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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
volume of bolewood (HG 2010)
timber assortment classes (HG 2010) · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
timber assortment classes (HG 2010)	main tree species	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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	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	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
smallwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2083	11	98	44	420	24	257	26	350	20	354	30	205	12	272	23	131	23	70	25	40	19	293	18	289	10	97	15	4958	6
	fir	1889	9	40	57	211	28	216	21	136	25	87	33	157	16	111	33	23	24	79	73	8	30	92	68	8	29	13	23	3070	7
	pine	182	46	17	51	35	56	15	54	8	28	0	.	12	56	2	89	7	43	6	46	5	85	61	20	62	17	4	51	417	21
	larch	2	72	3	51	78	65	1	68	12	45	6	75	1	89	2	90	7	55	0	.	8	75	78	15	48	23	79	17	326	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	8	25	18	22	0	100	27	17
	other conifers	17	45	2	45	8	71	25	88	8	37	0	.	3	59	2	58	2	73	1	73	1	66	0	56	2	36	0	100	71	34
	beech	54	8	21	13	12	19	24	13	29	11	7	27	26	15	22	14	9	21	6	27	9	21	4	36	8	28	35	14	267	4
	maple	7	20	3	26	1	43	2	29	6	23	3	29	7	25	7	22	4	23	2	36	5	32	2	35	3	44	4	35	56	8
	ash	4	27	3	37	3	35	4	26	9	23	1	45	4	44	5	25	1	38	2	42	1	40	3	42	1	54	5	28	47	9
	oak	8	28	4	34	5	35	4	26	5	21	1	67	1	63	1	68	0	71	0	71	0	100	4	36	1	74	8	28	41	11
	sweet chestnut	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	1	49	0	100	42	15	44	14
	other broadleaves	5	17	2	36	4	32	3	34	4	25	1	46	2	29	2	36	4	28	0	66	1	48	6	25	4	31	27	13	66	7
	indeterminable	0	72	0	0	0	0	0	0	0	0	0	0	0	0	0	100	0	0	0	0	0	0	0	0	0	0	0	0	0	68
	total	4252	7	194	30	778	18	550	18	568	15	459	25	417	10	427	19	188	17	166	42	78	16	551	16	444	8	315	7	9387	4
classes 1 and 2	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2683	10	702	23	1064	20	2678	14	3813	11	3303	14	5944	9	4763	11	4000	11	1920	17	1598	18	5622	10	10049	7	3431	12	51570	3
	fir	2449	10	364	32	333	27	2073	14	1098	16	914	24	2910	10	1219	16	521	22	630	26	173	28	385	25	219	27	440	26	13728	5
	pine	495	25	247	28	107	49	187	39	322	20	0	.	221	58	8	55	58	57	167	44	52	78	1241	18	1017	19	111	46	4232	9
	larch	4	100	27	46	73	65	30	52	165	31	12	100	14	94	43	68	146	35	0	.	93	60	2507	11	1660	11	2026	11	6798	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	367	23	715	19	8	91	1095	15
	other conifers	38	42	85	53	0	0	82	36	127	37	0	.	8	73	25	42	41	76	14	72	17	73	22	63	67	37	9	92	534	16
	beech	4245	8	1513	14	1022	17	1509	11	2324	9	561	25	2257	12	1633	15	703	20	544	26	616	25	521	28	380	27	2749	13	20577	4
	maple	1318	11	433	19	210	30	358	20	1043	15	341	24	652	21	728	19	551	21	194	31	351	26	319	25	177	30	310	25	6985	5
	ash	592	22	334	25	447	25	331	20	752	15	267	34	413	37	550	21	272	34	190	33	153	33	302	26	213	50	542	25	5357	7
	oak	413	29	125	35	316	32	136	34	177	22	17	60	26	58	24	69	27	64	13	59	2	100	149	34	15	71	471	21	1910	11
	sweet chestnut	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	65	65	10	74	2904	12	2981	12
	other broadleaves	640	17	232	20	303	27	441	21	754	14	195	25	282	20	453	26	392	20	216	33	165	29	718	15	744	20	3196	10	8731	5
	indeterminable	8	50	0	100	0	0	0	0	6	87	1	100	2	71	2	77	9	67	0	0	0	0	1	100	13	100	20	78	63	35
	total	12886	5	4060	10	3875	11	7825	8	10580	6	5611	11	12729	6	9448	8	6724	9	3887	14	3220	13	12220	7	15280	5	16216	4	124560	2
classes 3 and 4	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7819	7	1082	20	1906	23	3170	15	4724	10	4915	12	8055	9	8224	10	6022	11	2648	18	2493	16	6358	9	11925	7	5037	14	74377	3
	fir	7063	9	530	28	1012	25	3293	14	2341	14	1493	19	5160	10	3027	14	1092	22	921	28	453	32	770	30	583	25	853	26	28590	4
	pine	519	20	275	28	270	41	358	28	740	19	0	.	398	58	50	65	39	49	136	48	24	73	637	23	1052	20	97	51	4597	9
	larch	46	68	36	54	68	49	49	54	214	32	3	100	0	0	58	60	142	46	0	.	71	57	3481	12	2600	12	2192	11	8960	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	344	28	548	19	0	0	898	16
	other conifers	0	0	211	70	50	100	287	40	179	72	0	.	12	100	0	0	2	100	0	0	5	100	0	0	0	0	49	100	794	30
