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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 NFI1-NFI5
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	.	70	49	102	34	10	46	182	27
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	243	47	250	24	49	44	543	24
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	430	28	654	21	204	42	1288	16
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	748	30	1172	21	149	45	2068	16
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	675	30	1499	23	31	71	2206	18
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2167	22	3676	17	443	40	6286	13
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	.	8	58	49	39	2	100	51	32	40	58	15	75	220	26	224	28	44	29	656	14
	16-23 cm	0	.	0	.	5	100	0	.	0	.	27	53	263	36	25	100	151	27	99	62	46	92	632	19	763	19	254	25	2266	10
	24-35 cm	0	.	0	.	1	100	0	.	0	.	129	61	701	35	38	100	639	28	129	49	224	73	1388	19	2196	16	919	22	6365	9
	36-51 cm	0	.	0	.	22	100	0	.	0	.	391	50	1029	30	10	100	1231	26	315	47	318	47	2712	18	4478	13	1671	20	12177	8
	=52 cm	0	.	0	.	45	100	0	.	0	.	382	44	449	36	82	100	1118	29	458	51	461	46	4335	17	5381	15	1910	20	14621	8
	total	0	.	0	.	77	100	0	.	0	.	938	43	2492	27	157	71	3190	24	1040	40	1064	46	9288	14	13041	11	4799	17	36086	7
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	.	100	50	85	29	40	51	148	24	38	44	26	50	394	16	579	18	132	20	1572	9
	16-23 cm	21	75	0	.	26	88	0	.	0	.	285	33	277	27	130	43	376	26	162	36	87	41	1463	16	1692	13	610	20	5130	7
	24-35 cm	0	.	0	.	35	100	0	.	0	.	690	35	748	27	229	39	905	21	661	33	190	52	3130	15	3253	14	1669	17	11509	7
	36-51 cm	28	73	0	.	79	71	0	.	0	.	1242	26	1114	24	615	40	1923	24	1054	37	208	48	4145	14	6523	11	3222	15	20154	6
	=52 cm	335	70	0	.	102	62	0	.	0	.	1591	28	1250	28	516	43	3895	20	769	33	914	48	5236	14	7532	13	4801	15	26942	6
	total	413	62	0	.	242	61	0	.	0	.	3907	24	3474	22	1531	31	7247	17	2685	30	1425	42	14368	11	19580	9	10435	12	65306	5
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	978	10	91	33	93	31	360	29	191	26	200	20	658	15	276	16	169	18	91	30	131	35	97	33	61	25	454	20	3849	6
	16-23 cm	3532	7	223	29	299	25	794	21	712	20	687	31	2151	11	1320	13	617	24	406	28	541	25	344	30	247	28	1291	15	13165	4
	24-35 cm	8551	7	831	25	660	31	1434	22	2182	16	2471	17	5164	10	5195	12	1536	16	955	27	1036	26	707	28	839	27	3072	19	34633	4
	36-51 cm	16254	5	1668	22	2007	23	2705	19	4449	13	3579	15	8616	9	9434	9	2791	18	1681	24	2554	17	1050	26	1548	23	2796	19	61131	3
	=52 cm	14943	7	1539	27	2851	23	4225	19	5956	16	4535	16	13405	9	13127	10	3026	20	2711	26	3295	19	801	37	2386	22	3150	21	75948	4
	total	44258	4	4352	20	5911	20	9518	15	13489	12	11471	13	29994	7	29352	8	8139	15	5844	21	7556	15	2998	24	5081	18	10762	15	188726	3
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	239	31	352	18	240	24	623	16	560	14	3	100	82	49	35	71	48	42	61	86	12	100	26	76	26	48	0	.	2306	8
	16-23 cm	610	18	788	16	900	26	1496	14	1826	12	8	100	369	35	243	38	145	38	105	47	32	85	88	55	75	43	0	.	6686	6
	24-35 cm	1569	17	1578	17	1756	18	2967	13	4570	11	37	100	691	31	397	45	291	41	329	58	39	100	85	89	216	45	0	.	14525	6
	36-51 cm	3036	14	3203	15	2856	17	5587	11	6437	9	31	100	1173	29	831	33	394	39	333	46	59	78	75	58	205	43	0	.	24220	5
	=52 cm	2698	19	3190	17	2645	18	8305	12	10020	10	43	100	1086	30	559	38	604	54	309	64	93	100	58	77	308	44	0	.	29918	5
	total	8153	13	9111	12	8397	14	18978	9	23413	7	122	100	3401	23	2063	31	1482	35	1138	45	235	72	332	47	829	37	0	.	77654	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	.	16	58	0	.	15	66	0	.	0	.	0	.	0	.	0	.	0	.	88	30	0	.	604	16	725	14
	16-23 cm	0	.	0	.	95	66	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	254	29	3	100	1952	11	2321	10
	24-35 cm	81	74	0	.	107	49	0	.	30	100	0	.	0	.	0	.	0	.	0	.	0	.	273	38	11	100	3377	11	3879	10
	36-51 cm	81	71	0	.	217	61	0	.	88	60	0	.	0	.	0	.	0	.	0	.	0	.	305	33	36	100	2781	11	3508	10
	=52 cm	0	.	0	.	113	100	0	.	19	100	0	.	0	.	0	.	0	.	0	.	0	.	185	62	62	100	2218	18	2596	17
	total	165	71	0	.	547	50	0	.	169	63	0	.	0	.	0	.	0	.	0	.	0	.	1104	28	113	100	10932	8	13029	8
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	1248	10	443	15	352	18	983	15	766	12	311	21	874	12	353	15	416	13	229	29	184	27	894	11	992	13	1244	10	9290	4
	16-23 cm	4163	7	1011	14	1325	19	2290	11	2555	10	1008	23	3060	9	1718	12	1289	14	773	19	706	21	3024	10	3031	9	4157	7	30110	3
	24-35 cm	10202	6	2409	14	2559	14	4401	11	6783	9	3326	15	7305	8	5859	11	3371	11	2074	19	1488	22	6013	10	7169	8	9241	8	72200	2
	36-51 cm	19399	5	4871	12	5181	13	8292	9	10973	7	5243	12	11933	8	10890	9	6338	12	3384	18	3138	15	9034	9	13962	7	10619	7	123258	2
	=52 cm	17976	6	4729	14	5755	14	12530	10	15995	8	6550	13	16189	8	14283	10	8643	12	4247	19	4764	16	11291	9	17167	8	12111	8	152231	2
	total	52988	4	13463	10	15173	11	28496	7	37072	6	16438	11	39361	6	33103	7	20058	9	10707	15	10280	13	30257	7	42320	6	37372	5	387089	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. 1234291. Birmensdorf, Swiss Federal Research Institute WSL

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