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total number of stems #3
Number of stems of all living and dead trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm. The total number of stems is the sum of the number of stems and the number of deadwood stems.

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)

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
total number of stems
altitudinal vegetation belts (NaiS, 3 classes) · diameter at breast height (10 classes)
regional demarcation: biogeographical region
unit: 1000 n
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
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	50	100	0	.	5521	19	6661	15	9875	14	2660	19	24767	8
	21-30 cm	67	100	0	.	3330	18	3596	15	5382	12	2403	19	14778	8
	31-40 cm	64	100	0	.	2465	18	2430	15	3846	11	1301	18	10105	7
	41-50 cm	33	100	0	.	1530	16	1560	16	2531	11	849	19	6503	7
	51-60 cm	27	100	0	.	617	18	845	16	1405	12	555	19	3448	7
	61-70 cm	7	100	0	.	303	21	467	19	546	16	143	25	1466	10
	71-80 cm	0	.	0	.	155	25	174	30	234	20	92	29	655	13
	81-90 cm	0	.	0	.	28	50	64	43	94	37	65	46	251	22
	91-100 cm	0	.	0	.	16	71	29	50	35	53	7	100	88	31
	>100 cm	0	.	0	.	14	71	7	100	21	58	7	100	50	38
	total	247	100	0	.	13979	15	15832	12	23970	10	8082	15	62110	6
montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	25944	6	12869	11	48087	5	12258	11	14140	10	15866	11	129164	3
	21-30 cm	14500	6	7448	10	28846	5	7042	13	8303	10	8922	10	75061	3
	31-40 cm	10108	6	4674	10	18608	5	3619	12	4566	10	4697	11	46272	3
	41-50 cm	6403	5	3329	9	11299	4	1902	12	2900	9	2411	9	28244	3
	51-60 cm	3299	6	2341	9	7251	5	1107	14	1835	10	1437	17	17270	3
	61-70 cm	1278	10	912	12	3263	7	316	20	726	13	623	13	7117	4
	71-80 cm	435	14	498	15	1444	9	163	28	282	18	284	21	3106	6
	81-90 cm	97	31	210	20	312	17	44	41	118	24	128	29	909	10
	91-100 cm	44	41	43	46	106	26	37	45	20	58	51	41	300	16
	>100 cm	28	50	7	100	43	41	7	100	8	100	32	59	127	26
	total	62135	4	32331	8	119258	4	26496	10	32900	8	34452	9	307571	2
hyperinsubric, colline, submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	8567	13	30224	6	4495	21	2900	28	726	37	16833	11	63744	5
	21-30 cm	4262	13	15216	6	2472	21	1073	27	491	40	8775	10	32289	5
	31-40 cm	2615	13	8350	6	1251	20	376	31	208	45	3226	11	16027	5
	41-50 cm	1558	12	5704	6	635	21	135	35	116	40	1349	12	9497	5
	51-60 cm	750	14	3539	6	352	22	59	66	87	42	511	17	5298	5
	61-70 cm	317	19	1503	8	117	35	0	.	6	100	191	20	2134	7
	71-80 cm	78	32	630	12	39	46	0	.	6	100	127	29	880	10
	81-90 cm	7	100	215	19	28	58	0	.	0	.	53	47	302	17
	91-100 cm	0	.	54	35	0	.	7	100	7	100	38	60	106	29
	>100 cm	0	.	38	54	0	.	0	.	0	.	44	57	82	40
	total	18154	10	65473	4	9388	17	4550	25	1647	36	31147	8	130359	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	34561	5	43092	5	58102	5	21819	8	24741	8	35359	6	217675	2
	21-30 cm	18828	5	22664	5	34648	4	11712	9	14176	7	20101	6	122129	2
	31-40 cm	12786	5	13025	5	22324	4	6424	9	8621	7	9224	7	72404	2
	41-50 cm	7994	4	9033	4	13464	4	3597	9	5547	7	4609	6	44244	2
	51-60 cm	4076	5	5880	5	8219	4	2011	10	3327	7	2503	11	26016	2
	61-70 cm	1602	8	2415	6	3683	6	783	14	1278	10	957	10	10717	3
	71-80 cm	514	13	1128	9	1638	8	337	21	521	13	503	14	4641	5
	81-90 cm	104	30	424	14	368	15	108	30	213	21	246	22	1462	8
	91-100 cm	44	41	97	28	122	24	73	31	63	37	96	33	494	13
	>100 cm	28	50	45	48	57	35	14	71	30	50	84	39	258	19
	total	80536	3	97803	3	142625	3	46878	7	58518	6	73681	5	500040	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. 1318913. Birmensdorf, Swiss Federal Research Institute WSL

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