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growing stock (stemwood) #21
Stemwood volume of living trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm with bark.

development stage #20
Stage of stand development, defined by the dominant diameter at breast height (dbhdom = dbh of the 100 largest [thickest] trees per hectare). Young growth/thicket: dbhdom <12 cm, pole timber: dbhdom 12-30 cm, young timber: dbhdom 31-40 cm, medium timber : dbhdom 41-50 cm, old timber: dbhdom >50 cm, mixed: trees of different development stages, no development stage predominant or groups of different development stages covering < 500 m². Reference: Field Survey (MID 261: Entwicklungsstufe)

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).

canton #827
Regional demarcation with the cantons as a unit, with the two half-cantons, Basel-Land and Basel-Stadt, combined into one canton for statistical reasons.

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
growing stock (stemwood)
development stage · altitudinal vegetation belts (NaiS; 6 classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
development stage	altitudinal vegetation belts (NaiS; 6 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	subalpine	0	.	0	.	0	.	168	94	0	.	0	.	0	.	0	.	223	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100	0	.	0	.	414	50
	high-montane	0	.	0	.	0	.	2	71	0	.	0	.	0	.	17	100	422	44	0	.	4	100	0	.	0	.	0	.	68	100	0	.	0	.	0	.	0	.	59	81	0	.	0	.	21	73	0	.	0	.	592	34
	lower and upper montane	0	.	0	.	36	100	34	100	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	97	100	11	100	0	.	0	.	0	.	186	92	10	100	0	.	0	.	375	54
	submontane	0	.	0	.	0	.	73	100	0	.	34	100	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	140	83	0	.	0	.	104	71	0	.	0	.	3	100	384	42
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0
	total	0	.	0	.	36	100	277	64	0	.	34	100	0	.	19	90	645	35	17	100	4	100	0	.	0	.	0	.	68	100	0	.	111	88	11	100	140	83	59	81	0	.	289	64	54	55	0	.	3	100	1766	22
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	0	.	0	.	5	100	0	.	0	.	88	55
	subalpine	0	.	0	.	0	.	264	64	0	.	0	.	0	.	0	.	611	44	0	.	0	.	0	.	0	.	23	100	1	100	0	.	0	.	0	.	0	.	2	100	6	100	0	.	40	68	0	.	0	.	947	34
	high-montane	0	.	0	.	0	.	460	45	0	.	165	62	0	.	0	.	1508	35	0	.	0	.	3	100	0	.	7	100	28	100	0	.	0	.	72	100	0	.	8	54	37	100	91	82	91	59	0	0	0	.	2469	24
	lower and upper montane	0	.	0	0	133	72	844	44	0	.	155	76	0	.	46	100	191	59	35	73	414	57	49	100	0	.	14	77	127	58	66	100	24	94	155	57	117	100	56	92	25	100	254	57	0	.	78	71	241	50	3025	18
	submontane	310	45	0	.	0	.	592	44	74	68	132	80	0	.	0	.	0	.	63	77	130	70	52	100	0	.	0	.	29	74	13	100	33	100	0	.	0	.	0	.	0	.	164	63	0	.	0	.	178	70	1771	21
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	24	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	2	100	0	.	0	.	53	60
	total	310	45	0	0	133	72	2160	24	74	68	452	42	24	71	46	100	2354	25	98	56	544	46	104	69	0	.	44	60	184	44	80	85	57	70	227	50	117	100	131	53	69	67	509	38	137	44	78	71	420	41	8352	11
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	365	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	124	49	0	.	0	.	74	65	0	.	0	.	563	23
	subalpine	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	974	33	0	.	0	.	0	.	0	.	94	100	0	.	0	.	0	.	16	100	0	.	327	47	39	85	0	.	506	41	0	.	0	.	1994	21
	high-montane	0	.	0	.	0	.	818	36	0	.	0	.	0	.	0	.	2056	19	0	.	160	100	0	.	0	.	8	100	72	74	0	.	0	.	77	100	0	.	379	40	326	70	418	51	1685	22	0	.	0	.	5998	12
