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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)
deadwood: mechanical strength of wood (5 classes) · deadwood: bark cover (3 classes)
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
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
deadwood: mechanical strength of wood (5 classes)	deadwood: bark cover (3 classes)	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
	0-30%	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-60%	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>60%	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	0	0	0	0	0	0	0	0	0	0	0	0	0
fresh wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	13	100	14	100	12	100	0	.	0	.	8	71	47	50
	31-60%	0	.	0	.	42	71	0	.	16	100	20	80	78	48
	>60%	356	27	94	44	95	36	34	55	40	56	32	57	650	18
	total	369	26	107	41	149	35	34	55	56	60	59	42	774	16
solid wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	1232	13	1131	16	609	19	568	17	378	19	892	14	4810	6
	31-60%	783	19	685	18	331	25	378	24	426	24	372	18	2975	9
	>60%	3787	9	2003	14	1496	13	1292	13	1529	13	1507	15	11614	5
	total	5802	8	3818	11	2436	12	2239	12	2333	12	2772	11	19400	4
rotten wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	1796	12	2161	13	937	14	827	17	633	22	496	29	6850	6
	31-60%	361	29	300	25	336	25	125	29	207	29	86	32	1414	12
	>60%	377	22	491	26	181	31	225	24	289	26	224	27	1787	11
	total	2535	11	2952	11	1454	12	1176	14	1129	16	806	20	10052	5
mouldering wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	890	14	1510	15	657	22	443	21	595	20	361	23	4456	8
	31-60%	112	49	140	37	64	50	36	51	40	60	14	65	407	22
	>60%	52	48	23	51	24	63	48	49	30	50	73	47	249	22
	total	1054	15	1673	15	746	21	527	19	664	19	447	21	5112	7
mull wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	113	31	242	25	43	42	44	50	64	36	100	35	605	14
	31-60%	0	.	0	.	0	.	0	.	0	.	5	100	5	100
	>60%	0	.	3	100	0	.	0	.	5	100	0	.	8	74
	total	113	31	245	25	43	42	44	50	69	35	105	33	619	14
total	n/a	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	0-30%	4044	8	5057	10	2258	12	1882	12	1670	14	1857	12	16768	4
	31-60%	1257	16	1125	14	773	17	540	19	689	20	496	15	4879	7
	>60%	4572	9	2613	12	1796	12	1598	12	1893	12	1836	13	14309	5
	total	9873	6	8795	8	4828	9	4020	10	4251	11	4190	9	35956	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. 1268034. Birmensdorf, Swiss Federal Research Institute WSL

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