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
grid: 1.4-km grid
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	.	5	100	0	.	5	100
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	4	100
	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	.	9	100	0	.	9	100
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	.	3	100	0	.	138	24	215	19	35	43	391	14
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	21	100	0	.	339	25	444	18	70	44	879	14
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	9	100	0	.	371	23	687	18	96	39	1167	13
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	282	20	572	20	85	33	944	14
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	190	26	482	23	27	56	699	17
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	38	208	30	46	64	383	22
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	42	120	36	47	58	235	25
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	59	73	51	0	.	124	38
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	71	0	.	0	.	42	71
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	62	59	0	.	78	51
	total	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	38	100	0	.	1627	16	2863	14	406	29	4941	10
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	3	100	0	.	0	.	39	44	144	30	15	71	125	22	17	47	46	40	451	15	724	12	146	20	1710	7
	21-30 cm	0	.	0	.	4	100	0	.	0	.	114	40	300	26	35	90	423	25	52	45	124	44	1328	15	1769	12	402	19	4551	7
	31-40 cm	0	.	0	.	12	100	0	.	0	.	193	40	354	27	3	316	688	25	115	56	252	46	1607	13	2807	11	667	18	6698	7
	41-50 cm	0	.	0	.	18	100	0	.	0	.	169	38	243	28	8	100	521	27	97	50	211	40	1746	13	2874	10	744	16	6630	6
	51-60 cm	0	.	0	.	4	100	0	.	0	.	150	43	123	36	16	91	250	28	129	57	79	52	1169	14	1883	13	417	25	4219	8
	61-70 cm	0	.	0	.	0	.	0	.	0	.	97	52	91	41	15	100	259	36	74	54	61	54	489	27	680	17	229	27	1995	11
	71-80 cm	0	.	0	.	0	.	0	.	0	.	14	100	44	84	0	.	82	51	34	71	47	58	351	28	311	27	129	40	1013	15
	81-90 cm	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	60	51	86	50	124	42	96	41	382	22
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	14	100	32	100	78	58	14	100	155	40
	>100 cm	0	.	0	.	0	.	0	.	0	.	32	100	0	.	0	.	0	.	0	.	52	100	31	100	0	.	0	.	114	60
	total	0	.	0	.	42	100	0	.	0	.	824	32	1298	21	92	63	2365	21	517	40	946	31	7289	10	11249	8	2843	14	27466	5
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	14	43	0	.	16	70	0	.	0	.	297	24	301	20	142	38	333	21	186	29	63	36	1188	11	1085	10	473	13	4098	5
	21-30 cm	80	43	0	.	41	71	0	.	0	.	678	23	776	19	255	30	695	21	453	26	223	33	2042	11	2719	10	1008	14	8970	5
	31-40 cm	131	38	0	.	57	62	0	.	0	.	1098	20	977	18	490	29	1290	17	651	24	233	40	2483	10	4318	9	1592	14	13319	5
	41-50 cm	165	39	0	.	46	62	0	.	0	.	1026	21	746	19	476	27	1499	16	383	25	316	38	2227	11	4296	9	1353	14	12533	5
	51-60 cm	77	61	0	.	19	100	0	.	0	.	612	25	442	22	336	37	1053	18	234	29	340	43	2034	14	3260	11	1196	14	9603	6
	61-70 cm	28	73	0	.	13	100	0	.	0	.	115	39	293	28	66	51	403	30	152	61	318	44	643	19	1763	14	662	24	4458	9
	71-80 cm	48	100	0	.	0	.	0	.	0	.	0	.	91	46	116	63	263	37	103	71	69	78	439	25	891	20	396	26	2417	12
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	34	75	0	.	27	72	45	72	139	49	158	39	178	42	68	73	649	21
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	59	74	20	100	161	55	59	71	330	34
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	20	100	0	.	0	.	0	0	28	100	234	46	281	40
	total	543	34	0	.	192	49	0	.	0	.	3826	17	3660	15	1881	22	5584	13	2238	21	1760	27	11236	8	18699	7	7040	10	56658	4
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	3491	6	411	20	431	17	592	17	833	14	1075	13	2100	7	1819	9	636	14	407	18	714	17	335	21	304	17	1185	11	14334	3
	21-30 cm	7874	5	838	17	1026	17	1241	16	2106	11	2040	12	4273	7	4977	9	1114	13	810	19	1410	15	538	20	617	20	1354	11	30218	3
	31-40 cm	10586	5	1221	17	2064	18	2462	14	3355	11	2746	13	7572	7	6561	8	1878	14	962	20	1992	14	436	21	805	18	1113	14	43753	3
