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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
number of deadwood stems
deadwood: mechanical strength of wood (3 classes) · deadwood: colonization by shrubs (5 classes)
regional demarcation: canton
unit: 1000 n
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
deadwood: mechanical strength of wood (3 classes)	deadwood: colonization by shrubs (5 classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	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	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
	0-1	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	.
	2-10	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	.
	11-20	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	.
	21-50	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	.
	>50	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	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	0	0	0	0	0	0	0
fresh/firm wood	n/a	313	27	213	43	177	34	3037	12	421	26	877	20	79	76	271	39	4236	10	994	22	1139	22	402	25	214	47	505	38	1398	18	266	41	497	27	856	27	358	38	5346	12	444	25	2012	16	3240	13	135	45	505	27	27935	4
	0-1	340	32	61	79	139	46	1898	15	326	34	317	27	54	73	667	46	3629	14	269	36	529	28	263	31	51	58	257	30	583	18	83	52	327	43	259	35	94	41	2256	14	223	42	755	17	2675	11	0	.	368	25	16424	5
	2-10	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	32	64
	11-20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	21-50	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	.
	>50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100
	total	654	23	274	43	316	31	4935	11	746	24	1195	20	133	54	938	37	7880	9	1263	22	1684	21	665	22	265	41	762	32	1999	15	348	35	824	24	1115	27	452	31	7603	10	667	25	2768	13	5915	10	135	45	879	23	44413	4
rotten wood	n/a	49	54	28	63	83	45	608	18	14	71	153	43	0	.	161	44	496	19	155	38	376	40	79	58	7	100	134	46	290	25	0	.	84	47	342	33	17	100	523	20	313	30	330	21	425	21	53	66	155	38	4877	7
	0-1	284	27	15	100	207	49	1703	15	127	45	589	22	17	100	192	38	1976	14	368	27	478	24	126	37	96	42	258	36	513	18	87	53	297	31	292	37	110	78	1031	17	240	36	947	20	2058	13	25	77	410	46	12448	5
	2-10	0	.	0	.	0	.	35	71	0	.	6	100	0	.	0	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	61	46
	11-20	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	.
	21-50	0	.	0	.	0	.	48	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	73
	>50	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	333	25	43	73	290	39	2394	13	140	43	748	21	17	100	354	33	2485	12	523	22	853	23	205	37	103	41	392	29	803	16	87	53	382	28	634	26	127	69	1561	13	553	30	1277	16	2483	13	78	53	565	38	17433	4
mouldering/mull wood	n/a	0	.	0	.	36	100	209	36	0	.	40	52	0	.	39	61	147	41	35	70	104	52	0	.	6	100	48	52	75	53	7	100	17	100	43	87	0	.	145	43	73	50	68	60	79	45	0	.	7	100	1178	14
	0-1	122	40	0	.	94	45	1389	17	111	44	672	29	0	.	138	39	1467	14	289	30	546	25	124	43	54	53	133	41	363	25	24	77	185	32	193	44	0	.	890	17	170	37	642	20	1194	16	80	59	303	31	9186	6
	2-10	17	100	0	.	0	.	60	51	0	.	0	.	0	.	0	.	38	55	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	132	32
	11-20	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	71
	21-50	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	.
	>50	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	139	41	0	.	129	51	1666	16	111	44	712	28	0	.	177	40	1653	13	324	31	650	25	124	43	60	51	181	35	445	24	31	64	202	31	253	38	0	.	1035	16	242	34	710	20	1274	15	80	59	310	31	10511	5
total	n/a	362	24	242	40	296	34	3854	11	435	26	1071	18	79	76	471	35	4879	10	1184	22	1618	22	481	23	227	45	687	31	1762	17	273	40	598	26	1242	22	375	37	6014	11	829	22	2411	14	3744	12	187	37	667	23	33990	4
	0-1	746	21	76	66	440	34	4991	11	563	27	1578	21	71	61	998	33	7072	10	927	22	1554	18	513	23	201	36	648	29	1459	15	194	36	810	25	743	29	204	46	4178	11	633	29	2345	14	5927	10	105	52	1080	29	38057	4
	2-10	17	100	0	.	0	.	95	41	0	.	6	100	0	.	0	.	59	41	0	.	0	.	0	.	0	.	0	.	25	77	0	.	0	.	9	100	0	.	6	100	0	.	0	.	0	.	0	.	7	100	224	24
	11-20	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	58
	21-50	0	.	0	.	0	.	48	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	73
	>50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100
	total	1126	18	317	40	736	28	8996	9	998	23	2655	17	150	49	1468	28	12018	8	2110	19	3187	17	994	19	428	34	1335	27	3247	14	467	30	1408	20	2003	22	579	32	10199	8	1462	23	4755	11	9672	9	293	35	1754	23	72357	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. 1411554. Birmensdorf, Swiss Federal Research Institute WSL

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