Selection from the list of topics

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
total wood volume (stemwood)
tree species (5 classes) · tree state (living/dead)
regional demarcation: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
tree species (5 classes)	tree state (living/dead)	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	.
	living	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	dead	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
spruce	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	18439	6	27337	6	71814	4	13237	10	31290	7	12973	12	175091	2
	dead	2177	18	1846	16	9178	7	2179	13	3052	13	1392	20	19825	5
	total	20616	6	29183	6	80992	4	15416	10	34342	6	14366	12	194916	2
fir	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	16803	7	18788	8	24208	7	1081	31	1776	22	2068	24	64723	4
	dead	1253	15	1070	18	2216	14	88	54	145	53	224	35	4997	8
	total	18056	7	19858	7	26425	6	1169	31	1920	22	2291	23	69720	4
other conifers	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	2122	16	5755	12	1864	21	12414	10	12012	8	7917	10	42085	4
	dead	360	22	261	29	208	27	1324	13	984	17	674	21	3811	8
	total	2482	16	6017	12	2073	20	13738	9	12996	8	8591	10	45896	4
beech	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	20091	5	24711	6	20478	7	611	37	1941	22	6788	11	74620	3
	dead	1084	16	728	20	956	15	50	77	156	39	250	27	3224	9
	total	21175	5	25439	6	21434	7	661	35	2097	23	7037	11	77844	3
other broadleaves	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	8324	8	19575	6	13552	7	2067	16	2087	16	13160	7	58766	3
	dead	782	22	962	16	1450	12	232	20	280	22	2199	10	5905	6
	total	9107	8	20537	6	15002	7	2300	16	2368	16	15359	7	64671	3
indeterminable	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	dead	94	39	59	46	319	21	87	42	118	33	52	51	729	14
	total	94	39	59	46	319	21	87	42	118	33	52	51	729	14
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	65779	3	96167	3	131916	3	29411	7	49106	5	42905	4	415285	1
	dead	5752	9	4927	9	14328	6	3960	10	4734	10	4790	8	38491	3
	total	71531	3	101094	3	146244	3	33371	7	53840	5	47696	4	453776	1

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. 1201619. Birmensdorf, Swiss Federal Research Institute WSL

back