Selection from the list of topics

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)
diameter at breast height (main diameter classes) · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
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
diameter at breast height (main diameter classes)	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	.
12-15 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	239	24	75	36	42	37	405	25	182	20	157	31	404	22	130	25	213	16	101	29	60	32	354	16	735	15	210	20	3308	6
	fir	323	18	16	38	59	34	207	22	43	77	64	25	235	19	84	27	18	34	23	26	6	60	56	53	3	59	24	31	1159	9
	pine	2	100	0	.	2	100	4	58	3	100	0	.	10	72	0	.	5	71	6	82	1	100	54	39	100	29	7	65	194	19
	larch	0	.	7	88	18	71	2	100	8	61	3	100	1	100	4	100	10	58	0	.	6	53	120	32	59	28	88	39	325	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	69	56	48	1	73	104	40
	other conifers	13	73	7	62	0	.	7	46	18	50	0	.	2	71	3	71	6	59	5	100	2	77	6	79	17	35	1	100	87	20
	beech	364	14	184	21	102	38	160	22	199	17	50	55	114	17	65	22	40	26	28	59	79	52	34	30	13	34	288	15	1719	7
	maple	119	56	66	31	46	44	46	32	106	18	19	43	54	33	40	35	80	24	21	33	18	41	84	33	26	44	67	32	792	11
	ash	41	44	34	40	31	71	59	64	85	52	14	56	14	109	11	53	26	38	61	70	11	70	37	39	17	53	94	33	535	16
	oak	44	53	26	82	7	50	6	72	12	51	1	100	0	0	0	.	4	100	2	100	2	100	29	38	0	.	35	35	167	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	209	28	211	28
	other broadleaves	107	18	32	28	67	27	100	29	139	20	59	28	105	22	51	26	119	26	40	32	40	36	236	17	170	23	451	15	1716	6
	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	1251	10	446	15	374	17	995	14	794	12	367	18	938	11	388	14	522	12	286	24	227	23	1058	10	1197	11	1475	9	10318	3
16-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	850	14	174	32	370	52	738	20	932	19	587	21	1208	15	610	18	624	16	462	22	352	29	1291	16	2375	10	667	15	11240	5
	fir	1124	12	65	42	114	34	569	20	208	33	55	253	807	17	223	25	127	39	150	43	32	48	90	36	28	51	77	34	3670	8
	pine	85	78	-6	570	0	0	31	64	32	80	0	.	43	82	0	.	7	71	25	48	-11	100	404	29	260	23	7	100	877	18
	larch	0	.	0	.	58	100	3	100	86	60	12	81	9	100	0	0	24	65	0	.	10	100	600	17	257	21	387	20	1446	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	127	50	161	25	2	100	290	26
	other conifers	50	37	11	64	6	100	12	83	65	41	0	.	2	100	17	50	16	74	7	80	6	71	8	82	25	34	0	.	226	18
	beech	1199	12	385	19	302	26	447	15	567	14	186	33	614	18	468	19	242	41	177	32	157	36	179	41	76	41	1097	15	6098	6
	maple	448	19	146	30	138	38	137	24	253	29	132	26	248	25	198	29	211	26	24	77	99	35	150	25	40	43	161	32	2386	8
	ash	136	27	160	30	94	40	51	33	162	30	120	60	96	35	149	38	54	64	71	42	49	47	139	36	85	53	211	28	1576	10
	oak	83	50	13	59	154	47	38	76	18	63	0	0	0	.	7	100	14	65	0	.	0	.	70	40	3	100	134	34	535	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	5	100	867	18	881	17
	other broadleaves	249	23	73	34	88	40	275	29	305	19	135	36	143	25	167	36	154	28	128	49	103	42	411	17	478	24	1181	13	3892	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	4225	7	1021	14	1325	19	2302	11	2628	10	1228	20	3171	9	1840	12	1473	13	1044	17	797	19	3477	9	3793	8	4792	6	33117	3
