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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: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
timber assortment classes (HG 2010)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
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	.
smallwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2171	11	1127	13	930	13	200	18	254	11	89	16	4772	6
	fir	1944	9	603	13	442	21	21	70	10	25	13	23	3034	7
	pine	197	43	59	36	22	35	61	20	56	18	4	51	399	22
	larch	5	45	96	54	12	41	61	17	37	28	63	21	273	20
	Arolla pine	0	.	0	.	0	100	7	27	11	27	1	85	20	19
	other conifers	19	41	41	55	5	50	0	68	2	43	0	72	68	35
	beech	72	7	73	7	68	8	4	39	9	24	34	14	260	4
	maple	10	16	11	16	22	13	1	52	3	34	3	39	51	8
	ash	6	25	18	14	12	20	2	55	2	51	4	31	43	10
	oak	7	22	19	16	1	39	3	40	1	72	7	28	39	11
	sweet chestnut	0	0	0	0	0	100	1	69	0	0	42	15	43	15
	other broadleaves	6	17	11	16	10	17	4	34	3	32	24	14	58	8
	indeterminable	0	72	0	0	0	100	0	0	0	0	0	0	0	68
	total	4437	7	2058	9	1525	11	365	13	389	9	285	8	9059	4
classes 1 and 2	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3352	10	8369	7	18414	5	4842	11	8930	7	3118	13	47026	3
	fir	2839	9	4484	9	5255	8	276	31	262	24	449	26	13565	5
	pine	645	20	721	17	409	37	1197	19	948	20	134	44	4054	9
	larch	33	51	276	25	198	31	2032	12	1217	13	1524	13	5281	7
	Arolla pine	0	.	0	.	5	100	322	25	477	23	26	77	830	16
	other conifers	93	34	234	27	67	39	17	76	58	41	14	68	484	17
	beech	5514	7	5648	6	5456	7	317	35	536	27	2614	14	20085	4
	maple	1581	10	1702	11	2591	9	142	35	239	31	213	29	6468	5
	ash	820	18	1749	11	1596	14	163	32	192	54	493	27	5012	7
	oak	395	26	781	18	62	35	129	39	23	75	443	23	1832	11
	sweet chestnut	0	0	0	0	4	79	28	88	4	100	2876	12	2911	12
	other broadleaves	720	14	1582	10	1284	12	573	16	576	22	2824	11	7559	6
	indeterminable	7	54	7	74	13	50	1	100	13	100	4	58	46	36
	total	16001	4	25552	4	35354	4	10039	7	13478	6	14733	5	115155	2
classes 3 and 4	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8917	7	11290	7	28507	5	5295	10	11041	7	4879	15	69930	3
	fir	7603	8	8063	8	10438	7	495	36	714	22	841	26	28155	4
	pine	707	17	1516	14	498	46	628	24	1027	20	87	54	4464	9
	larch	97	42	342	23	180	40	3018	13	2277	13	2038	12	7952	7
	Arolla pine	0	.	0	.	5	100	264	32	480	21	13	100	761	17
	other conifers	66	57	672	34	5	100	0	0	0	0	49	100	791	30
	beech	9855	6	11156	6	8750	8	253	42	968	26	1999	12	32980	3
	maple	1432	12	1157	13	1805	10	47	100	187	33	84	40	4712	6
	ash	923	19	3047	11	1326	16	54	82	70	52	173	41	5592	8
	oak	417	23	1735	13	112	39	25	63	49	95	383	27	2720	10
	sweet chestnut	0	0	0	0	12	77	17	57	0	0	1142	15	1172	15
	other broadleaves	436	33	1054	16	606	24	84	44	56	34	798	16	3034	10
	indeterminable	11	100	0	0	13	100	0	0	0	0	0	0	23	71
	total	30464	4	40032	3	52256	3	10181	8	16867	6	12486	6	162285	1
classes 5 and 6	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2634	14	3828	12	11127	7	1563	16	3732	11	2322	19	25205	4
	fir	2044	16	3349	11	4819	10	138	55	495	29	461	31	11305	6
	pine	89	51	329	27	17	71	32	51	221	30	0	0	688	18
	larch	6	100	103	34	54	45	983	17	777	17	875	22	2797	10
	Arolla pine	0	.	0	.	0	0	129	37	153	32	20	100	302	23
	other conifers	5	100	643	42	0	0	0	0	0	0	18	100	666	41
	beech	2105	13	5480	9	2792	13	32	74	266	39	463	20	11138	6
	maple	170	31	298	34	125	28	0	0	33	53	0	0	626	19
	ash	168	31	822	21	229	34	0	0	22	100	38	100	1279	16
	oak	44	66	1134	29	34	55	0	0	7	100	42	52	1261	26
	sweet chestnut	0	0	0	0	8	100	16	100	0	0	848	28	871	28
	other broadleaves	44	69	270	28	228	78	22	58	7	100	28	50	598	33
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	7308	8	16257	6	19431	5	2915	12	5713	9	5112	11	56736	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17073	6	24614	6	58978	4	11901	9	23958	7	10409	13	146933	2
	fir	14430	7	16499	7	20953	7	931	32	1481	22	1764	24	56058	4
	pine	1639	17	2625	14	946	34	1917	18	2252	17	225	43	9605	8
	larch	141	41	817	19	444	28	6094	12	4308	12	4499	12	16303	6
	Arolla pine	0	.	0	.	10	100	722	24	1121	20	59	88	1913	15
	other conifers	183	35	1591	33	77	40	17	76	61	40	81	92	2010	26
	beech	17545	5	22357	5	17065	7	606	34	1780	24	5110	11	64463	3
	maple	3193	9	3168	10	4543	8	190	42	462	28	300	29	11856	5
	ash	1916	16	5635	10	3163	14	218	40	285	46	708	28	11926	7
	oak	863	19	3669	14	209	31	157	41	80	80	874	21	5852	10
	sweet chestnut	0	0	0	0	24	83	61	63	4	100	4908	12	4997	11
	other broadleaves	1207	19	2917	11	2127	17	684	18	641	21	3674	11	11250	6
	indeterminable	18	64	7	74	26	56	1	100	13	100	4	58	70	34
	total	58210	3	83899	3	108566	3	23499	7	36446	6	32616	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. 1385232. Birmensdorf, Swiss Federal Research Institute WSL

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