NFI Results

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.

NFI1
total number of stems NFI1
main tree species
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
	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
main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	.
spruce	4849	17	2110	30	3385	29	36118	7	992	37	11248	15	20	100	3491	27	34353	7	3603	27	10871	14	5235	19	1746	44	5111	22	9872	14	2359	32	2482	23	5920	20	3850	21	5035	15	4540	22	13610	11	18907	10	839	42	7670	13	198215	3
fir	1891	24	529	42	863	37	15250	9	551	38	2273	26	0	.	368	47	1263	23	3914	21	5817	16	4043	18	263	48	1185	30	2559	18	56	65	1882	23	1626	23	768	29	952	32	1363	34	6276	14	1311	29	314	38	1851	18	57166	4
pine	645	28	0	.	8	100	1542	25	421	42	401	46	255	88	0	.	5450	17	428	45	307	73	160	61	0	.	440	87	56	76	525	49	778	30	117	95	272	36	309	59	460	51	500	46	6659	19	0	.	1198	21	20932	9
larch	298	36	0	.	0	.	321	39	45	62	79	77	0	.	7	100	6080	12	7	100	24	77	7	100	0	.	0	.	208	57	52	74	0	.	15	100	86	100	3090	16	28	100	244	47	7427	12	14	100	208	42	18241	7
Arolla pine	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	2313	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1121	23	0	.	0	.	3442	15
other conifers	102	57	0	.	18	100	220	37	209	100	57	100	0	.	18	100	70	78	17	100	96	77	17	100	0	.	0	.	181	52	35	100	120	51	18	100	17	100	0	.	77	75	171	50	104	85	0	.	228	70	1777	20
beech	5987	14	456	45	620	32	19024	8	3221	21	4536	18	0	.	1492	28	2989	21	4349	17	4234	17	2443	18	748	40	2293	32	6223	17	3088	23	5586	16	1634	22	1714	21	9669	15	1203	36	8506	11	1840	31	234	54	4789	14	96878	3
maple	979	22	36	85	376	50	3109	14	502	31	459	32	0	.	533	37	492	30	643	23	627	22	553	40	234	35	316	43	2256	18	163	68	874	20	505	31	483	37	211	42	277	44	2276	18	591	37	122	72	1504	21	18119	6
ash	981	26	0	.	542	49	1997	19	380	45	1386	27	34	100	578	58	725	54	1071	29	710	33	343	60	665	60	84	69	1830	23	292	52	671	38	563	40	862	29	959	27	178	65	2120	22	515	32	160	74	1485	20	19132	7
oak	381	29	0	.	0	.	846	25	329	48	326	39	1177	52	0	.	105	64	284	51	316	42	261	69	22	100	0	.	160	48	283	35	441	53	6	100	604	26	2045	28	55	65	2261	27	520	39	0	.	552	26	10974	11
sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1087	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11631	14	0	.	0	.	175	61	0	.	0	.	12893	13
other broadleaves	1413	24	30	71	103	71	3366	16	644	31	983	29	34	100	374	48	4329	18	1175	29	793	25	849	43	170	53	403	51	1828	26	139	50	732	26	1079	43	1231	31	10341	11	845	53	3029	20	2823	19	33	63	1545	22	38291	5
indeterminable	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	.
total	17527	10	3162	29	5915	24	81802	5	7293	17	21748	11	1521	47	6862	20	59256	6	15492	12	23794	10	13912	12	3848	28	9831	17	25173	10	6992	20	13565	12	11482	15	9887	14	44241	6	9026	18	38993	8	41993	7	1717	33	21029	9	496061	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. 1240894. Birmensdorf, Swiss Federal Research Institute WSL

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