	beech	7260	7	3184	13	2042	19	3504	11	3944	10	769	29	3619	14	3673	13	1040	23	664	29	982	22	297	36	662	31	2044	12	33685	3
	maple	1106	13	320	29	117	36	248	25	660	17	152	31	427	21	749	17	319	22	159	30	313	25	92	59	152	43	99	36	4913	6
	ash	651	23	308	27	673	27	709	29	1464	15	148	52	335	36	569	23	192	34	142	55	212	38	98	50	45	46	193	38	5739	8
	oak	447	24	302	34	395	31	365	24	654	19	29	79	49	58	22	56	9	73	51	62	0	0	43	52	79	72	369	27	2816	10
	sweet chestnut	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	36	45	9	100	1141	15	1190	15
	other broadleaves	313	42	200	36	334	34	187	34	514	23	13	71	77	34	113	35	249	49	49	63	133	45	159	35	106	38	915	15	3361	9
	indeterminable	0	0	11	100	0	0	0	0	0	0	0	0	0	0	0	0	27	71	0	0	0	0	0	0	0	0	0	0	37	58
	total	25226	4	6458	10	6868	12	12171	8	15435	6	7522	11	18130	6	16485	7	9142	9	4770	15	4685	13	12314	7	17761	6	12989	6	169956	1
classes 5 and 6	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2280	15	466	36	465	38	1036	19	1411	18	1716	16	2920	12	3276	14	2237	15	1072	23	1388	24	2085	15	4132	11	2512	18	26997	4
	fir	1894	17	125	39	452	38	1190	18	1054	20	610	29	2397	14	1258	18	769	27	443	46	234	45	216	41	414	30	466	30	11522	6
	pine	84	53	5	100	24	100	113	53	157	36	0	.	25	76	17	100	0	0	9	100	0	0	32	51	226	30	7	100	699	17
	larch	0	0	5	100	10	100	19	76	42	47	0	0	0	0	38	63	41	55	0	.	21	72	1171	16	937	17	878	21	3162	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	179	34	173	31	0	0	352	23
	other conifers	0	0	113	82	0	0	278	53	229	91	0	.	28	100	0	0	0	0	0	0	0	0	0	0	0	0	18	100	666	41
	beech	1552	15	1029	21	703	23	1656	17	2710	14	214	48	1252	23	831	24	300	34	237	38	328	36	97	53	209	45	471	19	11589	6
	maple	114	37	74	54	13	100	5	100	280	36	20	58	12	71	74	40	48	46	14	100	29	60	0	0	16	75	0	0	698	18
	ash	98	44	106	47	91	34	210	49	345	28	25	100	33	100	209	49	49	57	34	100	42	65	0	0	22	100	52	78	1316	15
	oak	36	62	71	63	172	53	189	40	755	41	0	0	7	100	25	100	0	0	21	78	0	0	7	100	13	71	35	58	1329	25
	sweet chestnut	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	45	62	0	0	870	28	915	26
	other broadleaves	13	73	69	51	231	51	55	74	170	73	0	0	6	100	16	73	197	90	23	72	6	100	22	58	51	88	33	45	892	29
	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
	total	6071	9	2063	15	2161	17	4750	11	7151	10	2585	15	6681	9	5745	11	3641	13	1853	20	2047	19	3855	10	6193	9	5342	10	60138	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14866	6	2348	18	3855	19	7141	12	10298	9	10288	11	17124	8	16535	9	12389	10	5710	16	5519	16	14358	9	26395	6	11077	12	157902	2
	fir	13295	7	1058	27	2008	23	6772	12	4630	13	3104	19	10624	9	5615	13	2405	21	2072	27	869	30	1462	26	1224	24	1772	24	56910	4
	pine	1280	21	544	26	437	43	674	28	1226	18	0	.	656	45	77	60	104	44	318	43	82	65	1971	17	2357	16	220	43	9945	8
	larch	53	64	70	41	230	37	99	50	432	26	21	83	14	94	140	47	335	33	0	.	192	50	7237	11	5246	11	5176	10	19246	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	898	23	1454	18	8	92	2371	14
	other conifers	55	34	411	68	58	93	672	40	543	65	0	.	50	82	27	41	44	77	15	72	24	74	23	62	68	36	75	99	2065	26
	beech	13111	6	5748	12	3781	17	6692	10	9007	9	1551	24	7154	12	6160	12	2052	19	1450	25	1934	20	920	28	1259	27	5299	11	66118	3
	maple	2546	10	830	19	340	25	613	18	1989	14	515	22	1097	17	1558	15	922	19	370	27	697	22	412	25	348	30	412	25	12652	5
	ash	1344	20	751	22	1214	25	1253	25	2570	14	441	38	785	34	1333	20	514	30	368	37	408	35	403	28	281	45	792	25	12459	7
	oak	903	22	502	31	888	27	693	22	1591	24	47	65	83	48	72	55	36	55	85	58	2	100	202	34	108	71	883	20	6095	9
	sweet chestnut	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	148	54	20	84	4958	12	5129	11
	other broadleaves	972	21	502	25	872	29	686	20	1442	18	209	25	367	18	585	23	842	38	288	32	305	29	905	16	904	20	4171	10	13049	6
	indeterminable	8	50	11	100	0	0	0	0	6	87	1	100	2	71	2	77	36	57	0	0	0	0	1	100	13	100	20	78	101	31
	total	48434	3	12775	9	13682	11	25295	7	33734	5	16176	10	37956	6	32106	7	19695	9	10676	13	10031	12	28940	6	39678	5	34862	4	364041	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. 1197761. Birmensdorf, Swiss Federal Research Institute WSL

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