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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)
aspect (9 classes)
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
aspect (9 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	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
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	.
indetermined	2737	21	0	.	0	.	8527	14	940	44	1863	30	485	54	0	0	806	51	1211	37	2520	26	1479	33	0	.	197	81	1509	47	913	49	1152	37	223	71	4142	20	286	71	218	100	5206	17	123	72	336	63	4506	18	39380	6
N	2056	30	558	74	433	72	11798	13	841	42	2680	27	0	.	739	47	8028	15	3568	24	1650	30	1439	37	1045	51	681	50	3897	24	769	47	2197	26	1362	38	1038	40	4841	17	1088	39	1868	27	4838	17	314	100	2908	25	60637	5
NE	916	32	0	.	868	55	5801	19	703	62	649	54	0	.	595	54	7166	15	401	58	3600	24	231	64	338	83	476	52	3470	26	350	71	628	46	1356	47	1115	35	3216	21	976	48	1070	35	1351	33	38	100	2180	26	37493	7
E	1026	39	322	91	981	65	5477	20	276	52	1144	38	0	.	475	51	9059	14	682	58	2431	29	876	41	460	58	838	44	3706	27	338	76	679	53	381	65	651	62	4610	16	920	52	3057	24	4645	19	222	100	1518	33	44776	6
SE	1248	40	1251	44	412	78	9338	15	737	40	2554	27	0	.	372	62	5368	18	516	62	2011	30	3161	26	302	71	1242	41	2864	27	358	59	1915	29	1428	37	482	66	4073	18	632	77	7135	16	3229	21	0	.	1178	40	51804	6
S	592	44	880	71	184	100	6789	16	498	58	2045	31	0	.	1816	41	5352	18	1033	39	1100	52	1076	40	0	.	630	51	1760	39	397	61	1686	28	1509	33	538	57	2523	20	543	64	3190	25	3192	25	223	77	1599	35	39153	7
SW	820	44	0	.	198	100	4304	21	232	63	989	45	0	.	482	61	6652	15	1281	43	1545	40	639	52	42	100	1413	48	2208	30	209	75	33	100	1188	39	426	58	4588	19	479	62	1508	31	2383	27	148	73	700	39	32467	7
W	976	38	383	72	506	77	4874	19	770	47	1604	40	0	.	690	48	7693	14	992	54	1868	37	242	76	623	63	963	52	2194	34	1034	39	653	60	1103	42	399	73	2852	20	1219	39	2789	25	5174	18	270	100	1452	41	41325	7
NW	1756	32	357	100	1168	46	10441	14	645	47	3641	27	0	.	519	45	7156	15	2278	29	2594	26	2253	29	528	66	1039	48	3334	25	375	63	785	42	2527	28	171	62	4086	34	1210	37	6493	18	7989	16	485	58	1185	34	63015	6
total	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

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. 1195695. Birmensdorf, Swiss Federal Research Institute WSL

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