	50	22	102	22	119	48	40	25	19	36	67	19
	oak	1	100	32	45	1	100	7	65	0	.	9	36
	sweet chestnut	0	.	1	100	0	.	1	100	23	32	3	29
	other broadleaves	197	19	243	17	261	14	146	13	86	23	190	8
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	2763	9	2882	9	1720	10	770	13	294	16	1679	5
DBH 4-7.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	38	19	52	15	108	11	111	11	36	22	77	7
	fir	45	17	48	17	39	18	8	29	10	45	29	9
	pine	0	100	1	70	1	100	14	34	3	100	5	30
	larch	0	.	2	73	0	.	9	22	8	40	4	19
	Arolla pine	0	.	0	.	0	.	6	33	1	100	2	32
	other conifers	0	.	2	47	0	.	0	.	0	.	0	47
	beech	116	13	98	13	54	15	17	19	34	36	60	7
	maple	38	16	33	18	15	21	11	22	4	43	20	9
	ash	8	33	18	26	9	42	5	30	5	40	9	15
	oak	0	.	2	37	0	.	1	61	2	74	1	30
	sweet chestnut	0	.	0	.	0	.	0	.	5	36	1	36
	other broadleaves	32	16	38	13	41	16	39	18	58	20	40	8
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	276	7	295	7	266	7	221	8	166	13	248	4
DBH 8-11.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16	19	34	15	46	12	61	10	21	25	40	6
	fir	28	17	17	21	24	19	6	24	5	50	15	10
	pine	0	.	1	74	0	.	8	27	4	100	3	26
	larch	0	100	1	61	1	79	7	22	6	50	3	19
	Arolla pine	0	.	0	.	0	.	3	35	0	.	1	35
	other conifers	0	100	0	100	0	.	0	.	0	.	0	71
	beech	46	13	40	13	20	17	6	28	27	24	25	7
	maple	17	19	12	21	5	31	7	28	3	52	9	11
	ash	3	39	4	36	3	72	4	40	3	45	4	22
	oak	6	74	4	47	0	.	2	50	1	100	2	37
	sweet chestnut	0	.	0	.	0	.	0	.	3	45	0	45
	other broadleaves	12	23	20	15	17	21	17	19	34	20	19	9
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	130	8	133	7	117	8	120	7	105	11	122	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	917	21	1709	22	1959	12	1600	10	178	28	1402	8
	fir	1427	14	2825	11	1728	17	192	24	90	34	1229	8
	pine	12	100	19	47	1	100	115	31	13	89	44	27
	larch	1	78	10	66	1	79	95	22	31	40	36	19
	Arolla pine	0	.	0	.	0	.	59	39	29	77	22	35
	other conifers	46	69	59	39	7	100	0	.	1	100	22	34
	beech	11660	11	8161	12	4438	19	1888	27	1444	34	5329	7
	maple	8411	16	10220	14	3930	18	789	18	402	25	4617	9
	ash	4920	15	5250	14	2887	17	2273	32	4014	31	3672	9
	oak	1371	36	941	25	63	37	172	28	408	56	548	19
	sweet chestnut	0	.	1	100	0	.	8	66	164	30	22	28
	other broadleaves	1838	15	1732	17	1676	14	2261	15	796	23	1794	8
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	30603	9	30927	7	16690	9	9451	11	7569	19	18736	4

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. 1196583. Birmensdorf, Swiss Federal Research Institute WSL

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