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–NFI5
increment (gross increment)
altitude (in 400 m classes) · main tree species
regional demarcation: economic region
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI1-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
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	.	1	100	0	.	18	47	0	.	0	.	30	26	75	26	17	43	142	17
	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	.	1	79	10	42	0	100	11	37
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	32	47	21	18	40	108	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	49	30	35	0	100	41	29
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	44	0	.	0	44
	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	100	0	100	0	100	0	74
	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	.	1	100	0	.	18	47	0	.	0	.	85	20	162	16	36	30	302	11
1401-1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12	58	0	.	1	697	0	.	0	.	64	26	72	25	33	42	118	21	27	56	40	40	198	16	362	11	97	19	1023	7
	fir	6	64	0	.	0	.	0	.	0	.	18	61	4	106	2	96	15	43	-1	130	0	.	7	68	5	53	19	53	75	25
	pine	0	.	0	.	0	.	0	.	0	.	0	.	4	82	0	.	2	69	2	100	0	.	6	66	9	41	-4	91	19	41
	larch	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	8	59	0	0	1	94	36	39	38	28	60	25	144	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	2	80	-1	109	0	.	3	111
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	100	0	100	0	100	0	.	0	63
	beech	1	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	19	37	21	35
	maple	3	87	0	.	0	90	0	.	0	.	-1	164	3	59	0	100	-1	376	0	.	0	.	1	58	0	177	2	123	7	95
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	100
	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	1	73	0	.	0	.	0	.	0	.	-1	122	1	47	0	100	3	43	1	85	1	62	12	37	21	47	4	42	41	27
	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	23	59	0	.	1	421	0	.	0	.	81	30	84	24	35	43	146	20	28	56	43	38	261	15	435	11	197	14	1332	6
1001-1400 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	184	14	0	.	2	214	1	76	1	97	114	18	131	17	155	18	82	24	56	32	68	31	52	24	147	21	56	28	1050	6
	fir	194	13	0	.	11	66	3	75	7	71	44	49	129	21	63	22	31	39	26	51	8	57	32	37	8	73	18	38	575	9
	pine	0	116	0	.	0	.	0	.	0	.	0	.	0	.	0	73	0	0	1	89	0	.	10	41	18	56	2	90	31	36
	larch	0	.	0	.	0	.	0	.	0	.	1	100	0	100	0	.	1	92	0	.	0	.	16	38	7	47	16	61	40	29
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	100	0	.	0	.	0	.	0	.	0	.	0	100	2	100	0	100	0	100	0	.	0	100	0	.	0	.	3	75
	beech	79	16	0	.	2	102	3	92	5	58	15	53	27	47	54	30	24	46	14	48	22	43	1	593	7	78	80	25	334	11
	maple	29	29	0	.	1	71	0	100	0	.	9	39	9	47	14	36	14	54	5	43	13	48	4	44	1	90	3	100	101	15
	ash	2	61	0	.	0	.	0	.	0	100	2	63	4	47	0	367	1	605	7	61	6	65	4	49	0	.	13	76	38	34
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	100	0	.	0	.	1	48	0	.	1	58	2	35
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	70	0	70
	other broadleaves	3	60	0	.	0	100	0	100	0	.	3	49	8	46	1	75	2	45	-2	113	3	66	6	70	7	54	39	38	71	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
	total	491	8	0	.	16	70	7	71	13	62	187	18	308	14	290	15	155	20	107	24	120	25	126	19	195	18	228	15	2245	5
601-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	88	20	19	35	100	25	161	18	141	16	19	36	111	20	144	20	25	36	14	45	22	37	12	59	55	24	28	67	937	7
	fir	218	14	17	35	61	26	150	18	67	25	11	43	222	14	118	22	16	41	6	81	9	52	3	56	13	46	1	100	912	7
	pine	11	54	1	73	2	48	4	84	9	48	0	.	11	64	2	101	0	.	2	96	0	.	4	59	10	46	0	.	56	22
	larch	1	105	0	113	6	76	1	56	6	59	0	.	0	100	4	60	2	99	0	.	-1	113	4	55	7	55	3	93	34	26
