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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)

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 #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: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		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, 3 classes)	diameter at breast height (10 classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	90	100	0	.	90	100
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	20	100
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	110	100	0	.	110	100
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	44	100	0	.	0	.	232	39	1577	27	236	71	1320	24	201	42	400	37	5419	11	9383	9	1963	17	20775	6
	21-30 cm	0	.	0	.	18	100	0	.	0	.	217	40	903	26	94	82	1159	24	191	43	282	46	4488	11	6010	9	1325	16	14687	6
	31-40 cm	0	.	0	.	46	100	0	.	0	.	201	39	454	26	23	71	718	23	156	49	212	41	2364	11	3752	9	835	17	8761	6
	41-50 cm	0	.	0	.	25	100	0	.	0	.	111	39	189	28	4	100	330	25	68	48	140	35	1377	10	2085	9	582	15	4909	5
	51-60 cm	0	.	0	.	7	100	0	.	0	.	64	43	53	35	12	58	118	26	52	53	28	47	617	12	943	10	199	20	2093	6
	61-70 cm	0	.	0	.	0	.	0	.	0	.	27	51	26	41	4	100	84	34	20	53	20	53	207	20	265	15	92	25	745	10
	71-80 cm	0	.	0	.	0	.	0	.	0	.	4	100	13	74	0	.	20	53	8	71	12	58	110	23	116	20	47	31	329	13
	81-90 cm	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	16	50	27	38	40	32	24	41	111	19
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	4	100	12	58	12	58	4	100	36	33
	>100 cm	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	4	100	12	58	12	58	0	.	32	35
	total	0	.	0	.	140	100	0	.	0	.	864	34	3214	23	373	69	3752	20	696	36	1118	32	14633	9	22618	7	5069	13	52478	4
montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	25118	5	2746	19	2967	17	3475	16	5377	13	10123	10	17549	6	13566	8	7815	10	4795	14	5467	14	13524	9	11844	8	14991	7	139356	2
	21-30 cm	15529	5	1572	17	1924	16	2098	15	3670	11	5395	10	9877	6	9688	8	4046	10	2755	14	3456	13	6459	9	7181	8	6642	7	80289	2
	31-40 cm	9497	5	1066	16	1659	16	1780	14	2482	11	3357	10	6839	6	5802	7	2826	10	1557	14	2005	12	3306	9	4523	8	3011	8	49710	2
	41-50 cm	4774	4	504	17	837	14	1048	14	1358	11	1782	10	4138	6	3280	7	1705	10	774	15	1063	13	1617	9	2711	7	1462	8	27054	2
	51-60 cm	1813	7	122	22	317	18	429	16	481	15	720	12	1887	7	1459	9	877	11	320	18	414	17	894	12	1273	9	762	10	11768	3
	61-70 cm	494	10	27	43	89	29	96	25	112	23	227	18	556	11	373	14	200	18	110	31	147	26	202	18	467	12	281	16	3380	4
	71-80 cm	176	19	4	100	24	53	23	45	20	45	28	43	162	18	172	22	94	26	35	48	49	35	103	24	164	18	138	22	1192	7
	81-90 cm	24	41	0	.	0	.	8	71	8	71	0	.	62	26	21	45	21	45	17	50	28	47	33	35	36	33	31	42	288	12
	91-100 cm	16	50	0	.	4	100	0	.	4	100	12	58	8	71	4	100	0	.	8	71	12	58	4	100	21	52	13	57	105	20
	>100 cm	4	100	0	.	0	.	0	.	0	.	0	.	4	100	4	100	14	77	0	.	0	.	4	100	4	100	38	37	72	27
	total	57445	4	6040	15	7820	12	8957	12	13511	9	21643	8	41084	5	34368	6	17597	8	10372	12	12639	11	26146	7	28225	6	27368	6	313215	2
hyperinsubric, colline, submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	6243	12	5655	12	7199	12	9394	10	15198	7	164	71	2128	25	1756	30	1055	30	781	42	234	59	3127	19	540	35	15406	8	68880	3
	21-30 cm	4518	10	3622	11	4137	11	5796	9	8061	7	43	78	892	23	857	25	551	31	455	38	198	51	960	21	311	35	4731	9	35131	3
	31-40 cm	2258	11	2265	11	2517	11	3805	8	6068	7	108	54	682	22	455	30	263	30	144	48	69	53	304	23	216	38	1317	11	20469	3
	41-50 cm	1106	12	1036	12	1090	12	2477	8	3548	7	64	55	402	25	201	28	100	34	32	62	45	59	117	28	114	37	525	14	10857	3
	51-60 cm	344	17	419	14	349	15	1000	10	1425	8	20	59	157	29	76	38	62	38	13	72	11	74	56	33	60	41	218	17	4210	4
	61-70 cm	73	30	141	20	140	24	257	16	323	13	7	100	46	41	48	51	14	78	5	100	12	75	16	50	4	100	58	28	1144	7
	71-80 cm	11	75	20	45	36	33	107	23	120	22	0	.	31	50	0	.	0	.	4	100	0	.	4	100	4	100	88	34	425	12
	81-90 cm	0	.	16	50	17	50	46	35	33	35	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	26	41	143	18
	91-100 cm	0	.	0	.	4	100	4	100	12	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	43	51	31
	>100 cm	0	.	5	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	32	71	30
	total	14552	9	13178	9	15487	9	22886	7	34792	5	406	60	4343	20	3394	23	2045	27	1433	36	569	48	4584	17	1249	30	22464	7	141381	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	31361	4	8401	10	10210	10	12869	8	20575	6	10518	10	21254	6	15557	8	10190	9	5777	13	6101	13	22070	6	21858	6	32360	5	229101	2
	21-30 cm	20047	4	5193	9	6078	9	7894	7	11730	6	5655	10	11672	6	10639	7	5756	9	3401	12	3936	12	11907	6	13522	6	12698	5	130128	2
	31-40 cm	11755	4	3331	9	4222	9	5585	7	8550	5	3666	10	7975	6	6280	7	3807	9	1857	13	2286	12	5974	6	8492	6	5162	6	78940	1
	41-50 cm	5880	4	1540	10	1952	9	3525	7	4906	5	1957	10	4729	6	3486	7	2135	9	874	14	1248	12	3111	7	4911	5	2568	6	42820	2
	51-60 cm	2157	6	541	12	673	12	1429	8	1906	7	804	12	2097	7	1547	8	1057	10	385	17	453	15	1567	8	2276	6	1179	7	18071	2
	61-70 cm	566	10	168	18	228	18	353	13	435	11	261	17	629	10	425	13	298	16	135	26	179	23	425	13	736	9	430	12	5269	3
	71-80 cm	187	19	24	41	59	29	129	20	140	20	32	40	206	17	172	22	113	23	47	39	61	30	218	16	284	13	273	16	1946	6
	81-90 cm	24	41	16	50	17	50	55	32	41	32	4	100	66	25	21	45	21	45	17	50	44	35	60	26	76	23	81	24	542	9
	91-100 cm	16	50	0	.	8	71	4	100	16	50	12	58	8	71	4	100	4	100	8	71	16	50	16	50	33	39	48	33	192	15
	>100 cm	4	100	5	100	0	.	0	.	4	100	4	100	4	100	4	100	14	77	0	.	4	100	16	50	16	50	101	24	175	18
	total	71996	3	19219	7	23447	7	31843	6	48303	4	22913	8	48641	4	38136	6	23394	7	12501	11	14326	10	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. 1329883. Birmensdorf, Swiss Federal Research Institute WSL

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