	lower and upper montane	22	100	0	.	266	88	1975	23	192	54	465	42	0	.	448	64	300	52	532	62	1435	31	456	45	187	76	118	67	819	35	46	100	495	42	977	36	147	74	1534	22	480	49	1140	27	106	69	120	71	468	57	12725	9
	submontane	1380	24	0	.	0	.	1007	24	580	38	528	54	0	.	0	.	33	100	151	72	390	46	146	51	0	.	159	100	52	100	273	58	118	69	0	.	784	34	0	.	0	.	1274	31	239	56	0	.	1722	22	8835	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	195	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2052	20	0	.	71	100	185	43	0	.	24	100	2526	18
	total	1402	23	0	.	266	88	3837	15	771	32	994	35	0	.	448	64	3923	14	682	51	1986	25	602	36	187	76	379	53	942	32	318	52	613	36	1069	34	931	31	4416	12	845	39	2903	19	2794	17	120	71	2213	21	32642	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	511	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	211	74	0	.	0	.	513	48	0	.	0	.	1235	29
	subalpine	0	.	0	.	0	.	275	58	0	.	166	100	0	.	0	.	471	57	0	.	20	100	0	.	200	100	519	77	193	72	0	.	0	.	0	.	0	.	294	54	53	100	118	100	439	46	0	.	0	.	2746	24
	high-montane	0	.	0	.	0	.	262	64	0	.	85	100	0	.	0	.	2757	24	0	.	76	100	0	.	64	100	113	100	52	100	0	.	0	.	0	.	0	.	292	55	131	100	259	71	2171	29	0	.	0	.	6262	16
	lower and upper montane	72	100	218	100	284	71	2872	22	439	52	1707	33	0	.	408	52	831	44	1161	39	818	44	280	81	434	78	431	49	1714	35	310	70	516	46	129	100	479	61	2576	24	492	55	1791	28	385	51	222	100	1386	36	19957	9
	submontane	1401	31	0	.	0	.	2146	27	162	100	533	49	0	.	0	.	358	60	16	405	704	49	0	.	0	.	153	100	170	74	578	45	360	51	89	100	540	46	0	.	346	77	1277	29	11	100	0	.	1430	31	10273	11
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	308	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2807	19	0	.	71	100	267	51	0	.	0	.	3453	17
	total	1473	30	218	100	284	71	5555	16	602	47	2491	26	0	.	408	52	5236	17	1177	39	1618	31	280	81	698	57	1216	40	2129	30	889	38	875	34	218	72	1019	37	6180	13	1022	40	3515	19	3784	19	222	100	2816	23	43925	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	755	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	210	100	0	.	0	.	966	36
	subalpine	0	.	0	.	0	.	588	57	0	.	0	.	0	.	97	100	2718	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	398	66	0	.	96	100	736	58	0	.	0	.	4634	20
	high-montane	0	.	0	.	0	.	565	60	0	.	583	74	0	.	0	.	5222	19	0	.	0	.	0	.	45	100	0	.	319	71	0	.	0	.	210	100	0	.	1157	37	1044	59	373	58	2078	32	0	.	0	.	11595	13
	lower and upper montane	254	75	812	59	1000	60	6443	18	710	48	2319	29	0	.	1079	38	1207	41	1549	32	983	39	1212	38	984	53	657	59	4219	23	555	51	2366	28	1451	35	97	100	1046	36	704	60	4639	19	787	46	333	71	2652	27	38057	7
	submontane	1954	27	0	.	0	.	2253	28	982	43	1247	46	0	.	0	.	108	100	439	62	1543	40	120	100	0	.	0	.	68	100	963	41	1243	34	401	72	794	38	0	.	0	.	1611	33	0	.	300	71	1357	30	15382	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	301	71	0	.	88	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1418	33	0	.	94	100	215	66	0	.	60	100	2175	25
	total	2208	25	812	59	1000	60	9849	14	1692	32	4150	24	301	71	1176	36	10098	13	1988	28	2526	29	1331	36	1029	51	657	59	4606	22	1518	32	3608	22	2062	30	891	36	4019	19	1748	43	6813	16	4027	22	632	50	4069	20	72809	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1102	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	559	46	0	.	0	.	1660	27
	subalpine	0	.	0	.	0	.	1048	42	0	.	0	.	0	.	0	.	6408	18	0	.	0	.	0	.	255	72	0	.	278	76	0	.	0	.	349	72	0	.	1030	37	228	100	301	61	4303	21	0	.	0	.	14199	11
	high-montane	0	.	0	.	0	.	3194	29	0	.	1112	42	0	.	859	63	10327	15	0	.	207	100	0	.	0	.	995	48	495	72	0	.	0	.	611	52	0	.	2492	28	669	54	2554	36	5283	21	0	.	0	.	28799	9
