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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.

NFI4
number of stems
development stage · conifers and broadleaves
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		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
development stage	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	15	78	0	0	0	.	448	33	0	.	148	64	0	.	0	.	452	32	0	.	95	60	0	.	7	100	39	63	4	100	0	.	0	.	192	67	0	.	28	100	0	.	28	86	267	40	0	0	0	.	1723	17
	broadleaves	24	84	0	.	0	.	62	57	0	.	81	46	4	100	0	.	33	89	0	.	44	65	0	.	0	.	11	100	60	62	0	.	0	.	61	72	0	.	8	100	0	.	15	78	0	.	0	.	4	100	407	23
	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	39	73	0	0	0	.	510	30	0	.	228	55	4	100	0	.	485	31	0	.	139	54	0	.	7	100	50	54	64	64	0	.	0	.	253	59	0	.	36	82	0	.	43	70	267	40	0	0	4	100	2130	15
young growth/ thicket	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	303	36	0	.	106	62	1388	19	35	73	223	37	0	.	205	48	1444	17	110	44	293	38	77	79	11	100	168	44	327	28	9	100	61	61	246	43	87	53	230	40	223	45	349	25	567	25	29	63	209	34	6701	7
	broadleaves	524	25	0	.	52	84	798	20	96	51	222	39	0	.	223	47	274	51	45	55	225	39	38	100	49	76	135	50	355	29	21	100	73	48	85	61	114	39	386	36	103	57	452	30	366	31	15	100	334	28	4983	8
	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	827	23	0	.	158	64	2186	15	131	52	445	29	0	.	428	36	1718	17	155	40	517	33	115	71	60	65	303	38	682	24	30	100	133	40	331	43	201	42	616	27	326	45	801	23	933	23	44	67	542	25	11684	6
pole timber	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	2334	24	160	100	423	66	6200	15	367	58	1757	30	0	.	428	37	12502	12	1410	32	3020	21	966	53	765	58	1240	45	1754	29	787	44	913	34	1543	26	2051	31	3621	18	581	40	4659	18	7463	14	492	51	2692	24	58126	5
	broadleaves	3037	23	283	93	96	70	3885	16	922	33	1259	29	40	100	680	37	5762	16	1215	28	918	26	878	33	241	83	168	55	1517	27	419	59	1298	28	1881	27	1723	24	18217	9	744	48	3752	18	5843	13	134	57	2224	19	57134	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	5371	18	443	72	519	58	10085	12	1289	32	3016	23	40	100	1108	31	18264	10	2625	25	3937	19	1844	33	1007	49	1407	43	3271	22	1206	38	2210	25	3423	21	3774	22	21838	8	1325	36	8411	14	13306	10	626	47	4915	17	115260	3
young timber	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	1077	27	226	71	398	71	4606	16	332	43	2489	26	58	87	422	52	7657	13	580	35	1648	26	741	41	526	49	932	35	2337	24	400	42	927	31	836	36	931	35	2228	24	671	36	2965	19	5579	17	351	57	1464	25	40383	5
	broadleaves	1502	26	62	71	206	85	4571	14	712	38	1575	31	322	51	462	51	2295	20	881	31	303	36	346	51	294	71	853	47	1982	26	1068	30	1893	23	614	37	260	43	9142	12	863	40	3460	17	1870	24	37	62	1699	22	37272	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	2579	22	288	71	604	58	9177	12	1044	33	4065	21	380	50	884	40	9953	12	1461	28	1951	26	1087	38	820	44	1785	33	4319	21	1468	26	2820	22	1450	30	1191	31	11370	11	1534	33	6425	15	7449	14	388	55	3163	19	77655	4
medium timber	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	713	24	804	34	614	39	5884	12	383	28	2783	20	0	.	813	38	11298	10	2303	18	1050	27	782	28	86	49	660	43	2233	20	485	27	923	22	1470	24	998	27	2070	20	1487	31	3798	18	4396	16	279	67	1695	18	48007	4
	broadleaves	1962	18	313	47	363	44	5231	15	1548	23	1318	24	105	100	674	34	1020	27	1911	19	798	32	680	29	465	44	600	40	1873	20	973	27	2104	18	692	27	946	23	2482	20	86	55	3274	16	631	36	201	62	1624	17	31876	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	2676	16	1116	32	977	35	11115	11	1931	22	4102	17	105	100	1487	30	12319	9	4214	15	1848	23	1462	23	551	43	1260	33	4106	16	1459	24	3027	16	2162	23	1943	21	4552	15	1573	30	7072	14	5027	15	480	50	3319	15	79883	3
old timber	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	1102	19	744	36	1071	25	10314	8	590	30	1650	19	10	100	627	37	8876	9	934	23	2989	16	1620	18	153	47	1324	27	2962	14	180	43	744	22	2160	17	486	30	1858	18	330	40	4614	12	5333	12	192	47	1622	16	52485	3
	broadleaves	2410	16	104	63	563	34	5425	10	1302	22	1439	21	141	74	289	38	915	24	910	25	1741	20	1140	19	193	53	435	41	2426	16	407	35	1156	19	851	24	884	27	1605	20	176	54	3276	12	507	32	223	45	2030	14	30549	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
	total	3511	14	848	37	1635	24	15739	7	1893	21	3089	16	151	74	916	31	9791	9	1844	20	4730	15	2760	16	346	48	1758	25	5389	13	588	33	1900	18	3011	16	1370	23	3463	14	506	36	7890	10	5839	11	415	44	3652	13	83034	3
mixed	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	493	36	143	64	154	59	14273	8	160	38	1498	24	0	.	1142	30	19258	7	1813	19	3264	16	3510	15	252	55	2936	22	3539	19	0	.	851	31	1028	26	247	33	5014	13	2064	23	5189	12	14382	8	452	44	490	27	82150	3
	broadleaves	953	28	47	72	213	60	6214	11	536	36	1263	26	0	.	760	33	2885	17	2630	19	2155	22	1109	19	170	56	568	28	3110	20	113	100	1318	27	882	35	885	29	10732	10	388	39	4600	15	2730	17	84	69	840	24	45184	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
	total	1446	25	190	60	366	55	20487	7	697	32	2760	20	0	.	1902	26	22142	7	4443	16	5419	16	4618	14	422	48	3503	20	6648	17	113	100	2169	26	1910	27	1132	28	15746	8	2452	22	9789	11	17112	8	535	44	1330	23	127334	3
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	6038	12	2077	22	2766	20	43113	4	1868	18	10549	10	68	76	3637	16	61487	4	7150	10	12358	9	7696	11	1800	30	7299	14	13156	9	1862	22	4418	13	7475	10	4799	16	15049	7	5356	14	21602	7	37987	5	1795	24	8171	10	289574	1
	broadleaves	10412	9	810	39	1493	23	26187	6	5116	12	7158	11	613	36	3087	16	13185	9	7592	10	6184	11	4190	12	1412	27	2769	19	11323	9	3001	17	7841	10	5067	14	4812	12	42572	4	2360	22	18828	7	11946	9	693	27	8755	8	207404	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
	total	16450	8	2886	21	4259	17	69300	4	6984	11	17706	8	681	36	6724	13	74671	4	14742	8	18542	8	11886	9	3212	22	10067	12	24479	7	4863	15	12260	9	12542	10	9611	11	57621	4	7716	13	40430	5	49933	5	2489	21	16926	7	496978	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. 1325146. Birmensdorf, Swiss Federal Research Institute WSL

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