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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)
forest type (17 classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
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
forest type (17 classes)	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	.
inaccessible forest	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	.
shrub forest	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	.
permanently unstocked forest area	206	72	31	100	0	.	1461	37	198	82	969	42	0	.	376	87	364	38	283	100	855	56	299	65	0	.	0	.	715	59	154	100	79	100	177	87	193	73	148	92	0	.	979	43	1016	62	0	.	147	100	8652	16
temporarily unstocked forest area	0	.	0	.	36	100	277	64	0	.	34	100	0	.	19	90	645	35	17	100	4	100	0	.	0	.	0	.	68	100	0	.	111	88	11	100	140	83	59	81	0	.	289	64	54	55	0	.	3	100	1766	22
aisle, embankment	197	100	0	.	0	.	303	52	49	100	0	.	0	.	0	.	102	75	0	.	11	100	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	25	100	67	100	0	.	0	.	144	100	908	34
permanently open stand	0	.	0	.	0	0	814	36	16	100	145	85	0	0	325	52	2448	19	151	91	99	79	271	49	165	72	193	61	413	52	0	.	99	120	167	66	0	.	993	32	52	95	1640	24	1411	29	0	.	4	100	9405	10
(nut) orchard, plantation	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1079	36	0	.	486	73	120	100	0	.	0	.	1758	31
coppice forest	0	.	0	.	0	.	149	46	19	100	231	61	0	.	25	100	765	38	214	99	138	100	0	.	42	100	0	.	246	100	458	73	0	.	0	.	162	94	5035	14	0	.	112	69	387	37	0	.	0	.	7982	12
coppice with standards	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	206	72	0	.	0	.	34	100	0	.	0	.	0	.	162	100	133	101	0	.	0	.	1657	25	0	.	149	100	116	86	0	.	98	100	2556	21
selection type high forest	121	85	0	.	0	.	6465	17	0	.	847	39	0	.	146	76	5327	16	79	100	2079	27	2008	28	200	100	329	54	553	43	0	.	370	51	333	59	149	71	699	37	204	65	1216	27	3300	18	251	73	132	73	24809	7
non-uniform high forest	785	34	291	100	200	71	9836	13	456	45	1425	36	0	.	1352	38	8010	13	2359	28	2966	25	2758	24	227	87	1731	32	4503	22	121	100	890	42	925	38	580	47	7324	21	1523	37	4730	21	6467	15	0	.	1554	30	61013	5
young growth/ thicket	310	45	0	0	133	72	2126	25	55	85	272	50	24	71	46	100	2128	27	96	57	544	46	104	69	0	.	27	87	184	44	80	85	57	70	227	50	117	100	86	68	45	84	490	39	104	48	78	71	415	42	7748	12
poletimber	1306	24	0	.	266	88	3777	16	723	33	943	36	0	.	424	68	3286	16	682	51	1986	25	568	38	145	93	284	62	1191	34	318	52	632	35	1029	35	931	31	2375	17	818	40	2697	20	2128	20	120	71	2213	21	28841	6
young timber	1473	30	218	100	284	71	5441	16	602	47	1896	30	0	.	300	62	4956	17	1177	39	1522	33	280	81	434	78	1216	40	2129	30	734	41	742	36	218	72	1019	37	3383	19	1022	40	2972	21	3838	20	222	100	2816	23	38895	6
medium timber	2211	25	781	61	1000	60	9540	15	1692	32	3943	24	301	71	1132	37	10141	13	1776	30	2256	31	1331	36	1029	51	657	59	4606	22	1518	32	3608	22	2193	29	891	36	3605	20	1748	43	5766	17	3953	23	632	50	3924	20	70235	5
old timber	5518	18	2430	36	2831	33	28093	9	1833	35	6689	19	160	94	1870	39	19556	10	5129	21	7054	19	3914	24	1097	46	3029	31	10952	16	1199	42	3570	24	5926	20	4789	21	5001	19	2009	32	10903	15	10654	14	734	60	5776	19	150717	3
incomplete survey	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	68278	5	5643	16	17394	11	485	54	6015	18	58007	5	11963	12	19513	10	11568	11	3339	26	7478	17	25559	9	4744	18	10291	12	11207	13	8970	13	31445	6	7447	17	32496	7	33548	6	2036	30	17228	9	415285	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. 1232910. Birmensdorf, Swiss Federal Research Institute WSL

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