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.

NFI1
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (10 classes)
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		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
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (10 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	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	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	.
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	133	39	209	25	40	48	382	20
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	336	33	379	23	32	70	747	19
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	322	31	772	24	60	58	1155	18
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	236	29	450	26	51	51	736	19
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	124	40	504	31	0	.	628	26
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	59	264	37	0	.	376	31
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	52	129	47	0	.	208	35
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	99	58	0	.	122	51
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	34	100
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	82	72	0	.	112	59
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1429	22	2888	19	182	44	4500	14
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	7	100	0	.	0	.	25	62	189	38	16	100	138	28	28	52	39	61	414	20	615	16	161	26	1630	10
	21-30 cm	0	.	0	.	8	100	0	.	0	.	115	53	407	32	64	100	525	31	75	51	82	68	999	21	1535	17	436	26	4246	10
	31-40 cm	0	.	0	.	23	100	0	.	0	.	196	61	482	32	18	100	653	38	184	59	140	53	1507	19	2652	15	691	25	6546	9
	41-50 cm	0	.	0	.	34	100	0	.	0	.	156	43	346	33	15	100	629	36	162	54	217	56	1750	18	2515	14	796	22	6620	9
	51-60 cm	0	.	0	.	8	100	0	.	0	.	88	75	158	40	14	156	185	39	228	56	111	64	1280	18	1775	18	500	33	4347	11
	61-70 cm	0	.	0	.	0	.	0	.	0	.	35	100	107	50	27	100	174	45	106	63	65	59	593	35	639	24	232	38	1977	15
	71-80 cm	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	26	100	59	71	31	100	445	33	362	33	99	59	1046	20
	81-90 cm	0	.	0	.	0	.	0	.	0	.	33	100	0	.	0	.	0	.	0	.	51	72	83	71	104	58	20	100	292	34
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	58	100	99	72	0	.	180	52
	>100 cm	0	.	0	.	0	.	0	.	0	.	65	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	65	100
	total	0	.	0	.	80	100	0	.	0	.	737	42	1689	26	153	72	2330	29	841	43	760	43	7128	14	10294	11	2936	19	26948	7
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	15	59	0	.	20	95	0	.	0	.	344	33	275	28	155	63	202	24	224	38	34	53	1246	16	1206	14	394	17	4114	8
	21-30 cm	57	55	0	.	41	100	0	.	0	.	644	31	638	26	301	43	523	23	553	33	137	49	1966	14	3052	13	1047	19	8959	7
	31-40 cm	102	59	0	.	95	67	0	.	0	.	1025	27	845	27	432	41	1550	22	706	34	78	61	2300	14	4396	12	1645	18	13174	7
	41-50 cm	65	73	0	.	28	87	0	.	0	.	682	28	615	28	528	36	1891	20	418	32	260	61	2079	16	4347	12	1650	18	12563	7
	51-60 cm	83	90	0	.	36	100	0	.	0	.	603	32	360	31	405	50	1222	24	183	47	233	65	1426	18	2954	15	1263	19	8767	8
	61-70 cm	51	73	0	.	25	100	0	.	0	.	69	74	181	44	32	100	342	43	58	71	343	58	544	27	1258	21	918	28	3820	13
	71-80 cm	88	100	0	.	0	.	0	.	0	.	0	.	59	71	229	63	199	47	0	.	126	78	373	33	628	27	432	34	2133	16
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	33	100	120	59	117	59	94	72	121	74	513	30
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	74	37	100	49	100	58	100	252	46
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	0	.	0	0	0	.	248	59	284	53
	total	460	52	0	.	245	58	0	.	0	.	3367	24	2973	21	2081	29	5993	18	2176	28	1439	41	10088	11	17983	10	7774	13	54581	5
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	3685	8	456	26	357	25	748	21	892	18	1128	18	2142	10	1741	12	621	20	277	28	672	22	259	29	225	23	1075	16	14277	4
	21-30 cm	7795	7	790	23	1011	23	1322	20	2322	13	1916	18	4231	9	4970	12	1150	17	743	25	1447	21	509	29	529	25	1185	17	29919	4
	31-40 cm	10293	6	1083	24	1979	25	2820	17	3832	13	2439	19	6879	10	6401	10	1798	19	813	27	1704	18	437	29	672	24	808	20	41956	4
	41-50 cm	8703	6	1010	25	1401	22	2726	18	3155	15	2394	18	7877	9	6321	10	1653	19	1003	27	1774	19	411	30	817	24	722	19	39966	4
	51-60 cm	5082	10	364	31	919	26	1574	20	1344	20	1307	24	5512	11	3901	13	1663	20	847	32	1237	24	171	42	774	25	729	27	25424	5
