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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.

NFI5
growing stock (stemwood)
site quality · conifers and broadleaves
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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
site quality	conifers and broadleaves	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	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	.
	conifers	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	.
	broadleaves	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	.
	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	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	.
low	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	.
	conifers	96	79	352	71	0	.	4993	16	156	51	951	49	0	.	1337	40	9158	13	806	35	823	47	718	39	390	65	2046	30	1360	38	0	.	299	39	1575	30	206	100	682	42	1373	35	1928	27	3793	21	0	.	159	100	33202	7
	broadleaves	270	51	16	100	0	.	2873	21	582	42	744	48	0	.	248	55	4	71	1226	33	376	81	173	39	51	85	612	50	370	77	121	100	1460	31	229	52	2	100	2	100	242	95	787	31	622	39	0	.	223	83	11233	11
	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	.	0	0
	total	367	56	368	71	0	.	7866	14	738	39	1695	36	0	.	1584	37	9162	13	2032	30	1199	45	891	38	441	59	2659	27	1730	35	121	100	1759	29	1804	30	208	100	684	42	1615	36	2715	24	4416	19	0	.	382	68	44435	6
moderate	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	.
	conifers	0	.	454	58	104	100	12511	12	0	.	2934	26	0	.	842	51	29766	7	779	59	791	46	2835	25	401	63	1428	49	2613	27	0	.	271	53	2000	31	0	.	9065	12	1377	42	7176	17	22764	8	312	71	0	.	98423	4
	broadleaves	0	.	183	74	6	100	2328	20	0	.	461	35	0	.	659	45	2398	18	147	62	32	83	629	30	332	57	114	57	1043	32	0	.	179	59	309	45	0	.	3432	16	371	68	1626	21	1589	18	0	.	7	100	15845	7
	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	.	0	0
	total	0	.	636	59	110	100	14839	11	0	.	3396	23	0	.	1501	39	32164	7	926	55	823	45	3465	24	733	56	1542	47	3656	25	0	.	450	50	2309	30	0	.	12496	10	1747	39	8802	15	24353	8	312	71	7	100	114268	3
good	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	.
	conifers	1377	30	2367	37	3057	28	16098	10	427	40	4766	20	0	.	1232	40	7616	15	2125	31	7111	16	4578	20	791	50	1471	34	6263	17	847	33	782	36	3366	25	822	35	2444	48	2431	28	7154	14	861	48	524	54	3248	23	81761	4
	broadleaves	2069	27	149	63	987	32	6848	13	1168	33	1785	24	0	.	481	45	2406	22	1513	26	3139	21	802	26	702	52	644	42	4538	18	1151	30	1739	24	1611	30	1106	35	12393	8	779	35	4872	17	497	48	259	47	2865	19	54501	4
	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	.	0	.	0	.	0	.	0	0
	total	3447	22	2516	37	4043	26	22946	9	1595	29	6551	17	0	.	1713	34	10021	13	3638	23	10250	14	5380	19	1493	40	2116	31	10802	15	1998	26	2521	23	4977	21	1928	30	14837	11	3210	25	12026	12	1359	38	783	48	6113	18	136262	3
very good	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	.
	conifers	3143	21	0	.	500	76	11635	12	1285	38	1700	32	75	91	295	59	0	.	2835	25	4831	18	1116	33	362	100	489	52	3845	24	1157	35	2067	25	336	47	3400	20	0	.	2	100	2925	21	0	.	171	73	5603	15	47772	5
	broadleaves	4978	15	0	.	97	75	6941	13	1692	25	3007	25	401	58	332	50	0	.	2532	22	1814	24	512	33	135	100	354	62	2963	24	1468	28	2794	22	836	48	3311	19	327	62	184	100	4264	17	129	70	278	65	5004	14	44352	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	0	0
	total	8121	13	0	.	597	75	18576	10	2977	25	4707	22	475	55	627	48	0	.	5367	19	6646	16	1627	28	497	78	843	47	6808	19	2624	27	4861	19	1172	44	6711	16	327	62	186	100	7189	15	129	70	449	58	10607	11	92123	4
total	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	.
	conifers	4617	17	3173	30	3661	26	45237	6	1869	28	10351	13	75	91	3706	23	46540	6	6544	16	13556	11	9247	12	1945	33	5434	20	14082	12	2004	24	3419	18	7277	16	4428	17	12192	13	5183	19	19183	9	27418	7	1007	38	9010	12	261157	2
	broadleaves	7318	13	347	47	1090	30	18989	8	3441	18	5998	16	401	58	1720	25	4808	14	5419	14	5362	16	2116	15	1219	35	1725	27	8914	13	2739	20	6171	14	2985	22	4419	17	16153	7	1575	30	11549	10	2838	15	537	41	8100	11	125932	2
	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	.	0	.	0	0	0	.	0	0	0	0
	total	11935	11	3520	29	4750	24	64227	5	5310	17	16349	11	475	55	5426	19	51347	5	11963	12	18918	10	11363	11	3164	27	7159	18	22996	10	4744	18	9590	12	10261	14	8847	14	28344	7	6758	18	30732	7	30257	7	1544	33	17110	9	387089	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. 1371920. Birmensdorf, Swiss Federal Research Institute WSL

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