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.

NFI2–NFI3
fellings
altitude (in 400 m classes) · main tree species
regional demarcation: economic region
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI2/NFI3
grid: 1.4-km grid
change 1993/95–2004/06
		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
altitude (in 400 m classes)	main tree species	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	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	.
>1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	2	100	0	0	2	100	9	74	14	35	0	0	27	32
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	13	63	3	61	1	100	16	51
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	4	91	10	61	0	0	14	51
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	0
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0
	indeterminable	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	.	2	100	0	0	2	100	25	47	27	31	1	100	57	26
1401-1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	83	0	.	4	80	0	.	0	.	25	41	37	45	18	59	66	42	3	68	14	64	37	31	155	19	19	59	386	13
	fir	1	100	0	.	0	0	0	.	0	.	1	100	0	0	0	0	0	0	0	0	0	.	0	100	2	85	0	0	4	55
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	0	2	55	0	0	2	55
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	100	21	39	16	33	1	71	40	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	3	73	0	0	0	.	3	73
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	1	100	0	0	0	0	1	100
	beech	0	100	0	.	0	.	0	.	0	.	0	100	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	1	72
	maple	0	100	0	.	0	0	0	.	0	.	0	100	0	0	0	0	0	100	0	.	0	0	0	0	0	0	0	0	0	65
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	1	100	0	0	0	0	0	100	0	72	0	59	2	65
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	0
	total	8	70	0	.	4	80	0	.	0	.	26	40	37	45	18	59	68	41	3	68	14	62	63	26	174	18	20	54	438	12
1001-1400 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	159	15	0	.	7	68	0	0	24	93	131	23	257	17	188	25	80	41	24	41	22	48	31	45	129	23	12	51	1050	9
	fir	69	25	0	.	6	83	2	100	6	63	21	33	58	23	31	43	1	67	6	76	0	77	12	60	8	57	0	100	211	13
	pine	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	16	53	1	71	1	100	21	48
	larch	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0	26	61	0	0	0	0	30	61
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	0	0	.	0	.	0	.	0	.	0	100	0	0	0	0	0	0	0	100	0	.	0	0	0	.	0	.	0	72
	beech	51	24	0	0	4	66	0	100	1	100	6	43	19	52	9	53	1	100	5	58	0	100	0	0	1	71	8	54	100	17
	maple	1	69	0	.	0	0	0	0	0	.	0	100	8	74	1	78	3	57	1	100	0	100	0	0	0	0	0	100	14	43
	ash	0	0	0	.	0	.	0	0	0	0	1	89	0	0	0	0	0	0	1	100	0	0	1	89	0	0	0	100	4	52
	oak	2	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	100	0	0	0	0	2	97
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0
	other broadleaves	1	46	0	.	0	0	0	.	0	0	1	53	1	71	1	73	1	61	0	0	0	100	2	55	0	62	4	55	12	24
	indeterminable	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	283	14	0	0	16	56	2	84	31	74	160	20	343	16	230	24	85	39	36	33	23	46	89	31	140	22	25	36	1444	7
601-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	133	18	24	31	311	18	244	19	177	21	99	32	209	17	129	25	18	63	15	58	31	45	3	59	13	36	0	0	1403	7
	fir	134	15	1	94	48	29	124	20	74	25	26	53	247	14	53	33	3	100	1	95	5	96	1	100	0	0	0	0	705	8
	pine	7	45	5	57	5	60	5	58	11	44	0	.	1	100	0	0	0	0	3	75	0	0	1	100	12	68	0	.	50	23
	larch	0	0	0	0	3	88	1	100	2	100	0	0	3	72	0	0	6	79	0	.	5	72	0	0	9	86	1	100	32	37
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	100	0	0	8	77	3	70	1	92	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	.	0	.	13	52
