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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
growing stock (stemwood)
forest type (12 classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	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
forest type (12 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
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	.
inaccessible forest	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	.
shrub forest	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	.
permanently unstocked forest area	206	72	31	100	0	.	1461	37	198	82	969	42	0	.	376	87	364	38	283	100	855	56	299	65	0	.	0	.	715	59	154	100	79	100	177	87	193	73	148	92	0	.	979	43	1016	62	0	.	147	100	8652	16
forest aisle, embankment	197	100	0	.	0	.	303	52	49	100	0	.	0	.	0	.	102	75	0	.	11	100	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	25	100	67	100	0	.	0	.	144	100	908	34
permanently open stand	0	.	0	.	0	0	814	36	16	100	145	85	0	0	325	52	2448	19	151	91	99	79	271	49	165	72	193	61	413	52	0	.	99	120	167	66	0	.	993	32	52	95	1640	24	1411	29	0	.	4	100	9405	10
(nut) orchard, plantation	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1079	36	0	.	486	73	120	100	0	.	0	.	1758	31
coppice forest	0	.	0	.	0	.	149	46	19	100	231	61	0	.	25	100	765	38	214	99	138	100	0	.	42	100	0	.	246	100	458	73	0	.	0	.	162	94	5035	14	0	.	112	69	387	37	0	.	0	.	7982	12
coppice with standards	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	206	72	0	.	0	.	34	100	0	.	0	.	0	.	162	100	133	101	0	.	0	.	1657	25	0	.	149	100	116	86	0	.	98	100	2556	21
selection type high forest	121	85	0	.	0	.	6465	17	0	.	847	39	0	.	146	76	5327	16	79	100	2079	27	2008	28	200	100	329	54	553	43	0	.	370	51	333	59	149	71	699	37	204	65	1216	27	3300	18	251	73	132	73	24809	7
non-uniform high forest	785	34	291	100	200	71	9836	13	456	45	1425	36	0	.	1352	38	8010	13	2359	28	2966	25	2758	24	227	87	1731	32	4503	22	121	100	890	42	925	38	580	47	7324	21	1523	37	4730	21	6467	15	0	.	1554	30	61013	5
incomplete survey	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	.
uniform high forest	10819	12	3429	30	4550	25	49252	6	4905	18	13777	12	485	54	3791	24	40712	6	8877	14	13365	12	6198	18	2705	30	5214	22	19130	11	3849	20	8720	14	9604	15	7886	15	14510	9	5642	19	23117	9	20731	9	1786	33	15148	10	298202	2
total	12128	11	3751	28	4750	24	68278	5	5643	16	17394	11	485	54	6015	18	58007	5	11963	12	19513	10	11568	11	3339	26	7478	17	25559	9	4744	18	10291	12	11207	13	8970	13	31445	6	7447	17	32496	7	33548	6	2036	30	17228	9	415285	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. 1280074. Birmensdorf, Swiss Federal Research Institute WSL

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