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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.

NFI1
growing stock (stemwood)
higher/lower altitude zone · diameter at breast height (10 classes)
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
higher/lower altitude zone	diameter at breast height (10 classes)	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	.
	12-20 cm	0	.	0	.	0	.	0	.	5	100	0	.	5	100
	21-30 cm	0	.	0	.	0	.	0	.	4	100	0	.	4	100
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	9	100	0	.	9	100
lower altitude zone	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	10898	3	2223	8	1662	11	485	16	156	22	3068	6	18492	2
	21-30 cm	24063	3	4151	7	3925	10	549	18	302	28	2938	7	35927	2
	31-40 cm	36250	3	6884	8	4643	10	346	18	417	26	1981	9	50522	2
	41-50 cm	32687	3	6879	8	4111	10	251	25	401	24	1577	10	45906	2
	51-60 cm	18433	4	4581	9	2199	13	250	30	356	26	1018	14	26837	3
	61-70 cm	6664	6	1872	13	766	22	55	49	106	42	445	18	9909	5
	71-80 cm	2482	11	819	24	367	38	39	61	27	100	479	28	4213	9
	81-90 cm	1055	18	202	41	34	100	19	100	21	100	152	39	1484	15
	91-100 cm	296	34	49	100	0	.	0	.	0	.	129	41	474	26
	>100 cm	126	59	0	.	0	.	0	.	0	.	602	34	728	30
	total	132953	2	27662	6	17708	8	1994	14	1786	20	12388	5	194491	2
higher altitude zone	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	1787	8	3297	6	2139	8	1962	8	2228	7	654	10	12066	3
	21-30 cm	3945	7	6987	6	5196	8	4009	8	5385	7	1480	11	27002	3
	31-40 cm	5538	7	11131	6	7375	7	4768	7	8397	6	2355	11	39563	3
	41-50 cm	5343	6	11180	6	7385	7	4570	7	8389	6	2182	10	39049	3
	51-60 cm	3363	9	7867	7	5106	9	3595	9	6106	7	1640	12	27675	3
	61-70 cm	1404	13	3295	10	2421	12	1307	14	2837	11	937	19	12202	5
	71-80 cm	742	24	1173	16	1113	19	858	17	1399	14	571	21	5857	7
	81-90 cm	75	59	399	27	433	26	295	27	411	25	164	39	1777	12
	91-100 cm	56	71	168	47	193	41	94	51	238	42	74	60	822	20
	>100 cm	0	.	122	65	71	77	47	74	90	51	234	46	564	28
	total	22254	5	45619	4	31432	6	21504	5	35480	5	10289	7	166578	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	12684	3	5520	5	3801	6	2446	7	2388	6	3722	5	30562	2
	21-30 cm	28008	2	11138	4	9121	6	4558	7	5691	6	4418	5	62933	2
	31-40 cm	41788	2	18015	4	12018	6	5114	7	8814	6	4336	6	90085	2
	41-50 cm	38030	2	18059	4	11496	6	4821	7	8790	6	3758	7	84955	2
	51-60 cm	21796	3	12448	5	7304	7	3845	9	6462	7	2657	8	54512	2
	61-70 cm	8069	6	5168	8	3188	11	1362	14	2943	10	1381	14	22110	4
	71-80 cm	3224	10	1993	13	1480	17	897	17	1426	14	1051	17	10071	6
	81-90 cm	1130	18	602	23	467	25	314	26	432	24	315	27	3261	10
	91-100 cm	352	31	217	43	193	41	94	51	238	42	203	34	1296	16
	>100 cm	126	59	122	65	71	77	47	74	90	51	835	28	1292	21
	total	155207	1	73281	3	49140	4	23499	5	37275	4	22677	4	361078	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. 1363300. Birmensdorf, Swiss Federal Research Institute WSL

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