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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)
diameter at breast height (10 classes) · conifers and broadleaves
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/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
diameter at breast height (10 classes)	conifers and broadleaves	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	.
	conifers	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	.
	indeterminable	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	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	1686	10	243	27	490	32	1338	16	1022	15	623	24	1785	12	661	17	762	14	445	20	318	25	1870	12	2604	9	957	12	14804	4
	broadleaves	1858	9	767	15	634	19	952	14	1275	11	365	20	920	14	654	15	669	18	336	27	363	26	937	13	631	17	3267	8	13626	3
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	3544	7	1010	14	1124	18	2290	12	2296	10	987	17	2706	10	1314	12	1431	12	781	19	680	19	2806	9	3236	8	4224	6	28430	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	3566	9	569	21	852	24	2185	16	2265	14	1505	20	3378	9	2189	14	1537	14	1018	18	672	28	4084	11	5379	8	2430	12	31630	3
	broadleaves	3706	8	1208	15	1148	18	1241	14	2471	11	961	22	2019	14	1888	15	835	19	625	24	588	26	1123	16	804	20	5531	8	24150	4
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	7272	6	1776	13	2000	15	3427	12	4736	9	2466	15	5398	8	4078	11	2373	12	1643	16	1260	21	5207	9	6183	8	7961	6	55779	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	5521	9	813	22	1095	21	2483	14	2770	13	2730	15	4853	10	3977	12	2607	13	1504	19	952	24	5386	10	7617	8	3955	12	46265	3
	broadleaves	5395	8	1779	15	1626	17	1995	13	3188	10	786	24	2282	14	2611	13	1104	19	710	28	670	23	703	21	502	18	4140	9	27493	3
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	0
	total	10916	6	2592	13	2720	14	4479	10	5958	8	3516	13	7135	8	6588	9	3712	11	2215	16	1622	18	6090	9	8120	7	8095	8	73757	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	7015	7	980	20	1402	19	3100	14	4116	11	3101	14	6046	9	4467	11	3798	14	2002	21	1361	18	6029	9	9702	7	4308	11	57428	3
	broadleaves	5632	8	2302	14	1838	18	2171	13	3315	10	595	27	2379	16	2778	14	973	23	505	28	929	20	322	32	510	28	2913	10	27163	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12647	5	3282	13	3240	14	5271	10	7431	8	3696	13	8426	8	7246	9	4772	12	2507	18	2290	15	6351	9	10212	7	7221	7	84590	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	6796	8	869	25	1513	21	3369	13	3524	12	3081	14	6522	10	6012	11	3335	14	1529	20	1534	19	5500	10	8438	8	4353	12	56373	3
	broadleaves	3335	11	1728	20	1642	19	2827	15	4141	10	473	30	1926	18	1584	17	764	26	377	34	691	27	204	43	414	28	1525	15	21630	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	10131	6	2597	15	3155	15	6196	10	7665	8	3554	13	8447	8	7597	9	4099	12	1906	17	2224	17	5704	10	8852	8	5878	10	78004	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	4373	12	619	27	878	23	2694	16	2410	15	2177	19	4155	13	4071	14	2391	16	1025	23	803	27	3199	13	5185	11	2351	13	36331	4
	broadleaves	1242	19	885	24	838	22	1261	19	2370	16	79	71	685	31	694	27	486	37	252	48	303	51	108	53	273	35	813	21	10290	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5615	10	1504	18	1716	17	3956	13	4780	11	2256	19	4841	12	4765	13	2877	15	1277	22	1106	24	3307	13	5457	10	3164	10	46621	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	2253	15	379	47	476	36	1270	21	1690	20	723	30	2362	16	2118	18	1756	18	1044	31	963	29	2071	19	2580	14	1646	20	21330	5
	broadleaves	278	45	487	31	294	36	622	36	1087	22	169	47	405	39	279	61	201	45	77	70	41	100	47	73	161	56	395	26	4544	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2531	14	867	27	770	26	1892	18	2776	15	892	26	2766	14	2397	18	1957	17	1122	29	1004	29	2118	18	2741	14	2040	17	25874	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	737	31	61	100	406	42	646	31	1038	27	320	46	1076	26	811	29	341	39	135	62	317	39	798	27	1438	21	938	29	9061	9
	broadleaves	48	100	66	100	70	100	0	.	459	36	0	.	44	100	251	50	0	.	134	58	27	100	0	0	0	.	325	32	1423	19
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	785	30	126	71	476	39	646	31	1497	22	320	46	1120	25	1062	25	341	39	269	43	343	37	798	27	1438	21	1263	23	10484	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	340	45	0	.	0	.	122	71	172	58	180	58	109	71	58	100	296	45	68	100	288	46	409	32	495	34	456	39	2994	14
	broadleaves	0	.	0	.	55	100	111	72	379	43	0	.	0	.	55	100	0	.	0	.	25	100	29	100	0	.	145	59	798	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	340	45	0	.	55	100	233	50	551	35	180	58	109	71	113	71	296	45	68	100	312	43	439	31	495	34	601	32	3792	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	255	59	0	.	71	100	84	100	0	.	36	100	263	61	179	58	49	100	0	.	90	71	103	73	448	39	314	59	1891	20
	broadleaves	0	.	0	.	63	100	66	100	287	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	411	55	827	39
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	255	59	0	.	134	71	150	71	287	75	36	100	263	61	179	58	49	100	0	.	90	71	103	73	448	39	725	40	2719	19
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	32543	5	4533	16	7182	15	17292	9	19006	8	14475	11	30549	7	24543	8	16872	10	8771	15	7295	15	29449	7	43887	6	21708	8	278106	2
	broadleaves	21494	5	9222	10	8208	13	11245	10	18973	7	3427	18	10661	11	10794	10	5033	15	3017	20	3636	17	3475	14	3296	15	19464	5	131944	2
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	0
	total	54037	4	13755	10	15390	11	28538	7	37979	5	17902	10	41210	6	35338	7	21905	9	11788	14	10932	13	32923	7	47182	5	41172	4	410050	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. 1353909. Birmensdorf, Swiss Federal Research Institute WSL

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