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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.

NFI4
number of deadwood stems
deadwood: mechanical strength of wood (3 classes) · deadwood: number of fungi species (3 classes)
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
deadwood: mechanical strength of wood (3 classes)	deadwood: number of fungi species (3 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	±%
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	.
	1	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	2 or more	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
fresh/firm wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	10	100
	0	2754	8	785	16	703	16	619	16	1070	12	1459	13	2694	12	1729	14	1820	12	718	19	836	16	4747	8	4473	8	6409	8	30815	3
	1	487	15	84	34	107	30	135	28	142	25	254	22	295	20	272	23	290	28	86	36	154	26	494	15	344	17	1048	13	4193	6
	2 or more	66	40	4	100	4	100	42	55	0	.	4	100	39	44	23	60	23	55	8	71	13	77	95	52	14	77	130	27	464	16
	total	3307	7	873	15	814	15	796	15	1212	11	1716	12	3028	12	2024	13	2133	12	812	18	1003	15	5345	8	4831	8	7588	7	35482	3
rotten wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	10	100
	0	1107	10	182	25	284	21	268	21	503	16	613	16	1150	13	665	15	1124	14	302	22	557	21	1662	10	1328	13	1886	10	11631	4
	1	303	19	28	54	129	24	114	40	100	29	177	26	352	19	193	26	259	24	58	56	61	33	273	20	266	19	411	18	2724	7
	2 or more	62	45	14	77	17	72	19	71	27	51	39	65	56	56	15	75	18	64	13	76	4	100	32	70	27	54	31	51	374	18
	total	1473	10	224	23	430	17	400	21	629	15	830	15	1558	12	873	13	1400	14	373	23	621	19	1977	10	1621	12	2328	9	14739	4
mouldering/mull wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0	749	13	185	26	262	25	298	20	340	18	404	22	748	14	431	19	730	19	313	24	467	24	1286	11	1240	11	921	14	8374	4
	1	147	29	16	62	62	36	101	29	123	28	80	32	153	30	94	28	81	31	42	42	55	41	141	26	203	22	264	23	1562	8
	2 or more	9	71	0	.	18	63	30	44	51	45	0	.	8	71	9	100	4	100	0	.	0	.	24	61	10	100	22	63	185	21
	total	905	13	202	24	342	24	428	17	514	16	484	20	910	14	534	17	815	18	356	24	523	23	1450	10	1453	10	1206	13	10121	4
total	n/a	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	20	71	0	0	0	0	20	71
	0	4610	6	1153	14	1250	13	1184	14	1912	9	2476	12	4592	9	2825	11	3674	10	1334	15	1860	13	7695	7	7040	7	9216	7	50821	2
	1	937	12	129	32	299	18	349	20	366	16	511	18	800	13	558	17	630	19	186	27	271	20	908	12	813	12	1723	11	8479	4
	2 or more	138	30	18	64	38	57	91	34	78	34	43	61	103	45	48	42	45	43	21	68	17	64	150	37	52	55	182	24	1023	11
	total	5686	6	1299	14	1587	12	1624	13	2356	9	3030	11	5496	9	3431	10	4348	10	1541	15	2147	13	8772	7	7905	7	11122	6	60343	2

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. 1196178. Birmensdorf, Swiss Federal Research Institute WSL

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