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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.

NFI1
total wood volume (stemwood) NFI1
altitudinal vegetation belts (NaiS; 6 classes) · conifers and broadleaves
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	conifers and broadleaves	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	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	.
	broadleaves	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	.
	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	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	.
upper subalpine	n/a	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	.
	conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3020	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	122	57	0	.	0	.	1522	23	0	.	0	.	4664	14
	broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	2	100
	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3020	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	122	57	0	.	0	.	1524	23	0	.	0	.	4665	14
subalpine	n/a	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	.
	conifers	0	.	0	.	0	.	3764	22	0	.	0	.	0	.	285	62	11560	11	0	.	2	100	0	.	229	71	641	48	627	47	0	.	0	.	219	74	0	.	1989	22	634	51	478	47	7411	14	0	.	0	.	27838	7
	broadleaves	0	.	0	.	0	.	7	70	0	.	0	.	0	.	0	.	14	66	0	.	0	.	0	.	0	.	2	100	3	72	0	.	0	.	0	.	0	.	15	73	0	.	24	100	16	74	0	.	0	.	82	38
	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	0	.	0	.	0	.	3771	22	0	.	0	.	0	.	285	62	11574	11	0	.	2	100	0	.	229	71	643	48	630	47	0	.	0	.	219	74	0	.	2005	22	634	51	501	46	7427	14	0	.	0	.	27920	7
high-montane	n/a	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	.
	conifers	0	.	0	.	0	.	7142	16	0	.	1293	36	0	.	838	55	21677	9	0	.	615	44	93	100	102	100	1613	32	1684	36	0	.	0	.	1120	36	0	.	3785	18	2110	30	2503	26	10157	11	110	100	0	.	54841	5
	broadleaves	0	.	0	.	0	.	146	64	0	.	5	79	0	.	0	.	623	31	0	.	2	102	1	100	0	.	6	100	2	100	0	.	0	.	14	74	0	.	159	44	8	73	74	51	278	26	0	.	0	.	1320	18
	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	0	.	0	.	0	.	7289	16	0	.	1298	36	0	.	838	55	22300	9	0	.	616	43	94	100	102	100	1619	32	1686	36	0	.	0	.	1134	36	0	.	3944	18	2118	30	2577	26	10435	11	110	100	0	.	56161	5
lower and upper montane	n/a	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	.
	conifers	649	42	2516	29	4149	24	29365	8	742	45	7584	17	0	.	2916	25	2796	20	4366	18	9781	14	7098	13	906	41	2998	30	10782	13	847	37	2579	21	5890	18	961	33	1463	28	1277	36	12012	11	1322	26	809	41	5137	17	118945	3
	broadleaves	882	39	267	48	1029	30	9407	10	1046	31	1835	22	0	.	1450	26	1544	27	3253	17	1868	22	1295	18	792	37	1683	32	5147	15	528	36	3510	18	1516	27	719	40	2931	15	542	35	4580	12	622	32	203	52	3176	18	49827	4
	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	1531	32	2784	29	5178	23	38772	7	1788	29	9418	15	0	.	4366	21	4340	18	7619	14	11650	13	8393	12	1698	33	4681	24	15930	11	1376	33	6089	16	7406	16	1680	31	4394	14	1819	30	16592	10	1944	23	1012	40	8313	14	168771	3
submontane	n/a	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	.
	conifers	8120	15	0	.	0	.	12095	12	1143	37	3215	25	0	.	0	.	733	42	1533	30	4410	20	672	42	0	.	136	100	837	58	1329	31	2782	26	14	100	3710	19	0	.	159	56	2814	24	95	100	23	100	7029	15	50851	5
	broadleaves	6483	13	0	.	0	.	7192	13	2586	21	1986	25	0	.	0	.	370	51	2114	26	2145	24	503	56	0	.	22	100	1444	33	1477	23	1854	23	401	56	2486	19	0	.	252	52	3725	17	281	52	187	71	3779	16	39285	5
	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	14604	11	0	.	0	.	19287	10	3729	20	5200	20	0	.	0	.	1103	38	3646	21	6555	17	1174	35	0	.	158	100	2281	33	2805	23	4636	20	415	57	6196	16	0	.	411	50	6539	16	376	48	211	72	10808	13	90137	4
hyperinsubric and colline	n/a	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	.
	conifers	0	.	0	.	0	.	0	.	0	.	0	.	46	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	100	104	47	0	.	0	.	720	30	0	.	171	100	1110	26
	broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	390	54	0	.	545	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	6142	10	0	.	131	60	238	43	0	.	102	73	7553	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	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	435	51	0	.	545	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	100	6246	10	0	.	131	60	958	26	0	.	272	80	8663	9
total	n/a	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	.
	conifers	8769	14	2516	29	4149	24	52366	6	1885	28	12091	13	46	63	4039	22	39785	6	5899	15	14808	11	7862	12	1237	34	5388	20	13931	12	2176	23	5361	17	7243	15	4741	16	7464	11	4179	20	17806	9	21227	7	942	37	12337	11	258249	2
	broadleaves	7365	13	267	48	1029	30	16753	8	3632	17	3825	17	390	54	1450	26	3097	17	5366	14	4015	17	1799	20	792	37	1713	31	6596	14	2005	19	5364	14	1931	24	3210	17	9247	8	803	29	8534	10	1437	19	390	43	7057	12	98068	3
	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	16134	10	2784	29	5178	23	69119	5	5517	16	15917	11	435	51	5489	19	42882	6	11265	11	18823	10	9661	12	2029	29	7101	18	20527	10	4181	19	10725	12	9174	14	7951	14	16711	6	4982	18	26340	8	22664	7	1333	33	19394	9	356317	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. 1358731. Birmensdorf, Swiss Federal Research Institute WSL

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