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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 #1876
Stockable part of the accessible forest without shrub forest, i.e. accessible forest without shrub forest excluding the area of 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
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	217272	18	242361	14	5866	71	758775	10
	fir	186696	16	397768	13	275014	21	46522	28	8261	46	914261	9
	pine	1497	100	1537	100	0	.	9547	38	0	.	12582	33
	larch	0	.	0	.	0	.	51167	90	2664	71	53831	85
	Arolla pine	0	.	0	.	0	.	11217	73	2547	100	13764	62
	other conifers	8210	71	9433	51	1403	100	0	.	0	.	19047	40
	beech	1052297	16	878380	17	488555	29	440048	34	140969	36	3000249	10
	maple	1356954	17	1691778	17	690533	19	217315	21	45616	27	4002196	10
	ash	849995	16	915352	16	472911	19	689953	34	469365	28	3397576	10
	oak	252493	36	176669	26	11684	39	44335	29	49569	56	534750	20
	sweet chestnut	0	.	0	.	0	.	2419	70	15045	40	17463	36
	other broadleaves	212487	20	196813	28	194316	16	615707	18	65369	25	1284692	10
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	4025733	11	4455902	9	2351688	12	2370591	14	805271	19	14009186	5
height 40-129 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	39699	30	134593	31	118833	16	214911	14	5623	35	513659	11
	fir	45127	27	159609	18	49354	25	13024	32	2429	60	269542	12
	pine	539	100	1879	61	506	100	16330	50	458	100	19712	42
	larch	0	.	553	100	0	.	26899	49	917	70	28370	47
	Arolla pine	0	.	0	.	0	.	8476	32	917	100	9393	30
	other conifers	0	.	1410	58	0	.	0	.	0	.	1410	58
	beech	697138	14	528203	13	235823	20	128437	21	28644	60	1618245	8
	maple	164054	28	379919	19	114232	27	37234	18	6401	33	701840	13
	ash	62092	19	196838	15	86035	23	60159	21	58371	45	463496	10
	oak	971	71	19586	65	588	100	8536	34	3469	59	33150	40
	sweet chestnut	0	.	0	.	0	.	0	.	3626	43	3626	43
	other broadleaves	87792	17	111633	15	93932	22	158163	14	34579	31	486099	8
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	1097411	12	1534225	9	699303	11	672169	9	145433	23	4148541	5
DBH 0-3.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15917	24	26539	16	48051	13	85039	12	5258	34	180805	8
	fir	21099	38	55741	21	16517	25	6689	26	3039	32	103085	14
	pine	371	71	344	71	0	.	9586	27	844	81	11145	25
	larch	177	100	935	60	0	.	13184	23	2554	49	16850	20
	Arolla pine	0	.	0	.	0	.	3595	25	0	.	3595	25
	other conifers	177	100	1251	50	0	0	0	0	184	100	1612	42
	beech	387512	11	331821	13	158014	17	79668	38	11727	39	968743	8
	maple	49385	17	125275	17	49273	19	16692	21	1635	48	242259	10
	ash	10002	21	22909	22	26010	44	14036	24	2590	34	75547	18
	oak	194	100	6942	45	177	100	2292	65	165	100	9770	35
	sweet chestnut	0	.	199	100	0	.	188	100	3489	29	3876	27
	other broadleaves	37743	19	54282	17	55931	14	67566	11	13682	20	229202	7
	species not assessed in all inventories	0	0	0	0	0	0	0	0	0	0	0	0
	total	522577	9	626238	9	353973	10	298534	11	45167	14	1846490	5
DBH 4-7.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7158	19	11553	15	22216	11	41067	10	5676	23	87670	6
	fir	8361	17	10357	17	7962	18	3149	25	1214	45	31042	9
	pine	76	100	156	70	138	100	5552	29	774	71	6695	26
	larch	69	100	444	73	0	.	4983	19	2108	28	7604	15
	Arolla pine	0	.	0	.	0	.	2668	27	64	100	2733	26
	other conifers	0	.	446	47	0	.	0	0	0	0	446	47
	beech	22255	12	21358	13	10840	15	6853	19	4303	35	65609	7
	maple	7077	16	7472	17	3640	19	4253	19	652	37	23094	9
	ash	1788	29	3944	26	2616	39	1934	27	668	40	10950	15
	oak	69	100	524	37	0	.	278	61	280	62	1151	28
	sweet chestnut	0	.	0	.	0	.	0	.	676	36	676	36
	other broadleaves	5994	16	8566	13	8932	15	18419	14	9341	17	51251	7
	species not assessed in all inventories	0	0	0	0	0	0	0	0	0	0	0	0
	total	52845	8	64819	7	56344	7	89157	7	25757	11	288921	3
DBH 8-11.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2935	19	7394	15	9368	12	22247	9	3723	26	45666	6
	fir	5249	17	3769	20	4842	19	2102	23	714	45	16675	10
	pine	69	100	300	62	0	.	3501	22	709	73	4579	21
	larch	137	71	311	61	235	79	3531	18	1959	32	6172	15
	Arolla pine	0	.	0	.	0	.	1831	27	0	.	1831	27
	other conifers	69	100	78	100	0	0	0	0	0	0	147	71
	beech	8921	13	8571	13	4595	17	2429	25	3644	23	28160	7
	maple	3206	19	2653	20	1670	33	2678	24	491	47	10699	11
	ash	979	48	951	35	802	60	1394	37	442	41	4567	20
	oak	1101	70	767	47	71	100	628	46	136	71	2703	33
	sweet chestnut	0	.	0	.	0	.	0	.	365	45	365	45
	other broadleaves	2514	21	4378	15	3722	20	7717	16	5608	16	23940	8
	species not assessed in all inventories	0	0	0	0	0	0	0	0	0	0	0	0
	total	25181	8	29172	7	25304	8	48057	6	17791	10	145505	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	170812	21	368252	22	415741	12	605625	10	26145	25	1586575	7
	fir	266533	14	627243	11	353688	17	71485	22	15657	30	1334606	8
	pine	2552	91	4217	46	644	81	44515	27	2785	66	54713	23
	larch	383	61	2244	66	235	79	99764	59	10202	27	112827	52
	Arolla pine	0	.	0	.	0	.	27788	33	3529	77	31317	31
	other conifers	8457	69	12619	39	1403	100	0	0	184	100	22662	34
	beech	2168123	11	1768333	12	897827	19	657436	25	189287	32	5681006	7
	maple	1580675	16	2207096	14	859349	17	278172	17	54795	23	4980088	9
	ash	924856	15	1139993	14	588373	17	767476	31	531437	29	3952135	9
	oak	254828	36	204489	25	12520	37	56069	28	53619	53	581524	19
	sweet chestnut	0	.	199	100	0	.	2607	66	23201	28	26007	26
	other broadleaves	346528	15	375672	17	356833	13	867573	13	128579	19	2075185	7
	species not assessed in all inventories	0	0	0	0	0	0	0	0	0	0	0	0
	total	5723747	9	6710356	7	3486613	9	3478509	10	1039419	17	20438644	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. 1196524. Birmensdorf, Swiss Federal Research Institute WSL

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