Selection from the list of topics

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: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		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
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	±%
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
	no plant	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	.
	2 or more plants	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
fresh/firm wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no plant	2886	10	798	20	1048	20	1091	20	1958	15	2238	18	3651	16	2866	19	2424	18	745	28	1261	23	3669	14	3887	11	5124	10	33646	4
	1 plant	0	.	0	.	0	.	0	.	20	58	17	100	29	79	15	100	42	61	0	.	0	.	37	71	41	73	7	100	209	28
	2 or more plants	26	78	0	.	0	.	0	.	14	71	0	.	15	71	20	58	25	77	0	.	7	100	25	76	17	72	7	100	157	27
	total	2913	10	798	20	1048	20	1091	20	1993	15	2255	18	3695	15	2902	18	2491	18	745	28	1268	23	3730	14	3945	11	5138	10	34011	4
rotten wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no plant	516	22	177	61	402	33	241	33	241	32	566	25	623	27	546	22	697	29	162	38	230	31	1330	14	1276	15	1329	17	8336	6
	1 plant	0	.	0	.	0	.	0	.	17	100	33	71	15	100	15	100	36	71	18	100	43	61	22	58	14	71	0	.	213	26
	2 or more plants	0	.	7	100	0	.	0	.	17	100	28	81	21	77	13	71	88	41	44	100	40	56	95	34	64	62	8	100	425	20
	total	516	22	184	59	402	33	241	33	275	33	627	25	659	26	574	21	821	27	225	34	313	28	1447	14	1354	14	1337	17	8974	6
mouldering/mull wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no plant	450	28	84	60	153	35	102	46	134	36	186	36	240	27	263	35	579	25	167	43	83	45	823	21	975	20	650	20	4890	8
	1 plant	0	.	0	.	7	100	0	.	0	.	17	100	37	72	0	.	32	63	0	.	0	.	36	71	38	71	7	100	174	31
	2 or more plants	0	.	17	100	0	.	0	.	0	.	21	77	44	54	24	79	80	46	18	100	39	68	108	45	72	45	8	100	430	20
	total	450	28	101	52	160	35	102	46	134	36	225	33	321	25	287	33	690	25	185	40	122	37	967	21	1084	20	665	19	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
	no plant	3852	9	1058	20	1603	19	1433	18	2334	13	2990	17	4514	14	3675	16	3700	17	1074	23	1574	20	5822	12	6138	10	7104	8	46871	4
	1 plant	0	.	0	.	7	100	0	.	37	55	67	81	82	47	30	71	110	45	18	100	43	61	95	41	93	44	14	70	595	18
	2 or more plants	26	78	24	77	0	.	0	.	31	63	49	57	80	38	57	42	192	30	63	100	86	41	228	32	153	36	22	58	1012	14
	total	3878	9	1083	19	1610	19	1433	18	2401	13	3107	17	4676	14	3763	16	4002	16	1155	23	1703	19	6144	12	6383	10	7140	8	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. 1322006. Birmensdorf, Swiss Federal Research Institute WSL

back