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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)

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 #434
Forest of which less than two-thirds is covered with shrubs that can be accessed 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: production region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		production region
		Jura		Plateau		Pre-Alps		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	±%
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	.
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	50	100	3191	28	26448	7	5927	15	35616	7
	21-30 cm	0	.	67	100	1981	26	14326	7	3497	15	19871	7
	31-40 cm	0	.	64	100	1267	29	9222	7	1780	15	12332	6
	41-50 cm	0	.	33	100	782	23	5805	7	951	17	7572	6
	51-60 cm	0	.	27	100	232	27	2991	8	635	17	3885	7
	61-70 cm	0	.	7	100	128	33	1297	11	172	23	1604	9
	71-80 cm	0	.	0	.	62	36	599	14	78	30	739	12
	81-90 cm	0	.	0	.	0	.	272	20	91	37	362	18
	91-100 cm	0	.	0	.	9	100	86	31	7	100	102	28
	>100 cm	0	.	0	.	7	100	50	38	0	.	57	35
	total	0	.	247	100	7658	23	61095	6	13139	12	82139	5
montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	24866	6	12155	11	36842	5	54245	5	17994	9	146102	3
	21-30 cm	14224	6	6335	11	23712	5	29752	5	9926	9	83950	3
	31-40 cm	9899	6	3811	11	14930	5	16125	5	5188	10	49954	3
	41-50 cm	6259	5	2959	9	9012	4	9659	5	2646	9	30535	2
	51-60 cm	3178	6	2045	10	5976	5	5599	5	1501	16	18299	3
	61-70 cm	1172	10	873	13	2583	7	2306	7	637	13	7572	4
	71-80 cm	454	14	405	17	978	10	1158	10	312	19	3308	6
	81-90 cm	98	31	150	24	325	16	274	16	143	27	990	9
	91-100 cm	44	41	43	46	44	41	163	21	58	38	350	15
	>100 cm	28	50	0	.	52	38	38	45	32	59	150	23
	total	60222	4	28778	8	94454	4	119319	4	38437	8	341209	2
hyperinsubric, colline, submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	12281	11	26302	6	2855	24	6550	17	17897	10	65884	5
	21-30 cm	5542	10	13483	6	1972	23	2972	16	9108	9	33077	4
	31-40 cm	3169	11	7528	6	1007	21	1317	18	3341	11	16362	5
	41-50 cm	2195	10	4840	6	620	23	654	18	1376	12	9684	4
	51-60 cm	1061	11	3187	6	305	23	371	22	518	17	5442	5
	61-70 cm	490	15	1346	8	79	34	101	39	185	20	2200	7
	71-80 cm	154	23	532	13	78	36	27	50	127	29	918	10
	81-90 cm	29	50	185	21	18	71	17	72	53	47	302	17
	91-100 cm	0	.	73	32	0	.	14	71	38	60	125	27
	>100 cm	11	100	58	44	0	.	0	.	50	51	120	32
	total	24931	8	57532	4	6934	19	12024	14	32692	8	134114	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	37147	4	38507	5	42887	5	87242	3	41817	6	247602	2
	21-30 cm	19766	4	19885	5	27665	5	47050	3	22531	5	136898	2
	31-40 cm	13068	5	11402	5	17203	4	26664	3	10309	6	78647	2
	41-50 cm	8454	4	7832	4	10414	4	16118	3	4974	6	47791	2
	51-60 cm	4239	5	5259	4	6512	4	8962	4	2654	10	27626	2
	61-70 cm	1661	8	2226	6	2790	7	3705	5	994	9	11375	3
	71-80 cm	608	12	938	10	1118	9	1784	7	517	14	4965	4
	81-90 cm	127	27	335	15	343	16	563	12	286	20	1654	7
	91-100 cm	44	41	116	26	53	38	263	17	103	32	577	12
	>100 cm	40	46	58	44	59	35	87	29	83	39	327	17
	total	85153	3	86557	3	109045	3	192439	2	84268	4	557462	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. 1318941. Birmensdorf, Swiss Federal Research Institute WSL

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