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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).

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, 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: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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
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	±%
n/a	n/a	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	.
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	158	100	0	.	0	.	23	100	178	70	45	100	414	50
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	4	100	68	100	2	71	0	.	17	100	21	73	413	45	68	71	592	34
	lower and upper montane	97	100	0	.	16	100	0	.	0	.	170	100	0	.	36	100	34	100	11	100	2	100	10	100	0	.	0	.	375	54
	submontane	17	100	0	.	85	72	87	85	143	81	0	.	0	.	0	.	52	100	0	.	0	.	0	.	0	.	0	.	384	42
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0
	total	114	86	0	.	101	63	87	85	143	81	170	100	13	77	104	74	246	69	11	100	19	90	54	55	590	38	113	58	1766	22
young growth/ thicket	n/a	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	.	22	81	48	60	39	100	109	47
	subalpine	0	.	0	.	0	.	0	.	0	.	145	100	140	66	0	.	23	89	6	100	1	100	63	55	584	46	30	92	991	32
	high-montane	3	100	0	.	0	.	0	.	0	.	249	53	98	93	72	100	376	48	39	95	28	100	92	58	988	43	579	53	2524	23
	lower and upper montane	347	44	66	100	14	207	221	87	314	51	155	76	875	43	496	33	177	58	39	74	79	68	0	.	191	59	57	90	3032	18
	submontane	286	44	246	50	231	57	466	48	330	46	0	.	47	100	29	74	136	100	0	.	0	.	0	.	0	.	0	.	1771	21
	hyperinsubric and colline	0	.	0	.	24	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	81	0	.	27	100	59	55
	total	636	31	313	45	269	51	687	43	644	34	549	41	1160	35	597	30	711	35	84	57	107	56	184	36	1811	28	732	43	8485	11
pole timber	n/a	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	.	145	46	311	31	178	40	634	21
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	38	100	149	68	0	.	506	41	974	33	342	45	2044	21
	high-montane	56	76	0	.	0	.	0	.	0	.	212	77	168	96	124	72	980	33	375	62	24	100	1685	22	1878	20	558	35	6060	12
	lower and upper montane	1871	25	186	54	525	39	1086	33	862	38	682	36	2106	23	1707	28	626	37	580	42	869	47	106	69	59	100	1867	20	13130	9
	submontane	799	32	1029	26	1474	31	1416	21	3515	15	0	.	159	100	0	.	181	64	0	.	0	.	239	56	33	100	0	.	8845	10
	hyperinsubric and colline	0	.	0	.	71	100	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	208	40	0	.	2247	19	2549	17
	total	2725	19	1214	23	2070	24	2502	18	4401	14	894	33	2468	22	1831	26	1825	23	1104	32	893	46	2888	16	3256	15	5191	12	33263	5
young timber	n/a	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	.	513	48	588	37	211	74	1312	28
	subalpine	0	.	0	.	0	.	0	.	0	.	166	100	830	55	0	.	302	58	53	100	193	72	458	45	568	50	320	50	2888	23
	high-montane	0	.	0	.	118	100	0	.	0	.	177	71	253	59	0	.	311	64	131	100	52	100	2473	28	2757	24	292	55	6564	16
	lower and upper montane	4199	19	767	40	256	61	1533	34	1623	30	1710	33	1576	31	2073	33	770	35	573	49	1146	38	385	51	489	61	2918	22	20017	9
	submontane	836	36	884	38	957	35	2875	23	3227	20	0	.	308	71	170	74	212	77	435	65	0	.	11	100	358	60	0	.	10273	11
	hyperinsubric and colline	71	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	267	51	0	.	3115	18	3453	17
	total	5106	17	1651	27	1331	29	4408	19	4850	16	2053	29	2967	24	2243	31	1595	26	1192	35	1391	33	4105	19	4760	18	6857	13	44507	6
medium timber	n/a	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	.	210	100	767	37	22	100	999	35
