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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.

NFI2–NFI3
bolewood harvested
type of last silvicultural treatment
regional demarcation: economic region
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI2/NFI3
grid: 1.4-km grid
change 1993/95–2004/06
	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
type of last silvicultural treatment	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%
n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
no treatment	56	24	13	43	21	42	36	27	22	31	7	48	17	44	7	37	8	42	7	44	15	67	7	42	35	27	15	47	254	10
tending	61	29	87	32	61	33	142	24	132	25	20	76	31	59	45	44	19	47	1	71	14	54	4	75	29	47	0	100	643	11
thinning	156	14	77	19	79	19	178	15	220	13	28	32	64	22	51	31	1	72	15	48	17	45	12	45	27	31	10	41	928	6
secondary felling	99	23	18	52	27	43	27	42	80	29	36	41	81	28	25	46	5	100	11	43	9	100	32	45	35	35	0	0	485	11
final felling	83	27	52	42	31	54	67	38	27	40	10	61	22	53	32	61	24	51	17	62	12	69	19	62	55	32	15	61	459	12
selection felling	61	20	0	.	5	100	1	100	0	.	0	.	82	32	0	.	0	.	0	.	0	.	0	100	1	72	1	100	154	20
thinning of mountain forest	0	.	0	.	1	100	0	.	0	.	4	64	13	47	3	100	11	80	0	0	0	.	37	36	48	27	1	100	118	19
coppice cut	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	80	3	64
conversion	11	73	0	0	0	.	1	100	11	60	0	.	5	100	6	85	0	.	0	.	0	.	0	.	0	.	0	.	33	38
transformation	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	0	0	.	0	.	1	100	1	100	0	.	1	100	4	53
sanitary felling	224	15	41	30	193	19	390	13	536	13	137	23	410	13	195	20	57	44	10	74	19	39	18	31	54	25	5	71	2244	5
thinning of continuous cover forest	2	100	0	.	4	100	7	81	57	26	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	100	72	23
tending of forest edge	17	100	7	63	3	68	7	71	12	66	0	.	4	100	34	72	1	100	0	.	0	0	7	64	0	0	0	0	88	34
planting	18	63	24	82	61	48	36	52	55	41	12	100	6	73	6	95	21	72	0	.	0	0	0	0	0	.	2	100	243	21
agrosilvicultural treatments	12	50	1	93	5	76	2	73	6	78	0	0	9	59	18	68	20	61	0	0	0	0	3	49	2	97	8	44	84	24
total	802	7	322	14	492	11	895	8	1158	7	254	16	755	9	428	14	165	22	60	26	83	24	139	18	285	12	60	23	5688	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. 1299587. Birmensdorf, Swiss Federal Research Institute WSL

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