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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)
site quality · main tree species
regional demarcation: economic 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
		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
site quality	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	.
low	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	23	44	2	100	0	21586	1	157	4	82	21	42	73	25	13	45	69	27	17	58	50	36	23	33	112	21	24	33	433	10
	fir	52	23	3	97	0	.	3	59	4	81	0	84	0	395	1	95	17	57	4	86	3	107	2	76	0	.	0	.	90	19
	pine	6	81	1	63	0	.	2	69	2	88	0	.	4	82	0	.	1	93	2	73	0	712	0	79	10	41	0	100	29	28
	larch	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	100	29	38	64	21	43	27	135	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	38	14	50	0	100	27	32
	other conifers	0	68	1	100	0	.	0	77	0	100	0	.	0	96	0	.	0	100	0	100	0	100	0	0	1	43	0	.	3	35
	beech	53	24	1	515	6	100	28	41	14	75	-1	533	11	96	0	658	3	177	7	71	9	49	4	72	0	.	0	.	135	19
	maple	6	53	1	54	0	100	0	83	1	64	2	40	2	156	1	61	4	45	0	884	4	55	4	63	0	.	0	.	25	24
	ash	0	965	1	85	0	0	-1	243	1	69	7	100	3	103	0	72	1	73	0	100	0	88	0	77	0	.	0	.	13	59
	oak	4	71	0	100	0	.	-1	158	0	103	0	100	4	101	0	.	0	371	1	100	0	100	1	77	0	.	0	.	9	53
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	76	0	.	0	.	1	76
	other broadleaves	1	149	0	208	0	.	1	67	0	515	1	93	3	61	0	78	1	59	0	65	0	3866	8	53	0	68	0	74	17	30
	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
	total	146	17	10	75	6	123	36	35	26	49	30	39	101	24	16	39	97	24	31	40	66	31	84	20	201	15	67	25	916	7
moderate	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	64	23	0	.	2	215	0	100	1	100	120	19	42	34	93	25	176	17	35	41	44	33	288	12	488	10	150	15	1501	5
	fir	89	21	0	.	4	56	1	100	0	.	43	35	26	38	29	33	29	33	6	106	1	71	25	33	11	49	37	32	304	11
	pine	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	100	1	65	2	100	-1	100	20	32	40	30	-1	300	60	24
	larch	0	.	0	.	0	.	0	.	0	.	1	100	0	100	0	.	10	48	0	.	0	135	97	20	84	20	85	20	276	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	3	104	24	37	0	100	29	32
	other conifers	0	100	0	.	0	.	0	.	0	.	0	.	0	100	2	100	0	100	0	.	0	0	0	93	0	100	0	.	3	83
	beech	38	26	0	.	1	194	2	100	2	100	9	58	18	45	34	32	13	71	3	75	11	59	1	462	17	39	52	32	200	14
	maple	9	43	0	.	1	62	0	100	0	.	4	82	3	59	3	51	7	132	5	73	9	57	12	43	10	54	7	55	69	21
	ash	1	189	0	.	0	.	0	.	0	.	3	57	3	56	1	171	-1	554	5	86	6	65	14	45	4	60	3	56	39	26
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	100	0	.	0	.	5	48	1	100	2	70	7	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	100	13	56	14	54
	other broadleaves	2	37	0	.	0	.	0	100	0	.	2	69	-2	130	5	98	6	32	1	70	1	67	35	25	34	30	34	52	119	19
	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
	total	202	15	0	.	8	77	4	100	2	100	182	18	90	23	167	20	243	16	57	33	70	28	499	10	713	8	383	11	2622	4
good	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	146	17	23	29	55	32	40	35	84	22	76	25	176	15	260	15	40	30	62	31	42	39	17	50	159	19	65	34	1245	6
	fir	191	15	5	45	37	34	69	25	52	26	26	73	263	15	111	18	14	46	21	60	12	42	14	65	15	45	3	93	834	7
	pine	5	64	8	72	2	97	3	65	10	46	0	.	11	64	1	212	0	.	1	68	0	.	0	.	0	.	0	.	40	27
	larch	1	77	1	86	2	107	0	73	3	108	1	100	0	100	0	.	1	90	0	0	1	277	0	.	0	1223	11	72	20	45
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	2	71	4	92	2	82	17	48	4	77	0	.	1	92	1	71	0	100	0	0	0	124	0	.	0	.	2	92	33	30
