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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
number of stems
tree age (in 40-year classes) · 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
tree age (in 40-year classes)	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	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	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	.	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	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
=40 years	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	2381	24	0	.	697	66	9272	13	636	49	1786	33	86	82	323	64	4044	19	1198	25	4818	22	1270	37	86	61	1439	47	2469	29	323	68	1406	42	883	52	1232	36	1391	31	453	56	4038	24	2306	15	124	51	1909	31	44572	6
	broadleaves	4112	16	0	.	399	45	7684	12	1606	31	2421	21	69	79	561	37	3533	20	1589	22	1969	21	1629	27	447	43	584	31	2350	24	939	37	2542	29	398	36	2010	22	7043	16	214	39	4857	18	2325	22	126	64	4349	17	53756	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	6492	15	0	.	1097	49	16956	10	2242	28	4207	20	155	71	883	36	7577	15	2787	18	6787	18	2900	24	533	39	2023	36	4820	21	1263	39	3948	25	1282	39	3242	22	8434	15	667	45	8895	16	4632	14	250	50	6258	16	98328	4
41-80 years	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	1482	26	525	39	856	32	15420	9	803	30	3544	21	59	100	998	32	18741	9	2005	20	5343	16	3359	17	1099	52	2209	24	3330	20	768	40	1437	25	1979	23	2347	26	5183	17	2690	22	7840	14	13273	12	669	46	3717	21	99677	3
	broadleaves	2744	16	131	47	628	31	11943	9	2148	23	2783	20	248	66	1075	30	6196	20	2769	22	3067	16	1986	19	1341	42	1034	43	5282	16	1454	26	3043	20	1949	26	1763	20	26422	8	1565	30	7928	13	6374	15	322	53	3325	14	97520	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	4226	16	656	35	1484	28	27363	7	2951	20	6327	16	307	62	2073	24	24936	8	4774	17	8410	13	5345	14	2441	34	3244	22	8612	15	2223	24	4480	18	3928	20	4110	18	31605	7	4254	21	15768	11	19647	10	991	38	7042	14	197198	2
81-120 years	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	431	26	800	35	800	33	7337	9	282	37	1699	20	0	.	455	35	10349	10	1480	24	1759	17	1592	23	640	64	1998	28	2429	17	388	34	703	33	1332	22	469	27	2721	18	1343	24	3511	13	7214	13	46	71	1047	19	50826	4
	broadleaves	873	21	82	64	287	51	4152	14	862	27	1678	21	15	71	470	28	916	24	1478	21	769	21	396	24	202	41	568	50	2155	20	522	29	1034	23	943	27	581	25	2544	15	539	37	3411	15	526	26	53	48	1031	16	26087	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	1304	17	882	34	1088	31	11489	9	1144	26	3377	16	15	71	925	26	11265	10	2958	16	2528	15	1988	21	842	52	2565	25	4584	14	911	25	1737	21	2275	20	1050	21	5265	12	1882	23	6922	11	7740	12	99	54	2078	15	76912	3
121-160 years	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	67	66	402	46	114	56	4359	13	63	59	892	30	0	.	399	37	6849	11	516	27	916	22	794	20	176	42	1137	31	1097	25	56	53	258	37	579	29	141	42	1960	17	793	40	1723	17	4587	13	57	68	379	27	28312	5
	broadleaves	138	37	50	55	14	71	916	18	197	46	191	46	37	100	78	54	187	39	262	30	317	34	108	39	61	100	69	37	308	25	97	54	757	35	129	51	100	37	734	46	31	77	562	21	428	59	29	100	252	33	6053	10
	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	204	33	452	44	128	51	5275	11	260	41	1083	27	37	100	477	35	7036	11	778	24	1233	20	903	20	236	44	1206	29	1405	21	153	49	1014	30	708	27	241	32	2694	17	824	38	2285	15	5016	13	86	77	631	23	34365	4
>160 years	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	.	14	71	7	100	3384	13	70	53	705	66	0	.	487	33	7644	11	29	61	505	48	350	32	91	78	448	33	582	27	0	.	73	75	852	30	0	.	1485	17	751	36	1173	21	5158	14	0	.	0	.	23808	6
	broadleaves	7	100	0	.	0	.	313	36	7	100	0	.	0	.	6	100	34	61	62	66	21	100	8	100	0	.	107	78	61	51	35	72	389	62	34	74	62	69	799	30	63	58	116	34	129	67	0	.	27	50	2282	17
	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	7	100	14	71	7	100	3697	13	77	52	705	66	0	.	494	32	7678	10	91	59	527	46	358	32	91	78	555	32	643	25	35	72	462	55	886	29	62	69	2284	15	814	34	1288	19	5287	14	0	.	27	50	26089	5
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	4361	17	1740	30	2475	27	39772	6	1855	24	8627	14	145	71	2662	26	47626	6	5227	14	13340	12	7366	13	2091	44	7231	20	9907	12	1535	27	3877	20	5626	16	4189	19	12740	13	6030	19	18285	10	32539	8	897	38	7052	15	247194	2
	broadleaves	7873	12	263	50	1328	28	25009	7	4820	18	7073	14	370	55	2190	24	10866	14	6160	14	6143	13	4127	15	2052	36	2362	26	10157	13	3048	21	7764	14	3454	20	4516	16	37542	7	2412	27	16874	10	9782	12	529	41	8984	11	185698	2
	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	.	0	.	0	0	0	.	0	0	0	0
	total	12234	11	2003	29	3803	24	64780	5	6674	16	15699	11	515	54	4852	19	58492	6	11387	12	19483	10	11493	11	4143	30	9593	17	20065	11	4584	19	11641	13	9080	14	8705	14	50282	6	8442	17	35159	8	42322	7	1426	32	16036	10	432892	2

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. 1303944. Birmensdorf, Swiss Federal Research Institute WSL

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