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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.

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).

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)

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.

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
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (10 classes)
regional demarcation: production region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (10 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	342	21	40	48	382	20
	21-30 cm	0	.	0	.	0	.	715	20	32	70	747	19
	31-40 cm	0	.	0	.	0	.	1095	19	60	58	1155	18
	41-50 cm	0	.	0	.	0	.	685	20	51	51	736	19
	51-60 cm	0	.	0	.	0	.	628	26	0	.	628	26
	61-70 cm	0	.	0	.	0	.	376	31	0	.	376	31
	71-80 cm	0	.	0	.	0	.	208	35	0	.	208	35
	81-90 cm	0	.	0	.	0	.	122	51	0	.	122	51
	91-100 cm	0	.	0	.	0	.	34	100	0	.	34	100
	>100 cm	0	.	0	.	0	.	112	59	0	.	112	59
	total	0	.	0	.	0	.	4317	14	182	44	4500	14
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	7	100	230	33	1233	11	161	26	1630	10
	21-30 cm	0	.	8	100	586	27	3216	11	436	26	4246	10
	31-40 cm	0	.	23	100	696	28	5136	11	691	25	6546	9
	41-50 cm	0	.	34	100	516	26	5273	10	796	22	6620	9
	51-60 cm	0	.	8	100	260	36	3578	12	500	33	4347	11
	61-70 cm	0	.	0	.	169	41	1576	18	232	38	1977	15
	71-80 cm	0	.	0	.	24	100	924	21	99	59	1046	20
	81-90 cm	0	.	0	.	33	100	238	39	20	100	292	34
	91-100 cm	0	.	0	.	0	.	180	52	0	.	180	52
	>100 cm	0	.	0	.	65	100	0	0	0	.	65	100
	total	0	.	80	100	2579	21	21354	8	2936	19	26948	7
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	15	59	20	95	774	22	2911	9	394	17	4114	8
	21-30 cm	57	55	41	100	1583	18	6232	8	1047	19	8959	7
	31-40 cm	102	59	95	67	2302	17	9030	8	1645	18	13174	7
	41-50 cm	65	73	28	87	1825	17	8995	8	1650	18	12563	7
	51-60 cm	83	90	36	100	1368	22	6018	10	1263	19	8767	8
	61-70 cm	51	73	25	100	281	35	2546	15	918	28	3820	13
	71-80 cm	88	100	0	.	288	52	1326	19	432	34	2133	16
	81-90 cm	0	.	0	.	0	.	393	32	121	74	513	30
	91-100 cm	0	.	0	.	0	.	194	52	58	100	252	46
	>100 cm	0	.	0	.	0	.	37	100	248	59	284	53
	total	460	52	245	58	8422	14	37680	6	7774	13	54581	5
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	4141	7	1997	12	5010	6	2054	10	1075	16	14277	4
	21-30 cm	8585	6	4654	10	11117	6	4378	10	1185	17	29919	4
	31-40 cm	11375	6	8630	9	15719	6	5424	10	808	20	41956	4
	41-50 cm	9714	6	7281	10	16592	6	5657	9	722	19	39966	4
	51-60 cm	5446	9	3837	12	10720	7	4692	12	729	27	25424	5
	61-70 cm	1762	16	1423	19	4357	11	1586	18	213	32	9341	7
	71-80 cm	1053	23	490	36	1953	19	628	29	329	57	4454	12
	81-90 cm	128	59	43	100	653	31	172	48	44	100	1040	23
	91-100 cm	227	51	108	74	111	76	36	100	38	100	519	33
	>100 cm	68	100	0	.	0	.	0	.	0	.	68	100
	total	42498	4	28464	7	66232	4	24627	8	5142	13	166964	3
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	1836	11	4682	7	567	22	451	24	0	.	7536	6
	21-30 cm	4293	10	10066	6	1147	23	660	22	0	.	16166	5
	31-40 cm	5224	10	15759	5	1670	22	757	24	0	.	23410	5
	41-50 cm	4500	12	15582	6	1444	24	733	26	0	.	22260	5
	51-60 cm	2162	15	8877	8	665	33	683	26	0	.	12388	6
	61-70 cm	964	24	3249	12	520	39	132	47	0	.	4865	10
	71-80 cm	94	73	1471	17	228	50	68	78	0	.	1861	15
	81-90 cm	203	58	760	32	0	.	0	.	0	.	963	28
	91-100 cm	0	.	174	60	0	.	0	.	0	.	174	60
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.
	total	19276	8	60620	4	6241	19	3485	19	0	.	89623	4
hyperinsubric and colline	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	50	76	262	41	0	.	217	31	2156	10	2685	10
	21-30 cm	18	73	132	43	0	.	298	31	1727	12	2175	11
	31-40 cm	0	.	192	68	0	.	196	30	950	16	1338	16
	41-50 cm	5	100	187	55	0	.	128	60	540	19	860	19
	51-60 cm	0	.	68	75	0	.	82	61	284	23	434	22
	61-70 cm	0	.	0	.	0	.	18	100	129	40	147	37
	71-80 cm	0	.	0	.	0	.	19	100	254	47	273	45
	81-90 cm	0	.	0	.	0	.	0	.	118	60	118	60
	91-100 cm	0	.	0	.	0	.	0	.	80	61	80	61
	>100 cm	0	.	0	.	0	.	0	.	364	58	364	58
	total	72	73	841	39	0	.	959	26	6601	9	8473	9
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	6042	5	6969	5	6581	5	7208	5	3825	7	30625	2
	21-30 cm	12951	5	14901	4	14434	5	15498	4	4427	7	62212	2
	31-40 cm	16701	4	24699	4	20387	5	21637	4	4153	9	87578	2
	41-50 cm	14283	5	23113	4	20378	5	21473	4	3758	9	83004	2
	51-60 cm	7691	8	12826	6	13013	6	15681	5	2777	12	51988	3
	61-70 cm	2776	13	4697	10	5326	10	6234	9	1492	19	20526	5
	71-80 cm	1236	22	1961	15	2492	17	3173	12	1114	24	9975	7
	81-90 cm	331	42	803	30	686	30	925	20	302	41	3048	14
	91-100 cm	227	51	282	47	111	76	444	33	175	48	1239	21
	>100 cm	68	100	0	.	65	100	149	51	611	42	893	32
	total	62306	3	90251	3	83474	3	92422	3	22635	5	351089	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. 1321119. Birmensdorf, Swiss Federal Research Institute WSL

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