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.

NFI3–NFI4
increment (gross increment)
site quality · main tree species
regional demarcation: economic region
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI3/NFI4
grid: 1.4-km grid
change 2004/06–2009/17
		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	33	25	1	193	5	42	3	49	7	71	32	32	92	17	23	31	57	22	33	28	43	26	42	27	98	18	13	31	485	7
	fir	26	28	1	61	0	100	5	74	4	83	2	51	6	58	3	72	13	41	2	57	1	159	3	74	0	100	0	.	68	17
	pine	4	72	1	134	0	.	0	194	1	91	0	.	3	96	0	100	0	64	2	166	0	100	4	52	4	49	0	100	21	33
	larch	0	.	0	.	0	.	0	.	0	0	0	.	0	100	0	.	0	100	0	.	1	100	44	25	34	22	20	34	94	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	100	2	87	1	100	0	.	13	34	23	23	0	72	38	18
	other conifers	1	67	1	82	0	.	0	100	0	100	0	.	1	56	0	100	0	100	0	73	0	100	0	0	1	34	0	.	4	31
	beech	72	18	14	39	2	110	11	45	3	75	8	63	15	51	5	62	15	55	5	89	8	42	1	201	0	.	0	.	163	13
	maple	4	80	0	1067	0	98	1	59	1	64	3	50	6	65	0	50	-1	239	3	84	4	42	2	59	0	.	0	.	23	30
	ash	8	33	3	62	0	0	1	77	1	47	3	71	0	272	1	202	1	63	0	81	2	83	1	216	0	.	0	.	21	25
	oak	2	71	1	120	0	100	2	95	1	55	0	90	0	84	0	.	0	88	0	101	0	100	2	45	0	.	0	.	8	32
	sweet chestnut	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	138	0	.	0	.	1	100
	other broadleaves	5	33	2	86	0	.	0	299	1	73	1	61	1	45	0	269	1	39	3	65	0	75	5	53	0	69	0	100	20	23
	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	155	14	25	31	8	48	23	37	19	39	48	27	126	16	32	25	89	20	48	25	59	22	117	15	161	13	34	26	947	6
moderate	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	121	15	0	.	12	39	1	73	0	100	137	16	89	18	73	22	147	15	37	32	32	32	273	10	407	9	213	12	1549	4
	fir	63	17	0	.	4	51	1	78	0	0	63	25	27	29	32	27	6	108	12	36	8	52	36	28	16	30	33	23	308	9
	pine	0	100	0	.	0	.	0	.	0	.	0	0	0	.	0	1034	1	58	0	74	-1	301	47	24	23	37	8	51	76	20
	larch	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	108	0	.	0	444	145	12	71	20	86	18	294	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-1	100	0	.	0	.	4	42	6	45	0	78	8	40
	other conifers	0	100	0	.	0	.	0	.	0	.	0	.	0	.	-1	100	1	93	0	0	0	100	0	134	0	79	0	100	0	666
	beech	56	20	0	.	3	55	-1	132	5	68	10	46	9	40	8	49	-3	417	-1	137	9	36	20	41	15	46	27	49	167	15
	maple	9	32	0	.	1	117	0	9715	0	.	6	37	5	40	2	125	11	36	1	77	4	43	8	28	8	44	10	53	67	15
	ash	1	58	0	.	0	.	0	100	1	100	4	34	2	68	2	105	6	74	1	79	3	73	6	118	7	33	6	49	38	25
	oak	0	0	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	100	0	.	1	100	1	82	-1	156	8	42	8	45
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	2109	0	2109
	other broadleaves	3	42	0	.	0	.	0	.	0	100	5	30	3	62	0	294	5	59	-1	168	6	73	39	21	27	22	38	24	121	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
	total	253	11	0	.	19	34	1	129	6	68	223	14	136	16	116	18	174	16	50	27	62	28	579	8	580	8	428	9	2636	4
good	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	195	11	29	39	71	23	47	25	111	20	120	19	215	11	250	12	66	23	58	27	49	24	7	63	145	17	45	26	1418	5
	fir	168	11	12	54	37	22	64	20	46	23	57	26	243	12	113	16	12	35	18	30	10	41	12	48	28	34	9	79	850	6
	pine	12	37	0	932	0	66	1	55	0	640	0	.	4	64	0	438	0	54	0	80	0	.	0	.	1	100	1	114	20	37
	larch	1	87	3	65	4	49	1	52	5	48	1	100	3	97	1	66	2	108	0	100	5	82	1	100	3	73	2	203	31	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	1	174	0	63	7	72	7	48	0	907	0	0	0	60	1	55	0	100	0	0	0	100	0	100	0	.	1	100	19	41
