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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.

NFI5
growing stock (stemwood)
site quality · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
site quality	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
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	.
low	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1552	22	8949	13	6214	17	1741	32	5445	17	830	34	24732	8
	fir	2185	21	894	39	586	44	67	85	0	.	0	.	3732	17
	pine	637	26	269	68	109	58	28	74	404	31	22	100	1470	19
	larch	1	100	0	0	99	100	1790	23	3073	18	1029	21	5992	12
	Arolla pine	0	.	0	.	0	.	838	26	1107	22	0	100	1946	17
	other conifers	64	53	19	68	15	61	0	.	30	41	0	.	128	31
	beech	5435	14	2034	26	1023	37	203	73	0	.	0	.	8696	12
	maple	364	24	589	24	184	31	119	47	0	.	0	.	1256	15
	ash	219	32	130	67	40	70	93	87	0	.	0	.	482	29
	oak	319	36	81	73	52	96	17	71	0	.	0	.	468	29
	sweet chestnut	0	.	0	.	0	.	60	71	0	.	0	.	60	71
	other broadleaves	225	36	164	31	65	39	189	46	6	59	4	72	652	20
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	11001	11	13129	11	8388	15	5146	17	10064	12	1886	21	49613	5
moderate	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4886	14	18471	10	7760	15	13033	10	19987	9	8944	12	73081	4
	fir	3819	17	4025	18	1696	28	1255	30	498	40	1765	26	13058	10
	pine	21	100	32	55	146	81	1890	19	2313	19	142	54	4544	13
	larch	0	.	248	42	74	97	8104	13	4234	14	5088	13	17748	8
	Arolla pine	0	.	12	100	0	.	243	36	665	30	8	100	929	23
	other conifers	7	100	42	82	7	100	13	58	18	54	0	.	87	44
	beech	2208	17	1741	22	1684	24	348	43	838	26	2367	19	9186	9
	maple	555	20	873	20	726	27	254	37	221	32	177	39	2806	11
	ash	63	51	442	30	405	40	337	35	131	46	83	59	1462	17
	oak	0	.	14	72	0	.	195	42	64	100	194	41	467	28
	sweet chestnut	0	.	0	.	0	.	11	100	5	100	698	34	714	34
	other broadleaves	79	33	333	21	162	36	933	21	722	26	966	27	3195	12
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	11639	11	26234	9	12660	13	26616	8	29695	7	20434	7	127277	3
good	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13944	9	13654	9	15109	11	686	49	5614	17	3004	40	52010	5
	fir	11695	10	9951	11	5933	14	320	62	899	31	303	61	29101	6
	pine	1749	21	500	54	166	52	0	.	46	100	0	.	2461	19
	larch	393	32	30	74	101	71	0	.	196	71	731	39	1452	24
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	695	43	84	96	-9	225	0	.	0	.	82	98	853	37
	beech	13640	8	6521	14	5046	13	316	56	473	58	4212	15	30208	5
	maple	2695	13	1204	17	1821	16	103	57	211	52	314	32	6348	8
	ash	2631	18	1046	34	1444	22	45	71	142	65	799	33	6108	12
	oak	1861	18	164	44	80	54	20	100	85	100	781	24	2991	13
	sweet chestnut	0	.	6	100	0	.	26	100	18	100	4714	14	4763	14
	other broadleaves	1357	17	759	22	830	22	22	53	382	39	4042	13	7391	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	50661	5	33920	7	30521	8	1537	35	8065	15	18981	9	143686	3
very good	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21385	7	2472	24	1411	33	0	.	0	.	0	.	25268	7
	fir	14242	9	2711	21	1880	29	0	.	0	.	0	.	18833	8
	pine	2020	15	0	.	82	71	0	.	0	.	0	.	2102	15
	larch	836	22	189	58	192	48	0	.	0	.	0	.	1217	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	1138	41	0	.	19	57	0	.	0	.	0	.	1157	40
	beech	21476	6	2080	25	2712	22	53	71	0	.	208	78	26530	6
	maple	3507	10	396	30	446	34	8	100	0	.	0	.	4357	9
	ash	5514	11	422	45	657	34	11	89	0	.	30	74	6634	10
	oak	3792	16	12	100	56	84	45	78	0	.	42	80	3946	16
	sweet chestnut	0	.	0	.	0	.	68	100	0	.	0	0	68	100
	other broadleaves	3789	12	427	68	270	33	65	76	0	.	46	72	4597	12
	indeterminable	0	0	0	.	0	0	0	.	0	.	0	.	0	0
	total	77697	4	8710	15	7724	17	249	60	0	.	327	62	94709	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	41767	5	43546	5	30494	7	15460	9	31045	7	12778	12	175091	2
	fir	31941	5	17581	8	10095	11	1642	26	1397	24	2068	24	64723	4
	pine	4427	11	801	41	503	34	1918	18	2762	17	165	49	10577	8
	larch	1230	18	467	33	466	36	9895	12	7503	10	6848	11	26409	6
	Arolla pine	0	.	12	100	0	.	1082	21	1772	18	9	94	2875	13
	other conifers	1904	29	146	61	32	81	13	58	47	32	82	98	2224	25
	beech	42760	4	12376	9	10466	10	920	30	1310	27	6788	11	74620	3
	maple	7121	7	3063	10	3177	12	483	26	432	30	491	25	14767	5
	ash	8428	9	2040	21	2546	16	485	30	274	40	913	30	14686	7
	oak	5971	12	271	35	187	43	276	33	149	71	1017	20	7872	10
	sweet chestnut	0	.	6	100	0	.	165	51	23	82	5412	13	5605	12
	other broadleaves	5450	9	1683	20	1327	16	1209	18	1109	21	5057	11	15836	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	150998	2	81993	4	59294	6	33548	6	47824	5	41627	4	415285	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. 1195165. Birmensdorf, Swiss Federal Research Institute WSL

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