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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
total number of stems
tree condition · conifers and broadleaves
regional demarcation: canton
unit: 1000 n
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
tree condition	conifers and broadleaves	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	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	.
standing dead	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	239	31	191	47	243	34	2728	12	167	45	596	21	7	100	186	38	3163	12	589	30	1214	26	315	28	170	57	489	36	1018	23	165	53	277	27	771	30	306	45	632	23	563	25	1351	17	2628	15	180	38	386	24	18573	5
	broadleaves	123	34	33	71	53	58	719	21	268	32	440	32	17	100	285	43	1132	20	595	30	386	28	166	39	57	56	180	64	679	24	108	52	321	38	264	39	70	50	5017	13	213	49	961	23	661	22	7	100	273	43	13030	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	.
	total	362	24	224	43	296	34	3447	11	435	26	1036	19	24	100	471	35	4295	10	1184	22	1600	22	481	23	227	45	669	31	1697	17	273	40	598	26	1036	24	375	37	5649	12	777	23	2312	15	3289	13	187	37	659	23	31603	4
standing living	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	4361	17	1734	30	2468	27	39696	6	1855	24	8612	14	145	71	2662	26	47188	6	5227	14	13340	12	7366	13	2091	44	7159	20	9890	13	1519	26	3877	20	5626	16	4189	19	12740	13	5995	19	18285	10	32204	8	897	38	7041	15	246166	2
	broadleaves	7775	12	263	50	1250	28	24927	7	4785	18	7073	14	370	55	2095	24	10829	14	6135	14	6088	13	4127	15	2034	35	2343	26	10044	13	3024	21	7711	14	3440	20	4480	16	36802	7	2412	27	16819	10	9528	13	529	41	8951	11	183831	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	.
	total	12135	11	1997	29	3718	24	64623	5	6639	16	15684	11	515	54	4757	20	58018	6	11362	12	19429	10	11493	11	4125	30	9502	17	19933	11	4542	19	11588	13	9065	14	8669	14	49542	6	8407	18	35104	8	41733	7	1426	32	15992	10	429997	2
lying dead	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	191	29	76	66	194	37	3840	13	204	37	1062	25	0	.	268	39	4971	12	351	38	895	22	302	27	105	45	476	35	688	21	59	52	299	36	380	38	58	52	614	21	380	32	1155	19	4288	12	71	60	482	59	21408	5
	broadleaves	573	26	0	.	246	43	1106	17	340	37	522	29	71	61	591	48	1211	21	576	25	657	25	211	40	89	60	172	36	608	20	135	46	476	34	311	46	146	61	3094	13	147	56	1120	18	981	21	35	100	581	23	14000	6
	indeterminable	0	.	0	.	0	.	15	100	19	100	0	.	0	.	0	.	15	100	0	.	17	100	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	0	.	18	100	0	.	17	100	137	40
	total	764	21	76	66	440	34	4962	11	563	27	1584	21	71	61	859	35	6197	11	927	22	1569	18	513	23	194	37	648	29	1296	15	194	36	810	25	692	30	204	46	3708	11	527	28	2275	14	5288	11	105	52	1080	29	35545	4
lying living	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	.	6	100	7	100	76	47	0	.	15	100	0	.	0	.	438	39	0	.	0	.	0	.	0	.	72	100	17	100	17	100	0	.	0	.	0	.	0	.	35	100	0	.	335	37	0	.	10	100	1028	23
	broadleaves	98	40	0	.	79	60	82	46	35	100	0	.	0	.	94	50	37	71	25	78	55	75	0	.	18	100	19	100	114	39	24	100	54	58	14	71	36	71	740	25	0	.	55	58	254	47	0	.	33	71	1867	14
	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	98	40	6	100	86	61	158	38	35	100	15	100	0	.	94	50	474	37	25	78	55	75	0	.	18	100	92	82	131	41	41	72	54	58	14	71	36	71	740	25	35	100	55	58	589	29	0	.	44	59	2895	12
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	4791	16	2006	30	2912	25	46340	6	2226	23	10285	14	152	68	3116	25	55760	6	6167	15	15449	12	7983	13	2366	43	8196	20	11613	12	1760	26	4453	19	6777	16	4552	19	13986	13	6973	18	20791	9	39455	8	1148	37	7920	15	287176	2
	broadleaves	8569	12	296	47	1627	27	26834	7	5427	18	8035	14	458	51	3066	23	13209	14	7331	14	7186	13	4504	16	2198	35	2714	26	11445	13	3291	21	8562	14	4029	21	4732	16	45653	7	2773	27	18954	10	11425	12	571	41	9839	11	212728	2
	indeterminable	0	.	0	.	0	.	15	100	19	100	0	.	0	.	0	.	15	100	0	.	17	100	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	0	.	18	100	0	.	17	100	137	40
	total	13360	11	2302	29	4540	22	73189	5	7672	16	18320	11	610	52	6182	19	68984	6	13497	12	22652	10	12487	11	4564	29	10910	17	23058	10	5050	19	13050	13	10807	14	9285	14	59639	6	9746	17	39745	8	50898	7	1719	31	17775	10	500040	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. 1359228. Birmensdorf, Swiss Federal Research Institute WSL

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