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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 #410
NFI's sampling grid with a mesh size of 1.4 km. The 1.4-km grid is the grid size covering all the previous terrestrial Inventories, which is why it is also called the 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.

NFI4
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
state 2009/17
		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	120350	23	300415	24	269854	19	315200	15	11074	34	1016893	10
	fir	162688	14	475887	13	207543	12	44382	23	6249	39	896749	8
	pine	2524	100	1578	71	836	100	20357	29	774	100	26071	25
	larch	0	.	2524	100	0	.	36085	56	2329	58	40937	50
	Arolla pine	0	.	0	.	0	.	43489	29	0	.	43489	29
	other conifers	3130	50	1578	100	1637	71	734	100	774	100	7853	35
	beech	2120989	15	1432787	18	546085	14	512884	34	79087	49	4691832	9
	maple	1184784	14	2066235	15	599502	17	234859	21	93436	27	4178817	9
	ash	1033447	13	1626390	10	754881	15	522699	15	560237	26	4497654	7
	oak	209633	48	185834	53	4190	45	52346	32	29947	43	481951	30
	sweet chestnut	779	100	9136	100	0	.	790	100	26867	22	37573	29
	other broadleaves	214378	14	197383	16	194402	13	388144	10	106872	16	1101179	6
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	5052704	9	6299747	8	2578930	9	2171969	11	917647	18	17020997	5
height 40-129 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	38945	20	82984	17	132117	13	185706	10	17801	38	457554	7
	fir	49247	23	87933	16	42964	21	15261	44	1963	50	197369	11
	pine	303	100	1193	61	1434	54	16396	27	2779	91	22105	23
	larch	0	.	1495	83	596	71	27746	27	5016	41	34853	23
	Arolla pine	0	.	0	.	0	.	11073	23	0	.	11073	23
	other conifers	0	0	1671	53	0	.	0	0	0	.	1671	53
	beech	784897	9	625549	11	313374	18	85963	22	4558	26	1814341	6
	maple	181362	14	302968	16	90915	17	43400	18	7394	39	626039	9
	ash	158741	14	288015	17	133486	15	65618	16	63820	24	709679	9
	oak	4367	41	10481	21	328	100	5804	42	828	75	21807	17
	sweet chestnut	0	.	606	100	0	.	0	.	4572	32	5178	30
	other broadleaves	86538	18	106208	15	83259	12	144893	10	29579	18	450477	6
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	1304400	7	1509102	7	798474	9	601860	6	138309	14	4352145	4
DBH 0-3.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16998	18	27936	15	49263	11	71919	10	9037	19	175154	6
	fir	17402	16	26140	16	19881	15	7264	24	1665	34	72352	9
	pine	313	76	636	72	1013	49	13926	25	1003	100	16891	22
	larch	95	100	327	57	108	100	18979	18	3207	26	22716	15
	Arolla pine	0	.	0	.	0	.	4166	20	194	100	4361	20
	other conifers	101	100	710	43	0	0	0	0	0	0	811	40
	beech	366579	9	322341	11	122442	16	35416	14	18653	59	865432	6
	maple	64285	13	111863	15	33115	19	17658	17	2287	30	229209	9
	ash	54686	15	87481	12	54037	25	24792	17	17927	49	238923	9
	oak	319	58	2836	34	320	73	524	53	298	58	4298	25
	sweet chestnut	0	.	109	100	0	.	205	70	3227	43	3540	39
	other broadleaves	37798	13	52982	12	59000	12	75444	13	18695	14	243919	6
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	558577	7	633362	7	339180	8	270294	6	76193	20	1877606	4
DBH 4-7.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7604	14	14438	11	20500	8	44240	7	7210	15	93993	4
	fir	9171	10	9146	14	8909	14	4586	19	1369	33	33181	7
	pine	159	61	206	53	208	53	7334	20	806	58	8711	17
	larch	151	61	396	73	39	100	7795	14	2762	22	11144	11
	Arolla pine	0	.	0	.	0	.	2399	19	0	.	2399	19
	other conifers	158	100	553	39	0	0	0	0	0	0	711	37
	beech	19210	10	21461	9	10326	11	8261	16	4471	19	63728	5
	maple	7874	16	9302	13	3913	17	4578	16	918	25	26584	8
	ash	5607	17	9351	17	3175	21	2689	21	1562	26	22385	9
	oak	372	46	1684	33	39	100	596	36	314	49	3005	22
	sweet chestnut	0	.	40	100	0	.	40	100	523	32	603	29
	other broadleaves	6676	12	7105	13	7951	11	17015	10	9250	12	47997	5
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	56981	6	73682	5	55062	5	99533	4	29183	7	314441	2
DBH 8-11.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4734	15	8374	12	9633	9	24909	6	4345	15	51995	4
	fir	5439	12	4913	14	4692	13	2349	24	570	40	17963	7
	pine	82	71	333	56	252	71	3522	19	347	61	4537	16
	larch	77	71	213	66	39	100	4412	15	1602	28	6343	13
	Arolla pine	0	.	0	.	0	.	1442	24	0	.	1442	24
	other conifers	198	66	238	47	249	52	0	0	0	0	684	32
	beech	6842	12	7925	11	5068	12	3408	16	2977	19	26220	6
	maple	1996	19	2318	16	1832	20	2069	15	652	36	8868	9
	ash	1332	30	2580	15	1034	24	1068	25	969	29	6983	10
	oak	517	70	712	47	0	.	982	28	393	39	2604	22
	sweet chestnut	0	.	39	100	0	.	0	.	757	28	796	27
	other broadleaves	2875	14	3536	14	2773	16	7316	11	6233	12	22733	6
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	24091	7	31181	6	25572	5	51477	4	18845	7	151167	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	188632	16	434148	18	481367	13	641975	9	49467	21	1795589	7
	fir	243946	13	604019	12	283989	11	73843	18	11816	27	1217613	7
	pine	3381	91	3946	42	3743	43	61535	17	5709	82	78314	16
	larch	323	67	4955	76	783	56	95017	26	14916	23	115994	22
	Arolla pine	0	.	0	.	0	.	62569	22	194	100	62763	22
	other conifers	3587	44	4750	39	1885	62	734	100	774	100	11731	25
	beech	3298517	10	2410063	12	997295	13	645932	27	109745	37	7461552	7
	maple	1440302	13	2492687	14	729278	15	302563	17	104687	25	5069516	8
	ash	1253813	11	2013817	10	946614	14	616865	14	644514	24	5475623	6
	oak	215207	48	201547	49	4878	39	60253	30	31780	41	513665	28
	sweet chestnut	779	100	9929	93	0	.	1035	78	35946	19	47689	24
	other broadleaves	348265	12	367214	12	347385	9	632812	7	170629	12	1866305	5
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.
	total	6996753	7	8547074	7	3797218	7	3195132	8	1180177	15	23716355	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. 1196501. Birmensdorf, Swiss Federal Research Institute WSL

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