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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
volume of deadwood (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · tree state (standing/lying)
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 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
altitudinal vegetation belts (NaiS; 6 classes)	tree state (standing/lying)	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	.
	standing	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lying	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	.
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	standing	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	164	38	160	33	47	47	370	23
	lying	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	90	34	263	32	60	39	413	22
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	254	31	423	28	107	33	784	18
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	standing	0	.	0	.	71	100	0	.	0	.	17	100	266	39	17	100	163	41	74	49	35	70	519	23	592	29	226	30	1980	13
	lying	0	.	0	.	35	100	0	.	0	.	75	61	154	51	38	100	405	33	37	64	55	56	971	19	1116	20	283	44	3169	11
	total	0	.	0	.	106	100	0	.	0	.	92	53	420	40	55	100	568	30	110	40	90	52	1490	17	1708	20	509	32	5149	10
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	standing	12	100	0	.	2	100	0	.	0	.	265	41	317	32	255	40	480	28	243	36	68	61	648	21	563	19	654	27	3507	10
	lying	9	100	0	.	15	100	0	.	0	.	290	45	234	32	157	58	589	25	126	41	149	42	1314	16	1410	16	769	22	5061	8
	total	21	72	0	.	16	100	0	.	0	.	555	33	551	28	412	40	1068	20	369	34	217	38	1962	15	1974	14	1423	20	8568	7
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	standing	2369	14	171	36	159	39	393	33	439	36	455	30	1510	17	1396	16	473	31	262	29	403	29	236	30	198	39	362	25	8825	7
	lying	1329	14	199	44	186	38	183	35	400	27	899	28	1902	17	875	19	461	28	149	37	465	21	137	30	326	30	426	30	7935	7
	total	3698	12	370	32	345	32	575	27	838	24	1354	23	3412	14	2270	14	934	23	411	28	868	20	373	26	524	28	788	22	16760	5
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	standing	317	24	588	33	249	36	322	27	549	23	0	0	47	59	143	58	69	48	21	79	48	75	34	58	8	71	0	.	2395	12
	lying	279	27	523	27	143	45	462	24	503	25	5	100	86	43	50	53	113	51	11	81	21	74	26	54	3	100	0	.	2224	11
	total	596	20	1111	24	391	30	784	19	1052	18	5	100	133	37	193	51	181	42	32	65	69	71	60	55	11	66	0	.	4619	9
hyperinsubric and colline	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	standing	0	0	0	.	25	67	0	.	31	72	0	.	0	.	0	.	0	.	0	.	0	.	61	38	0	0	998	13	1116	12
	lying	2	100	0	.	26	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	167	35	0	.	721	19	916	16
	total	2	100	0	.	51	50	0	.	31	72	0	.	0	.	0	.	0	.	0	.	0	.	229	31	0	0	1720	12	2033	11
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	standing	2698	13	759	27	506	26	715	22	1019	20	737	23	2141	13	1810	14	1184	18	599	20	554	24	1663	12	1520	14	2288	10	18193	4
	lying	1619	12	722	23	404	26	645	20	902	18	1269	22	2375	14	1120	17	1567	16	323	25	690	18	2705	11	3118	11	2259	12	19718	4
	total	4317	10	1481	19	909	21	1359	16	1922	14	2006	18	4516	11	2930	13	2751	13	922	19	1244	16	4368	9	4639	10	4547	8	37911	3

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. 1285989. Birmensdorf, Swiss Federal Research Institute WSL

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