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	134	100	0	.	551	58	0	.	423	80	736	58	2587	27	529	55	4959	20
	high-montane	0	.	0	.	0	.	0	.	0	.	728	62	213	82	529	58	624	54	1044	59	0	.	2078	32	3570	22	2811	25	11597	13
	lower and upper montane	11354	12	1518	31	882	49	1297	45	3402	24	2389	28	3485	25	6719	19	1920	31	1186	44	1600	31	787	46	1335	39	1298	32	39172	7
	submontane	1726	30	2994	24	2395	30	3799	22	3106	20	0	.	532	72	627	54	96	100	0	.	0	.	0	.	108	100	0	.	15382	10
	hyperinsubric and colline	94	100	0	.	301	71	0	.	60	100	0	.	0	.	0	.	0	.	0	.	0	.	215	66	0	.	1506	32	2175	25
	total	13173	11	4512	18	3577	24	5096	20	6567	15	3118	26	4364	22	7876	17	3191	24	2230	36	2023	30	4027	22	8366	14	6166	16	74285	5
old timber	n/a	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	.	559	46	1021	35	81	100	1660	27
	subalpine	0	.	0	.	0	.	0	.	0	.	595	56	668	49	82	100	341	62	576	59	282	76	4303	21	5379	20	2059	28	14284	11
	high-montane	396	65	0	.	55	100	0	.	0	.	2417	33	1543	37	914	48	3651	29	778	48	1050	54	5283	21	8196	17	4623	21	28908	9
	lower and upper montane	14615	11	1666	39	4118	26	4568	24	6696	19	3957	23	14021	13	16335	12	3534	26	3280	31	2648	29	711	57	2187	27	2285	31	80622	5
	submontane	2804	28	3061	24	2170	30	8520	15	11190	12	122	100	1390	39	151	100	744	59	407	71	142	100	83	100	118	100	0	.	30902	7
	hyperinsubric and colline	0	.	0	.	160	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	100	0	.	1648	30	1951	28
	total	17815	10	4727	20	6503	19	13087	12	17887	10	7091	18	17623	11	17483	12	8270	17	5042	23	4122	24	11081	14	16901	11	10697	12	158328	3
mixed	n/a	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	.	1068	25	1591	26	763	28	3422	16
	subalpine	0	.	0	.	77	100	0	.	0	.	198	87	1175	36	75	100	2395	29	399	58	846	53	3876	22	5016	17	2154	23	16211	10
	high-montane	0	.	0	.	69	100	0	.	0	.	1027	41	1399	33	218	51	2173	27	636	43	271	62	4556	17	3693	19	2541	24	16584	9
	lower and upper montane	12815	10	422	50	334	60	1004	39	1177	35	3200	26	9201	13	4066	22	1891	31	870	46	1869	30	1235	37	1515	31	3536	39	43136	6
	submontane	2083	24	917	38	1261	41	2323	29	2234	22	0	.	965	39	1086	47	61	100	514	75	92	100	120	100	212	74	0	.	11868	12
	hyperinsubric and colline	0	.	0	.	0	0	0	.	86	100	0	.	0	.	0	.	0	.	0	.	0	.	353	49	113	100	2877	19	3428	17
	total	14898	9	1338	30	1742	32	3327	23	3497	18	4425	21	12740	11	5445	19	6520	17	2419	27	3079	24	11209	11	12139	10	11872	14	94649	4
total	n/a	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	.	2517	20	4327	15	1294	22	8138	10
	subalpine	0	.	0	.	77	100	0	.	0	.	1103	39	2991	24	157	71	3807	22	1183	36	1745	35	9965	13	15285	10	5480	15	41793	6
	high-montane	454	57	0	.	242	61	0	.	0	.	4811	21	3679	21	1925	29	8116	16	3003	27	1442	41	16188	10	21496	9	11473	11	72829	4
	lower and upper montane	45297	4	4625	19	6144	19	9709	15	14075	12	12264	12	31265	7	31432	8	8952	14	6540	19	8213	15	3234	23	5775	17	11961	14	199485	2
	submontane	8551	13	9130	12	8573	14	19486	9	23745	7	122	100	3401	23	2063	31	1482	35	1356	41	235	72	452	43	829	37	0	.	79426	4
	hyperinsubric and colline	165	71	0	.	556	49	0	.	169	63	0	.	0	.	0	.	0	.	0	.	0	.	1192	26	113	100	11420	8	13615	7
	total	54467	4	13755	10	15592	11	29195	7	37989	5	18300	10	41336	6	35578	7	22357	9	12082	13	11634	13	33548	6	47824	5	41627	4	415285	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. 1362739. Birmensdorf, Swiss Federal Research Institute WSL

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