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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)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (main diameter classes)
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
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (main diameter classes)	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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=52 cm	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	.
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	92	40	140	26	70	28	302	18
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	285	41	402	18	160	27	846	17
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	495	25	889	18	535	29	1919	13
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	849	27	1369	19	309	29	2528	14
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	796	27	1526	23	220	39	2542	16
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2517	20	4327	15	1294	22	8138	10
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	3	100	0	.	0	.	15	56	64	34	2	100	63	28	56	44	20	58	237	24	292	23	87	27	840	12
	16-23 cm	0	.	0	.	5	100	0	.	0	.	52	55	300	33	25	100	199	24	146	46	68	66	715	18	1087	16	373	20	2970	9
	24-35 cm	0	.	0	.	1	100	0	.	0	.	196	53	751	33	38	100	727	26	198	46	262	63	1551	17	2833	14	1080	20	7638	8
	36-51 cm	0	.	0	.	22	100	0	.	0	.	458	45	1188	27	10	100	1455	24	325	46	794	47	2903	17	5090	12	1714	19	13959	7
	=52 cm	0	.	0	.	45	100	0	.	0	.	382	44	687	33	82	100	1363	27	458	51	601	38	4558	16	5983	14	2226	18	16385	8
	total	0	.	0	.	77	100	0	.	0	.	1103	39	2991	24	157	71	3807	22	1183	36	1745	35	9965	13	15285	10	5480	15	41793	6
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	29	82	0	.	0	0	0	.	0	.	115	44	92	27	41	50	191	21	49	36	29	46	492	14	651	17	165	17	1852	8
	16-23 cm	21	75	0	.	26	88	0	.	0	.	356	29	288	26	158	37	471	22	211	31	101	38	1722	14	1903	12	684	18	5941	7
	24-35 cm	13	100	0	.	35	100	0	.	0	.	767	32	862	25	277	35	1103	19	743	30	190	52	3817	14	3732	12	1852	15	13390	6
	36-51 cm	37	60	0	.	79	71	0	.	0	.	1561	24	1140	23	762	36	2283	22	1136	35	208	48	4626	13	7018	10	3502	14	22352	6
	=52 cm	354	66	0	.	102	62	0	.	0	.	2013	25	1296	27	688	37	4069	19	865	31	914	48	5532	13	8192	12	5270	14	29294	6
	total	454	57	0	.	242	61	0	.	0	.	4811	21	3679	21	1925	29	8116	16	3003	27	1442	41	16188	10	21496	9	11473	11	72829	4
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	996	10	94	32	101	29	369	28	200	26	234	19	699	14	309	14	220	19	120	26	166	29	109	30	88	25	511	18	4217	5
	16-23 cm	3592	7	228	29	299	25	805	21	767	19	813	27	2214	11	1414	13	658	23	582	24	597	23	364	28	323	26	1520	13	14174	4
	24-35 cm	8703	7	915	24	682	30	1473	22	2215	16	2718	17	5404	10	5417	12	1669	16	1146	24	1143	24	761	27	1011	24	3506	17	36763	4
	36-51 cm	16510	5	1720	22	2210	23	2818	19	4574	13	3769	14	9259	9	10222	9	3002	17	1847	23	2760	16	1097	25	1808	21	3180	17	64777	3
	=52 cm	15496	7	1667	26	2851	23	4244	19	6320	15	4730	16	13689	9	14069	10	3404	19	2846	25	3547	18	903	34	2544	21	3244	21	79554	3
	total	45297	4	4625	19	6144	19	9709	15	14075	12	12264	12	31265	7	31432	8	8952	14	6540	19	8213	15	3234	23	5775	17	11961	14	199485	2
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	224	35	352	18	244	24	626	16	579	13	3	100	82	49	35	71	48	42	61	86	12	100	28	70	26	48	0	.	2321	8
	16-23 cm	613	18	792	16	900	26	1498	13	1844	12	8	100	369	35	243	38	145	38	105	47	32	85	97	51	75	43	0	.	6721	6
	24-35 cm	1585	17	1593	17	1756	18	2981	13	4580	11	37	100	691	31	397	45	291	41	377	52	39	100	111	72	216	45	0	.	14653	6
	36-51 cm	3168	14	3203	15	2856	17	5636	11	6492	9	31	100	1173	29	831	33	394	39	409	42	59	78	98	50	205	43	0	.	24553	5
	=52 cm	2960	18	3190	17	2817	17	8746	12	10251	10	43	100	1086	30	559	38	604	54	404	54	93	100	118	63	308	44	0	.	31178	5
	total	8551	13	9130	12	8573	14	19486	9	23745	7	122	100	3401	23	2063	31	1482	35	1356	41	235	72	452	43	829	37	0	.	79426	4
hyperinsubric and colline	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	3	100	0	.	26	52	0	.	15	66	0	.	0	.	0	.	0	.	0	.	0	.	101	27	0	.	642	15	786	13
	16-23 cm	0	.	0	.	95	66	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	294	27	3	100	2055	11	2465	10
	24-35 cm	81	74	0	.	107	49	0	.	30	100	0	.	0	.	0	.	0	.	0	.	0	.	287	36	11	100	3599	11	4115	10
	36-51 cm	81	71	0	.	217	61	0	.	88	60	0	.	0	.	0	.	0	.	0	.	0	.	326	32	36	100	2857	11	3604	10
	=52 cm	0	.	0	.	113	100	0	.	19	100	0	.	0	.	0	.	0	.	0	.	0	.	185	62	62	100	2267	18	2646	16
	total	165	71	0	.	556	49	0	.	169	63	0	.	0	.	0	.	0	.	0	.	0	.	1192	26	113	100	11420	8	13615	7
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	1251	10	446	15	374	17	995	14	794	12	367	18	938	11	388	14	522	12	286	24	227	23	1058	10	1197	11	1475	9	10318	3
	16-23 cm	4225	7	1021	14	1325	19	2302	11	2628	10	1228	20	3171	9	1840	12	1473	13	1044	17	797	19	3477	9	3793	8	4792	6	33117	3
	24-35 cm	10383	6	2508	14	2581	14	4454	11	6824	8	3718	14	7709	8	6129	11	3790	10	2463	17	1634	21	7022	9	8692	8	10571	7	78478	2
	36-51 cm	19796	5	4923	12	5384	13	8454	9	11153	7	5820	11	12761	8	11824	8	7133	11	3716	16	3821	15	9899	8	15527	6	11562	6	131773	2
	=52 cm	18811	6	4857	14	5928	14	12990	10	16590	8	7168	13	16758	8	15397	9	9439	12	4572	18	5155	16	12092	9	18616	7	13227	7	161599	2
	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. 1234301. Birmensdorf, Swiss Federal Research Institute WSL

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