	beech	97	14	47	24	31	29	56	24	101	23	31	37	98	24	60	30	26	39	16	44	28	46	7	60	4	80	88	21	689	7
	maple	26	34	10	50	11	46	12	41	33	27	2	197	13	50	38	25	4	74	3	80	10	62	4	72	6	53	14	39	185	12
	ash	11	34	3	90	21	61	7	93	25	54	8	55	10	38	27	37	5	78	8	49	3	63	1	66	7	71	24	48	160	17
	oak	3	73	5	51	10	50	10	49	9	35	2	88	1	72	1	84	0	100	1	65	0	.	2	100	2	100	16	34	61	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	100	0	100	89	22	89	22
	other broadleaves	14	33	5	40	8	54	5	61	8	35	5	56	17	27	12	29	3	146	4	98	5	50	1	73	28	41	115	20	230	12
	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
	total	496	9	112	20	179	21	219	17	331	15	151	23	591	10	509	12	93	22	116	22	100	26	47	35	220	17	427	10	3586	4
very good	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	83	20	39	24	74	28	266	13	339	11	9	51	59	32	31	35	7	55	2	71	4	61	0	.	0	.	0	.	911	7
	fir	129	20	23	39	44	32	197	15	110	20	5	61	73	23	52	39	0	100	2	93	1	198	0	.	0	.	0	.	635	8
	pine	11	60	7	92	3	74	2	210	13	32	0	.	0	0	1	100	0	.	0	100	0	.	0	.	0	.	0	.	37	30
	larch	0	742	2	116	13	52	4	64	11	61	0	.	1	100	5	53	2	90	0	.	0	100	0	.	0	.	0	.	39	28
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	3	93	9	83	5	100	11	52	13	49	0	.	0	.	0	83	0	.	0	.	0	.	0	.	0	.	0	.	41	30
	beech	94	18	83	18	57	31	149	16	177	11	4	79	25	40	35	39	7	59	-1	409	10	54	-3	106	0	.	5	87	641	7
	maple	41	32	13	39	8	48	25	27	39	33	1	100	1	399	18	89	8	50	2	87	0	59	0	100	0	.	0	.	156	17
	ash	18	30	21	40	25	55	24	32	64	20	9	87	7	66	11	38	3	70	0	.	4	74	1	66	0	.	1	75	188	13
	oak	9	43	13	64	20	49	8	35	23	40	0	100	0	.	2	90	0	.	0	100	0	.	1	71	0	.	1	89	78	21
	sweet chestnut	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	100	1	88
	other broadleaves	4	66	11	33	27	34	38	27	51	28	1	100	4	64	9	46	3	206	1	98	1	126	1	293	0	.	3	93	153	14
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	392	12	221	14	276	16	724	9	839	7	28	47	169	18	165	23	31	38	5	145	22	39	-1	955	0	.	10	61	2880	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	315	10	64	18	131	21	308	12	428	10	227	14	350	11	396	12	292	13	115	22	140	20	328	11	758	8	239	13	4090	3
	fir	461	9	32	31	85	22	270	12	165	16	75	33	363	12	192	15	61	25	32	45	17	38	41	30	26	33	41	30	1864	5
	pine	22	41	15	55	5	59	7	62	24	26	0	.	15	51	2	96	2	63	6	48	-1	112	21	31	50	25	-2	253	166	14
	larch	1	81	3	82	15	48	4	60	14	53	1	71	1	79	5	53	14	40	0	0	0	371	125	18	148	14	139	16	469	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	16	36	38	30	0	87	56	23
	other conifers	6	54	13	60	7	72	28	35	17	41	0	.	2	68	3	70	0	79	0	100	0	144	0	93	1	39	2	92	80	20
	beech	283	9	131	14	94	22	235	12	293	11	42	31	151	18	128	19	49	31	25	40	58	27	8	88	21	35	145	17	1665	4
	maple	83	20	24	29	19	32	37	22	73	21	8	58	19	42	60	31	22	46	10	48	23	36	20	32	15	39	21	32	435	9
	ash	30	23	25	35	46	41	30	33	90	20	26	42	23	30	40	28	9	66	13	44	14	39	16	40	11	50	28	41	401	9
	oak	16	33	19	48	29	36	18	33	33	30	2	78	5	76	3	75	0	725	2	52	0	100	8	38	2	78	19	30	155	13
	sweet chestnut	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	2	56	0	89	103	21	105	20
	other broadleaves	21	27	16	26	36	29	45	24	59	25	9	38	22	27	26	27	13	57	6	65	6	40	44	22	62	25	153	19	518	8
	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	0
	total	1237	5	342	11	468	12	983	7	1198	6	390	12	951	7	856	9	465	11	210	17	258	15	629	9	1133	6	887	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. 1261367. Birmensdorf, Swiss Federal Research Institute WSL

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