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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
volume of deadwood (stemwood)
deadwood: mechanical strength of wood (5 classes) · deadwood: bark cover (3 classes)
regional demarcation: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
deadwood: mechanical strength of wood (5 classes)	deadwood: bark cover (3 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	0	0	0	0	0	0	0
	0-30%	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-60%	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>60%	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	0	0	0	0	0	0	0	0	0	0	0	0	0
fresh wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	13	100	12	100	14	100	0	.	0	.	8	71	47	50
	31-60%	0	.	0	.	42	71	0	.	16	100	20	80	78	48
	>60%	155	36	205	38	183	29	25	68	50	49	32	57	650	18
	total	168	34	217	37	239	28	25	68	66	53	59	42	774	16
solid wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	675	16	691	19	1565	13	555	18	396	18	929	13	4810	6
	31-60%	567	23	237	26	1020	15	317	25	426	23	408	18	2975	9
	>60%	2432	13	1502	13	3426	10	1169	14	1520	13	1565	14	11614	5
	total	3674	11	2430	11	6012	8	2041	13	2342	12	2902	11	19400	4
rotten wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	851	17	1104	15	2987	10	737	17	661	21	510	28	6850	6
	31-60%	225	31	275	36	489	19	123	30	208	29	94	30	1414	12
	>60%	231	29	175	31	652	21	210	25	281	27	238	26	1787	11
	total	1307	14	1554	15	4128	9	1070	15	1150	16	841	19	10052	5
mouldering wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	385	18	594	18	2103	13	401	21	611	20	363	23	4456	8
	31-60%	102	53	12	61	193	31	36	51	45	56	18	55	407	22
	>60%	37	53	15	100	55	38	40	55	19	55	83	43	249	22
	total	524	21	621	18	2350	12	477	19	675	18	463	20	5112	7
mull wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	64	35	54	51	277	23	44	50	67	35	100	35	605	14
	31-60%	0	.	0	.	0	.	0	.	0	.	5	100	5	100
	>60%	0	.	0	.	3	100	0	.	5	100	0	.	8	74
	total	64	35	54	51	280	23	44	50	72	34	105	33	619	14
total	n/a	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	0-30%	1988	11	2455	11	6945	8	1736	13	1735	13	1908	12	16768	4
	31-60%	895	19	524	22	1745	11	477	20	695	20	544	15	4879	7
	>60%	2854	12	1897	12	4319	9	1444	13	1875	12	1918	13	14309	5
	total	5738	9	4876	9	13009	6	3657	10	4305	11	4371	9	35956	3

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. 1268036. Birmensdorf, Swiss Federal Research Institute WSL

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