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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: 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, 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: 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	3524	11	1132	19	896	21	676	20	1368	17	1324	18	3188	15	2422	16	2194	16	925	20	1447	24	5359	11	5363	10	8753	9	38569	4
	0	189	34	94	64	17	100	20	100	57	52	35	100	166	36	192	39	123	55	0	.	74	74	77	81	18	100	0	.	1061	17
	1	717	18	243	38	211	36	254	40	126	32	69	45	337	27	265	27	371	25	57	64	272	36	504	24	775	22	639	17	4839	7
	2 or more	120	37	49	54	14	71	0	.	47	74	0	.	55	67	40	55	0	.	0	.	0	.	49	54	14	71	28	77	414	20
	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	1353	14	231	31	280	28	370	25	577	23	774	20	1359	17	1211	19	1262	19	561	27	517	22	2021	14	1720	14	1534	13	13769	5
	0	93	60	43	62	0	.	17	100	0	.	17	100	120	41	67	60	18	100	0	.	37	61	0	.	0	.	0	.	413	23
	1	313	25	109	44	84	43	125	34	121	38	224	26	303	24	331	37	194	29	88	44	135	39	443	23	422	25	430	22	3322	8
	2 or more	7	100	0	.	0	.	0	.	14	71	0	.	47	62	18	100	25	78	0	.	0	.	18	100	0	.	108	43	238	27
	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	1002	16	76	56	180	37	262	24	335	28	792	23	962	17	505	24	780	27	276	29	209	34	1126	14	1180	14	1273	15	8958	6
	0	51	54	24	77	0	.	0	.	41	60	69	100	26	75	51	85	0	.	0	.	37	71	0	.	0	.	0	.	299	32
	1	103	51	7	100	36	71	41	69	24	77	113	38	173	32	208	36	36	45	45	60	29	62	148	41	92	38	179	33	1234	13
	2 or more	0	.	0	.	0	.	35	61	0	.	15	72	28	81	0	.	0	.	0	.	0	.	0	.	0	.	24	100	101	40
	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	5879	9	1439	18	1355	18	1308	15	2279	14	2889	15	5509	12	4138	14	4236	13	1762	18	2173	19	8506	10	8263	9	11559	8	61296	3
	0	333	37	161	47	17	100	36	71	98	39	121	100	312	27	310	44	141	56	0	.	148	64	77	81	18	100	0	.	1774	16
	1	1133	14	359	30	331	28	420	27	271	23	405	24	813	17	804	24	600	20	190	36	437	29	1096	18	1289	17	1248	13	9395	5
	2 or more	127	37	49	54	14	71	35	61	60	59	15	72	130	41	58	49	25	78	0	.	0	.	67	48	14	71	160	35	754	15
	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. 1410704. Birmensdorf, Swiss Federal Research Institute WSL

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