	61-70 cm	1658	17	104	59	361	42	410	36	652	27	648	31	2233	16	1475	20	453	31	433	43	294	38	60	71	347	41	213	32	9341	7
	71-80 cm	1007	24	46	100	227	60	137	59	126	58	141	54	941	26	872	31	190	52	92	100	192	45	52	75	102	74	329	57	4454	12
	81-90 cm	128	59	0	.	0	.	0	.	43	100	0	.	421	40	232	50	91	71	34	100	0	.	0	.	47	78	44	100	1040	23
	91-100 cm	227	51	0	.	70	100	0	.	38	100	0	.	77	100	35	100	0	.	36	100	0	.	0	.	0	.	38	100	519	33
	>100 cm	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	100
	total	38645	5	3853	19	6324	18	9737	15	12403	11	9973	15	30312	7	25948	8	7619	15	4279	23	7318	16	1900	23	3511	20	5142	13	166964	3
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	975	16	861	16	813	19	1786	14	2083	10	1	100	349	29	216	38	123	44	146	52	29	75	80	55	72	47	0	.	7536	6
	21-30 cm	2205	14	2087	17	2095	17	4001	11	3969	10	22	100	515	32	610	34	177	37	161	47	89	71	30	60	204	46	0	.	16166	5
	31-40 cm	2528	15	2696	15	2736	16	6217	10	6806	9	23	100	980	28	667	36	336	37	100	69	42	71	18	73	261	45	0	.	23410	5
	41-50 cm	2235	17	2264	18	1910	18	6005	11	7667	10	41	100	975	31	429	38	301	41	38	82	74	100	57	68	264	46	0	.	22260	5
	51-60 cm	787	23	1376	20	841	21	3486	13	4551	12	25	100	515	40	126	54	283	43	33	100	0	.	152	55	215	47	0	.	12388	6
	61-70 cm	236	38	728	29	484	32	1197	21	1569	18	0	.	252	43	268	64	31	100	31	100	0	.	39	80	31	100	0	.	4865	10
	71-80 cm	0	.	94	73	251	41	532	28	688	25	0	.	228	50	0	.	0	.	18	100	0	.	0	.	50	100	0	.	1861	15
	81-90 cm	0	.	203	58	227	50	384	51	149	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	963	28
	91-100 cm	0	.	0	.	34	100	0	.	140	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	174	60
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8967	12	10309	12	9390	14	23608	8	27622	7	112	100	3813	25	2316	32	1251	34	527	43	234	74	376	48	1097	38	0	.	89623	4
hyperinsubric and colline	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	50	76	0	.	231	46	0	.	31	62	0	.	0	.	0	.	0	.	0	.	0	.	216	31	1	100	2156	10	2685	10
	21-30 cm	18	73	0	.	88	45	0	.	44	92	0	.	0	.	0	.	0	.	0	.	0	.	295	31	3	100	1727	12	2175	11
	31-40 cm	0	.	0	.	44	82	0	.	147	85	0	.	0	.	0	.	0	.	0	.	0	.	173	31	23	100	950	16	1338	16
	41-50 cm	5	100	0	.	85	95	0	.	103	64	0	.	0	.	0	.	0	.	0	.	0	.	108	69	20	100	540	19	860	19
	51-60 cm	0	.	0	.	46	100	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	82	61	0	.	284	23	434	22
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	129	40	147	37
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	254	47	273	45
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	118	60	118	60
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	80	61	80	61
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	364	58	364	58
	total	72	73	0	.	494	47	0	.	347	66	0	.	0	.	0	.	0	.	0	.	0	.	912	27	48	100	6601	9	8473	9
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	4725	7	1317	13	1428	14	2534	11	3006	8	1499	15	2955	8	2128	11	1084	13	674	20	773	20	2348	10	2329	9	3825	7	30625	2
	21-30 cm	10074	6	2877	13	3243	13	5323	9	6335	7	2697	15	5791	8	5946	10	2375	12	1533	18	1754	19	4135	9	5701	9	4427	7	62212	2
	31-40 cm	12923	5	3778	13	4877	13	9037	8	10785	7	3684	15	9186	8	7517	9	4337	13	1803	19	1964	17	4758	9	8776	8	4153	9	87578	2
	41-50 cm	11008	6	3275	15	3457	13	8731	9	10925	8	3272	14	9813	8	7293	9	4474	12	1621	19	2325	17	4640	10	8413	8	3758	9	83004	2
	51-60 cm	5951	9	1740	17	1850	16	5060	11	5916	10	2022	19	6545	10	4446	13	3353	14	1290	24	1581	22	3236	11	6221	9	2777	12	51988	3
	61-70 cm	1944	15	832	26	869	25	1607	18	2221	15	752	28	2772	14	1802	19	1000	22	628	33	702	33	1366	19	2538	14	1492	19	20526	5
	71-80 cm	1096	23	140	59	478	35	670	26	814	23	164	48	1227	22	1100	28	415	33	170	61	350	38	968	21	1271	18	1114	24	9975	7
	81-90 cm	128	59	203	58	227	50	384	51	193	51	33	100	421	40	232	50	118	59	68	71	172	46	224	42	344	33	302	41	3048	14
	91-100 cm	227	51	0	.	104	75	0	.	178	60	0	.	77	100	35	100	0	.	36	100	131	63	129	59	147	58	175	48	1239	21
	>100 cm	68	100	0	.	0	.	0	.	0	.	65	100	0	.	0	.	37	100	0	.	0	.	30	100	82	72	611	42	893	32
	total	48144	4	14162	10	16534	10	33345	7	40372	5	14188	12	38787	6	30499	7	17193	10	7823	16	9751	14	21833	7	35822	6	22635	5	351089	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. 1321114. Birmensdorf, Swiss Federal Research Institute WSL

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