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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).

biogeographical region #2586
Demarcation of Switzerland into six regions with similar flora and fauna. The six regions correspond to the basic categories in the publication «The Biogeographical Regions of Switzerland», which was published by FOEN in 2022.

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: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-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	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	subalpine	0	.	0	.	168	94	23	100	178	70	45	100	414	50
	high-montane	0	.	0	.	90	77	8	100	377	48	117	53	592	34
	lower and upper montane	97	100	16	100	262	68	0	.	0	.	0	.	375	54
	submontane	17	100	315	48	52	100	0	.	0	.	0	.	384	42
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	0	0	0
	total	114	86	331	46	573	44	31	79	555	39	162	47	1766	22
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	22	81	48	60	39	100	109	47
	subalpine	0	.	0	.	319	54	52	64	590	45	30	92	991	32
	high-montane	3	100	0	.	862	30	92	58	988	43	579	53	2524	23
	lower and upper montane	439	38	640	41	1700	26	0	.	197	58	57	90	3032	18
	submontane	350	38	1219	26	201	72	0	.	0	.	0	.	1771	21
	hyperinsubric and colline	0	.	24	71	0	.	8	81	0	.	27	100	59	55
	total	792	27	1883	22	3081	18	173	38	1824	28	732	43	8485	11
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	145	46	311	31	178	40	634	21
	subalpine	0	.	0	.	190	57	436	45	979	33	440	39	2044	21
	high-montane	56	76	0	.	1974	24	1595	23	1832	21	605	33	6060	12
	lower and upper montane	2300	21	3227	20	5637	14	40	63	59	100	1867	20	13130	9
	submontane	1122	26	7022	11	607	41	60	100	33	100	0	.	8845	10
	hyperinsubric and colline	0	.	95	79	0	.	208	40	0	.	2247	19	2549	17
	total	3478	16	10344	9	8408	11	2485	18	3214	16	5336	11	33263	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	513	48	588	37	211	74	1312	28
	subalpine	0	.	0	.	1368	38	425	48	702	44	393	43	2888	23
	high-montane	118	100	0	.	982	33	2362	29	2628	25	474	44	6564	16
	lower and upper montane	5181	17	3498	21	7238	15	288	60	893	45	2918	22	20017	9
	submontane	1208	34	7681	13	1026	38	0	.	358	60	0	.	10273	11
	hyperinsubric and colline	0	.	71	100	0	.	267	51	0	.	3115	18	3453	17
	total	6507	15	11251	11	10615	12	3855	20	5169	17	7111	12	44507	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	210	100	767	37	22	100	999	35
	subalpine	0	.	0	.	1107	44	736	58	2587	27	529	55	4959	20
	high-montane	0	.	0	.	3139	29	2078	32	3462	22	2919	24	11597	13
	lower and upper montane	13015	11	6602	18	16568	11	355	71	1335	39	1298	32	39172	7
	submontane	3494	21	11233	12	547	57	0	.	108	100	0	.	15382	10
	hyperinsubric and colline	0	.	454	53	0	.	215	66	0	.	1506	32	2175	25
	total	16508	9	18289	10	21361	10	3595	24	8258	14	6274	15	74285	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	559	46	1021	35	81	100	1660	27
	subalpine	0	.	0	.	3062	24	3474	23	5379	20	2370	27	14284	11
	high-montane	451	58	0	.	11107	15	4531	23	8196	17	4623	21	28908	9
	lower and upper montane	17726	10	18615	12	39016	7	177	73	2804	26	2285	31	80622	5
	submontane	4023	21	24416	8	2345	30	0	.	118	100	0	.	30902	7
	hyperinsubric and colline	0	.	160	94	0	.	142	100	0	.	1648	30	1951	28
	total	22200	9	43191	6	55530	6	8882	15	17518	11	11007	12	158328	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	1068	25	1434	26	920	29	3422	16
	subalpine	77	100	0	.	5089	19	3829	23	5016	17	2201	23	16211	10
	high-montane	69	100	0	.	5784	16	4401	18	3693	19	2636	23	16584	9
	lower and upper montane	13617	10	3611	22	19594	9	739	41	2038	26	3536	39	43136	6
	submontane	2417	22	7182	15	1883	34	0	.	387	61	0	.	11868	12
	hyperinsubric and colline	0	.	86	100	0	.	353	49	0	.	2990	19	3428	17
	total	16180	9	10878	12	32349	7	10390	12	12569	10	12283	13	94649	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	2517	20	4170	15	1451	22	8138	10
	subalpine	77	100	0	.	11302	13	8975	14	15430	10	6009	15	41793	6
	high-montane	696	43	0	.	23937	9	15067	11	21176	9	11953	11	72829	4
	lower and upper montane	52375	4	36208	7	90015	4	1599	28	7326	15	11961	14	199485	2
	submontane	12631	10	59068	4	6662	16	60	100	1005	35	0	.	79426	4
	hyperinsubric and colline	0	.	890	35	0	.	1192	26	0	.	11533	8	13615	7
	total	65779	3	96167	3	131916	3	29411	7	49106	5	42905	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. 1362740. Birmensdorf, Swiss Federal Research Institute WSL

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