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.

NFI4
total wood volume (stemwood)
tree condition · diameter at breast height (≤ or >30 cm)
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		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
tree condition	diameter at breast height (≤ or >30 cm)	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	.
	=30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>30 cm	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	.
standing dead	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=30 cm	441	16	138	33	165	36	205	26	185	22	233	22	654	23	408	24	281	22	201	23	125	30	517	16	521	14	818	11	4892	6
	>30 cm	803	23	193	32	371	28	593	22	666	20	762	42	1149	19	788	19	950	23	278	30	376	32	720	19	846	19	805	17	9300	7
	total	1245	16	331	24	536	25	798	18	851	17	995	33	1803	16	1196	16	1231	20	479	22	501	26	1237	14	1368	14	1623	10	14192	5
standing living	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=30 cm	11477	6	3240	12	3491	13	5239	10	7746	8	3809	14	7837	7	5873	10	3740	11	2518	15	2170	17	7913	9	8499	7	11891	5	85442	2
	>30 cm	43960	4	12195	10	12940	11	24742	8	32928	6	13233	11	34158	6	29324	7	17292	10	8738	15	9139	14	22440	7	34992	6	24931	5	321010	1
	total	55436	3	15435	9	16431	10	29981	7	40674	5	17042	10	41995	6	35197	7	21032	9	11256	13	11308	13	30353	7	43491	5	36822	4	406452	1
lying dead	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=30 cm	556	13	117	35	137	26	131	25	214	22	285	25	370	15	229	19	408	21	78	30	209	24	707	14	561	15	606	13	4610	5
	>30 cm	610	22	300	34	299	29	295	31	405	29	944	31	1233	21	476	25	978	21	272	29	269	29	1451	14	1359	15	754	20	9646	6
	total	1166	14	417	28	436	23	426	23	620	23	1229	26	1603	17	706	20	1386	18	350	25	478	22	2159	12	1920	13	1360	13	14256	5
lying living	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=30 cm	20	54	0	.	7	100	1	100	39	51	14	64	34	45	20	45	27	48	13	47	30	57	108	28	76	40	101	31	490	13
	>30 cm	28	74	58	76	11	100	0	.	0	.	0	.	69	74	11	100	34	60	0	.	7	100	111	38	73	50	118	34	520	20
	total	48	49	58	76	18	72	1	100	39	51	14	64	103	52	31	50	61	44	13	47	37	51	219	26	149	32	219	28	1010	13
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=30 cm	12494	5	3495	12	3801	13	5575	9	8184	8	4340	13	8896	7	6530	9	4456	10	2811	15	2533	16	9245	8	9658	7	13416	5	95434	2
	>30 cm	45401	4	12746	10	13620	11	25631	8	33999	6	14939	11	36608	6	30600	7	19254	9	9287	14	9790	13	24723	7	37270	6	26608	5	340476	1
	total	57895	3	16241	9	17421	10	31206	7	42183	5	19279	10	45505	6	37130	7	23710	9	12098	13	12324	13	33968	6	46927	5	40024	4	435910	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. 1329392. Birmensdorf, Swiss Federal Research Institute WSL

back