	lower and upper montane	969	44	2430	36	2831	33	16983	12	706	65	4718	24	0	.	1011	48	2666	26	3478	25	5796	21	3534	26	943	51	2034	40	10206	17	284	100	1669	33	4930	23	931	50	1806	35	786	52	6959	17	711	57	734	60	3332	28	80445	5
	submontane	4746	20	0	.	0	.	7711	16	1169	38	1003	46	0	.	0	.	118	100	1651	39	1207	38	414	76	0	.	0	.	536	60	1199	42	1535	35	189	100	4050	23	0	.	218	100	1919	33	83	100	0	.	2591	24	30339	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	160	94	0	.	73	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1576	32	0	.	0	.	142	100	0	.	0	.	1951	28
	total	5715	18	2430	36	2831	33	28936	9	1875	34	6833	19	160	94	1870	39	20694	10	5129	21	7209	18	3948	24	1198	43	3029	31	11514	15	1482	39	3204	24	6079	20	4982	20	6904	16	1901	33	11734	14	11081	14	734	60	5923	19	157392	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2077	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	266	48	0	.	0	.	1050	26	0	.	0	.	3393	16
	subalpine	0	.	0	.	0	.	3042	26	0	.	0	.	0	.	498	58	5229	17	0	.	0	.	0	.	0	.	444	60	424	83	0	.	0	.	2	100	0	.	1768	26	395	58	309	51	3854	23	0	.	0	.	15965	10
	high-montane	0	.	0	.	0	.	2569	26	0	.	247	73	0	.	210	75	4504	17	0	.	371	60	0	.	0	.	832	45	113	71	0	.	0	.	376	48	0	.	1667	31	426	54	467	54	4464	18	0	.	0	.	16246	9
	lower and upper montane	189	71	291	100	200	71	9639	13	88	100	1454	32	0	.	1014	43	1909	28	1944	33	3356	23	4812	17	227	87	877	43	4058	23	121	100	771	46	1032	36	327	63	3073	44	367	74	4812	19	1235	37	251	73	344	46	42391	7
	submontane	831	34	0	.	0	.	1485	32	541	45	514	62	0	.	0	.	212	74	928	40	1706	36	318	59	0	.	0	.	905	49	336	71	489	47	0	.	469	51	0	.	514	75	964	44	120	100	0	.	1440	32	11771	12
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	398	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	100	2592	20	0	.	0	.	321	53	0	.	0	.	3397	18
	total	1020	30	291	100	200	71	16735	10	629	41	2215	27	0	0	1721	31	14329	9	2872	26	5433	18	5130	17	227	87	2153	27	5499	20	457	58	1260	33	1409	30	882	37	9366	17	1701	34	6553	16	11046	12	251	73	1784	28	93163	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4854	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	640	33	0	.	0	.	2411	20	0	.	0	.	7906	11
	subalpine	0	.	0	.	0	.	5424	18	0	.	166	100	0	.	595	51	16634	10	0	.	20	100	0	.	454	60	1081	45	895	48	0	.	0	.	366	68	0	.	3818	18	720	46	825	35	9901	13	0	.	0	.	40899	6
	high-montane	0	.	0	.	0	.	7870	16	0	.	2192	31	0	.	1085	52	26797	8	0	.	818	43	3	100	109	72	1955	31	1146	39	0	.	0	.	1345	32	0	.	6054	16	2633	30	4161	24	15793	10	0	0	0	.	71961	5
	lower and upper montane	1505	33	3751	28	4750	24	38790	7	2134	29	10820	14	0	.	4009	21	7105	15	8698	15	12802	12	10343	12	2775	29	4130	24	21142	10	1381	34	5937	16	8685	15	2098	29	10090	16	2854	26	19781	9	3234	23	1737	33	8423	15	196975	2
	submontane	10622	12	0	.	0	.	15266	10	3509	20	3991	22	0	.	0	.	829	37	3265	24	5679	18	1050	38	0	.	312	70	1759	32	3362	22	3790	20	679	53	6777	16	0	.	1078	48	7313	15	452	43	300	71	8722	12	78755	4
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	485	54	0	.	1061	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	100	10472	9	0	.	236	58	1132	27	0	.	83	77	13555	7
	total	12128	11	3751	28	4750	24	67349	5	5643	16	17168	11	485	54	5689	18	57280	5	11963	12	19319	10	11396	11	3339	26	7478	17	24943	9	4744	18	9727	12	11076	13	8960	13	31074	6	7284	17	32316	7	32923	7	2036	30	17228	9	410050	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. 1343034. Birmensdorf, Swiss Federal Research Institute WSL

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