24-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2593	13	410	35	723	26	1404	21	2343	15	2012	19	2997	13	2538	14	2059	14	1034	22	814	32	3334	12	5940	10	2110	19	30310	4
	fir	2229	12	248	37	263	37	1112	21	640	27	609	29	1776	16	819	33	313	29	319	36	51	55	310	35	87	46	288	39	9063	7
	pine	307	43	138	53	48	58	53	73	47	51	0	.	139	63	0	.	28	100	124	54	30	80	675	23	547	29	41	86	2177	14
	larch	0	.	14	100	96	72	9	100	52	52	0	.	9	100	34	100	96	53	0	.	56	71	1393	20	849	19	1460	16	4067	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	231	27	531	22	5	100	768	17
	other conifers	8	100	24	87	0	.	22	81	79	74	0	.	8	100	16	71	27	72	0	.	-25	100	0	0	5	100	0	.	164	45
	beech	3393	11	991	20	644	24	982	19	1716	15	466	30	1813	14	1319	19	627	25	549	28	258	39	310	36	243	34	2209	16	15520	5
	maple	911	15	279	28	54	53	272	30	901	19	269	29	572	23	739	23	283	32	235	36	329	38	152	34	135	32	187	38	5317	7
	ash	311	32	122	48	393	35	213	29	477	28	287	45	222	67	359	29	151	48	125	58	74	49	180	36	102	44	271	37	3288	11
	oak	363	34	160	42	191	35	100	50	140	35	27	71	33	62	0	0	51	74	0	0	0	.	98	40	6	100	378	28	1546	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	71	5	100	1595	17	1657	16
	other broadleaves	271	27	121	34	171	42	288	32	429	21	48	39	140	37	305	30	155	32	77	43	47	51	282	30	240	32	2027	15	4601	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
	total	10383	6	2508	14	2581	14	4454	11	6824	8	3718	14	7709	8	6129	11	3790	10	2463	17	1634	21	7022	9	8692	8	10571	7	78478	2
36-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5223	9	638	27	1467	26	2328	18	3667	12	4070	13	5930	10	5558	11	4812	13	2014	22	1875	22	4965	11	10548	8	4219	15	57314	3
	fir	4929	10	382	34	580	30	1983	16	1318	19	783	24	2721	12	1928	15	545	30	762	29	423	29	530	32	250	32	503	32	17638	5
	pine	690	26	333	31	200	46	217	38	734	22	0	.	329	55	17	71	31	51	157	44	25	75	624	21	1065	25	69	63	4490	10
	larch	12	100	34	67	99	49	39	74	149	45	10	100	0	.	13	100	103	69	0	.	80	77	2788	15	2294	13	1944	14	7565	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	288	27	549	21	0	.	849	16
	other conifers	0	.	80	57	56	100	120	38	29	76	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	16	100	306	29
	beech	6382	8	2437	16	1767	20	2634	13	2648	12	594	33	2826	17	3021	14	1032	25	438	29	892	25	215	50	517	32	1736	14	27138	4
	maple	1081	13	279	31	92	36	257	26	535	20	123	35	355	27	550	20	259	29	112	39	309	27	97	67	130	52	76	49	4255	7
	ash	643	22	300	30	618	29	515	31	1054	17	176	54	421	36	663	27	203	38	153	62	131	42	114	54	36	50	189	39	5215	9
	oak	467	28	295	38	365	37	246	28	439	26	34	76	91	66	29	60	16	71	46	78	0	.	57	53	28	82	350	28	2463	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	21	70	5	100	1212	19	1244	19
	other broadleaves	368	30	147	36	140	41	114	40	580	24	30	58	87	37	46	52	110	57	35	55	85	47	201	39	104	46	1248	17	3296	10
	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	19796	5	4923	12	5384	13	8454	9	11153	7	5820	11	12761	8	11824	8	7133	11	3716	16	3821	15	9899	8	15527	6	11562	6	131773	2
=52 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7030	10	1201	29	1641	29	3083	18	4012	16	4735	15	7657	10	8885	12	6081	14	2818	20	3242	20	5515	13	11447	10	5571	14	72919	3
