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NFI4
number of deadwood stems
mechanical strength of wood (3 classes) · regeneration on dead wood (3 classes)
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 x 1.4 km grid
state 2009/17
		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
mechanical strength of wood (3 classes)	regeneration on dead wood (3 classes)	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	±%
fresh / firm wood	no plant	2731	10	769	21	618	25	740	19	929	16	1624	18	3050	17	2219	19	1947	18	810	24	700	22	3911	13	3495	11	5660	10	29204	4
	1 plant	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	18	100	0	.	7	100	37	67	84	43	8	100	160	31
	2 or more plant	0	.	0	.	0	.	0	.	0	.	0	.	22	100	9	100	25	77	0	.	7	100	19	100	69	43	18	100	168	30
	total	2731	10	769	21	618	25	740	19	929	16	1624	18	3072	17	2234	20	1990	17	810	24	714	22	3968	13	3647	11	5686	10	29532	4
rotten wood	no plant	1583	13	175	35	411	22	409	22	605	19	775	21	1596	17	694	19	1322	21	378	28	452	31	1722	14	1311	15	1946	14	13378	5
	1 plant	0	.	7	100	0	.	0	.	16	100	52	59	55	58	9	100	72	49	9	100	0	.	32	61	54	49	18	100	323	22
	2 or more plant	0	.	0	.	7	100	0	.	14	71	18	70	37	53	42	70	64	40	9	100	38	55	46	53	61	52	8	100	342	19
	total	1583	13	182	34	417	22	409	22	634	20	844	20	1689	16	744	19	1458	20	395	29	490	29	1800	14	1426	15	1972	14	14043	5
mouldering / mull wood	no plant	857	19	203	32	375	31	457	23	561	21	511	28	772	18	571	23	808	24	219	37	412	29	1259	15	1082	16	923	18	9010	6
	1 plant	44	62	0	.	17	100	7	100	7	100	6	100	40	59	22	100	65	48	0	.	7	100	13	71	89	50	7	100	324	22
	2 or more plant	8	100	7	100	0	.	0	.	33	100	75	45	63	41	33	81	69	38	13	71	42	72	43	41	113	35	44	70	543	17
	total	909	18	210	31	391	30	464	22	601	20	592	26	875	17	626	21	942	23	232	36	461	29	1315	15	1285	16	974	18	9877	6
total	no plant	5172	8	1147	19	1404	18	1605	15	2095	12	2909	16	5418	13	3484	15	4077	15	1407	20	1564	19	6892	11	5888	10	8529	8	51592	3
	1 plant	44	62	7	100	17	100	7	100	23	76	58	54	95	42	37	66	155	39	9	100	14	100	83	40	227	28	33	80	807	15
	2 or more plant	8	100	7	100	7	100	0	.	47	87	93	39	122	32	84	49	157	30	22	58	87	43	108	36	243	25	69	53	1054	12
	total	5224	8	1161	19	1427	18	1612	15	2165	12	3059	16	5636	13	3604	15	4390	14	1437	20	1665	18	7083	11	6359	10	8631	8	53452	3

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.

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. 1322059. Birmensdorf, Swiss Federal Research Institute WSL

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Selection from the list of topics

Estimates and standard error of the estimates

Confidence interval of the estimate

Significance of the estimate

Dealing with missing values

Changes

Table citation