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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
growing stock (stemwood)
diameter at breast height (10 classes) · conifers and broadleaves
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	conifers and broadleaves	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	1686	10	243	27	490	32	1345	16	1022	15	626	24	1789	12	661	17	778	14	456	20	318	25	1957	12	2679	9	974	12	15024	4
	broadleaves	1861	9	767	15	642	19	967	14	1285	11	368	20	938	14	657	15	695	18	338	27	411	25	950	12	633	17	3304	7	13817	3
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	3547	7	1010	14	1132	18	2313	12	2307	10	994	17	2727	10	1318	12	1473	12	795	19	729	19	2907	9	3312	8	4278	6	28841	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	3566	9	569	21	852	24	2197	16	2265	14	1515	19	3382	9	2189	14	1545	14	1043	18	672	28	4203	10	5680	8	2444	11	32122	3
	broadleaves	3706	8	1208	15	1148	18	1260	14	2471	11	984	21	2045	14	1888	15	854	18	672	23	595	25	1162	16	804	20	5587	8	24386	3
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	7272	6	1776	13	2000	15	3457	12	4736	9	2499	15	5427	8	4078	11	2399	11	1715	16	1267	21	5365	9	6485	8	8031	6	56508	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	5542	9	813	22	1095	21	2458	14	2770	13	2801	15	4871	10	4050	11	2672	12	1513	19	1110	25	5627	10	7698	8	3955	12	46974	3
	broadleaves	5395	8	1779	15	1626	17	2047	13	3188	10	795	24	2291	14	2611	13	1156	18	764	27	670	23	715	20	502	18	4175	9	27714	3
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	0
	total	10937	6	2592	13	2720	14	4504	10	5958	8	3596	13	7161	8	6661	9	3828	11	2277	16	1780	18	6342	9	8200	7	8131	8	74688	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	7102	7	980	20	1539	20	3116	14	4116	11	3174	14	6071	9	4517	11	3846	13	2026	21	1569	19	6083	9	9801	7	4355	11	58296	3
	broadleaves	5711	8	2302	14	1838	18	2271	13	3315	10	595	27	2386	16	2817	14	1070	22	520	27	940	20	322	32	510	28	2987	10	27586	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12813	5	3282	13	3378	14	5387	10	7431	8	3769	12	8457	8	7334	9	4916	12	2547	18	2509	15	6405	9	10311	7	7343	7	85882	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	6860	8	869	25	1569	21	3421	13	3524	12	3277	14	6522	10	6041	11	3373	14	1529	20	1597	19	5558	10	8484	8	4390	12	57013	3
	broadleaves	3335	11	1728	20	1642	19	2949	15	4141	10	483	29	1926	18	1584	17	784	25	407	33	691	27	204	43	414	28	1536	15	21824	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	10195	6	2597	15	3211	15	6370	10	7665	8	3759	13	8447	8	7625	9	4157	12	1936	17	2287	16	5762	10	8898	8	5926	9	78837	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	4373	12	619	27	878	23	2732	16	2410	15	2177	19	4172	13	4071	14	2421	16	1025	23	870	26	3199	13	5223	11	2378	13	36548	4
	broadleaves	1242	19	885	24	838	22	1324	18	2370	16	79	71	685	31	694	27	486	37	252	48	303	51	108	53	273	35	813	21	10353	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5615	10	1504	18	1716	17	4056	13	4780	11	2256	19	4858	12	4765	13	2907	15	1277	22	1173	24	3307	13	5496	10	3191	10	46901	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	2253	15	379	47	476	36	1344	20	1690	20	723	30	2362	16	2118	18	1791	18	1121	29	998	28	2071	19	2580	14	1717	19	21622	5
	broadleaves	278	45	487	31	294	36	622	36	1087	22	169	47	405	39	279	61	201	45	77	70	41	100	47	73	161	56	395	26	4544	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2531	14	867	27	770	26	1966	18	2776	15	892	26	2766	14	2397	18	1992	17	1198	28	1040	28	2118	18	2741	14	2112	16	26166	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	800	30	61	100	406	42	646	31	1038	27	320	46	1076	26	858	28	341	39	135	62	346	37	798	27	1438	21	966	29	9228	8
	broadleaves	48	100	66	100	70	100	0	.	459	36	0	.	44	100	251	50	0	.	134	58	27	100	0	0	0	.	325	32	1423	19
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	848	29	126	71	476	39	646	31	1497	22	320	46	1120	25	1108	24	341	39	269	43	373	35	798	27	1438	21	1291	23	10652	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	340	45	0	.	0	.	122	71	172	58	180	58	109	71	58	100	296	45	68	100	363	42	409	32	495	34	456	39	3069	14
	broadleaves	0	.	0	.	55	100	223	62	379	43	0	.	0	.	55	100	0	.	0	.	25	100	29	100	0	.	145	59	911	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	340	45	0	.	55	100	345	47	551	35	180	58	109	71	113	71	296	45	68	100	388	40	439	31	495	34	601	32	3980	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	367	51	0	.	71	100	84	100	0	.	36	100	263	61	179	58	49	100	0	.	90	71	103	73	448	39	314	59	2003	20
	broadleaves	0	.	0	.	63	100	66	100	287	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	411	55	827	39
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	367	51	0	.	134	71	150	71	287	75	36	100	263	61	179	58	49	100	0	.	90	71	103	73	448	39	725	40	2830	18
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	32890	5	4533	16	7376	14	17464	9	19006	8	14827	11	30616	7	24741	8	17111	10	8917	15	7932	15	30010	7	44527	5	21949	8	281899	2
	broadleaves	21577	5	9222	10	8216	13	11731	10	18983	7	3473	18	10720	11	10837	10	5246	15	3165	20	3702	17	3538	14	3297	15	19678	5	133386	2
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	0
	total	54467	4	13755	10	15592	11	29195	7	37989	5	18300	10	41336	6	35578	7	22357	9	12082	13	11634	13	33548	6	47824	5	41627	4	415285	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. 1353915. Birmensdorf, Swiss Federal Research Institute WSL

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