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	6	56	1	76	2	82	19	43	9	57	0	.	2	77	1	56	0	.	0	.	0	113	0	0	0	.	0	.	39	27
	beech	170	13	32	36	75	23	108	21	103	20	27	39	109	19	50	30	19	51	2	306	31	38	7	49	13	40	33	37	778	7
	maple	43	26	6	62	14	43	12	37	20	33	0	1114	5	113	40	44	7	56	0	1002	8	71	7	59	12	45	3	60	177	15
	ash	14	38	4	79	12	83	12	50	36	40	25	45	19	35	31	33	2	150	6	59	7	54	4	77	7	74	4	53	183	15
	oak	7	41	0	77	5	60	4	65	5	37	2	78	4	86	0	0	0	205	0	100	0	.	4	58	2	78	6	46	40	19
	sweet chestnut	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	1	100	0	89	69	28	69	27
	other broadleaves	9	33	5	51	11	46	9	38	9	32	3	59	11	42	14	28	3	119	0	64	2	47	11	40	11	51	31	38	129	14
	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
	total	566	9	84	23	288	16	479	12	406	12	86	26	494	10	404	13	74	26	31	36	78	24	57	26	132	18	179	16	3355	4
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	27	32	45	22	25	33	146	17	285	13	0	.	17	70	12	63	1	90	0	180	0	.	0	.	0	.	3	73	559	8
	fir	37	40	15	52	12	56	118	17	85	23	0	.	7	100	2	92	0	.	-2	91	0	100	0	.	0	.	0	.	274	12
	pine	11	62	15	58	4	82	3	92	15	29	0	.	0	.	0	.	0	.	1	100	0	.	-2	83	0	.	0	.	46	27
	larch	0	100	4	80	8	60	3	80	8	84	0	.	1	100	1	100	2	100	0	.	0	.	0	0	0	.	0	100	25	36
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	12	66	5	100	9	59	8	59	0	.	0	.	0	133	0	.	0	.	0	.	0	.	0	.	2	92	36	33
	beech	31	26	90	16	17	67	125	15	172	12	0	.	1	59	24	46	3	100	8	67	1	178	0	1955	0	.	3	57	475	8
	maple	5	139	14	37	4	48	25	28	50	28	0	.	-1	91	3	124	2	95	0	100	0	100	3	96	0	.	4	78	109	18
	ash	13	31	19	42	34	47	18	44	50	23	0	.	0	71	7	51	4	59	-1	124	1	100	1	49	1	100	9	52	158	15
	oak	8	49	18	49	25	42	13	40	25	38	0	.	0	.	2	86	0	.	1	65	0	.	0	76	0	.	10	47	102	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	67	0	.	29	37	29	36
	other broadleaves	5	68	11	31	22	41	34	30	54	25	0	.	0	85	7	59	2	386	2	105	0	177	4	66	4	100	41	43	184	15
	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	137	19	243	13	155	22	494	10	751	8	0	.	25	58	58	35	13	61	9	67	3	136	7	103	5	100	100	23	1997	5
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	310	10	64	18	128	21	308	12	427	10	197	13	331	11	345	12	244	14	97	25	130	21	291	12	638	9	202	15	3711	3
	fir	455	9	32	31	85	22	270	12	160	16	73	33	361	12	185	16	61	25	29	49	17	38	42	30	26	33	38	32	1837	5
	pine	22	41	15	55	5	59	7	62	24	26	0	.	15	51	2	96	2	69	5	54	0	.	20	32	47	26	-2	223	163	14
	larch	1	81	3	82	15	48	4	60	14	53	1	71	1	79	5	53	12	44	0	0	1	267	100	21	99	15	97	20	351	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	13	44	29	37	0	100	43	28
	other conifers	6	54	13	60	7	72	28	35	17	41	0	.	2	72	3	70	0	100	0	100	0	144	0	93	1	41	2	92	80	20
	beech	281	9	122	15	94	22	235	12	279	10	42	31	138	18	127	19	46	32	24	42	55	28	8	93	20	36	136	18	1608	4
	maple	81	20	20	32	19	33	37	22	70	22	8	59	15	49	57	32	22	46	5	66	21	40	14	37	14	42	12	43	394	9
	ash	29	23	24	38	46	41	30	33	86	21	26	42	23	30	38	29	7	78	12	47	13	39	9	42	8	64	26	43	378	10
	oak	16	33	19	48	29	36	17	34	30	32	2	78	4	83	2	86	0	725	2	56	0	.	6	41	2	78	17	32	144	14
	sweet chestnut	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	1	64	0	89	97	22	99	21
	other broadleaves	17	28	15	26	33	31	43	25	63	22	5	45	20	30	21	26	10	76	2	175	6	40	33	24	43	29	115	22	425	9
	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	1217	5	328	11	462	13	980	7	1170	6	355	13	911	7	785	9	405	12	176	19	244	16	536	9	927	7	739	7	9232	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. 1399444. Birmensdorf, Swiss Federal Research Institute WSL

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