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.

NFI4
growing stock (stemwood)
forest type (12 classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
	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	526	55	0	.	0	.	2589	33	321	63	992	46	0	.	300	71	429	38	222	81	1067	60	121	100	0	.	32	100	827	68	297	72	83	95	202	75	159	100	173	64	9	100	475	54	1482	48	0	.	236	95	10543	15
forest aisle, embankment	0	.	0	.	0	.	0	.	0	.	0	.	0	.	80	100	122	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	143	100	91	81	0	.	103	100	6	100	0	.	47	100	591	38
permanently open stand	0	.	0	.	72	100	300	44	12	100	0	.	18	100	251	53	2907	19	108	100	27	100	50	92	128	74	227	50	227	96	145	100	-6	100	342	64	0	.	752	31	115	97	1630	23	1379	30	0	.	0	0	8684	11
(nut) orchard, plantation	0	.	0	.	0	.	0	.	0	.	262	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	233	62	0	.	442	72	0	.	0	.	0	.	937	47
coppice forest	0	.	0	.	0	.	104	60	0	.	40	100	99	100	15	100	1333	30	168	100	4	100	0	.	0	.	37	100	124	71	0	.	144	100	0	.	126	100	6845	12	154	87	287	45	827	45	0	.	0	.	10305	10
coppice with standards	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	160	100	0	.	0	.	0	.	0	.	0	.	143	100	283	72	0	.	0	.	0	.	548	41	0	.	100	100	12	100	0	.	0	.	1247	31
selection type high forest	228	79	-1	100	0	.	6062	18	0	.	370	58	0	.	243	86	5207	16	529	45	568	47	3265	21	195	100	401	51	703	46	0	.	107	79	133	62	98	100	1873	27	582	50	1030	33	3678	18	113	100	883	40	26266	7
non-uniform high forest	900	39	0	.	0	.	12190	13	681	42	1595	31	0	.	626	52	8346	15	2174	28	3559	23	1869	28	436	70	1900	29	3688	24	0	.	1627	32	872	41	775	46	4612	16	1055	38	4668	18	6400	15	88	100	698	47	58759	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	12031	11	3363	28	5570	23	48717	6	5306	18	13708	12	515	60	4937	22	35095	7	9083	14	15188	12	5937	17	2131	30	4123	24	18146	11	4443	19	8300	14	9343	14	8659	15	11955	10	4993	21	23592	9	16788	9	1625	34	16582	10	290130	2
total	13685	11	3362	28	5641	22	69962	5	6320	16	16966	11	632	51	6452	18	53598	5	12284	12	20415	10	11241	11	2890	26	6720	17	23858	9	5169	18	10254	12	10892	13	9960	14	27082	5	6908	17	32328	7	30572	7	1826	31	18446	9	407462	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. 1280028. Birmensdorf, Swiss Federal Research Institute WSL

back