Selection from the list of topics

back

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.

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 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
site quality · main tree species
regional demarcation: production 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
		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	9	129	1	683	58	33	110	32	13	43	192	22
	fir	43	31	6	58	-4	129	23	46	0	.	69	27
	pine	4	126	3	53	4	82	6	89	0	.	18	48
	larch	0	100	0	.	0	.	50	32	14	47	64	27
	Arolla pine	0	.	0	.	0	.	17	47	0	.	17	47
	other conifers	1	70	1	62	0	96	0	470	0	.	2	58
	beech	28	54	47	34	5	228	10	100	0	.	89	30
	maple	6	68	-2	86	4	86	7	43	0	.	14	43
	ash	0	548	-1	176	8	90	-1	270	0	.	6	127
	oak	2	77	-1	176	3	101	2	60	0	.	7	62
	sweet chestnut	0	.	0	.	0	.	1	76	0	.	1	76
	other broadleaves	-3	123	0	399	3	61	2	240	0	100	2	267
	indeterminable	0	0	0	0	0	.	0	0	0	.	0	0
	total	92	29	53	36	82	32	227	19	28	33	481	13
moderate	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13	162	1	408	153	19	653	12	45	78	863	11
	fir	77	24	5	50	88	22	63	24	28	43	263	13
	pine	0	100	0	.	0	100	29	54	-3	123	25	63
	larch	0	.	0	.	1	84	116	19	52	41	169	18
	Arolla pine	0	.	0	.	0	.	19	47	0	100	20	46
	other conifers	0	100	0	.	2	100	0	69	0	.	3	86
	beech	30	32	4	68	54	25	31	44	43	38	164	17
	maple	7	63	1	56	10	38	30	41	4	76	52	27
	ash	1	189	0	.	4	70	12	67	3	56	20	44
	oak	0	.	0	.	0	100	4	55	0	826	4	79
	sweet chestnut	0	.	0	.	0	.	1	82	6	138	6	126
	other broadleaves	2	47	0	100	-3	108	15	72	-5	375	9	236
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0
	total	129	25	12	64	310	13	973	9	175	31	1598	7
good	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	113	25	155	18	330	14	227	17	42	34	868	9
	fir	167	17	129	18	318	16	51	37	-1	552	664	10
	pine	1	520	7	62	7	105	1	104	0	.	16	70
	larch	2	59	0	11382	0	72	-1	506	11	72	13	78
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	6	62	23	38	2	65	0	124	2	92	33	30
	beech	81	27	140	18	141	19	63	28	69	26	494	10
	maple	32	30	54	20	36	35	20	42	5	81	147	15
	ash	-9	133	37	52	3	495	1	634	15	76	46	65
	oak	3	93	28	27	2	66	1	185	-2	456	33	34
	sweet chestnut	0	.	0	.	0	.	0	381	-2	976	-2	958
	other broadleaves	6	86	18	34	21	30	-3	310	45	44	86	28
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	402	14	592	11	860	10	361	15	184	22	2397	6
very good	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	63	43	589	9	73	30	7	74	0	.	731	9
	fir	111	26	315	12	114	23	0	733	0	.	540	10
	pine	9	113	9	73	1	100	0	.	0	.	18	63
	larch	2	114	28	35	4	47	3	80	0	.	37	28
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	12	66	29	34	0	83	0	.	0	.	41	30
	beech	136	16	357	10	36	49	8	106	4	86	541	8
	maple	45	29	68	22	18	93	7	58	0	.	138	19
	ash	13	61	85	23	4	388	8	49	1	75	110	23
	oak	13	78	44	30	3	82	0	100	1	89	61	28
	sweet chestnut	0	.	0	100	0	.	0	.	-1	100	-1	101
	other broadleaves	10	55	87	28	8	58	-3	233	3	92	104	26
	indeterminable	0	0	0	0	0	0	0	.	0	.	0	0
	total	413	13	1610	5	261	18	31	46	8	65	2319	5
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	199	23	747	8	615	10	997	9	100	38	2652	5
	fir	399	11	456	10	515	11	138	19	28	47	1536	6
	pine	14	94	19	41	12	66	36	46	-3	123	78	31
	larch	4	69	28	39	5	39	168	16	78	31	283	13
	Arolla pine	0	.	0	.	0	.	37	33	0	100	37	33
	other conifers	19	44	52	25	5	53	0	254	2	92	78	20
	beech	275	13	548	8	236	15	113	23	117	21	1287	6
	maple	89	19	120	15	68	31	64	24	9	56	351	10
	ash	4	346	120	23	19	111	21	62	19	61	182	22
	oak	19	58	71	21	8	52	7	45	-1	1416	104	20
	sweet chestnut	0	.	0	100	0	.	1	60	3	940	4	630
	other broadleaves	15	55	104	24	29	31	11	160	43	61	201	21
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	1037	8	2268	4	1512	7	1592	7	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. 1399000. Birmensdorf, Swiss Federal Research Institute WSL

back