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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: regeneration (3 classes)
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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: regeneration (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
	no plant	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	1 plant	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	2 or more plants	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	3194	10	900	21	812	22	673	20	1035	19	976	21	2510	15	2140	15	1553	18	674	20	837	23	3258	13	3313	12	6352	10	28229	4
	no plant	1355	16	616	25	325	27	276	39	556	18	451	21	1186	18	779	19	1119	19	300	36	939	34	2711	11	2834	15	3023	13	16471	5
	1 plant	0	.	0	.	0	.	0	.	0	.	0	.	39	65	0	.	0	.	7	100	0	.	7	100	0	.	8	100	61	46
	2 or more plants	0	.	0	.	0	.	0	.	7	100	0	.	11	100	0	.	14	71	0	.	17	100	13	71	24	77	36	63	123	32
	total	4550	9	1517	17	1137	20	950	19	1598	15	1427	18	3745	14	2919	14	2687	15	982	20	1793	22	5990	10	6171	10	9419	9	44884	3
rotten wood	n/a	502	20	62	45	58	56	132	34	194	32	198	36	665	25	722	20	393	22	371	26	199	37	425	21	290	24	757	16	4968	7
	no plant	1250	15	307	25	306	28	379	25	493	24	789	20	1070	16	851	27	976	23	278	32	476	22	1912	14	1797	15	1282	15	12167	5
	1 plant	0	.	7	100	0	.	0	.	0	.	15	100	13	71	29	79	70	50	0	.	0	.	72	49	6	100	20	100	232	26
	2 or more plants	14	71	7	100	0	.	0	.	23	77	13	71	80	50	25	77	61	44	0	.	14	71	74	41	49	38	14	71	375	18
	total	1766	13	383	24	364	26	512	21	711	20	1015	18	1829	14	1627	18	1499	17	649	27	689	21	2483	13	2142	14	2072	11	17742	4
mouldering/mull wood	n/a	176	42	7	100	20	74	37	77	7	100	33	52	185	34	116	46	89	43	110	47	39	61	79	45	51	55	283	33	1232	14
	no plant	961	15	94	49	196	34	283	23	376	25	890	24	832	17	598	23	605	32	202	32	205	30	999	16	1035	15	1141	17	8418	6
	1 plant	19	100	0	.	0	.	0	.	17	100	18	100	37	66	0	.	34	62	9	100	7	100	50	54	44	61	37	82	271	25
	2 or more plants	0	.	7	100	0	.	17	100	0	.	48	56	134	38	50	63	88	54	0	.	25	79	146	37	142	32	15	70	671	16
	total	1156	14	108	44	216	33	337	21	399	25	989	23	1189	16	764	21	816	26	320	29	275	29	1274	15	1272	13	1476	15	10592	5
total	n/a	3873	10	969	21	890	21	843	17	1236	17	1207	18	3361	14	2978	13	2035	15	1155	18	1075	22	3763	12	3654	11	7392	9	34429	4
	no plant	3566	10	1017	19	827	20	939	19	1426	14	2130	17	3088	12	2228	18	2701	16	781	24	1620	22	5622	10	5666	11	5446	9	37056	4
	1 plant	19	100	7	100	0	.	0	.	17	100	33	71	89	40	29	79	104	39	16	71	7	100	129	35	50	55	64	57	564	17
	2 or more plants	14	71	14	70	0	.	17	100	30	64	61	46	225	29	75	63	163	34	0	.	56	50	233	28	216	26	65	41	1169	12
	total	7472	8	2007	16	1717	17	1799	14	2708	12	3431	15	6763	11	5310	13	5002	12	1951	18	2758	18	9747	9	9585	9	12967	7	73219	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. 1413650. Birmensdorf, Swiss Federal Research Institute WSL

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