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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)
diameter at breast height (10 classes) · conifers and broadleaves
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/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
diameter at breast height (10 classes)	conifers and broadleaves	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	.
	conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	4780	7	3170	9	1423	12	1870	12	2604	9	957	12	14804	4
	broadleaves	5485	5	1954	10	1353	12	937	13	631	17	3267	8	13626	3
	indeterminable	0	0	0	0	0	.	0	.	0	.	0	.	0	0
	total	10264	4	5124	7	2776	9	2806	9	3236	8	4224	6	28430	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	9437	6	6421	7	3879	10	4084	11	5379	8	2430	12	31630	3
	broadleaves	9774	5	3815	10	3101	11	1123	16	804	20	5531	8	24150	4
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	.	0	0
	total	19211	4	10236	6	6980	8	5207	9	6183	8	7961	6	55779	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	12682	6	10191	7	6433	9	5386	10	7617	8	3955	12	46265	3
	broadleaves	13983	5	4172	10	3991	10	703	21	502	18	4140	9	27493	3
	indeterminable	0	0	0	0	0	.	0	.	0	0	0	.	0	0
	total	26666	4	14363	6	10424	7	6090	9	8120	7	8095	8	73757	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	16613	5	12946	6	7830	9	6029	9	9702	7	4308	11	57428	3
	broadleaves	15258	4	3947	12	4213	11	322	32	510	28	2913	10	27163	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	31871	3	16893	6	12043	7	6351	9	10212	7	7221	7	84590	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	16071	5	12937	7	9075	8	5500	10	8438	8	4353	12	56373	3
	broadleaves	13673	5	3162	13	2652	13	204	43	414	28	1525	15	21630	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	29744	3	16100	6	11727	7	5704	10	8852	8	5878	10	78004	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	10974	7	8723	9	5899	11	3199	13	5185	11	2351	13	36331	4
	broadleaves	6597	8	1250	23	1249	22	108	53	273	35	813	21	10290	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	17571	5	9973	8	7148	10	3307	13	5457	10	3164	10	46621	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	6068	10	4840	11	4125	14	2071	19	2580	14	1646	20	21330	5
	broadleaves	2768	14	775	26	398	46	47	73	161	56	395	26	4544	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8836	8	5615	10	4523	13	2118	18	2741	14	2040	17	25874	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	2887	15	1736	20	1263	22	798	27	1438	21	938	29	9061	9
	broadleaves	644	31	44	100	411	37	0	0	0	.	325	32	1423	19
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3531	14	1780	19	1674	19	798	27	1438	21	1263	23	10484	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	634	32	585	32	414	38	409	32	495	34	456	39	2994	14
	broadleaves	544	35	0	.	80	76	29	100	0	.	145	59	798	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1179	23	585	32	494	34	439	31	495	34	601	32	3792	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	410	45	347	50	269	45	103	73	448	39	314	59	1891	20
	broadleaves	416	56	0	.	0	.	0	.	0	.	411	55	827	39
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	826	36	347	50	269	45	103	73	448	39	725	40	2719	19
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	80556	3	61897	5	40610	7	29449	7	43887	6	21708	8	278106	2
	broadleaves	69143	3	19121	8	17447	8	3475	14	3296	15	19464	5	131944	2
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	.	0	0
	total	149699	2	81017	4	58057	6	32923	7	47182	5	41172	4	410050	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. 1353908. Birmensdorf, Swiss Federal Research Institute WSL

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