NFI Results

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 bolewood (HG 2010)
timber assortment classes (HG 2010) · tree species (5 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
timber assortment classes (HG 2010)	tree species (5 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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
smallwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3159	8	646	17	356	18	276	19	250	11	84	17	4772	6
	fir	2461	8	267	14	194	35	91	68	8	29	12	24	3034	7
	other conifers	414	25	33	29	15	35	130	13	106	14	62	21	760	14
	beech	139	5	41	11	35	11	4	36	7	29	34	14	260	4
	other broadleaves	83	7	26	13	25	13	12	20	7	23	80	9	233	5
	indeterminable	0	72	0	0	0	100	0	0	0	0	0	0	0	68
	total	6257	6	1012	12	626	17	514	17	379	9	272	8	9059	4
classes 1 and 2	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10791	6	11822	6	7488	9	5155	10	8774	7	2997	13	47026	3
	fir	6287	7	4307	9	1954	13	383	25	211	27	423	27	13565	5
	other conifers	1987	10	455	31	296	30	3661	10	2727	11	1524	13	10649	5
	beech	10505	4	3418	9	2677	12	512	28	359	28	2614	14	20085	4
	other broadleaves	9082	5	3123	11	2658	12	1213	14	961	21	6746	7	23784	4
	indeterminable	14	46	12	55	2	77	0	0	13	100	4	58	46	36
	total	38667	3	23136	5	15075	6	10924	7	13045	6	14309	5	115155	2
classes 3 and 4	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18454	5	17386	6	12192	8	6100	10	10994	7	4804	15	69930	3
	fir	14079	6	7685	8	4216	12	756	30	583	25	835	26	28155	4
	other conifers	3302	10	575	42	247	31	4050	11	3829	10	1965	12	13968	5
	beech	19736	4	5163	11	5134	11	285	37	662	31	1999	12	32980	3
	other broadleaves	9695	6	1940	13	2414	12	357	30	287	31	2535	10	17229	4
	indeterminable	11	100	13	100	0	0	0	0	0	0	0	0	23	71
	total	65277	2	32762	4	24204	6	11548	8	16356	6	12138	7	162285	1
classes 5 and 6	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5577	9	6206	8	5398	11	1977	15	3741	11	2306	19	25205	4
	fir	4613	10	3767	11	1843	17	208	42	414	30	461	31	11305	6
	other conifers	1080	27	93	43	85	40	1262	15	1211	14	723	23	4453	9
	beech	7312	7	1742	18	1324	18	89	57	209	45	463	20	11138	6
	other broadleaves	2772	14	349	53	456	27	52	43	58	47	948	26	4636	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	21353	4	12157	7	9105	9	3588	11	5633	9	4900	11	56736	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	37981	4	36060	5	25434	8	13509	9	23759	7	10190	13	146933	2
	fir	27441	5	16025	8	8207	11	1437	26	1216	24	1732	24	56058	4
	other conifers	6783	10	1155	28	643	26	9102	9	7873	9	4273	12	29830	5
	beech	37692	4	10364	9	9170	10	890	29	1237	28	5110	11	64463	3
	other broadleaves	21632	5	5438	11	5553	11	1635	15	1313	20	10310	7	45881	3
	indeterminable	25	51	24	59	2	77	0	0	13	100	4	58	70	34
	total	131554	2	69067	4	49010	6	26574	7	35412	6	31619	5	343236	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. 1400836. Birmensdorf, Swiss Federal Research Institute WSL

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