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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
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	291	30	60	39	441	21
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	254	31	450	26	107	33	811	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	298	36	17	100	163	41	74	49	35	70	519	23	594	29	240	28	2027	13
	lying	0	.	0	.	35	100	0	.	0	.	75	61	154	51	38	100	405	33	37	64	55	56	974	19	1126	20	285	44	3184	11
	total	0	.	0	.	106	100	0	.	0	.	92	53	452	38	55	100	568	30	110	40	90	52	1494	17	1720	20	524	31	5211	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	494	27	316	36	68	61	650	21	582	19	654	27	3615	10
	lying	9	100	0	.	15	100	0	.	0	.	392	42	234	32	157	58	652	25	126	41	149	42	1316	16	1410	16	770	22	5230	8
	total	21	72	0	.	16	100	0	.	0	.	658	32	551	28	412	40	1146	20	442	33	217	38	1966	15	1992	14	1424	20	8844	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	408	32	439	36	455	30	1609	17	1396	16	473	31	263	29	444	28	236	30	198	39	372	24	8991	7
	lying	1329	14	199	44	186	38	198	34	400	27	899	28	1929	16	875	19	461	28	149	37	468	21	137	30	326	30	426	30	7980	7
	total	3698	12	370	32	345	32	606	26	838	24	1354	23	3538	14	2270	14	934	23	412	28	911	20	373	26	524	28	798	22	16971	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	.	169	35	0	.	721	19	918	16
	total	2	100	0	.	51	50	0	.	31	72	0	.	0	.	0	.	0	.	0	.	0	.	231	31	0	0	1720	12	2034	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	730	21	1019	20	737	23	2271	13	1810	14	1198	17	673	21	595	23	1665	12	1541	14	2312	10	18514	4
	lying	1619	12	722	23	404	26	660	20	902	18	1372	22	2402	14	1120	17	1631	15	323	25	693	18	2713	10	3156	11	2261	12	19977	4
	total	4317	10	1481	19	909	21	1390	16	1922	14	2108	18	4673	11	2930	13	2829	13	997	19	1287	16	4377	9	4697	10	4573	8	38491	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. 1286001. Birmensdorf, Swiss Federal Research Institute WSL

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