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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) · tree species (5 classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/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
timber assortment classes (HG 2010)	tree species (5 classes)	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	.
	spruce	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	.
	fir	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	.
	other 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	.
	beech	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	.
	other 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	.
smallwood	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	.
	spruce	87	52	50	58	57	53	603	14	29	67	406	28	2	93	19	27	314	10	226	24	93	19	837	18	20	43	85	26	184	27	63	62	66	72	35	20	104	32	72	20	36	22	1137	17	293	18	4	44	136	26	4958	6
	fir	53	42	9	89	35	78	600	15	34	65	119	36	3	100	3	47	13	24	609	18	91	19	607	20	2	49	70	82	74	36	3	65	27	47	16	31	38	54	9	27	13	33	516	16	92	68	4	52	33	36	3070	7
	other conifers	6	42	0	.	1	100	152	54	11	55	58	43	1	81	0	100	145	11	41	39	24	89	22	66	0	100	7	56	20	41	9	74	8	38	4	63	6	44	70	20	3	48	89	56	147	12	0	0	16	37	840	13
	beech	15	17	1	49	2	47	43	10	8	22	10	22	0	.	4	30	10	24	13	21	13	22	5	28	2	48	5	33	20	15	7	23	17	16	6	30	6	26	33	15	3	36	25	14	4	36	1	62	11	16	267	4
	other broadleaves	10	20	0	100	2	43	22	13	5	26	7	26	3	56	5	36	21	18	5	25	7	23	4	28	4	50	1	55	14	19	2	39	7	23	5	27	7	25	74	10	3	39	20	16	15	18	1	61	11	19	252	4
	indeterminable	0	0	0	.	0	100	0	100	0	100	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	0	.	0	0	0	68
	total	171	32	61	58	95	49	1421	12	88	49	600	22	9	70	32	20	502	7	893	16	228	20	1475	15	29	33	167	42	312	20	84	48	125	40	66	16	160	27	257	9	58	18	1787	13	551	16	10	36	207	22	9387	4
classes 1 and 2	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	.
	spruce	931	22	717	32	602	29	8522	7	183	40	2384	17	1	100	837	24	11370	6	266	40	2344	14	384	25	680	39	1624	21	2623	14	401	35	464	27	1589	17	1032	24	2110	17	1487	20	3119	12	5622	10	320	36	1957	18	51570	3
	fir	327	34	86	48	340	40	3564	10	215	30	585	30	0	0	52	46	356	23	657	23	1802	16	341	24	73	40	303	36	516	21	42	78	514	26	449	25	455	33	303	33	495	30	1333	15	385	25	165	44	369	22	13728	5
	other conifers	209	31	0	.	10	76	565	22	151	36	75	52	40	100	2	77	4042	9	100	60	75	49	57	65	4	100	168	73	229	34	151	41	164	29	70	63	108	35	1571	13	120	50	280	32	4137	9	2	100	331	21	12659	5
	beech	1031	16	54	59	106	47	3718	9	718	23	907	19	0	.	225	28	607	23	756	21	979	17	422	18	234	44	407	32	1574	17	546	27	1175	18	421	20	411	21	2522	14	355	36	1727	12	521	28	83	45	1078	13	20577	4
	other broadleaves	832	16	5	73	346	34	2488	10	558	21	963	17	103	47	380	28	2007	15	797	22	595	18	467	23	362	50	227	48	1443	16	199	32	711	20	573	27	702	20	6576	8	409	30	2293	13	1553	13	66	52	1309	15	25963	3
	indeterminable	0	0	0	.	1	100	2	59	1	74	0	0	0	.	0	0	28	71	1	100	1	100	0	0	0	0	1	100	2	100	0	.	4	100	0	0	1	100	4	58	0	0	11	58	1	100	0	.	5	100	63	35
	total	3331	12	862	30	1404	24	18858	5	1826	16	4915	12	143	52	1495	18	18409	5	2577	14	5796	10	1671	13	1353	28	2730	17	6386	10	1339	20	3032	13	3103	13	2709	15	13087	6	2865	17	8763	8	12220	7	637	28	5049	10	124560	2
classes 3 and 4	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	.
