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increment (gross increment) #11
Increase in 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.

site quality #894
Production capacity of the site in kilograms per hectare and year based on the total mean increment (TMI) according to Keller (1978) - in four classes. Low: TMI ≤1500 kg/(ha/year), moderate: TMI 1501-3000 kg/(ha/year), good: TMI 3001-4500 kg/(ha/year), very good: TMI >4500 kg/(ha/year).

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)

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 NFI3/NFI4 #1429
Forest that was less than two-thirds covered with shrubs in both NFI3 (2004-2006) and NFI4 (2009-2017) and could be reached 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.

NFI3–NFI4
increment (gross increment)
site quality · main tree species
regional demarcation: production region
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI3/NFI4
grid: 1.4-km grid
change 2004/06–2009/17
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
site quality	main tree species	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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
low	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	34	25	16	36	146	14	276	10	13	31	485	7
	fir	28	26	9	55	11	39	19	32	0	.	68	17
	pine	5	64	2	84	3	94	11	45	0	100	21	33
	larch	0	.	0	0	0	100	78	17	20	34	94	15
	Arolla pine	0	.	0	.	1	71	38	19	0	72	38	18
	other conifers	2	53	0	71	1	50	1	29	0	.	4	31
	beech	85	16	15	37	27	35	28	34	0	.	163	13
	maple	4	86	2	41	9	49	8	47	0	.	23	30
	ash	11	29	2	41	3	89	4	63	0	.	21	25
	oak	3	61	3	59	0	69	2	44	0	.	8	32
	sweet chestnut	0	.	0	100	0	.	1	138	0	.	1	100
	other broadleaves	7	33	0	254	2	36	10	36	0	100	20	23
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	0
	total	180	13	49	24	204	12	476	8	34	26	947	6
moderate	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	121	15	13	35	297	10	903	6	213	12	1549	4
	fir	63	17	5	43	119	15	79	18	33	23	308	9
	pine	0	100	0	.	0	1034	69	21	8	51	76	20
	larch	0	100	0	.	0	.	213	10	86	18	294	9
	Arolla pine	0	.	0	.	0	.	8	41	0	78	8	40
	other conifers	0	100	0	.	-1	100	2	70	0	100	0	666
	beech	56	20	7	60	28	26	41	38	27	49	167	15
	maple	9	32	1	123	14	33	33	19	10	53	67	15
	ash	1	58	1	78	8	41	23	37	6	49	38	25
	oak	0	0	0	.	0	100	1	221	8	42	8	45
	sweet chestnut	0	.	0	.	0	.	0	0	0	2109	0	2109
	other broadleaves	3	42	0	100	7	39	75	15	38	24	121	12
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0
	total	253	11	27	28	470	9	1446	5	428	9	2636	4
good	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	226	11	229	13	577	7	326	11	45	26	1418	5
	fir	182	10	147	13	413	9	79	17	9	79	850	6
	pine	13	46	1	285	3	71	1	56	1	114	20	37
	larch	4	52	10	32	4	64	11	48	2	203	31	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	1	137	14	49	2	47	0	88	1	100	19	41
	beech	184	11	248	11	226	11	75	17	87	22	836	6
	maple	25	26	17	43	37	22	16	45	10	54	106	15
	ash	28	22	41	24	45	23	4	154	41	25	160	12
	oak	14	31	13	38	2	115	5	48	17	34	51	18
	sweet chestnut	0	.	0	.	0	.	0	106	111	19	100	19
	other broadleaves	28	25	28	22	13	29	49	26	115	13	223	9
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	704	6	749	7	1323	5	567	8	439	9	3814	3
very good	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	188	13	810	6	122	16	29	40	8	100	1157	5
	fir	173	12	385	9	113	15	8	89	0	.	679	6
	pine	9	58	34	17	0	151	4	59	0	.	46	17
	larch	-1	141	35	25	3	43	3	68	0	.	41	23
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	11	58	21	38	0	58	0	.	0	.	33	32
	beech	190	10	422	7	81	18	30	26	4	89	727	5
	maple	30	23	92	16	7	102	3	67	0	100	133	13
	ash	54	17	150	13	19	29	13	47	1	84	238	10
	oak	28	26	76	16	1	97	1	83	0	99	106	13
	sweet chestnut	0	.	4	93	0	.	0	842	0	73	4	102
	other broadleaves	29	25	121	14	9	54	10	31	-1	237	168	12
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	712	7	2150	4	354	11	102	19	12	74	3331	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	570	7	1068	5	1139	5	1532	4	280	10	4606	2
	fir	445	6	547	7	656	6	185	11	42	24	1934	4
	pine	26	31	36	18	7	62	84	18	9	46	162	12
	larch	4	68	45	21	8	40	304	8	108	16	448	7
	Arolla pine	0	.	0	.	1	71	47	17	0	53	46	16
	other conifers	14	46	36	30	1	107	3	39	1	94	58	23
	beech	515	6	693	5	365	8	176	13	118	20	1932	3
	maple	68	15	111	15	67	19	60	17	20	37	334	8
	ash	93	12	195	11	75	17	45	28	48	23	468	7
	oak	43	19	91	14	4	76	9	38	25	26	178	10
	sweet chestnut	0	.	4	88	0	.	1	325	111	21	106	20
	other broadleaves	67	15	149	12	30	21	144	12	152	12	537	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	1845	3	2974	3	2354	3	2590	3	913	5	10810	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. 1261340. Birmensdorf, Swiss Federal Research Institute WSL

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