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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 NFI4/NFI5 #2282
Forest that was less than two-thirds covered with shrubs in both NFI4 (2009-2017) and NFI5 (2018-2026) and is 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: production region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
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	3016	29	25215	8	5687	15	33968	7
	21-30 cm	0	.	67	100	1875	27	13652	7	3429	16	19023	7
	31-40 cm	0	.	64	100	1252	29	8951	7	1780	15	12047	7
	41-50 cm	0	.	33	100	775	23	5646	7	945	17	7399	6
	51-60 cm	0	.	27	100	232	27	2962	8	635	17	3856	7
	61-70 cm	0	.	7	100	121	35	1297	11	172	23	1597	9
	71-80 cm	0	.	0	.	55	39	591	14	78	30	724	12
	81-90 cm	0	.	0	.	0	.	264	20	84	39	349	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	7341	24	58714	6	12817	12	79120	5
montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	24866	6	11877	11	36580	5	52756	5	17499	10	143578	3
	21-30 cm	14224	6	6302	11	23531	5	29111	5	9767	9	82936	3
	31-40 cm	9882	6	3768	11	14809	5	15599	5	5135	11	49193	3
	41-50 cm	6220	5	2837	9	8857	4	9467	5	2605	9	29985	3
	51-60 cm	3154	6	2011	10	5896	5	5522	5	1486	17	18069	3
	61-70 cm	1172	10	873	13	2577	7	2253	7	630	13	7505	4
	71-80 cm	454	14	405	17	978	10	1127	10	297	20	3262	6
	81-90 cm	90	33	150	24	316	17	274	16	143	27	973	9
	91-100 cm	44	41	43	46	44	41	155	22	58	38	343	15
	>100 cm	28	50	0	.	43	41	38	45	32	59	142	24
	total	60135	4	28268	8	93630	4	116302	4	37652	8	335987	2
hyperinsubric, colline, submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	12250	11	26180	6	2855	24	6408	17	17897	10	65590	5
	21-30 cm	5542	10	13430	6	1972	23	2852	16	9108	9	32904	4
	31-40 cm	3169	11	7492	6	1007	21	1299	18	3341	11	16309	5
	41-50 cm	2149	10	4828	6	620	23	654	18	1376	12	9626	5
	51-60 cm	1061	11	3145	6	305	23	371	22	518	17	5400	5
	61-70 cm	490	15	1324	9	79	34	101	39	185	20	2179	7
	71-80 cm	154	23	520	13	78	36	27	50	127	29	906	10
	81-90 cm	29	50	185	21	18	71	17	72	53	47	302	17
	91-100 cm	0	.	61	33	0	.	14	71	38	60	113	28
	>100 cm	0	.	58	44	0	.	0	.	50	51	108	33
	total	24843	8	57223	4	6934	19	11745	14	32692	8	133437	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	37116	5	38107	5	42450	5	84380	3	41082	6	243136	2
	21-30 cm	19766	4	19799	5	27379	5	45615	4	22304	5	134863	2
	31-40 cm	13051	5	11324	5	17068	4	25849	3	10256	6	77548	2
	41-50 cm	8369	4	7698	4	10251	4	15766	3	4926	6	47010	2
	51-60 cm	4215	5	5183	4	6433	4	8856	4	2639	10	27325	2
	61-70 cm	1661	8	2205	6	2777	7	3651	5	987	9	11281	3
	71-80 cm	608	12	926	10	1111	9	1745	8	502	14	4892	4
	81-90 cm	119	28	335	15	334	16	556	13	280	20	1624	7
	91-100 cm	44	41	103	27	53	38	256	17	103	32	558	12
	>100 cm	28	50	58	44	50	37	87	29	83	39	306	17
	total	84978	3	85738	3	107906	3	186761	2	83161	4	548544	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. 1318926. Birmensdorf, Swiss Federal Research Institute WSL

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