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.

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 NFI4/NFI5 #2282
Forest that was less than two-thirds covered with shrubs in both NFI4 (2009-2017) and NFI5 (2018-2026) and is 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) · main tree species
regional demarcation: production region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/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)	main tree species	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	.
	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	.
smallwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2181	11	1027	13	831	15	822	8	97	15	4958	6
	fir	1929	9	563	14	356	15	209	40	13	23	3070	7
	pine	199	42	58	37	15	49	141	12	4	51	417	21
	larch	5	43	91	56	9	55	140	12	79	17	326	17
	Arolla pine	0	.	0	.	0	.	27	17	0	100	27	17
	other conifers	19	40	41	55	4	42	6	29	0	100	71	34
	beech	75	7	65	7	56	9	35	11	35	14	267	4
	maple	11	16	9	17	16	15	16	15	4	35	56	8
	ash	7	22	17	16	10	23	9	20	5	28	47	9
	oak	12	22	14	16	2	39	5	29	8	28	41	11
	sweet chestnut	0	.	0	0	0	.	1	45	42	15	44	14
	other broadleaves	7	16	11	17	5	21	15	15	27	13	66	7
	indeterminable	0	72	0	0	0	100	0	0	0	0	0	68
	total	4446	7	1896	9	1303	11	1427	8	315	7	9387	4
classes 1 and 2	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3386	9	7555	7	14010	5	23188	4	3431	12	51570	3
	fir	2813	10	3504	10	5043	8	1929	12	440	26	13728	5
	pine	742	19	616	18	230	56	2534	12	111	46	4232	9
	larch	31	42	268	26	68	50	4406	8	2026	11	6798	6
	Arolla pine	0	.	0	.	0	.	1086	15	8	91	1095	15
	other conifers	122	39	209	26	33	36	161	28	9	92	534	16
	beech	5758	6	4855	6	4451	8	2764	11	2749	13	20577	4
	maple	1751	9	1611	11	1721	12	1592	11	310	25	6985	5
	ash	925	17	1530	11	1230	17	1130	16	542	25	5357	7
	oak	538	24	628	18	67	36	205	27	471	21	1910	11
	sweet chestnut	0	.	0	0	0	.	77	56	2904	12	2981	12
	other broadleaves	872	13	1498	10	930	15	2235	9	3196	10	8731	5
	indeterminable	8	48	6	87	5	47	24	62	20	78	63	35
	total	16946	4	22279	3	27788	3	41331	3	16216	4	124560	2
classes 3 and 4	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8901	7	9800	7	21193	5	29446	4	5037	14	74377	3
	fir	7593	8	6646	9	9679	7	3819	12	853	26	28590	4
	pine	794	16	1369	15	447	52	1889	14	97	51	4597	9
	larch	82	45	331	24	61	57	6295	8	2192	11	8960	6
	Arolla pine	0	.	0	.	0	.	898	16	0	0	898	16
	other conifers	211	70	516	35	12	100	7	74	49	100	794	30
	beech	10445	5	9491	6	8061	8	3645	11	2044	12	33685	3
	maple	1427	12	1025	13	1328	12	1034	13	99	36	4913	6
	ash	959	17	2845	12	1052	18	689	20	193	38	5739	8
	oak	749	20	1415	13	100	38	182	38	369	27	2816	10
	sweet chestnut	0	.	0	0	0	.	48	39	1141	15	1190	15
	other broadleaves	513	29	1036	17	203	23	695	22	915	15	3361	9
	indeterminable	11	100	0	0	0	0	27	71	0	0	37	58
	total	31684	3	34474	3	42137	3	48672	2	12989	6	169956	1
classes 5 and 6	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2746	14	2912	12	7913	8	10915	6	2512	18	26997	4
	fir	2019	16	2696	12	4265	10	2076	16	466	30	11522	6
	pine	89	51	294	29	42	61	267	26	7	100	699	17
	larch	5	100	71	37	38	63	2170	11	878	21	3162	10
	Arolla pine	0	.	0	.	0	.	352	23	0	0	352	23
	other conifers	113	82	507	50	28	100	0	0	18	100	666	41
	beech	2581	12	5068	9	2297	15	1171	17	471	19	11589	6
	maple	188	31	297	34	106	31	107	32	0	0	698	18
	ash	204	32	645	22	268	41	148	38	52	78	1316	15
	oak	106	47	1115	29	32	81	41	49	35	58	1329	25
	sweet chestnut	0	.	0	0	0	.	45	62	870	28	915	26
	other broadleaves	83	45	456	38	22	60	298	61	33	45	892	29
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	8134	7	14062	6	15011	5	17590	5	5342	10	60138	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17214	6	21294	6	43946	4	64371	3	11077	12	157902	2
	fir	14353	7	13409	8	19343	6	8033	11	1772	24	56910	4
	pine	1824	16	2337	14	734	41	4831	11	220	43	9945	8
	larch	123	36	761	20	176	40	13011	7	5176	10	19246	6
	Arolla pine	0	.	0	.	0	.	2363	14	8	92	2371	14
	other conifers	466	60	1273	35	77	55	174	28	75	99	2065	26
	beech	18859	5	19480	5	14865	7	7615	9	5299	11	66118	3
	maple	3377	8	2943	10	3171	10	2749	10	412	25	12652	5
	ash	2095	15	5037	11	2560	16	1975	15	792	25	12459	7
	oak	1405	18	3172	15	202	31	432	27	883	20	6095	9
	sweet chestnut	0	.	0	0	0	.	172	47	4958	12	5129	11
	other broadleaves	1475	16	3000	12	1160	13	3243	13	4171	10	13049	6
	indeterminable	19	61	6	87	5	47	51	48	20	78	101	31
	total	61209	2	72712	3	86238	3	109020	2	34862	4	364041	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. 1197769. Birmensdorf, Swiss Federal Research Institute WSL

back