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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
total wood volume (stemwood)
tree species (5 classes) · tree state (living/dead)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
tree species (5 classes)	tree state (living/dead)	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	±%	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	dead	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
spruce	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	2617	21	2795	30	2096	30	28801	7	769	35	8462	14	4	75	3353	23	36021	6	2239	23	7023	14	4483	15	1721	34	4173	20	10647	13	1388	28	1755	26	5520	16	2572	21	7267	19	4327	21	12480	10	15359	9	1039	37	5788	14	172699	2
	dead	172	35	91	40	283	48	3728	12	59	45	1004	29	0	.	368	33	3568	12	340	39	1187	22	267	50	113	47	477	33	898	20	272	66	119	43	673	28	219	46	808	28	477	28	1294	19	2397	13	75	55	527	35	19416	5
	total	2789	20	2886	30	2378	28	32529	7	828	35	9466	14	4	75	3720	22	39590	6	2579	22	8210	13	4750	15	1834	33	4650	20	11545	13	1660	27	1874	25	6193	16	2791	20	8075	19	4804	20	13774	10	17755	9	1114	35	6315	14	192115	2
fir	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	1398	28	609	41	1551	36	16417	9	549	37	2259	24	4	100	501	48	2105	23	3889	20	6298	14	4592	17	368	56	1259	34	3926	17	184	59	1315	22	2149	24	1377	32	1360	27	888	32	7047	13	1642	26	398	44	2054	20	64140	4
	dead	202	37	2	100	95	59	1208	18	67	54	249	49	0	.	13	80	277	36	325	29	624	29	90	37	43	79	93	68	300	41	0	.	148	40	149	39	8	100	83	53	160	40	476	31	156	41	59	58	121	43	4947	9
	total	1600	26	610	41	1647	35	17625	8	615	37	2508	24	4	100	514	48	2382	22	4215	19	6923	14	4682	17	411	55	1352	32	4226	17	184	59	1463	21	2299	23	1385	32	1443	28	1048	31	7523	13	1797	26	457	41	2174	19	69087	4
other conifers	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	627	29	0	.	14	74	2195	21	576	47	428	46	67	100	53	95	13712	8	416	35	626	45	172	49	4	100	244	74	815	46	432	38	396	29	144	59	479	37	5130	13	271	52	778	24	12449	10	40	100	1199	20	41266	5
	dead	0	.	0	.	0	.	264	28	53	51	24	74	14	100	0	.	1299	16	58	55	0	.	25	93	0	.	16	82	47	46	29	58	83	58	23	69	23	76	260	24	8	73	90	39	1346	13	0	.	110	47	3772	8
	total	627	29	0	.	14	74	2459	20	629	45	452	47	81	84	53	95	15010	8	474	36	626	45	197	50	4	100	260	71	862	44	460	38	480	27	167	58	503	36	5390	13	279	51	868	24	13794	9	40	100	1310	19	45038	4
beech	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	4603	16	323	48	494	36	14012	9	2525	19	3366	19	0	.	935	31	1978	22	3416	17	3640	17	1304	18	486	42	1366	30	5681	15	2159	21	4514	15	1924	24	1734	20	5971	12	1033	32	6732	12	920	30	314	43	4310	14	73741	3
	dead	329	37	2	78	39	58	453	25	275	38	143	37	0	.	71	42	99	46	106	39	169	41	59	63	17	63	59	46	320	27	56	44	247	30	82	56	2	100	222	29	9	74	277	28	82	53	12	100	86	35	3215	9
	total	4932	16	325	48	534	34	14465	9	2800	19	3509	19	0	.	1006	29	2077	22	3521	17	3809	17	1363	18	503	42	1425	30	6000	14	2215	21	4761	15	2005	24	1736	20	6193	12	1042	32	7009	12	1002	29	326	43	4396	14	76955	3
other broadleaves	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	2882	16	25	75	595	36	5924	10	1225	22	2653	20	410	57	848	32	3464	15	2003	21	1731	27	845	22	760	46	436	43	3875	15	581	29	1746	18	1338	26	2798	21	11346	8	765	31	5278	14	2554	14	245	42	3877	13	58204	3
	dead	188	47	0	.	91	52	421	24	109	39	185	42	22	63	184	46	510	21	412	39	140	31	51	45	71	71	24	52	295	26	16	67	106	32	123	36	97	50	1910	11	61	44	346	21	293	19	4	100	194	37	5854	6
	total	3070	17	25	75	686	35	6345	10	1334	22	2838	19	432	56	1032	30	3974	15	2415	22	1871	25	896	21	831	45	460	42	4169	14	597	28	1852	18	1461	26	2895	21	13257	8	826	31	5624	13	2847	14	249	42	4071	13	64057	3
indeterminable	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	dead	0	.	0	.	11	93	165	32	16	81	8	88	0	.	0	.	141	30	14	89	18	55	22	74	35	72	10	87	37	51	0	.	31	86	0	.	1	100	7	51	22	100	44	45	95	39	0	.	29	76	708	14
	total	0	.	0	.	11	93	165	32	16	81	8	88	0	.	0	.	141	30	14	89	18	55	22	74	35	72	10	87	37	51	0	.	31	86	0	.	1	100	7	51	22	100	44	45	95	39	0	.	29	76	708	14
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	12128	11	3751	28	4750	24	67349	5	5643	16	17168	11	485	54	5689	18	57280	5	11963	12	19319	10	11396	11	3339	26	7478	17	24943	9	4744	18	9727	12	11076	13	8960	13	31074	6	7284	17	32316	7	32923	7	2036	30	17228	9	410050	1
	dead	891	21	95	39	519	35	6238	9	579	26	1613	21	36	57	635	25	5895	9	1256	20	2138	18	514	31	279	34	679	28	1897	15	372	50	733	21	1049	21	351	35	3291	10	738	23	2526	14	4368	9	149	49	1067	21	37911	3
	total	13018	11	3847	28	5270	23	73588	5	6222	16	18781	10	521	54	6325	18	63175	5	13219	12	21457	10	11911	11	3618	25	8157	17	26839	9	5116	18	10461	12	12125	13	9311	13	34364	6	8022	17	34842	7	37291	6	2186	29	18295	9	447962	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. 1201611. Birmensdorf, Swiss Federal Research Institute WSL

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