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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; 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).

tree state (living/dead) #1265
Classification of trees and shrubs ≥12 cm in diameter at breast height (dbh) as living or dead. Reference: Field Survey (MID 58: Bemerkungen, MID 1018: Baumzustand)

economic region #366
Division of Switzerland into 14 regions (2 in the Jura, 3 on the Plateau, 3 in the Pre-Alps, 5 in the Alps and 1 in the Southern Alps). The economic regions are a subdivision of the production regions according to economic-geographical 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.

NFI2—NFI5
change: total number of stems
altitudinal vegetation belts (NaiS; 6 classes) · tree state (living/dead)
regional demarcation: economic region
unit: n/ha
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
change 1993/95–2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	tree state (living/dead)	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
n/a	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	dead	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
upper subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	36	83	44	70	108	18	47	44
	dead	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	15	45	40	39	35	54	30	30
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	51	59	84	38	144	25	77	27
subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	-50	0	.	.	.	.	-2	1454	64	77	-22	64	-10	461	185	54	83	128	16	169	11	201	68	39	29	46
	dead	.	.	.	.	100	0	.	.	.	.	23	50	55	51	22	64	44	62	42	40	7	115	39	30	41	23	17	82	37	16
	total	.	.	.	.	50	0	.	.	.	.	21	160	119	55	0	.	34	94	227	45	91	118	55	49	52	43	84	26	65	20
high-montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	250	73	.	.	-102	55	.	.	.	.	-70	141	-30	210	-19	724	53	89	-70	130	154	42	10	233	32	87	16	167	17	85
	dead	-26	76	.	.	-10	171	.	.	.	.	65	41	45	35	17	49	60	24	17	98	1	1438	39	24	31	30	49	20	39	12
	total	224	86	.	.	-112	63	.	.	.	.	-5	2050	15	381	-2	6635	113	37	-53	179	155	42	49	51	63	47	65	42	56	27
lower and upper montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-56	25	-105	58	-132	44	-13	428	-113	27	-188	25	-52	47	-124	22	7	387	-18	178	-147	37	4	1387	-40	87	91	49	-65	14
	dead	31	13	23	42	10	106	26	28	16	42	55	19	44	15	43	20	32	39	40	39	43	48	46	31	17	95	38	28	35	7
	total	-25	57	-82	76	-121	49	13	438	-97	32	-133	34	-8	307	-81	35	39	73	22	157	-104	59	50	102	-23	161	129	39	-30	32
submontane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-110	38	-53	66	-68	76	-101	30	-81	31	180	0	-81	118	-190	62	-4	1452	99	135	-190	45	86	147	-113	93	.	.	-82	17
	dead	21	35	34	22	9	114	11	31	13	31	0	.	23	45	64	33	53	41	14	130	130	22	108	35	25	64	.	.	19	13
	total	-88	47	-19	190	-59	93	-90	34	-67	38	180	0	-59	162	-126	96	48	148	114	123	-60	95	195	70	-88	129	.	.	-63	23
hyperinsubric and colline	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-341	15	.	.	-507	34	.	.	-105	107	.	.	.	.	.	.	.	.	.	.	.	.	-6	1035	20	0	9	333	-19	147
	dead	1	6530	.	.	28	74	.	.	-3	357	.	.	.	.	.	.	.	.	.	.	.	.	54	24	0	.	91	15	80	13
	total	-340	4	.	.	-479	39	.	.	-108	113	.	.	.	.	.	.	.	.	.	.	.	.	48	138	20	0	100	30	60	47
total	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-63	22	-67	45	-113	34	-76	35	-90	22	-145	27	-43	49	-120	22	18	120	5	792	-80	56	14	105	15	99	42	42	-40	15
	dead	28	13	31	20	11	66	15	21	14	25	55	17	44	13	42	17	45	20	32	31	35	43	40	14	33	17	56	11	34	5
	total	-35	40	-36	87	-103	39	-61	44	-76	26	-90	44	1	3658	-78	36	64	32	36	103	-45	107	54	28	48	33	99	19	-6	100

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. 1405546. Birmensdorf, Swiss Federal Research Institute WSL

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