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.

NFI3
growing stock (stemwood)
aspect (9 classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
	canton
	AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
aspect (9 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	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
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	.
indetermined	3991	15	0	.	0	.	10773	11	914	35	2101	22	526	41	4	100	893	37	1292	27	1987	23	1349	25	0	.	244	54	2184	25	794	35	2032	23	226	63	3824	18	469	46	154	100	7280	12	161	47	290	59	5070	15	46556	4
N	2317	22	358	57	1144	43	13187	10	1003	33	2324	23	0	.	652	41	6913	12	3988	17	1405	27	1362	25	403	46	591	39	2941	20	969	33	2910	19	1564	29	1110	31	4473	12	588	35	2302	22	4583	13	475	61	2736	19	60299	4
NE	1395	24	0	.	851	41	7121	13	841	44	1384	31	0	.	643	44	6815	11	383	44	3451	22	501	44	207	70	646	37	3313	22	292	58	880	43	1173	34	1090	31	2892	14	467	41	1745	24	2885	16	126	74	2488	22	41590	5
E	1459	24	213	90	711	50	5145	16	752	39	1171	33	40	100	896	60	7409	11	913	48	2034	27	966	33	369	50	810	35	3715	20	239	67	516	42	583	35	355	57	4128	15	662	43	2879	19	3608	15	484	48	1588	28	41644	5
SE	1745	30	834	38	1074	40	7960	13	729	31	2042	24	0	.	772	37	4805	13	707	34	1971	24	3166	18	212	58	957	34	2448	22	317	50	2049	22	1546	29	335	55	3103	14	726	43	7228	12	2927	18	237	73	1549	28	49439	4
S	1176	27	596	52	423	78	6423	13	807	37	1597	24	0	.	1094	38	4365	13	1263	26	1586	32	1317	27	68	74	688	36	1793	25	562	38	1542	26	1382	27	865	38	2070	16	411	50	3125	18	2754	19	227	68	1485	26	37619	5
SW	1029	37	120	79	256	71	3390	18	842	34	596	40	0	.	679	45	5992	13	1406	29	1535	32	354	54	9	100	752	49	2157	24	304	46	326	47	914	35	686	47	3698	14	337	43	2356	23	2180	19	151	66	1017	30	31084	6
W	1106	29	172	76	445	52	5912	15	521	42	1204	34	0	.	550	38	6728	12	806	38	1890	29	300	48	307	56	1003	33	2414	25	806	37	612	41	1420	32	275	61	2303	15	591	37	3514	20	4794	14	328	61	1263	31	39262	5
NW	1918	23	538	52	991	38	10720	11	610	45	4188	18	0	.	996	50	7761	12	3024	19	2838	22	1787	26	389	51	748	42	3295	21	233	59	1028	27	1775	24	471	43	2383	16	1037	33	5038	15	6301	12	135	62	1558	26	59765	4
total	16137	8	2830	22	5895	17	70632	4	7019	12	16608	8	565	39	6284	16	51680	4	13783	8	18697	8	11103	9	1964	21	6439	13	24258	7	4516	14	11896	9	10584	10	9010	11	25519	4	4974	14	35467	5	30193	5	2452	22	18752	7	407258	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. 1195622. Birmensdorf, Swiss Federal Research Institute WSL

back