	fir	6811	11	293	43	1271	26	4037	16	3118	17	1729	23	6048	13	3626	16	1751	25	889	40	760	39	656	35	1029	26	1176	27	33194	5
	pine	309	32	87	50	207	57	367	37	536	27	0	.	200	52	95	71	9	100	23	100	12	100	162	41	790	25	41	74	2838	12
	larch	63	74	28	100	56	72	83	60	316	35	0	.	0	.	142	58	189	49	0	.	122	72	4994	13	4044	13	2969	14	13006	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	389	30	475	27	0	.	864	20
	other conifers	0	.	277	80	0	.	630	48	389	88	0	.	81	100	0	.	0	.	0	.	0	.	0	.	0	.	65	100	1442	36
	beech	3653	12	2066	19	1304	22	3339	16	4895	13	528	37	2494	20	1699	21	751	31	566	32	751	34	182	56	460	41	1457	17	24144	5
	maple	326	28	155	43	43	76	14	100	418	33	133	40	157	46	351	35	167	39	72	60	80	63	0	.	101	46	0	.	2016	13
	ash	321	37	280	40	406	34	729	36	1192	23	43	100	95	100	456	45	118	49	95	83	140	43	15	100	33	100	148	63	4071	12
	oak	262	38	243	53	356	44	505	29	1438	31	0	.	0	0	45	100	0	.	56	74	0	.	22	100	112	71	120	46	3161	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	60	8	100	1529	24	1612	23
	other broadleaves	36	73	227	48	643	41	203	46	275	46	0	.	26	100	99	50	373	74	55	72	48	77	79	51	116	70	151	39	2332	19
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	18811	6	4857	14	5928	14	12990	10	16590	8	7168	13	16758	8	15397	9	9439	12	4572	18	5155	16	12092	9	18616	7	13227	7	161599	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15935	7	2497	19	4243	19	7957	12	11136	10	11562	12	18196	8	17721	10	13788	11	6430	17	6343	17	15460	9	31045	7	12778	12	175091	2
	fir	15416	8	1003	29	2287	22	7908	12	5327	14	3240	21	11587	10	6680	13	2754	21	2141	28	1273	30	1642	26	1397	24	2068	24	64723	4
	pine	1394	22	552	27	457	43	672	28	1352	19	0	.	721	45	111	61	80	48	334	45	58	60	1918	18	2762	17	165	49	10577	8
	larch	75	64	81	47	326	39	137	50	611	26	25	81	19	94	192	48	423	36	0	.	274	52	9895	12	7503	10	6848	11	26409	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	1082	21	1772	18	9	94	2875	13
	other conifers	71	34	399	66	62	91	791	41	580	61	0	.	92	88	36	41	54	68	12	71	-16	122	13	58	47	32	82	98	2224	25
	beech	14990	6	6063	12	4118	17	7562	11	10026	9	1823	23	7861	13	6571	12	2692	20	1758	23	2137	21	920	30	1310	27	6788	11	74620	3
	maple	2885	10	925	19	373	25	725	18	2213	14	676	22	1387	17	1879	15	1000	18	464	27	835	24	483	26	432	30	491	25	14767	5
	ash	1451	18	897	24	1542	25	1568	25	2970	15	640	38	847	38	1638	21	553	31	504	38	405	32	485	30	274	40	913	30	14686	7
	oak	1219	23	737	32	1073	28	895	21	2047	24	62	67	124	58	81	61	85	53	105	61	2	100	276	33	149	71	1017	20	7872	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	165	51	23	82	5412	13	5605	12
	other broadleaves	1031	19	601	26	1110	28	980	19	1728	16	271	25	501	18	668	23	911	36	334	32	324	29	1209	18	1109	21	5057	11	15836	6
	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	54467	4	13755	10	15592	11	29195	7	37989	5	18300	10	41336	6	35578	7	22357	9	12082	13	11634	13	33548	6	47824	5	41627	4	415285	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. 1280624. Birmensdorf, Swiss Federal Research Institute WSL

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