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total number of stems NFI1 #243
Number of stems of all living and dead trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm according to the method used in NFI1. In NFI1, dead trees were only included if their wood was still usable. The total number of stems NFI1 is the sum of the number of stems and the number of deadwood stems NFI1.

altitudinal vegetation belts (NaiS, 3 classes) #2668
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 three classes. The variable represents a simplification of the NaiS altititudinal vegetation belts in six classes (NAISHSTKOMB6KL) in which the class «hyperinsubric and colline» is mearged with «submontane» to form the class «hyperinsubric, colline, submontane», the class «lower and upper montane» with «high montane» to form the class «montane» and the class «subalpine» with «upper subalpine» to form the class «subalpine». 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).

diameter at breast height (10 classes) #1317
Diameter at breast height (dbh) of the trees and shrubs ≥12 cm dbh – in ten classes. Reference: Field Survey (MID 60: Brusthöhendurchmesser, MID 62: Umfang)

protection forest region #829
Demarcation of Switzerland used in NFI for protection forest analyses. The six protection forest regions were derived from the economic regions by combining individual regions according to natural and statistical criteria.

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.

NFI1
total number of stems NFI1
altitudinal vegetation belts (NaiS, 3 classes) · diameter at breast height (10 classes)
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
altitudinal vegetation belts (NaiS, 3 classes)	diameter at breast height (10 classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	84	100	3907	23	887	40	4846	16	7915	13	2108	21	19747	8
	21-30 cm	33	100	2965	20	473	41	3718	16	5091	13	1342	22	13624	8
	31-40 cm	87	100	1411	21	374	38	2135	16	3633	12	808	21	8448	8
	41-50 cm	47	100	742	22	243	36	1288	15	1757	11	574	20	4651	7
	51-60 cm	13	100	199	25	144	38	625	17	876	14	207	28	2065	9
	61-70 cm	0	.	100	32	57	40	238	28	256	20	85	39	736	13
	71-80 cm	0	.	13	71	21	58	142	29	122	26	22	58	320	17
	81-90 cm	0	.	9	100	14	71	22	58	42	41	7	100	94	28
	91-100 cm	0	.	0	.	7	100	14	71	14	71	0	.	36	45
	>100 cm	0	.	9	100	0	.	14	71	14	71	0	.	37	45
	total	264	100	9355	18	2221	31	13042	13	19721	10	5154	17	49757	6
montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	41625	6	35070	6	22141	9	12956	12	11563	11	12919	10	136274	3
	21-30 cm	24986	5	18171	6	15677	8	6173	12	7509	11	6043	11	78559	3
	31-40 cm	17021	5	12090	6	8582	8	3097	12	4425	11	2783	12	48000	3
	41-50 cm	8304	5	7251	6	4876	8	1554	13	2667	10	1479	12	26131	3
	51-60 cm	3055	7	3320	7	2180	10	622	16	1236	12	759	13	11170	4
	61-70 cm	750	12	871	11	613	16	168	25	363	18	322	20	3087	6
	71-80 cm	274	19	266	19	298	23	93	29	122	25	143	29	1196	9
	81-90 cm	28	50	74	32	75	35	22	58	25	50	25	59	249	18
	91-100 cm	42	41	6	100	29	50	7	100	8	100	15	71	107	26
	>100 cm	7	100	7	100	0	.	7	100	0	.	26	59	47	41
	total	96093	4	77126	5	54471	7	24698	10	27920	9	24513	8	304819	2
hyperinsubric, colline, submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	43094	6	3339	23	2873	29	2830	24	693	45	17210	10	70039	5
	21-30 cm	25557	5	1489	23	1625	25	949	28	420	41	4997	12	35038	4
	31-40 cm	16137	5	1101	22	679	30	270	30	313	40	1362	15	19861	4
	41-50 cm	9087	5	672	25	277	31	97	46	173	42	480	18	10786	5
	51-60 cm	3320	6	265	29	52	45	79	39	83	47	178	22	3977	6
	61-70 cm	877	11	66	39	74	56	22	58	7	100	60	40	1106	10
	71-80 cm	273	16	32	50	6	100	7	100	7	100	93	50	419	16
	81-90 cm	125	27	0	.	0	.	0	.	0	.	33	50	158	24
	91-100 cm	21	58	0	.	0	.	0	.	0	.	27	59	48	42
	>100 cm	0	.	0	.	0	.	0	.	0	.	52	49	52	49
	total	98490	4	6965	19	5586	24	4254	23	1697	36	24492	9	141484	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	84802	4	42316	6	25901	8	20632	9	20171	8	32237	6	226060	2
	21-30 cm	50577	3	22625	6	17775	7	10840	9	13020	8	12383	7	127221	2
	31-40 cm	33245	3	14602	6	9635	7	5501	9	8372	7	4953	8	76309	2
	41-50 cm	17438	3	8665	5	5397	7	2938	9	4597	7	2533	8	41568	2
	51-60 cm	6388	4	3784	7	2376	9	1326	11	2195	9	1144	10	17212	3
	61-70 cm	1627	8	1037	10	744	15	427	18	626	13	467	16	4929	5
	71-80 cm	547	12	312	17	325	21	242	21	252	17	257	24	1934	7
	81-90 cm	153	24	82	30	89	31	43	41	68	32	65	36	500	13
	91-100 cm	63	33	6	100	36	45	21	58	23	58	42	46	191	20
	>100 cm	7	100	16	71	0	.	21	58	14	71	78	38	137	26
	total	194847	2	93446	4	62277	6	41993	7	49338	6	54159	5	496061	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. 1329872. Birmensdorf, Swiss Federal Research Institute WSL

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