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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).

main tree species #90
Type of trees and shrubs ≥12 cm in diameter at breast height (dbh) with the ten most common species or species groups in Switzerland ('main tree species') categorised, and the classes 'other conifers' and 'other broadleaves' for the remaining species. 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 50: Baumart)

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.

NFI5
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	main tree species	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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	157	43	227	50	122	73	506	31
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	57	87	237	38	66	100	361	34
	larch	0	.	0	.	0	.	1240	26	1930	22	245	46	3415	16
	Arolla pine	0	.	0	.	0	.	712	28	1263	22	8	100	1983	17
	other conifers	0	.	0	.	0	.	0	.	16	60	0	.	16	60
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	2	100	2	100	2	100	6	59
	indeterminable	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	total	0	.	0	.	0	.	2167	22	3676	17	443	40	6286	13
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	77	100	6216	17	2252	29	5395	16	10354	12	2800	22	27094	7
	fir	0	.	14	77	0	.	0	.	0	.	0	.	14	77
	pine	0	.	273	67	0	.	72	57	147	34	0	.	492	39
	larch	0	.	49	100	1	100	3570	22	2278	21	1939	20	7837	13
	Arolla pine	0	.	0	.	0	.	162	42	234	48	0	.	397	33
	other conifers	0	.	1	100	0	.	8	82	19	53	0	.	28	43
	beech	0	.	0	.	0	.	0	.	0	.	24	100	24	100
	maple	0	.	24	91	0	.	0	.	0	.	26	100	50	68
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	43	68	8	64	80	45	8	54	10	88	150	32
	indeterminable	0	.	0	.	0	.	0	0	0	0	0	.	0	0
	total	77	100	6620	16	2261	29	9288	14	13041	11	4799	17	36086	7
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	609	47	11668	12	5119	20	7893	14	14362	11	6377	15	46029	6
	fir	0	.	2379	26	177	63	1036	32	638	37	934	36	5164	16
	pine	0	.	2	100	226	61	1241	25	1896	23	72	54	3437	16
	larch	0	.	184	50	51	100	3463	18	1645	22	2357	22	7700	12
	Arolla pine	0	.	12	100	0	.	0	.	1	100	0	.	14	90
	other conifers	0	.	0	.	7	91	1	100	0	.	0	.	8	85
	beech	0	.	0	.	0	.	22	97	45	72	165	55	232	42
	maple	43	66	287	30	6	63	117	42	168	45	84	56	705	19
	ash	0	.	8	85	0	.	116	49	149	62	18	100	291	38
	oak	0	.	0	.	0	.	9	100	0	.	0	.	9	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	36	72	36	72
	other broadleaves	2	100	87	31	54	45	470	23	675	30	392	56	1681	19
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	655	45	14628	12	5641	19	14368	11	19580	9	10435	12	65306	5
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	24352	6	20257	8	19687	9	858	39	2697	24	2093	56	69944	4
	fir	20841	7	14084	9	8916	12	571	47	633	35	1038	36	46084	5
	pine	1817	18	170	61	121	57	229	59	213	56	0	.	2551	16
	larch	331	34	55	66	232	48	95	56	179	51	852	29	1744	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	1010	43	101	81	34	41	4	100	0	.	0	.	1150	39
	beech	20359	6	10055	10	8531	11	734	35	1129	30	5344	13	46152	4
	maple	4106	9	2214	12	2617	13	200	48	92	44	136	50	9365	6
	ash	2723	15	1762	24	1887	19	171	60	20	89	107	59	6668	10
	oak	504	26	129	46	19	100	32	65	64	100	31	61	778	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	48	92	48	92
	other broadleaves	1485	17	778	20	708	20	103	54	54	50	1115	24	4242	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	77528	4	49604	5	42752	7	2998	24	5081	18	10762	15	188726	3
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15718	8	764	30	470	48	0	.	180	67	0	.	17133	7
	fir	10486	10	987	34	237	74	0	.	125	74	0	.	11835	10
	pine	2510	15	330	76	46	100	0	.	116	57	0	.	3001	15
	larch	898	21	152	68	82	71	0	.	89	74	0	.	1221	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	893	37	0	.	-15	138	0	.	0	.	0	.	879	38
	beech	21259	6	1723	30	1370	27	119	81	117	57	0	.	24587	6
	maple	2816	11	336	39	257	52	50	86	82	59	0	.	3541	10
	ash	5483	12	128	44	556	38	24	60	50	85	0	.	6242	11
	oak	4608	14	92	69	154	50	33	100	0	.	0	.	4887	13
	sweet chestnut	0	.	6	100	0	.	26	100	5	100	0	.	36	74
	other broadleaves	3380	12	488	60	279	42	81	63	65	63	0	.	4292	12
	indeterminable	0	0	0	0	0	.	0	.	0	.	0	.	0	0
	total	68052	4	5005	19	3436	24	332	47	829	37	0	.	77654	4
hyperinsubric and colline	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	15662	0	.	0	.	167	58	0	.	214	43	381	35
	fir	22	83	0	.	0	.	15	65	0	.	34	100	70	56
	pine	100	74	0	.	0	.	291	40	0	.	0	.	391	35
	larch	0	.	0	.	0	.	181	74	9	100	5	100	196	68
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	1	100	0	.	82	98	83	98
	beech	53	100	0	.	0	.	11	100	0	.	862	27	926	26
	maple	0	.	0	.	0	.	9	71	0	.	90	62	99	57
	ash	19	64	0	.	0	.	31	50	0	.	708	36	758	34
	oak	641	40	0	.	0	.	156	51	85	100	908	22	1790	19
	sweet chestnut	0	.	0	.	0	.	71	62	18	100	5246	13	5336	13
	other broadleaves	46	63	0	.	0	.	170	46	0	.	2782	15	2998	14
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	0
	total	881	36	0	.	0	.	1104	28	113	100	10932	8	13029	8
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	40757	5	38905	6	27528	8	14470	10	27821	7	11606	13	161087	3
	fir	31349	5	17464	8	9330	12	1621	26	1397	24	2006	24	63168	4
	pine	4427	11	775	42	393	41	1891	19	2609	18	138	56	10232	8
	larch	1230	18	440	34	365	37	8549	12	6131	12	5398	12	22114	6
	Arolla pine	0	.	12	100	0	.	874	24	1499	20	8	100	2393	15
	other conifers	1904	29	102	80	27	95	13	58	35	40	82	98	2163	26
	beech	41671	4	11778	9	9901	10	886	31	1290	27	6395	12	71921	3
	maple	6965	7	2862	11	2880	13	376	31	342	28	336	30	13760	5
	ash	8225	9	1898	22	2444	17	342	35	219	47	832	32	13959	7
	oak	5753	12	220	39	173	46	231	38	149	71	939	21	7465	10
	sweet chestnut	0	.	6	100	0	.	97	52	23	82	5330	13	5456	12
	other broadleaves	4913	9	1396	24	1050	17	906	17	805	26	4300	12	13369	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	147193	2	75857	4	54090	6	30257	7	42320	6	37372	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. 1372334. Birmensdorf, Swiss Federal Research Institute WSL

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