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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
deadwood quantity
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	13	87	9	100	0	.	469	84	0	0	27	78	0	.	139	100	109	67	100	100	14	84	5	100	0	.	0	.	160	92	1	100	1	100	9	100	9	71	3	71	0	.	62	61	61	67	0	.	0	0	1190	39
forest aisle, embankment	20	100	0	.	0	.	11	83	14	100	0	.	0	.	0	.	30	79	0	.	1	100	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	5	100	0	0	0	.	0	0	80	44
permanently open stand	0	.	0	.	0	0	91	65	0	0	2	100	5	100	50	82	295	38	2	100	61	100	5	100	12	100	1	100	160	52	0	.	10	100	12	100	0	.	174	50	0	0	215	50	212	50	0	.	0	0	1308	19
(nut) orchard, plantation	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	139	41	0	.	43	78	88	100	0	.	0	.	270	41
coppice forest	0	.	0	.	0	.	47	68	0	0	11	89	0	.	15	72	246	66	30	86	7	100	0	.	0	0	0	.	11	100	32	78	0	.	0	.	0	0	797	20	0	.	15	95	61	42	0	.	0	.	1273	19
coppice with standards	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	80	0	.	0	.	7	100	0	.	0	.	0	.	0	0	65	71	0	.	0	.	271	35	0	.	12	100	3	100	0	.	7	100	377	28
selection type high forest	14	100	0	.	0	.	677	30	0	.	182	51	0	.	4	100	604	28	68	100	97	42	140	45	14	100	16	62	251	53	0	.	77	58	80	95	6	71	152	43	31	100	349	53	528	27	34	74	16	75	3340	12
non-uniform high forest	46	51	1	100	97	92	1156	20	146	56	487	72	0	.	307	57	759	23	367	40	331	39	117	45	23	71	200	66	283	31	0	0	149	66	121	42	56	71	785	23	111	38	574	38	1311	23	0	.	186	47	7611	9
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	1089	20	173	47	349	33	5897	12	463	27	1712	19	33	68	732	30	5609	12	1085	19	1876	24	455	31	319	40	493	35	1499	18	462	41	873	20	1047	24	500	28	1900	18	637	31	2539	14	3216	12	83	34	1447	21	34487	4
total	1182	19	183	45	446	33	8347	10	622	24	2422	20	38	61	1246	25	7664	9	1650	17	2388	19	729	22	369	35	710	31	2365	15	494	39	1176	18	1268	21	572	26	4221	10	779	26	3813	12	5480	10	117	32	1656	19	49936	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. 1411689. Birmensdorf, Swiss Federal Research Institute WSL

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