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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).

main tree species #90
Type of trees and shrubs ≥12 cm in diameter at breast height (dbh) with the ten most common species or species groups in Switzerland ('main tree species') categorised, and the classes 'other conifers' and 'other broadleaves' for the remaining species. The main tree species are: spruce (Picea spp.), fir (Abies spp.), pine (Pinus sylvestris, P. nigra, P. strobus, P. mugo subsp. uncinata), larch (Larix spp.), Arolla pine (Pinus cembra), beech (Fagus sylvatica), maple (Acer spp.), ash (Fraxinus spp.), oak (Quercus spp.) and chestnut (Castanea sativa). Reference: Field Survey (MID 50: Baumart)

protection forest region #829
Demarcation of Switzerland used in NFI for protection forest analyses. The six protection forest regions were derived from the economic regions by combining individual regions according to natural and statistical 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)
altitudinal vegetation belts (NaiS; 6 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	main tree species	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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	163	42	314	39	333	39	811	23
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	57	87	238	38	89	79	384	32
	larch	0	.	0	.	0	.	1391	23	2266	19	860	23	4517	13
	Arolla pine	0	.	0	.	0	.	899	25	1482	19	9	94	2389	15
	other conifers	0	.	0	.	0	.	0	.	22	47	0	.	22	47
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	6	80	4	74	4	59	14	44
	indeterminable	0	.	0	.	0	.	0	.	0	0	0	0	0	0
	total	0	.	0	.	0	.	2517	20	4327	15	1294	22	8138	10
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	77	100	7376	15	2883	25	5686	16	12043	11	2911	21	30976	7
	fir	0	.	14	77	55	100	20	100	0	.	0	.	89	67
	pine	0	.	299	62	13	87	92	49	176	30	0	.	580	34
	larch	0	.	72	75	100	99	3884	21	2721	19	2443	18	9220	11
	Arolla pine	0	.	0	.	0	.	183	39	289	42	0	.	472	30
	other conifers	0	.	36	95	5	100	8	82	25	45	0	.	73	50
	beech	0	.	0	.	0	.	0	.	0	.	24	100	24	100
	maple	0	.	34	71	16	100	10	100	0	.	71	69	131	44
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	70	55	14	55	82	44	30	66	31	40	228	26
	indeterminable	0	.	0	.	0	.	0	0	0	0	0	.	0	0
	total	77	100	7901	15	3085	24	9965	13	15285	10	5480	15	41793	6
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	651	45	13516	11	5799	19	8394	13	15304	10	6961	14	50624	5
	fir	0	.	2426	25	177	63	1036	32	638	37	955	35	5231	15
	pine	0	.	2	100	226	61	1248	24	2019	22	72	54	3567	15
	larch	0	.	189	49	51	100	4330	18	2238	20	2683	20	9490	11
	Arolla pine	0	.	12	100	0	.	0	.	1	100	0	.	14	90
	other conifers	0	.	9	85	7	91	1	100	0	.	0	.	17	60
	beech	0	.	0	.	29	100	22	97	45	72	165	55	261	39
	maple	43	66	319	28	26	66	201	32	170	44	85	55	844	17
	ash	0	.	8	85	0	.	237	42	204	49	65	78	513	29
	oak	0	.	0	.	0	.	14	76	0	.	0	.	14	76
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	36	72	36	72
	other broadleaves	2	100	125	31	54	45	707	27	878	26	451	48	2218	17
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	696	43	16606	11	6370	18	16188	10	21496	9	11473	11	72829	4
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	25112	6	21890	8	21308	9	1050	34	3204	22	2359	50	74923	4
