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growing stock (stemwood) #21
Stemwood volume of living trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm with bark.

development stage #20
Stage of stand development, defined by the dominant diameter at breast height (dbhdom = dbh of the 100 largest [thickest] trees per hectare). Young growth/thicket: dbhdom <12 cm, pole timber: dbhdom 12-30 cm, young timber: dbhdom 31-40 cm, medium timber : dbhdom 41-50 cm, old timber: dbhdom >50 cm, mixed: trees of different development stages, no development stage predominant or groups of different development stages covering < 500 m². Reference: Field Survey (MID 261: Entwicklungsstufe)

altitudinal vegetation belts (NaiS; 6 classes) #2633
Altitudinal vegetation belts in the system used in the guidelines for monitoring the sustainability and performance of protection forests (NaiS; Frehner et al. 2005), reduced to six classes. The variable represents a simplification of the NaiS altitudinal vegetation belts with ten classes (NAISHSTKOMB) in which the classes «hyperinsubric» and «colline» are merged with «colline with beech» to form the class «hyperinsubric and colline» and the class «lower montane» with «upper montane» and «lower/upper montane» to form the class «lower and upper montane». The information is based on the altitudinal vegetation belts determined by experts (accessible forest sample plots of NFI4 on the 1.4-km network; Arge Frehner et al. 2020), as well as on the altitudinal vegetation belts modelled for the period 1981-2010 (other sample plots; Zischg et al. 2021).

biogeographical region #2586
Demarcation of Switzerland into six regions with similar flora and fauna. The six regions correspond to the basic categories in the publication «The Biogeographical Regions of Switzerland», which was published by FOEN in 2022.

accessible forest without shrub forest NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was 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
growing stock (stemwood)
development stage · altitudinal vegetation belts (NaiS; 6 classes)
regional demarcation: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
development stage	altitudinal vegetation belts (NaiS; 6 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	subalpine	0	.	0	.	168	94	23	100	178	70	45	100	414	50
	high-montane	0	.	0	.	74	92	8	100	377	48	103	58	562	36
	lower and upper montane	97	100	16	100	262	68	0	.	0	.	0	.	375	54
	submontane	17	100	315	48	52	100	0	.	0	.	0	.	384	42
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	0	0	0
	total	114	86	331	46	556	45	31	79	555	39	149	50	1736	22
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	subalpine	0	.	0	.	274	62	25	100	461	51	0	.	761	38
	high-montane	3	100	0	.	760	32	91	59	982	43	523	58	2359	25
	lower and upper montane	439	38	640	41	1618	27	0	.	123	74	51	100	2871	19
	submontane	331	39	1215	26	201	72	0	.	0	.	0	.	1747	21
	hyperinsubric and colline	0	.	24	71	0	.	0	.	0	.	27	100	51	62
	total	773	27	1879	22	2855	19	116	51	1565	32	601	51	7789	12
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	55	83	156	47	58	71	270	36
	subalpine	0	.	0	.	0	.	281	60	459	44	251	59	991	30
	high-montane	56	76	0	.	1648	27	1174	27	1339	25	479	39	4696	14
	lower and upper montane	2269	22	3215	20	4678	15	35	71	0	.	1527	23	11725	10
	submontane	1141	25	7001	11	607	41	60	100	33	100	0	.	8842	10
	hyperinsubric and colline	0	.	95	79	0	.	183	43	0	.	2068	20	2345	19
	total	3466	16	10311	9	6934	13	1789	21	1987	20	4383	13	28869	6
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	513	48	420	44	74	100	1007	31
	subalpine	0	.	0	.	1066	45	424	48	389	63	320	50	2199	27
	high-montane	118	100	0	.	677	40	1878	31	2335	27	424	48	5432	17
	lower and upper montane	4843	17	3498	21	6702	16	288	60	737	51	2545	24	18614	9
	submontane	1208	34	7639	13	1026	38	0	.	358	60	0	.	10231	11
	hyperinsubric and colline	0	.	71	100	0	.	267	51	0	.	2963	19	3301	18
	total	6169	15	11209	11	9472	13	3370	21	4238	19	6327	13	40784	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	210	100	648	41	0	.	859	39
	subalpine	0	.	0	.	685	51	637	65	2503	28	529	55	4354	21
	high-montane	0	.	0	.	2809	31	2078	32	3337	23	2635	26	10858	14
	lower and upper montane	13015	11	6400	19	15725	11	355	71	1135	43	1118	34	37748	7
	submontane	3494	21	11233	12	547	57	0	.	108	100	0	.	15382	10
	hyperinsubric and colline	0	.	454	53	0	.	215	66	0	.	1460	33	2129	26
	total	16508	9	18087	10	19765	10	3496	24	7731	15	5742	16	71329	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	559	46	1021	35	0	.	1580	28
	subalpine	0	.	0	.	2750	26	3406	23	5268	20	2243	28	13668	12
	high-montane	409	64	0	.	10400	16	4240	24	7764	17	4598	21	27411	9
	lower and upper montane	17391	10	18301	12	36881	8	110	100	2681	26	2285	31	77648	5
	submontane	3787	22	23565	8	2127	32	0	.	118	100	0	.	29598	7
	hyperinsubric and colline	0	.	151	100	0	.	142	100	0	.	1648	30	1941	28
	total	21587	9	42016	6	52159	6	8457	15	16852	11	10774	12	151846	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	830	29	1274	29	468	47	2572	19
	subalpine	77	100	0	.	4275	21	3617	24	3892	19	1838	26	13699	11
	high-montane	69	100	0	.	4625	18	4040	19	3162	21	2091	27	13989	10
	lower and upper montane	12979	10	3611	22	17418	10	739	41	1762	28	3236	42	39745	7
	submontane	2261	23	7059	15	1762	36	0	.	387	61	0	.	11470	12
	hyperinsubric and colline	0	.	86	100	0	.	297	56	0	.	2879	19	3262	18
	total	15386	9	10756	12	28081	8	9523	13	10478	11	10513	15	84736	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	2167	22	3519	17	600	39	6286	13
	subalpine	77	100	0	.	9218	14	8414	15	13150	11	5227	16	36086	7
	high-montane	655	45	0	.	20994	10	13509	11	19295	9	10854	12	65306	5
	lower and upper montane	51033	4	35681	7	83285	4	1528	29	6438	16	10762	15	188726	3
	submontane	12239	10	58026	4	6324	17	60	100	1005	35	0	.	77654	4
	hyperinsubric and colline	0	.	881	36	0	.	1104	28	0	.	11045	8	13029	8
	total	64003	3	94588	3	119820	3	26782	7	43407	6	38488	5	387089	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. 1343030. Birmensdorf, Swiss Federal Research Institute WSL

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