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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: 1000 n
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)	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	.
	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	105104	29	188172	26	197970	19	217344	15	5866	71	714456	11
	fir	183918	17	383973	13	266434	21	39787	32	5714	50	879825	9
	pine	1497	100	1537	100	0	.	6827	45	0	.	9861	38
	larch	0	.	0	.	0	.	4080	74	1274	100	5354	62
	Arolla pine	0	.	0	.	0	.	6930	100	2547	100	9478	78
	other conifers	8210	71	9433	51	1403	100	0	.	0	.	19047	40
	beech	1048209	16	875723	17	483234	30	423118	35	131747	39	2962031	10
	maple	1335149	17	1689067	17	630484	20	205840	21	41503	29	3902043	10
	ash	837647	16	908629	16	465922	20	669066	35	435288	30	3316552	10
	oak	251130	37	175313	26	11684	39	44335	29	48152	58	530615	20
	sweet chestnut	0	.	0	.	0	.	2419	70	13771	43	16190	38
	other broadleaves	209679	20	196813	28	180369	17	539245	19	48933	29	1175038	11
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	3980542	11	4428661	9	2237501	12	2158992	15	734794	21	13540490	5
height 40-129 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	39699	30	134593	31	114215	16	173722	16	4612	40	466841	12
	fir	45127	27	154701	18	47851	25	13024	32	1512	74	262214	13
	pine	539	100	1879	61	0	.	15301	53	0	.	17719	46
	larch	0	.	553	100	0	.	13084	35	458	100	14096	33
	Arolla pine	0	.	0	.	0	.	6418	33	917	100	7335	32
	other conifers	0	.	1410	58	0	.	0	.	0	.	1410	58
	beech	695175	14	521497	14	227094	21	111720	22	28080	61	1583566	8
	maple	162062	28	377037	19	99451	29	31032	19	5942	35	675524	13
	ash	60172	19	194828	15	81444	25	57172	21	57350	46	450966	10
	oak	971	71	19113	67	588	100	8536	34	1938	71	31146	43
	sweet chestnut	0	.	0	.	0	.	0	.	2605	44	2605	44
	other broadleaves	85220	17	111160	15	88962	23	132793	16	27493	38	445628	9
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	1088964	12	1516773	9	659606	11	562802	9	130907	25	3959051	5
DBH 0-3.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14707	24	26539	16	46304	13	74840	14	4414	38	166804	8
	fir	21099	38	55565	21	16517	25	5416	29	2158	39	100755	15
	pine	194	100	344	71	0	.	8149	31	844	81	9531	28
	larch	177	100	935	60	0	.	8709	28	367	70	10189	25
	Arolla pine	0	.	0	.	0	.	2855	29	0	.	2855	29
	other conifers	177	100	1251	50	0	0	0	0	184	100	1612	42
	beech	381406	11	330089	13	156486	18	73635	41	11194	41	952810	8
	maple	46837	18	124248	17	46521	20	14921	23	1305	55	233832	11
	ash	9288	22	22058	22	23695	48	12925	25	2406	36	70372	19
	oak	194	100	6942	45	177	100	2292	65	0	.	9605	36
	sweet chestnut	0	.	199	100	0	.	188	100	2791	32	3179	29
	other broadleaves	36466	19	52298	18	51881	14	47498	14	10670	24	198813	8
	species not assessed in all inventories	0	0	0	0	0	0	0	0	0	0	0	0
	total	510548	9	620468	9	341580	10	251428	13	36334	16	1760357	5
DBH 4-7.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7023	19	11287	15	21528	12	36230	11	4478	22	80545	7
	fir	8361	17	10290	17	7656	18	2585	29	1214	45	30106	9
	pine	76	100	156	70	138	100	4635	34	322	100	5327	30
	larch	0	.	444	73	0	.	2874	22	989	41	4307	19
	Arolla pine	0	.	0	.	0	.	1879	33	64	100	1943	32
	other conifers	0	.	446	47	0	.	0	0	0	0	446	47
	beech	21345	13	21158	13	10631	15	5581	19	4174	36	62889	7
	maple	6930	16	7203	18	3043	21	3554	22	517	43	21248	9
	ash	1436	33	3878	26	1705	42	1582	30	668	40	9269	15
	oak	0	0	524	37	0	.	278	61	215	74	1018	30
	sweet chestnut	0	.	0	.	0	.	0	.	676	36	676	36
	other broadleaves	5920	17	8216	13	8150	16	12884	19	7174	21	42344	8
	species not assessed in all inventories	0	0	0	0	0	0	0	0	0	0	0	0
	total	51091	8	63603	7	52851	8	72082	8	20493	13	260118	4
DBH 8-11.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2867	19	7327	15	9085	12	19908	10	2654	25	41842	6
	fir	5249	17	3628	21	4842	19	1900	24	570	50	16189	10
	pine	0	.	233	74	0	.	2505	27	451	100	3189	26
	larch	68	100	311	61	235	79	2190	22	689	50	3492	19
	Arolla pine	0	.	0	.	0	.	1111	35	0	.	1111	35
	other conifers	69	100	78	100	0	0	0	0	0	.	147	71
	beech	8572	13	8571	13	4037	17	2088	28	3315	24	26582	7
	maple	3206	19	2587	21	1029	31	2182	28	362	52	9367	12
	ash	565	39	885	36	627	72	1255	40	370	45	3702	22
	oak	1032	74	767	47	0	.	557	50	72	100	2428	37
	sweet chestnut	0	.	0	.	0	.	0	.	365	45	365	45
	other broadleaves	2302	23	4300	15	3426	21	5497	19	4149	20	19674	9
	species not assessed in all inventories	0	0	0	0	0	0	0	0	0	0	0	0
	total	23931	8	28687	7	23281	8	39194	7	12997	11	128090	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	169400	21	367919	22	389102	13	522044	10	22024	29	1470489	8
	fir	263755	14	608156	11	343300	17	62712	24	11168	34	1289090	8
	pine	2306	100	4150	47	138	100	37416	31	1618	89	45628	27
	larch	245	78	2244	66	235	79	30938	22	3777	40	37439	19
	Arolla pine	0	.	0	.	0	.	19193	39	3529	77	22722	35
	other conifers	8457	69	12619	39	1403	100	0	0	184	100	22662	34
	beech	2154707	11	1757037	12	881482	19	616142	27	178510	34	5587877	7
	maple	1554184	16	2200142	14	780529	18	257529	18	49629	25	4842014	9
	ash	909109	15	1130277	14	573393	17	742000	32	496083	31	3850862	9
	oak	253327	36	202661	25	12449	37	55998	28	50377	56	574811	19
	sweet chestnut	0	.	199	100	0	.	2607	66	20209	30	23015	28
	other broadleaves	339587	15	372787	17	332788	14	737917	15	98418	23	1881497	8
	species not assessed in all inventories	0	0	0	0	0	0	0	0	0	0	0	0
	total	5655076	9	6658192	7	3314818	9	3084497	11	935524	19	19648107	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. 1196518. Birmensdorf, Swiss Federal Research Institute WSL

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