	beech	170	15	50	30	63	26	73	29	35	31	18	37	54	24	38	27	19	59	1	90	10	59	0	72	9	56	6	93	538	9
	maple	10	33	4	59	4	72	4	53	11	42	0	0	2	55	12	42	2	60	0	0	2	55	0	100	0	0	0	100	49	17
	ash	17	33	4	69	11	37	0	0	11	35	2	55	8	51	6	40	1	100	1	100	6	100	0	100	2	90	1	100	67	17
	oak	0	100	1	71	11	52	4	56	1	100	0	100	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	18	36
	sweet chestnut	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	0	4	46	5	45
	other broadleaves	6	59	2	74	1	59	1	75	12	57	0	67	4	46	8	65	2	84	2	94	3	55	1	88	2	69	6	70	50	22
	indeterminable	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	477	10	90	23	465	14	460	13	334	16	145	29	528	12	246	20	51	37	22	51	61	32	7	48	47	35	19	52	2929	5
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	73	30	84	39	62	29	333	13	633	12	0	.	6	74	22	46	0	0	2	100	0	0	5	100	0	0	0	0	1189	8
	fir	28	40	11	38	7	58	118	19	96	24	0	.	0	0	2	88	0	0	3	71	0	0	0	.	0	.	0	.	260	13
	pine	6	49	14	43	8	48	3	61	44	21	0	.	0	0	1	100	0	0	0	0	0	.	1	100	0	100	0	0	77	16
	larch	0	0	7	91	5	51	7	66	37	54	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	100	0	0	55	38
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	1	74	1	79	3	100	4	73	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	0	9	47
	beech	60	28	133	22	16	51	115	19	181	14	0	.	3	71	7	58	0	0	1	100	3	100	0	0	0	0	0	0	512	9
	maple	1	60	20	35	5	59	6	38	11	31	0	.	2	100	0	0	2	100	1	100	0	0	0	0	0	.	0	0	47	19
	ash	21	34	6	54	9	46	9	38	44	26	0	.	2	73	0	100	0	0	0	100	0	0	0	0	0	100	0	0	89	16
	oak	15	68	12	47	10	52	20	31	30	28	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	100	87	19
	sweet chestnut	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	12	39	13	37
	other broadleaves	10	37	11	48	5	46	8	54	52	28	0	.	0	0	3	78	6	100	0	100	0	0	0	100	0	0	6	37	102	18
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0
	total	215	18	300	17	129	21	620	10	1133	9	0	.	12	61	35	41	8	100	7	56	3	100	7	74	1	76	18	30	2439	5
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	373	11	108	31	383	15	577	11	834	10	252	17	512	11	356	17	166	27	44	31	68	29	86	24	311	14	31	41	3988	4
	fir	231	12	12	36	61	25	245	14	176	17	47	32	306	12	86	26	4	78	9	51	5	92	13	57	10	49	0	100	1146	6
	pine	13	33	19	35	13	38	8	43	55	19	0	0	1	100	1	100	0	0	3	75	0	0	19	47	15	54	1	100	144	13
	larch	0	0	7	91	9	46	7	59	39	52	0	0	3	72	0	0	6	79	0	0	5	66	60	33	28	35	3	51	173	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	7	60	10	61	0	0	18	43
	other conifers	0	100	1	74	8	71	6	59	6	60	0	100	0	0	0	0	0	0	0	100	0	0	1	100	0	0	0	0	21	36
	beech	280	11	183	18	83	22	188	16	218	12	23	29	77	22	54	22	19	57	7	45	12	51	0	72	10	50	14	51	1109	6
	maple	13	28	24	31	9	46	9	31	22	26	0	71	12	54	14	39	6	43	1	71	2	48	1	100	0	0	0	81	107	13
	ash	37	24	10	43	20	29	9	38	54	22	3	48	10	44	6	39	0	100	2	59	6	100	2	74	2	82	2	88	153	11
	oak	17	60	13	43	21	37	24	27	31	27	0	100	0	0	0	0	0	0	0	0	0	.	0	100	0	0	0	100	102	16
	sweet chestnut	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	1	100	0	0	16	31	17	29
	other broadleaves	17	31	12	43	6	39	10	47	64	25	2	43	5	40	12	48	10	66	2	77	3	49	4	45	3	55	17	32	159	13
	indeterminable	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
	total	982	7	390	14	614	12	1083	8	1499	7	327	16	925	9	529	14	213	23	68	25	102	23	192	18	388	12	85	22	7139	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. 1242160. Birmensdorf, Swiss Federal Research Institute WSL

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