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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)
main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	canton
	AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
main tree species	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	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
spruce	2617	21	2795	30	2096	30	28801	7	769	35	8462	14	4	75	3353	23	36021	6	2239	23	7023	14	4483	15	1721	34	4173	20	10647	13	1388	28	1755	26	5520	16	2572	21	7267	19	4327	21	12480	10	15359	9	1039	37	5788	14	172699	2
fir	1398	28	609	41	1551	36	16417	9	549	37	2259	24	4	100	501	48	2105	23	3889	20	6298	14	4592	17	368	56	1259	34	3926	17	184	59	1315	22	2149	24	1377	32	1360	27	888	32	7047	13	1642	26	398	44	2054	20	64140	4
pine	143	58	0	.	0	.	1350	28	297	35	331	55	67	100	0	.	2730	17	383	37	246	54	123	59	4	100	217	83	152	50	330	39	320	32	78	75	404	42	151	52	265	53	181	46	1918	18	0	.	840	22	10531	8
larch	286	41	0	.	0	.	271	45	27	71	97	69	0	.	50	100	9171	9	22	100	28	59	41	100	0	.	18	100	319	45	36	59	20	100	57	100	70	60	4889	13	0	.	492	31	9470	12	40	100	285	37	25688	6
Arolla pine	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	1769	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	1047	22	0	.	0	.	2836	13
other conifers	198	50	0	.	14	74	561	41	252	100	0	.	0	.	3	100	42	35	11	74	352	71	8	100	0	.	9	91	344	98	66	100	57	58	9	72	5	72	82	98	7	100	105	57	13	58	0	.	75	49	2211	25
beech	4603	16	323	48	494	36	14012	9	2525	19	3366	19	0	.	935	31	1978	22	3416	17	3640	17	1304	18	486	42	1366	30	5681	15	2159	21	4514	15	1924	24	1734	20	5971	12	1033	32	6732	12	920	30	314	43	4310	14	73741	3
maple	636	22	25	75	266	42	2455	12	484	27	566	24	3	100	431	34	522	26	508	24	549	22	600	27	153	42	269	49	1802	17	101	53	730	22	380	27	473	39	389	29	216	39	1237	15	475	26	110	46	1299	18	14678	5
ash	743	29	0	.	254	55	1362	23	343	42	1343	28	9	100	262	44	356	35	770	29	575	49	99	46	511	59	116	76	1461	23	231	46	462	27	622	37	779	27	830	32	218	56	1369	22	463	31	115	60	1279	22	14573	7
oak	819	34	0	.	0	.	666	21	189	52	231	43	384	58	0	.	185	61	332	45	305	48	87	60	5	100	0	.	99	56	210	47	212	42	19	100	963	45	981	20	86	72	1153	24	276	33	12	100	585	25	7799	10
sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	515	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4920	13	0	.	6	100	165	51	0	.	0	.	5605	12
other broadleaves	684	24	0	.	75	69	1441	16	208	32	513	31	14	65	155	46	1885	20	393	34	302	28	59	37	91	51	50	39	513	25	39	48	342	37	317	33	583	33	4227	12	245	37	1514	28	1175	19	7	63	715	25	15548	6
indeterminable	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0
total	12128	11	3751	28	4750	24	67349	5	5643	16	17168	11	485	54	5689	18	57280	5	11963	12	19319	10	11396	11	3339	26	7478	17	24943	9	4744	18	9727	12	11076	13	8960	13	31074	6	7284	17	32316	7	32923	7	2036	30	17228	9	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. 1371165. Birmensdorf, Swiss Federal Research Institute WSL

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