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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.

NFI4–NFI5
increment (gross increment)
altitude (in 400 m classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
change 2009/17–2018/26
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-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	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
>1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	30	38	35	25	111	22	29	32	205	14
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	71	1	79	10	41	0	282	11	37
	larch	0	.	0	.	2	100	50	28	73	20	56	23	181	13
	Arolla pine	0	.	0	.	0	.	14	40	35	30	3	89	52	24
	other conifers	0	.	0	.	0	.	0	0	0	43	0	.	1	43
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	95	0	74	0	54	1	44
	indeterminable	0	.	0	.	0	.	0	0	0	0	0	0	0	0
	total	0	.	0	.	32	36	100	19	229	14	89	22	451	10
1401-1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17	50	0	.	441	11	187	17	419	11	126	16	1189	6
	fir	6	64	0	.	39	36	6	76	4	61	23	46	78	24
	pine	0	.	0	.	8	50	5	74	11	37	-3	144	22	36
	larch	0	.	0	.	8	54	48	31	44	26	70	23	170	15
	Arolla pine	0	.	0	.	2	100	2	74	0	321	0	.	4	83
	other conifers	0	.	0	.	0	69	0	0	0	71	0	100	0	49
	beech	1	71	0	.	0	.	0	100	1	100	23	33	24	31
	maple	4	78	0	.	6	102	0	100	0	224	4	63	14	51
	ash	0	.	0	.	0	81	0	.	0	.	0	.	0	81
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	1	73	0	.	7	30	11	39	20	42	22	47	61	23
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0	0	0
	total	27	51	0	.	512	11	260	15	498	10	265	12	1562	6
1001-1400 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	189	14	34	37	659	8	53	24	171	19	69	25	1176	6
	fir	207	13	11	56	313	13	16	41	12	55	18	38	579	9
	pine	0	116	0	100	1	81	11	40	17	60	2	74	30	36
	larch	0	.	1	100	1	79	22	36	21	46	16	59	60	26
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	100	2	100	1	65	0	100	0	0	0	.	3	75
	beech	83	15	4	50	155	17	-3	136	18	52	82	25	341	11
	maple	34	26	2	64	68	19	8	46	4	61	6	69	123	14
	ash	2	61	1	80	20	37	10	52	3	71	15	68	51	27
	oak	0	.	0	.	1	63	1	47	0	.	3	66	5	40
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	70	0	70
	other broadleaves	6	58	1	100	26	29	15	47	20	47	49	34	117	19
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	522	8	56	30	1243	7	135	19	267	16	260	14	2485	4
601-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	121	17	468	10	278	12	12	59	58	23	28	67	961	7
	fir	248	12	399	11	263	13	2	73	18	38	1	100	929	7
	pine	13	46	19	35	9	71	4	59	10	46	0	.	56	22
	larch	1	123	16	41	4	70	4	52	6	60	3	88	34	26
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	14	38	24	38	2	82	0	0	0	.	0	.	39	27
	beech	228	11	326	11	195	17	4	67	33	38	38	35	823	7
	maple	63	20	37	24	65	31	4	92	14	41	4	51	188	14
	ash	25	26	67	29	88	19	0	68	7	68	3	68	191	14
	oak	8	45	19	29	4	53	4	56	1	100	8	41	43	18
	sweet chestnut	0	100	0	0	0	100	1	100	0	100	70	27	71	27
	other broadleaves	18	24	30	23	32	22	11	39	14	43	36	34	142	13
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	739	8	1404	7	940	8	47	28	162	17	192	16	3478	4
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	37	25	503	9	19	61	0	.	0	.	3	73	560	8
	fir	40	37	227	13	10	74	0	.	0	.	0	.	277	12
	pine	20	40	27	35	1	97	-2	83	0	.	0	.	46	27
	larch	1	64	23	40	2	72	0	0	0	.	0	100	25	36
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	1	83	33	35	0	100	0	.	0	.	2	92	36	33
	beech	79	17	358	9	36	35	0	82	0	.	3	57	477	7
	maple	4	226	88	19	11	40	2	100	0	.	7	58	111	18
	ash	12	64	125	17	11	43	0	71	1	100	10	49	159	15
	oak	9	44	86	21	2	44	0	100	0	.	10	47	107	17
	sweet chestnut	0	.	0	.	1	93	0	76	0	.	33	33	34	32
	other broadleaves	7	39	119	17	12	66	3	59	4	100	52	37	197	15
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	total	209	15	1590	5	105	25	5	76	5	100	118	21	2028	5
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	363	9	1004	6	1427	5	286	12	756	8	256	13	4090	3
	fir	500	8	636	8	625	9	25	34	34	29	41	30	1864	5
	pine	33	30	46	25	19	40	19	33	49	26	0	1475	166	14
	larch	1	65	39	28	18	34	124	18	144	15	145	15	469	8
	Arolla pine	0	.	0	.	2	100	16	36	35	31	3	89	56	23
	other conifers	15	34	59	25	3	66	0	100	1	37	2	92	80	20
	beech	392	8	689	7	387	11	2	265	51	31	145	17	1665	4
	maple	104	17	127	15	150	17	15	39	18	35	21	31	435	9
	ash	39	26	194	15	118	16	11	49	12	48	28	41	401	9
	oak	17	31	105	18	7	34	5	42	1	100	21	28	155	13
	sweet chestnut	0	100	0	0	1	119	1	67	0	100	103	21	105	20
	other broadleaves	32	20	151	14	77	17	41	23	58	25	160	19	518	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	1497	5	3049	3	2834	4	546	9	1160	6	924	6	10005	2

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. 1305300. Birmensdorf, Swiss Federal Research Institute WSL

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