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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 number of stems
degree of damage · conifers and broadleaves
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-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
degree of damage	conifers and broadleaves	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	.
	conifers	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	.
	broadleaves	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	.
	indeterminable	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	.
no damage	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	.
	conifers	3373	17	1323	32	1833	30	30491	6	1530	25	6239	16	128	72	1687	32	32882	7	4332	15	10871	13	5977	14	1756	48	5493	21	7514	14	1216	28	3119	23	4392	16	3096	19	9148	14	4144	21	14394	10	23367	8	714	41	5394	16	184413	2
	broadleaves	6293	12	151	55	833	34	18299	8	3560	19	5472	15	316	59	1025	27	7114	16	4422	15	4379	14	3336	16	922	38	1337	28	6900	14	2379	22	5714	16	2423	21	3339	16	23308	8	1595	29	12981	10	6570	14	374	41	6964	11	130005	3
	indeterminable	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	9667	11	1474	31	2666	26	48789	6	5090	17	11712	12	444	56	2712	24	39997	6	8754	12	15250	11	9314	12	2678	35	6829	18	14414	11	3595	20	8833	14	6816	15	6435	14	32456	7	5739	19	27375	8	29937	7	1088	34	12358	10	314419	2
slight damage	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	.
	conifers	656	26	174	35	231	32	4768	10	182	42	1210	22	0	.	471	30	8459	8	305	24	1149	17	512	25	146	41	716	28	925	19	171	42	328	37	607	26	666	30	1581	17	972	23	1848	14	5022	11	78	55	721	22	31899	4
	broadleaves	248	24	25	77	108	47	1979	14	197	30	468	31	29	80	226	33	889	19	485	25	580	22	334	24	119	47	215	34	812	18	210	32	426	25	257	33	249	25	2989	14	146	52	958	18	737	25	17	100	439	23	13141	5
	indeterminable	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	904	20	199	36	340	29	6747	9	379	26	1679	19	29	80	697	25	9348	8	790	18	1729	14	846	18	265	33	932	24	1737	14	381	28	754	22	865	21	915	23	4570	11	1118	22	2805	12	5759	10	96	48	1159	17	45040	3
moderate damage	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	.
	conifers	207	36	114	59	283	43	2385	11	55	59	628	22	17	100	230	26	2850	10	243	27	564	20	443	25	98	49	383	31	750	19	111	36	135	40	423	23	308	26	1043	20	418	28	1037	16	1785	12	33	71	506	23	15049	4
	broadleaves	371	22	42	47	139	55	2500	12	500	30	432	34	25	100	304	31	1006	18	396	23	367	25	323	28	359	45	439	33	1244	20	196	46	736	20	376	27	205	29	3815	10	342	38	1207	18	847	21	31	63	596	18	16796	4
	indeterminable	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	578	21	156	49	422	37	4884	9	555	31	1060	19	42	100	533	22	3856	9	639	19	932	16	766	20	457	40	822	25	1994	15	307	33	871	19	800	18	512	20	4858	8	760	26	2244	12	2632	11	64	48	1102	16	31845	3
severe damage	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	.
	conifers	100	35	101	46	106	44	1492	11	14	70	439	22	0	.	162	31	2300	10	263	27	624	19	380	29	78	55	478	32	476	21	14	71	257	31	146	32	86	39	809	23	335	37	734	17	1448	13	51	54	267	23	11160	4
	broadleaves	549	20	32	81	91	45	1388	13	447	33	492	34	0	.	234	35	1277	23	553	26	564	25	97	41	445	59	266	43	727	20	155	36	492	23	299	27	464	26	4667	11	243	51	1422	15	941	24	99	59	588	20	16534	5
	indeterminable	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	650	18	134	40	197	39	2880	9	461	32	931	21	0	.	396	26	3578	11	816	20	1187	17	477	25	523	51	744	26	1203	15	169	34	749	19	444	25	550	23	5477	10	578	31	2156	12	2389	12	150	43	855	17	27693	3
very severe damage	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	.
	conifers	24	78	27	71	21	57	636	17	74	44	109	38	0	.	113	43	1135	20	83	41	133	35	54	48	13	71	161	70	243	28	24	77	38	61	57	58	34	53	159	25	162	56	272	24	917	20	21	100	164	29	4674	8
	broadleaves	412	23	13	71	157	42	844	15	116	41	209	39	0	.	400	32	580	23	305	27	253	29	36	71	207	59	105	48	474	23	108	51	397	27	98	36	259	37	2763	12	86	63	307	22	688	25	7	100	398	22	9222	6
	indeterminable	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	436	23	40	53	179	39	1480	12	190	33	318	29	0	.	513	27	1715	15	388	23	386	23	90	40	220	57	266	47	717	19	132	44	435	25	155	32	293	34	2922	11	248	43	579	17	1605	17	28	79	562	18	13896	5
dead	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	.
	conifers	430	23	266	43	437	32	6568	10	371	35	1658	21	7	100	454	37	8133	10	940	28	2109	22	617	22	275	44	965	33	1706	19	224	44	576	26	1152	31	363	42	1246	17	943	26	2506	14	6916	11	251	37	868	38	39981	4
	broadleaves	696	24	33	71	299	38	1826	15	608	29	962	26	89	52	876	37	2343	17	1171	23	1043	21	377	32	146	52	352	45	1287	19	243	37	798	26	575	38	216	51	8111	10	361	47	2081	15	1642	18	42	85	854	26	27030	5
	indeterminable	0	.	0	.	0	.	15	100	19	100	0	.	0	.	0	.	15	100	0	.	17	100	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	0	.	18	100	0	.	17	100	137	40
	total	1126	18	299	42	736	28	8409	9	998	23	2620	17	95	52	1330	29	10492	9	2110	19	3169	17	994	19	421	34	1317	27	2993	14	467	30	1408	20	1727	25	579	32	9357	9	1304	23	4587	11	8577	10	293	35	1739	23	67148	3
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	.
	conifers	4791	16	2006	30	2912	25	46340	6	2226	23	10285	14	152	68	3116	25	55760	6	6167	15	15449	12	7983	13	2366	43	8196	20	11613	12	1760	26	4453	19	6777	16	4552	19	13986	13	6973	18	20791	9	39455	8	1148	37	7920	15	287176	2
	broadleaves	8569	12	296	47	1627	27	26834	7	5427	18	8035	14	458	51	3066	23	13209	14	7331	14	7186	13	4504	16	2198	35	2714	26	11445	13	3291	21	8562	14	4029	21	4732	16	45653	7	2773	27	18954	10	11425	12	571	41	9839	11	212728	2
	indeterminable	0	.	0	.	0	.	15	100	19	100	0	.	0	.	0	.	15	100	0	.	17	100	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	0	.	18	100	0	.	17	100	137	40
	total	13360	11	2302	29	4540	22	73189	5	7672	16	18320	11	610	52	6182	19	68984	6	13497	12	22652	10	12487	11	4564	29	10910	17	23058	10	5050	19	13050	13	10807	14	9285	14	59639	6	9746	17	39745	8	50898	7	1719	31	17775	10	500040	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. 1258742. Birmensdorf, Swiss Federal Research Institute WSL

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