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net increment #80
Increase in the stemwood volume between two inventories of all surviving trees and shrubs with a diameter at breast height (dbh) ≥12 cm with bark, the stemwood volume of all ingrowing trees and shrubs with bark, and the modelled increase in the stemwood volume of the losses with bark during half the inventory period minus the volume of mortality. The net increment thus corresponds to the increment (=gross increment) minus the volume of mortality.

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 NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was 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.

NFI4–NFI5
net increment
altitudinal vegetation belts (NaiS; 6 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
change 2009/17–2018/26
		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/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%
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	.	2	63	5	65	5	79	12	44
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	1	108	2	254	0	100	3	160
	larch	0	.	0	.	0	.	14	61	16	65	9	54	40	37
	Arolla pine	0	.	0	.	0	.	10	48	29	29	0	100	39	24
	other conifers	0	.	0	.	0	.	0	.	-1	76	0	.	-1	76
	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	100	0	100	0	100	0	74
	indeterminable	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	total	0	.	0	.	0	.	27	38	51	31	15	44	93	21
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	-5	100	89	26	41	39	71	30	98	50	32	52	327	19
	fir	0	.	3	99	0	.	0	.	0	.	0	.	3	99
	pine	0	.	6	62	0	.	0	140	0	698	0	.	6	70
	larch	0	.	2	100	0	100	37	34	33	31	22	84	94	26
	Arolla pine	0	.	0	.	0	.	-2	187	-2	301	0	.	-4	192
	other conifers	0	.	0	100	0	.	0	100	0	58	0	.	0	54
	beech	0	.	0	.	0	.	0	.	0	.	0	100	0	100
	maple	0	.	0	92	0	.	0	.	0	.	2	100	2	81
	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	.	1	73	0	1593	3	100	0	62	0	0	4	74
	indeterminable	0	.	0	.	0	.	0	0	0	0	0	.	0	0
	total	-5	100	101	25	41	39	110	23	130	40	56	57	433	17
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	9	61	149	22	59	45	81	42	310	14	31	104	640	12
	fir	0	.	49	31	-1	312	22	36	5	111	16	66	91	23
	pine	0	.	0	100	4	62	-4	246	30	39	-4	109	26	58
	larch	0	.	7	62	1	100	30	45	24	37	33	41	95	22
	Arolla pine	0	.	2	100	0	.	0	0	0	100	0	.	2	102
	other conifers	0	.	0	.	0	72	0	0	0	.	0	.	0	72
	beech	0	.	0	.	0	.	1	90	0	2063	3	105	3	96
	maple	1	68	0	1275	0	72	3	60	4	85	2	145	9	73
	ash	0	.	0	75	0	.	3	56	2	336	1	100	6	105
	oak	0	.	0	.	0	.	0	100	0	.	0	.	0	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	1	74	1	74
	other broadleaves	0	298	0	310	1	66	-3	202	12	86	2	936	12	160
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	10	57	209	19	65	41	135	31	385	13	85	48	888	10
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	410	14	292	14	295	16	4	171	68	28	26	38	1094	8
	fir	498	10	332	15	176	20	14	55	8	59	11	68	1039	8
	pine	8	132	6	89	2	91	1	156	-1	335	0	.	16	74
	larch	5	91	1	160	1	128	1	53	3	56	14	47	25	34
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	35	29	2	63	3	77	0	0	0	.	0	.	40	26
	beech	369	9	138	21	119	23	-1	1107	13	52	101	23	740	8
	maple	109	15	31	40	55	25	8	54	1	118	3	75	206	12
	ash	25	75	25	61	7	195	1	302	0	81	0	28952	58	48
	oak	7	73	2	68	0	100	0	252	1	100	0	189	11	51
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	-2	108	-2	108
	other broadleaves	26	30	17	45	1	1556	3	116	-5	54	1	1352	42	39
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	1493	6	846	10	660	12	32	54	89	26	153	18	3270	5
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	532	10	31	40	8	84	0	.	2	61	0	.	573	9
	fir	353	11	40	38	0	1272	0	.	4	107	0	.	398	11
	pine	27	42	0	1334	0	100	0	100	2	124	0	.	30	43
	larch	27	39	2	149	1	72	0	.	1	138	0	.	31	35
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	36	33	0	.	0	1135	0	.	0	.	0	.	36	33
	beech	449	10	41	33	39	35	-5	84	3	48	0	.	527	10
	maple	100	19	3	108	17	93	2	96	7	64	0	.	129	20
	ash	98	25	-1	594	-2	587	1	54	1	157	0	.	97	27
	oak	69	23	4	99	2	103	0	100	0	.	0	.	75	22
	sweet chestnut	0	100	0	100	0	.	0	100	0	100	0	.	0	138
	other broadleaves	92	28	-6	130	4	86	1	209	6	67	0	.	97	28
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	1783	5	113	26	70	40	-1	1068	27	40	0	.	1994	5
hyperinsubric and colline	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	-4	80	0	.	0	.	5	60	0	.	4	52	5	97
	fir	1	73	0	.	0	.	0	181	0	.	1	100	2	77
	pine	-2	92	0	.	0	.	-2	149	0	.	0	.	-4	88
	larch	0	.	0	.	0	.	-1	544	0	100	0	100	-1	397
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	100	0	.	2	92	2	89
	beech	2	100	0	.	0	.	0	100	0	.	12	55	15	48
	maple	0	.	0	.	0	.	0	73	0	.	3	126	3	112
	ash	1	84	0	.	0	.	1	54	0	.	19	61	21	56
	oak	14	63	0	.	0	.	4	57	1	100	-1	861	18	66
	sweet chestnut	0	.	0	.	0	.	1	67	0	100	4	707	5	554
	other broadleaves	1	78	0	.	0	.	5	57	0	.	41	49	46	43
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	0	0	0
	total	14	57	0	.	0	.	13	50	2	100	84	42	112	33
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	942	8	562	10	403	14	162	25	482	14	100	38	2652	5
	fir	855	7	423	13	175	20	37	31	17	50	28	47	1536	6
	pine	33	46	12	65	6	52	-3	301	33	40	-3	123	78	31
	larch	32	35	11	49	4	58	82	25	77	22	78	31	283	13
	Arolla pine	0	.	2	100	0	.	8	69	26	40	0	100	37	33
	other conifers	72	22	2	63	3	76	0	93	-1	119	2	92	78	20
	beech	821	7	179	18	157	19	-5	174	16	44	117	21	1287	6
	maple	210	12	34	40	72	29	13	38	12	47	9	56	351	10
	ash	124	25	24	65	6	303	6	66	3	191	19	61	182	22
	oak	90	21	6	66	3	92	5	48	2	78	-1	1416	104	20
	sweet chestnut	0	100	0	100	0	.	1	64	0	89	3	940	4	630
	other broadleaves	118	22	13	84	5	161	9	91	13	84	43	61	201	21
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	3295	4	1269	7	835	10	315	17	682	11	394	17	6794	3

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. 1399688. Birmensdorf, Swiss Federal Research Institute WSL

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