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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.

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).

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)

production region #49
Demarcation of Switzerland into five regions (Jura, Plateau, Pre-Alps, Alps and Southern Alps) with relatively uniform growth and timber production conditions. The production regions were established by the Federal Office of Forestry long before the first National Forest Inventory (NFI1, 1983-1985). With a small exception along Lake Geneva, the boundaries of the production regions still follow the municipal boundaries of the time. Unlike the NFI, the Forestry Statistics of the Federal Statistical Office don't use the production regions as demarcation but rather the forestry zones, whose boundaries are somewhat different.

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
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (10 classes)
regional demarcation: production region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (10 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	5	100	0	.	5	100
	21-30 cm	0	.	0	.	0	.	4	100	0	.	4	100
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	9	100	0	.	9	100
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	356	15	35	43	391	14
	21-30 cm	0	.	0	.	4	100	805	14	70	44	879	14
	31-40 cm	0	.	0	.	4	100	1067	14	96	39	1167	13
	41-50 cm	0	.	0	.	0	.	859	15	85	33	944	14
	51-60 cm	0	.	0	.	0	.	672	18	27	56	699	17
	61-70 cm	0	.	0	.	0	.	337	23	46	64	383	22
	71-80 cm	0	.	0	.	0	.	188	27	47	58	235	25
	81-90 cm	0	.	0	.	0	.	124	38	0	.	124	38
	91-100 cm	0	.	0	.	0	.	42	71	0	.	42	71
	>100 cm	0	.	0	.	0	.	78	51	0	.	78	51
	total	0	.	0	.	8	100	4528	10	406	29	4941	10
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	3	100	198	24	1363	8	146	20	1710	7
	21-30 cm	0	.	4	100	448	21	3696	8	402	19	4551	7
	31-40 cm	0	.	12	100	550	22	5469	8	667	18	6698	7
	41-50 cm	0	.	18	100	419	22	5449	7	744	16	6630	6
	51-60 cm	0	.	4	100	289	27	3509	9	417	25	4219	8
	61-70 cm	0	.	0	.	203	32	1563	13	229	27	1995	11
	71-80 cm	0	.	0	.	59	68	825	17	129	40	1013	15
	81-90 cm	0	.	0	.	16	100	270	27	96	41	382	22
	91-100 cm	0	.	0	.	0	.	141	42	14	100	155	40
	>100 cm	0	.	0	.	32	100	83	73	0	.	114	60
	total	0	.	42	100	2214	17	22367	6	2843	14	27466	5
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	14	43	16	70	740	14	2855	6	473	13	4098	5
	21-30 cm	80	43	41	71	1710	13	6132	6	1008	14	8970	5
	31-40 cm	131	38	57	62	2564	12	8975	6	1592	14	13319	5
	41-50 cm	165	39	46	62	2248	13	8721	6	1353	14	12533	5
	51-60 cm	77	61	19	100	1390	16	6921	7	1196	14	9603	6
	61-70 cm	28	73	13	100	474	21	3280	11	662	24	4458	9
	71-80 cm	48	100	0	.	207	41	1766	14	396	26	2417	12
	81-90 cm	0	.	0	.	34	75	547	23	68	73	649	21
	91-100 cm	0	.	0	.	0	.	271	39	59	71	330	34
	>100 cm	0	.	0	.	0	0	48	72	234	46	281	40
	total	543	34	192	49	9367	10	39516	4	7040	10	56658	4
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	3902	5	1856	9	4994	5	2397	7	1185	11	14334	3
	21-30 cm	8711	5	4374	8	11290	4	4489	7	1354	11	30218	3
	31-40 cm	11807	4	7881	7	16879	4	6073	7	1113	14	43753	3
	41-50 cm	10199	4	7088	7	17440	4	5961	7	898	14	41586	3
	51-60 cm	5782	7	4117	9	11712	5	4176	9	692	19	26479	3
	61-70 cm	2213	11	1387	15	4715	8	1482	14	325	22	10122	5
	71-80 cm	984	19	418	28	1924	15	562	23	247	44	4135	10
	81-90 cm	162	41	117	50	577	24	212	35	79	56	1148	16
	91-100 cm	125	51	59	73	221	43	68	77	22	100	493	27
	>100 cm	37	100	0	.	20	100	70	100	102	60	229	45
	total	43922	3	27297	6	69771	3	25490	6	6017	9	172498	2
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	1793	8	4842	5	592	18	424	18	0	.	7651	4
	21-30 cm	4412	8	10230	5	1004	18	709	17	0	.	16355	4
	31-40 cm	5342	8	16388	4	1481	17	698	19	0	.	23909	4
	41-50 cm	4404	9	15991	4	1368	18	554	22	0	.	22317	4
	51-60 cm	2362	11	9394	6	820	22	490	23	0	.	13066	5
	61-70 cm	990	17	3438	9	422	34	167	41	0	.	5017	8
	71-80 cm	197	40	1549	14	216	50	38	77	0	.	2000	13
	81-90 cm	140	51	682	24	34	100	0	.	0	.	856	21
	91-100 cm	0	.	168	46	0	.	0	.	0	.	168	46
	>100 cm	54	100	36	100	0	.	0	.	0	.	89	72
	total	19693	6	62717	3	5938	14	3079	15	0	.	91427	3
hyperinsubric and colline	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	28	75	231	33	0	.	233	22	1883	8	2375	8
	21-30 cm	10	74	146	32	0	.	217	26	1584	9	1957	8
	31-40 cm	0	.	170	47	0	.	201	24	869	12	1239	11
	41-50 cm	3	100	116	51	0	.	148	35	679	16	946	14
	51-60 cm	0	.	39	73	0	.	81	50	326	19	446	18
	61-70 cm	0	.	0	.	0	.	18	73	119	29	137	27
	71-80 cm	0	.	28	100	0	.	11	100	232	33	270	31
	81-90 cm	0	.	29	100	0	.	0	.	73	54	102	48
	91-100 cm	0	.	0	.	0	.	0	.	108	45	108	45
	>100 cm	0	.	0	.	0	.	0	.	500	39	500	39
	total	40	73	759	30	0	.	908	19	6372	7	8079	7
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	5736	4	6948	4	6523	4	7633	3	3722	5	30562	2
	21-30 cm	13213	3	14795	3	14456	4	16051	3	4418	5	62933	2
	31-40 cm	17280	3	24509	3	21478	3	22483	3	4336	6	90085	2
	41-50 cm	14771	4	23259	3	21475	3	21691	3	3758	7	84955	2
	51-60 cm	8222	5	13574	4	14210	5	15849	4	2657	8	54512	2
	61-70 cm	3231	9	4838	8	5814	7	6846	7	1381	14	22110	4
	71-80 cm	1229	17	1995	12	2406	13	3390	9	1051	17	10071	6
	81-90 cm	302	32	828	21	662	22	1153	15	315	27	3261	10
	91-100 cm	125	51	227	39	221	43	521	26	203	34	1296	16
	>100 cm	90	72	36	100	51	73	279	38	835	28	1292	21
	total	64198	2	91008	2	87298	2	95897	2	22677	4	361078	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. 1321143. Birmensdorf, Swiss Federal Research Institute WSL

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