	beech	143	12	42	26	54	32	76	17	119	15	33	30	86	17	108	16	20	40	20	30	17	31	13	40	5	61	87	22	836	6
	maple	20	30	5	54	6	36	5	69	5	112	12	39	10	40	17	35	8	49	6	52	3	118	0	599	-2	167	10	54	106	15
	ash	20	28	8	31	9	67	17	35	15	35	8	55	22	27	16	48	1	331	4	94	-1	177	1	97	-2	176	41	25	160	12
	oak	6	36	8	47	-1	472	5	35	9	35	2	65	2	60	-2	131	4	65	0	59	0	.	0	111	1	88	17	34	51	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	100	0	100	111	19	100	19
	other broadleaves	22	31	7	33	12	36	7	37	9	35	1	168	8	28	4	69	7	44	7	45	6	49	5	50	24	46	115	13	223	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
	total	587	7	115	19	200	16	231	13	317	12	233	16	593	8	507	9	120	17	115	19	89	20	39	29	204	15	439	9	3814	3
very good	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	139	16	47	21	107	17	303	11	401	9	22	40	59	23	43	30	8	55	4	49	11	84	6	81	0	.	8	100	1157	5
	fir	148	13	22	27	39	29	202	13	145	13	3	114	76	18	31	25	1	64	5	94	4	61	-4	110	0	.	0	.	679	6
	pine	5	96	4	52	5	38	8	35	20	22	0	.	0	73	0	100	2	100	1	62	2	100	0	.	0	.	0	.	46	17
	larch	0	70	-1	110	13	39	12	46	9	45	2	83	0	100	1	50	1	172	0	.	3	79	0	100	0	.	0	.	41	23
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	2	173	9	59	6	54	5	52	10	70	0	.	0	0	0	58	0	.	0	.	0	.	0	.	0	.	0	.	33	32
	beech	110	13	81	17	96	17	150	13	176	11	7	66	53	21	20	39	8	53	3	87	17	35	2	51	0	.	4	89	727	5
	maple	14	35	17	32	12	46	22	27	59	21	1	75	5	46	2	421	1	150	0	239	1	46	0	61	0	.	0	100	133	13
	ash	27	20	28	29	45	25	25	32	80	17	-2	84	5	43	15	32	5	96	2	77	1	177	6	63	0	.	1	84	238	10
	oak	17	36	11	37	26	27	15	32	34	24	0	74	0	100	1	128	0	.	0	100	0	.	0	95	0	.	0	99	106	13
	sweet chestnut	0	.	0	.	4	94	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	842	0	.	0	73	4	102
	other broadleaves	12	23	18	39	33	28	29	35	59	18	1	100	4	59	4	106	4	54	1	47	1	54	4	58	0	.	-1	237	168	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
	total	473	9	237	12	386	10	771	7	992	6	34	38	202	14	118	21	30	37	16	39	40	33	15	43	0	.	12	74	3331	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	489	7	77	20	195	13	355	10	520	8	312	11	456	7	389	9	278	11	131	16	135	16	325	9	650	7	280	10	4606	2
	fir	405	7	35	25	80	18	273	10	194	11	126	17	349	9	178	12	33	29	37	23	23	29	46	26	44	24	42	24	1934	4
	pine	20	33	6	83	6	36	10	32	21	26	0	0	7	57	0	183	4	58	4	99	0	2034	51	22	28	32	9	46	162	12
	larch	2	71	2	120	17	32	13	44	14	33	2	65	3	78	2	40	4	69	0	100	9	53	190	11	109	15	108	16	448	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	100	1	280	1	100	0	.	17	28	29	21	0	53	46	16
	other conifers	4	99	11	52	14	46	12	35	10	78	0	0	1	42	1	247	1	82	0	73	0	107	0	86	1	36	1	94	58	23
	beech	380	7	137	13	154	15	236	10	302	8	58	22	162	12	142	14	42	40	27	30	50	18	36	28	20	38	118	20	1932	3
	maple	46	19	22	28	19	32	28	25	65	21	21	26	27	25	21	45	19	33	10	40	13	34	11	33	7	67	20	37	334	8
	ash	55	15	39	22	54	24	43	23	97	15	13	42	28	23	34	28	13	59	7	58	6	67	13	60	6	63	48	23	468	7
	oak	24	27	20	28	25	31	21	25	44	20	2	56	3	53	-1	340	4	66	0	126	1	95	4	37	0	389	25	26	178	10
	sweet chestnut	0	.	0	.	4	94	0	100	0	100	0	.	0	.	0	.	0	100	0	.	0	.	1	346	0	100	111	21	106	20
	other broadleaves	42	18	26	28	45	23	35	30	68	16	7	32	16	23	7	69	17	28	11	37	12	41	53	17	51	24	152	12	537	6
	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	1468	4	375	9	612	8	1027	6	1335	5	543	9	1052	5	773	7	415	10	228	13	249	12	747	6	943	6	913	5	10810	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. 1261342. Birmensdorf, Swiss Federal Research Institute WSL

back