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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
tree condition · conifers and broadleaves
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest
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
tree condition	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	±%
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	.
standing dead	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	2431	12	417	30	313	27	569	20	819	21	922	21	2594	16	2091	16	1343	17	768	21	488	29	2971	14	3010	12	1516	15	20251	5
	broadleaves	1441	17	552	27	577	28	274	30	417	30	285	30	767	24	888	23	692	27	387	33	587	27	791	21	644	22	5876	11	14178	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3873	10	969	21	890	21	843	17	1236	17	1207	18	3361	14	2978	13	2035	15	1155	18	1075	22	3763	12	3654	11	7392	9	34429	4
standing living	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	30156	5	4305	16	6327	16	16718	10	15516	9	13611	12	29780	8	16904	9	15970	10	9277	14	6664	16	36780	7	52109	6	24240	8	278358	2
	broadleaves	30111	6	10916	11	9224	13	12380	9	20020	7	5835	16	14670	10	12216	11	9976	13	4873	18	5413	16	12240	11	8140	15	45928	6	201942	2
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	60267	4	15221	10	15551	11	29098	7	35536	6	19446	11	44450	6	29120	8	25946	8	14150	13	12077	13	49020	6	60249	5	70169	4	480300	1
lying dead	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	1671	14	279	31	264	33	544	22	444	19	1730	19	2313	14	1324	26	2311	19	482	28	865	23	4825	11	5113	12	1563	16	23727	5
	broadleaves	1874	14	759	23	564	25	412	24	1012	18	494	30	1072	17	1007	21	641	23	315	44	818	35	1141	19	804	23	4013	11	14926	5
	indeterminable	54	74	0	.	0	.	0	.	17	100	0	.	17	100	0	.	15	100	0	.	0	.	18	100	15	100	0	.	137	40
	total	3599	10	1038	19	827	20	956	19	1472	14	2224	17	3403	12	2332	18	2968	16	797	24	1683	21	5984	10	5932	10	5575	9	38790	4
lying living	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	0	.	17	100	0	.	0	.	10	100	30	71	115	67	13	71	52	74	110	63	17	100	424	31	559	32	130	40	1479	17
	broadleaves	62	53	108	46	0	.	0	.	137	35	22	100	188	34	157	37	41	61	194	73	213	41	272	45	70	61	1001	22	2464	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	62	53	125	42	0	.	0	.	147	34	52	59	303	34	170	37	93	49	304	52	231	40	696	26	629	30	1132	20	3943	11
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	34259	5	5018	15	6904	15	17831	10	16789	9	16293	12	34803	7	20332	9	19676	9	10637	14	8034	15	45000	7	60790	6	27449	8	323815	2
	broadleaves	33488	6	12335	11	10365	13	13066	9	21586	7	6636	15	16697	9	14268	11	11350	12	5769	19	7031	15	14443	11	9658	14	56818	5	233510	2
	indeterminable	54	74	0	.	0	.	0	.	17	100	0	.	17	100	0	.	15	100	0	.	0	.	18	100	15	100	0	.	137	40
	total	67801	4	17353	10	17269	11	30897	7	38392	6	22929	11	51517	6	34600	8	31042	8	16406	13	15065	12	59462	6	70464	5	84268	4	557462	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. 1359247. Birmensdorf, Swiss Federal Research Institute WSL

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