	spruce	1046	22	1341	29	1082	30	12837	7	312	36	3802	15	0	0	1533	22	14001	7	1172	23	3321	14	2087	17	642	36	1663	22	4548	13	622	29	846	28	2586	17	943	22	2961	22	1785	22	5760	11	6358	9	552	38	2578	15	74377	3
	fir	684	31	311	41	785	38	7315	9	219	42	1050	22	0	0	232	52	840	23	1887	23	2823	15	1943	18	157	59	483	29	1856	17	100	58	532	22	1000	24	465	32	596	31	398	36	3132	13	770	30	106	50	905	20	28590	4
	other conifers	275	32	0	.	0	0	1001	27	282	53	199	52	44	78	14	100	4860	9	135	39	209	42	73	59	0	0	51	83	263	50	207	37	229	28	55	64	236	42	1678	13	117	54	267	30	4462	10	11	100	579	21	15248	5
	beech	2109	17	193	49	272	36	6435	10	1281	22	1621	21	0	.	505	33	849	25	1667	18	1689	18	729	22	205	56	652	33	2917	15	1065	23	2101	15	908	26	673	22	1858	13	399	41	3335	13	297	36	174	50	1751	14	33685	3
	other broadleaves	1067	20	11	71	174	39	2018	12	389	31	791	24	167	53	395	38	701	20	1067	24	689	36	233	32	278	47	82	46	1537	15	200	36	675	22	395	38	985	20	2407	11	157	37	1664	16	427	26	107	54	1403	15	18018	4
	indeterminable	0	0	0	.	0	0	14	100	11	100	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	13	100	0	0	0	.	0	0	37	58
	total	5181	12	1856	27	2313	25	29620	5	2493	18	7463	12	211	52	2680	19	21250	5	5928	13	8732	10	5065	12	1282	28	2931	18	11121	10	2195	19	4383	13	4944	14	3304	15	9499	8	2856	18	14170	8	12314	7	949	32	7215	10	169956	1
classes 5 and 6	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	.
	spruce	393	43	344	39	201	37	4701	10	180	56	1113	20	0	0	942	31	5150	9	542	41	1234	20	676	28	203	51	437	32	2413	18	274	51	183	44	995	26	400	35	1494	27	622	27	1648	17	2085	15	107	47	657	23	26997	4
	fir	362	32	84	66	174	49	3072	13	54	71	529	36	0	0	146	51	621	27	409	38	937	19	800	29	78	61	347	53	861	24	10	100	136	54	445	30	202	66	259	36	47	74	1198	21	216	41	66	85	467	26	11522	6
	other conifers	50	54	0	.	0	0	299	33	111	84	35	76	0	0	8	100	1627	13	43	85	200	67	0	0	0	0	0	0	254	82	22	81	6	100	5	100	62	57	612	25	9	100	19	73	1383	14	16	100	118	40	4879	9
	beech	1179	21	26	71	44	56	2239	15	404	36	335	29	0	.	134	48	281	36	464	31	656	29	60	56	0	0	216	44	934	27	259	44	799	26	326	48	448	31	400	21	175	49	1051	20	97	53	42	70	1021	22	11589	6
	other broadleaves	483	26	0	0	6	100	460	28	95	42	220	36	65	100	55	59	136	40	133	41	88	68	24	100	33	100	0	0	298	32	39	64	85	40	84	67	702	48	955	26	43	74	506	43	74	42	23	75	541	26	5150	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	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	total	2468	15	453	38	425	31	10771	7	843	25	2232	16	65	100	1286	25	7816	8	1591	20	3115	14	1560	20	315	39	1000	26	4761	13	605	32	1210	22	1855	20	1816	24	3719	13	896	26	4422	12	3855	10	254	38	2804	14	60138	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	.
	spruce	2457	20	2453	29	1941	28	26663	7	703	33	7705	14	3	75	3331	22	30835	6	2206	22	6992	13	3984	15	1546	34	3809	19	9769	13	1360	27	1559	25	5205	16	2480	20	6637	19	3930	20	11664	10	14358	9	984	36	5327	14	157902	2
	fir	1427	26	489	39	1334	36	14552	8	523	36	2283	22	3	100	432	48	1830	22	3561	19	5653	13	3691	17	310	51	1203	32	3307	16	156	59	1210	21	1910	24	1161	30	1167	28	953	31	6178	12	1462	26	341	41	1775	19	56910	4
	other conifers	541	28	0	.	10	74	2016	20	555	52	367	48	84	87	25	92	10674	8	320	36	509	45	153	50	4	100	225	74	766	47	389	38	407	26	135	56	413	35	3930	12	249	50	655	24	10129	9	28	100	1044	19	33627	4
	beech	4334	15	274	47	424	34	12436	9	2411	19	2874	19	0	.	869	29	1746	22	2900	17	3337	17	1216	18	441	42	1281	30	5445	14	1878	21	4092	15	1662	24	1538	20	4812	12	932	35	6139	12	920	28	299	44	3860	13	66118	3
	other broadleaves	2393	16	16	72	527	34	4987	10	1046	22	1981	19	338	55	835	31	2864	15	2001	22	1380	26	728	22	677	47	310	41	3291	14	441	28	1478	18	1057	26	2396	22	10012	8	612	29	4483	14	2070	14	197	45	3264	13	49384	3
	indeterminable	0	0	0	.	1	100	16	91	12	93	0	0	0	.	0	0	28	71	1	100	1	100	0	0	0	0	1	100	2	100	0	.	4	100	0	0	1	100	4	58	0	0	24	61	1	100	0	.	5	100	101	31
	total	11151	11	3232	28	4237	23	60670	5	5249	16	15210	11	428	52	5492	18	47978	5	10989	12	17872	10	9771	11	2978	25	6829	17	22579	9	4223	18	8750	12	9968	13	7988	13	26563	6	6676	16	29143	7	28940	6	1849	30	15275	9	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. 1323773. Birmensdorf, Swiss Federal Research Institute WSL

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