Selection from the list of topics

back

volume of bolewood (HG 2010) #211
Wood volume of the stem without bark or stump ≥7 cm in diameter (limit for merchantable wood) of all trees and shrubs with a diameter at breast height (dbh) ≥12 cm, based on the stem-form functions according to Kaufmann (2001). The definition of the assortments is based on the 2010 edition of the Swiss trading customs for raw timber (HG 2010).

timber assortment classes (HG 2010) #1788
Sorting of roundwood into four diameter classes on the basis of the 2010 and 2021 editions of the Swiss Trading Customs for Raw Timber (HG 2010, Waldwirtschaft Schweiz et al. 2010). Classes 1 and 2, 3 and 4, as well as 5 and 6 of the HG 2010 are each combined into one class.

tree species (5 classes) #1157
Type of trees and shrubs ≥12 cm in diameter at breast height (dbh). The three most common species or species groups in Switzerland (spruce - Picea spp.; fir - Abies spp.; beech - Fagus sylvatica) are in individual classes, while the remaining species are classified as «other conifers» or «other broadleaves». 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.

NFI5
volume of bolewood (HG 2010)
timber assortment classes (HG 2010) · tree species (5 classes)
regional demarcation: production region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
timber assortment classes (HG 2010)	tree species (5 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	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	.
smallwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2133	11	1026	13	791	16	738	9	84	17	4772	6
	fir	1899	9	562	14	352	15	208	41	12	24	3034	7
	other conifers	224	38	190	31	27	33	257	9	62	21	760	14
	beech	75	7	65	7	54	9	33	11	34	14	260	4
	other broadleaves	35	10	48	9	31	12	39	10	80	9	233	5
	indeterminable	0	72	0	0	0	100	0	0	0	0	0	68
	total	4366	7	1891	10	1255	11	1275	9	272	8	9059	4
classes 1 and 2	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3295	10	7495	7	12593	6	20646	4	2997	13	47026	3
	fir	2792	10	3495	10	5007	8	1848	12	423	27	13565	5
	other conifers	895	17	1093	13	331	40	6807	7	1524	13	10649	5
	beech	5701	7	4804	6	4363	8	2603	11	2614	14	20085	4
	other broadleaves	3953	8	5130	7	3622	10	4333	8	6746	7	23784	4
	indeterminable	8	48	6	87	5	47	22	66	4	58	46	36
	total	16645	4	22023	3	25920	4	36259	3	14309	5	115155	2
classes 3 and 4	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8715	7	9740	8	19245	5	27427	4	4804	15	69930	3
	fir	7505	8	6574	9	9546	7	3694	12	835	26	28155	4
	other conifers	1087	18	2216	13	520	45	8181	7	1965	12	13968	5
	beech	10304	5	9432	6	7740	8	3505	11	1999	12	32980	3
	other broadleaves	3569	10	6126	8	2641	10	2358	12	2535	10	17229	4
	indeterminable	11	100	0	0	0	0	13	100	0	0	23	71
	total	31190	3	34087	3	39692	3	45178	3	12138	7	162285	1
classes 5 and 6	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2683	14	2894	12	7202	8	10120	6	2306	19	25205	4
	fir	1981	16	2632	12	4185	10	2046	16	461	31	11305	6
	other conifers	207	50	873	31	108	41	2543	10	723	23	4453	9
	beech	2351	12	4961	9	2224	15	1140	18	463	20	11138	6
	other broadleaves	551	20	2220	17	419	28	497	38	948	26	4636	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	7773	7	13580	6	14137	6	16346	5	4900	11	56736	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16826	6	21155	6	39831	5	58931	4	10190	13	146933	2
	fir	14177	7	13264	8	19090	6	7796	11	1732	24	56058	4
	other conifers	2412	17	4371	13	985	32	17789	6	4273	12	29830	5
	beech	18431	5	19261	5	14381	7	7281	10	5110	11	64463	3
	other broadleaves	8109	8	13523	7	6713	9	7227	9	10310	7	45881	3
	indeterminable	19	61	6	87	5	47	35	56	4	58	70	34
	total	59974	3	71580	3	81004	3	99058	2	31619	5	343236	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. 1400839. Birmensdorf, Swiss Federal Research Institute WSL

back