	fir	21326	7	14155	9	9600	11	571	47	633	35	1080	35	47365	5
	pine	1817	18	170	61	178	43	229	59	213	56	4	100	2612	15
	larch	331	34	55	66	234	48	95	56	179	51	858	29	1752	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	1010	43	101	81	34	41	4	100	0	.	0	.	1150	39
	beech	20980	6	10653	10	9035	11	739	35	1149	30	5736	13	48292	4
	maple	4238	9	2374	12	2834	12	206	47	180	54	187	42	10019	6
	ash	2871	14	1904	23	1990	18	171	60	20	89	119	53	7075	10
	oak	530	25	180	40	33	68	52	55	64	100	75	56	934	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	70	70	70	70
	other broadleaves	1632	17	1000	17	939	18	116	49	133	46	1473	21	5293	9
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	79849	4	52481	5	46185	6	3234	23	5775	17	11961	14	199485	2
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15928	8	764	30	504	45	0	.	180	67	0	.	17376	7
	fir	10593	10	987	34	263	67	0	.	125	74	0	.	11968	10
	pine	2510	15	330	76	86	70	0	.	116	57	0	.	3042	15
	larch	898	21	152	68	82	71	0	.	89	74	0	.	1221	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	893	37	0	.	-15	138	0	.	0	.	0	.	879	38
	beech	21727	6	1723	30	1402	26	148	68	117	57	0	.	25117	6
	maple	2836	11	336	39	302	46	50	86	82	59	0	.	3606	10
	ash	5537	12	128	44	556	38	24	60	50	85	0	.	6296	11
	oak	4799	13	92	69	154	50	45	78	0	.	0	.	5090	13
	sweet chestnut	0	.	6	100	0	.	93	77	5	100	0	.	104	70
	other broadleaves	3763	12	488	60	319	39	92	57	65	63	0	.	4727	12
	indeterminable	0	0	0	0	0	.	0	.	0	.	0	.	0	0
	total	69485	4	5005	19	3654	24	452	43	829	37	0	.	79426	4
hyperinsubric and colline	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	15662	0	.	0	.	167	58	0	.	214	43	381	35
	fir	22	83	0	.	0	.	15	65	0	.	34	100	70	56
	pine	100	74	0	.	0	.	291	40	0	.	0	.	391	35
	larch	0	.	0	.	0	.	195	69	9	100	5	100	209	64
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	1	100	0	.	82	98	83	98
	beech	53	100	0	.	0	.	11	100	0	.	862	27	926	26
	maple	3	100	0	.	0	.	16	54	0	.	147	45	167	40
	ash	19	64	0	.	0	.	54	51	0	.	728	35	801	32
	oak	641	40	0	.	0	.	165	48	85	100	943	21	1834	19
	sweet chestnut	0	.	0	.	0	.	71	62	18	100	5306	13	5395	13
	other broadleaves	52	57	0	.	0	.	206	40	0	.	3098	14	3356	13
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	0
	total	890	35	0	.	0	.	1192	26	113	100	11420	8	13615	7
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	41767	5	43546	5	30494	7	15460	9	31045	7	12778	12	175091	2
	fir	31941	5	17581	8	10095	11	1642	26	1397	24	2068	24	64723	4
	pine	4427	11	801	41	503	34	1918	18	2762	17	165	49	10577	8
	larch	1230	18	467	33	466	36	9895	12	7503	10	6848	11	26409	6
	Arolla pine	0	.	12	100	0	.	1082	21	1772	18	9	94	2875	13
	other conifers	1904	29	146	61	32	81	13	58	47	32	82	98	2224	25
	beech	42760	4	12376	9	10466	10	920	30	1310	27	6788	11	74620	3
	maple	7121	7	3063	10	3177	12	483	26	432	30	491	25	14767	5
	ash	8428	9	2040	21	2546	16	485	30	274	40	913	30	14686	7
	oak	5971	12	271	35	187	43	276	33	149	71	1017	20	7872	10
	sweet chestnut	0	.	6	100	0	.	165	51	23	82	5412	13	5605	12
	other broadleaves	5450	9	1683	20	1327	16	1209	18	1109	21	5057	11	15836	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	150998	2	81993	4	59294	6	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. 1372345. Birmensdorf, Swiss Federal Research Institute WSL

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