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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
volume of deadwood (stemwood)
deadwood: mechanical strength of wood (5 classes) · deadwood: bark cover (3 classes)
regional demarcation: economic region
unit: 1000 m³
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 (5 classes)	deadwood: bark cover (3 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	±%
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
	0-30%	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-60%	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>60%	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 wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	13	100	0	.	0	.	0	.	0	.	0	.	14	100	12	100	0	.	0	.	0	.	0	.	0	.	8	71	47	50
	31-60%	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	23	100	0	.	16	100	20	80	78	48
	>60%	64	46	38	81	102	52	46	70	105	57	12	55	55	69	52	51	26	56	15	87	33	51	38	50	40	56	32	57	659	18
	total	78	42	38	81	102	52	46	70	105	57	12	55	69	59	83	42	26	56	15	87	57	65	38	50	56	60	59	42	783	16
solid wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	471	17	262	34	70	35	156	47	307	25	184	34	522	23	323	28	486	25	117	34	243	31	652	16	484	20	940	13	5218	6
	31-60%	410	24	160	55	38	90	71	43	118	36	51	43	400	25	208	33	250	26	62	61	61	41	385	24	472	22	422	17	3107	8
	>60%	1896	15	654	23	386	27	421	24	497	21	395	30	1386	19	1017	17	565	20	278	32	354	24	1398	13	1657	12	1673	14	12576	5
	total	2777	12	1075	22	494	23	648	21	922	17	630	23	2309	15	1548	16	1301	16	456	24	658	20	2435	11	2613	11	3035	10	20901	4
rotten wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	618	17	257	40	149	37	276	28	496	24	726	25	972	19	644	19	630	20	146	31	239	26	922	15	661	21	532	27	7269	6
	31-60%	171	37	16	84	33	80	40	62	132	59	50	45	129	39	196	35	159	36	81	45	72	46	136	28	223	28	103	30	1538	11
	>60%	202	32	25	64	10	62	57	54	83	50	78	41	307	38	77	33	163	30	107	48	18	53	236	23	336	25	263	25	1963	10
	total	991	16	299	36	191	32	373	24	711	26	854	23	1408	16	917	16	952	17	334	25	330	23	1293	13	1220	16	898	18	10770	5
mouldering wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	276	20	65	43	100	45	307	30	141	30	460	29	674	20	349	29	450	31	141	38	192	47	473	20	646	19	370	23	4645	7
	31-60%	102	53	0	.	2	100	0	.	8	85	61	95	111	44	23	54	26	73	21	95	22	100	40	47	40	60	14	62	470	22
	>60%	37	53	0	.	0	.	1	100	15	100	22	54	1	100	0	.	0	.	5	76	21	71	51	46	30	50	81	42	264	20
	total	416	25	65	43	102	44	309	30	164	32	542	27	786	20	372	28	477	30	166	38	235	42	565	18	716	18	465	20	5379	7
mull wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	55	39	4	100	20	100	13	77	19	75	68	53	101	32	11	65	73	52	25	61	9	77	47	47	87	33	111	33	644	14
	31-60%	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	5	100
	>60%	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	8	74
	total	55	39	4	100	20	100	13	77	19	75	71	51	101	32	11	65	73	52	25	61	9	77	47	47	93	32	116	31	657	14
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	0	0	0	0	0	0	0	0
	0-30%	1434	12	588	26	340	27	753	21	964	16	1438	22	2283	13	1339	16	1639	16	428	25	684	21	2094	11	1879	13	1960	12	17823	4
	31-60%	684	21	176	51	72	59	111	36	258	34	161	46	640	19	445	23	435	21	164	34	178	30	561	19	750	19	564	15	5198	7
	>60%	2199	14	717	23	497	27	526	21	701	20	509	25	1750	17	1146	16	755	18	405	26	426	23	1723	11	2068	11	2049	12	15470	4
	total	4317	10	1481	19	909	21	1390	16	1922	14	2108	18	4673	11	2930	13	2829	13	997	19	1287	16	4377	9	4697	10	4573	8	38491	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. 1268045. Birmensdorf, Swiss Federal Research Institute WSL

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