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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.

NFI3
number of deadwood stems
deadwood: mechanical strength of wood (3 classes) · deadwood: regeneration (3 classes)
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		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
deadwood: mechanical strength of wood (3 classes)	deadwood: regeneration (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	±%	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	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	0	0	0	0	0	0	0	0	0	0	0
	no plant	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	.
	1 plant	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	.
	2 or more plants	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	.
	total	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	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
fresh/firm wood	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	.
	no plant	673	21	156	58	383	33	4556	12	460	25	1794	21	211	60	754	29	4629	10	668	22	2095	23	308	31	205	37	517	33	1958	18	219	41	623	25	996	44	427	38	4382	11	643	32	2424	14	3669	14	103	53	792	21	33646	4
	1 plant	7	100	0	.	0	.	64	53	0	.	17	100	0	.	0	.	48	64	0	.	7	100	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	71	0	.	13	71	209	28
	2 or more plants	7	100	0	.	0	.	32	64	0	.	0	.	0	.	7	100	24	58	0	.	15	71	0	.	0	.	0	.	28	50	0	.	0	.	0	.	0	.	0	.	0	.	19	100	25	76	0	.	0	.	157	27
	total	686	21	156	58	383	33	4652	12	460	25	1812	20	211	60	761	29	4701	10	668	22	2117	23	308	31	205	37	517	33	2001	18	219	41	623	25	996	44	427	38	4382	11	643	32	2443	14	3730	14	103	53	805	21	34011	4
rotten wood	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	.
	no plant	213	54	30	71	127	44	1093	21	49	54	509	27	17	100	108	51	1507	15	186	33	84	47	60	77	36	71	347	44	343	27	35	70	186	43	168	42	0	.	1098	18	127	44	502	28	1330	14	0	.	182	37	8336	6
	1 plant	0	.	0	.	0	.	17	100	0	.	33	71	0	.	17	100	14	71	0	.	0	.	0	.	0	.	15	100	26	77	0	.	0	.	15	100	0	.	0	.	18	100	18	100	22	58	0	.	17	100	213	26
	2 or more plants	0	.	0	.	0	.	122	35	7	100	0	.	0	.	26	77	64	62	0	.	15	100	0	.	0	.	0	.	20	58	0	.	0	.	51	87	0	.	8	100	0	.	0	.	95	34	0	.	17	100	425	20
	total	213	54	30	71	127	44	1233	20	56	49	542	27	17	100	151	41	1586	14	186	33	99	42	60	77	36	71	362	43	389	26	35	70	186	43	234	36	0	.	1106	17	145	40	520	27	1447	14	0	.	215	38	8974	6
mouldering/mull wood	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	.
	no plant	96	56	18	100	0	.	787	20	24	77	163	38	0	.	48	58	1151	18	224	50	91	45	0	.	45	74	65	49	158	36	0	.	100	40	217	45	17	100	474	24	96	45	217	30	823	21	0	.	76	46	4890	8
	1 plant	0	.	0	.	0	.	47	54	0	.	17	100	0	.	0	.	38	71	0	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	36	71	0	.	0	.	174	31
	2 or more plants	17	100	0	.	18	100	130	34	0	.	15	100	0	.	7	100	79	42	0	.	0	.	0	.	0	.	0	.	38	69	0	.	0	.	18	100	0	.	0	.	0	.	0	.	108	45	0	.	0	.	430	20
	total	113	49	18	100	18	100	964	19	24	77	195	36	0	.	55	52	1269	18	224	50	113	41	0	.	45	74	65	49	195	32	0	.	100	40	235	42	17	100	481	23	96	45	224	30	967	21	0	.	76	46	5494	8
total	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	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
	no plant	981	20	204	49	510	32	6437	11	533	25	2466	19	228	62	910	26	7288	9	1078	21	2270	22	368	31	287	35	929	33	2459	16	254	36	909	23	1380	34	444	36	5954	10	866	27	3143	14	5822	12	103	53	1049	18	46871	4
	1 plant	7	100	0	.	0	.	129	41	0	.	67	81	0	.	17	100	100	42	0	.	29	79	0	.	0	.	15	100	41	61	0	.	0	.	15	100	0	.	7	100	18	100	25	78	95	41	0	.	30	64	595	18
	2 or more plants	24	77	0	.	18	100	283	24	7	100	15	100	0	.	40	55	167	33	0	.	31	61	0	.	0	.	0	.	86	37	0	.	0	.	70	90	0	.	8	100	0	.	19	100	228	32	0	.	17	100	1012	14
	total	1012	19	204	49	527	31	6849	11	540	25	2549	19	228	62	968	25	7555	9	1078	21	2329	22	368	31	287	35	944	33	2585	16	254	36	909	23	1465	33	444	36	5968	10	884	27	3188	14	6144	12	103	53	1096	18	48478	4

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. 1322004. Birmensdorf, Swiss Federal Research Institute WSL

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