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total number of stems NFI1 #243
Number of stems of all living and dead trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm according to the method used in NFI1. In NFI1, dead trees were only included if their wood was still usable. The total number of stems NFI1 is the sum of the number of stems and the number of deadwood stems NFI1.

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)

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 #434
Forest of which less than two-thirds is covered with shrubs that can be accessed on foot.

1.4-km grid #410
NFI's sampling grid with a mesh size of 1.4 km. The 1.4-km grid is the grid size covering all the previous terrestrial Inventories, which is why it is also called the 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
total number of stems NFI1
altitudinal vegetation belts (NaiS, 3 classes) · diameter at breast height (10 classes)
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern 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	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	90	100	0	.	90	100
	21-30 cm	0	.	0	.	0	.	0	.	20	100	0	.	20	100
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	110	100	0	.	110	100
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	44	100	3128	17	837	28	5419	11	9383	9	1963	17	20775	6
	21-30 cm	18	100	2280	16	567	30	4488	11	6010	9	1325	16	14687	6
	31-40 cm	46	100	1373	16	391	30	2364	11	3752	9	835	17	8761	6
	41-50 cm	25	100	629	17	211	28	1377	10	2085	9	582	15	4909	5
	51-60 cm	7	100	235	19	92	34	617	12	943	10	199	20	2093	6
	61-70 cm	0	.	137	25	44	35	207	20	265	15	92	25	745	10
	71-80 cm	0	.	37	40	20	45	110	23	116	20	47	31	329	13
	81-90 cm	0	.	4	100	16	50	27	38	40	32	24	41	111	19
	91-100 cm	0	.	4	100	4	100	12	58	12	58	4	100	36	33
	>100 cm	0	.	4	100	4	100	12	58	12	58	0	.	32	35
	total	140	100	7830	14	2188	23	14633	9	22618	7	5069	13	52478	4
montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	39683	4	35487	5	23828	6	13524	9	11844	8	14991	7	139356	2
	21-30 cm	24792	4	19318	5	15898	6	6459	9	7181	8	6642	7	80289	2
	31-40 cm	16484	4	13022	5	9364	6	3306	9	4523	8	3011	8	49710	2
	41-50 cm	8521	4	7626	4	5118	6	1617	9	2711	7	1462	8	27054	2
	51-60 cm	3162	5	3484	5	2192	7	894	12	1273	9	762	10	11768	3
	61-70 cm	817	8	983	8	630	11	202	18	467	12	281	16	3380	4
	71-80 cm	246	16	284	14	257	18	103	24	164	18	138	22	1192	7
	81-90 cm	41	32	82	22	65	28	33	35	36	33	31	42	288	12
	91-100 cm	23	41	20	45	24	41	4	100	21	52	13	57	105	20
	>100 cm	4	100	18	63	4	100	4	100	4	100	38	37	72	27
	total	93773	3	80324	3	57379	5	26146	7	28225	6	27368	6	313215	2
hyperinsubric, colline, submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	43689	4	3347	19	2770	23	3127	19	540	35	15406	8	68880	3
	21-30 cm	26133	4	1486	18	1510	19	960	21	311	35	4731	9	35131	3
	31-40 cm	16913	4	1053	17	667	24	304	23	216	38	1317	11	20469	3
	41-50 cm	9257	4	566	20	278	24	117	28	114	37	525	14	10857	3
	51-60 cm	3536	5	240	22	101	32	56	33	60	41	218	17	4210	4
	61-70 cm	933	8	67	34	65	41	16	50	4	100	58	28	1144	7
	71-80 cm	294	13	31	50	4	100	4	100	4	100	88	34	425	12
	81-90 cm	112	21	4	100	0	.	0	.	0	.	26	41	143	18
	91-100 cm	20	45	0	.	0	.	0	.	0	.	31	43	51	31
	>100 cm	9	72	0	.	0	.	0	.	0	.	63	32	71	30
	total	100895	3	6794	15	5396	18	4584	17	1249	30	22464	7	141381	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	83416	3	41962	4	27435	6	22070	6	21858	6	32360	5	229101	2
	21-30 cm	50942	2	23083	4	17976	5	11907	6	13522	6	12698	5	130128	2
	31-40 cm	33443	2	15447	4	10423	5	5974	6	8492	6	5162	6	78940	1
	41-50 cm	17802	2	8821	4	5608	5	3111	7	4911	5	2568	6	42820	2
	51-60 cm	6706	3	3958	5	2386	7	1567	8	2276	6	1179	7	18071	2
	61-70 cm	1751	6	1188	7	739	11	425	13	736	9	430	12	5269	3
	71-80 cm	540	10	352	13	281	16	218	16	284	13	273	16	1946	6
	81-90 cm	153	17	91	21	81	24	60	26	76	23	81	24	542	9
	91-100 cm	43	30	24	41	28	38	16	50	33	39	48	33	192	15
	>100 cm	12	59	22	54	7	71	16	50	16	50	101	24	175	18
	total	194808	1	94948	3	64963	4	45364	5	52202	4	54900	3	507185	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. 1329881. Birmensdorf, Swiss Federal Research Institute WSL

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