	41-50 cm	9206	5	993	18	1689	15	2337	14	3062	12	2682	13	8252	7	6507	8	1801	14	1028	19	1890	15	384	24	858	19	898	14	41586	3
	51-60 cm	5379	7	403	23	983	18	1490	16	1644	15	1588	16	5892	8	4232	10	1558	15	664	26	963	20	314	39	677	21	692	19	26479	3
	61-70 cm	2103	11	110	42	403	31	461	25	522	23	910	21	2242	12	1563	14	463	24	339	36	343	29	62	51	275	35	325	22	10122	5
	71-80 cm	957	20	27	100	148	53	130	45	140	46	147	45	932	20	845	24	202	41	98	62	157	38	28	75	77	60	247	44	4135	10
	81-90 cm	162	41	0	.	0	.	62	71	55	71	0	.	412	29	165	45	78	58	58	75	0	.	19	100	58	64	79	56	1148	16
	91-100 cm	125	51	0	.	37	100	0	.	21	100	105	60	94	71	21	100	0	.	19	100	48	100	0	.	0	.	22	100	493	27
	>100 cm	37	100	0	.	0	.	0	.	0	.	0	.	0	.	20	100	70	100	0	.	0	.	0	.	0	.	102	60	229	45
	total	39920	4	4002	14	6781	13	8776	11	11740	9	11293	10	31769	5	26709	6	7800	11	4387	16	7517	12	2116	18	3670	14	6017	9	172498	2
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	886	12	907	12	976	14	1571	11	2295	8	26	78	309	24	257	29	136	32	101	43	31	52	111	37	45	43	0	.	7651	4
	21-30 cm	2387	11	2025	12	2199	12	3392	9	4639	7	36	74	476	25	493	27	209	33	162	38	107	49	98	40	133	43	0	.	16355	4
	31-40 cm	2604	11	2738	11	3093	11	5274	8	8022	7	142	54	793	23	546	30	281	30	121	49	83	53	29	57	183	42	0	.	23909	4
	41-50 cm	2155	12	2249	13	2168	13	5875	9	7948	7	159	56	765	25	444	28	198	37	23	83	108	59	63	48	163	43	0	.	22317	4
	51-60 cm	997	18	1365	15	1073	17	3506	10	4816	9	59	59	528	29	234	38	194	39	37	71	41	73	94	50	124	48	0	.	13066	5
	61-70 cm	332	31	658	21	648	25	1278	16	1512	13	15	100	218	44	189	55	62	76	17	100	50	81	21	80	17	100	0	.	5017	8
	71-80 cm	71	76	126	45	214	34	639	22	696	23	0	.	216	50	0	.	0	.	11	100	0	.	0	.	27	100	0	.	2000	13
	81-90 cm	0	.	140	51	124	50	351	36	206	39	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	856	21
	91-100 cm	0	.	0	.	20	100	39	100	109	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	46
	>100 cm	0	.	54	100	0	.	0	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	89	72
	total	9433	9	10260	9	10514	10	21926	7	30277	5	436	56	3340	20	2163	24	1080	28	472	35	420	47	415	32	692	36	0	.	91427	3
hyperinsubric and colline	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	28	75	0	.	193	37	0	.	38	61	0	.	0	.	0	.	0	.	0	.	0	.	223	23	10	54	1883	8	2375	8
	21-30 cm	10	74	0	.	111	36	0	.	35	71	0	.	0	.	0	.	0	.	0	.	0	.	212	26	5	201	1584	9	1957	8
	31-40 cm	0	.	0	.	83	47	0	.	87	81	0	.	0	.	0	.	0	.	0	.	0	.	187	25	14	100	869	12	1239	11
	41-50 cm	3	100	0	.	50	91	0	.	66	58	0	.	0	.	0	.	0	.	0	.	0	.	120	39	28	71	679	16	946	14
	51-60 cm	0	.	0	.	28	95	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	44	66	37	76	326	19	446	18
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	73	0	.	119	29	137	27
	71-80 cm	0	.	0	.	0	.	0	.	28	100	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	232	33	270	31
	81-90 cm	0	.	0	.	0	.	0	.	29	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	54	102	48
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	45	108	45
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	500	39	500	39
	total	40	73	0	.	464	35	0	.	295	55	0	.	0	.	0	.	0	.	0	.	0	.	815	20	93	53	6372	7	8079	7
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	4419	5	1318	10	1619	10	2163	9	3166	6	1437	11	2854	6	2233	8	1230	10	714	14	854	15	2446	7	2388	6	3722	5	30562	2
	21-30 cm	10350	4	2863	9	3381	9	4634	8	6780	6	2868	10	5829	6	5760	8	2441	10	1497	14	1864	13	4558	7	5691	6	4418	5	62933	2
	31-40 cm	13321	4	3959	9	5308	9	7736	7	11464	5	4178	10	9699	6	7600	7	4137	9	1858	14	2560	12	5114	7	8814	6	4336	6	90085	2
	41-50 cm	11529	4	3242	10	3971	9	8212	7	11076	6	4035	10	10006	6	7434	7	4018	9	1536	14	2526	13	4821	7	8790	6	3758	7	84955	2
	51-60 cm	6454	6	1768	12	2106	12	4996	9	6472	7	2408	13	6985	7	4817	9	3055	11	1064	19	1423	17	3845	9	6462	7	2657	8	54512	2
	61-70 cm	2463	10	768	19	1065	19	1740	14	2033	11	1137	18	2843	10	1833	14	1187	16	583	27	772	23	1362	14	2943	10	1381	14	22110	4
	71-80 cm	1076	19	153	41	362	29	769	20	865	20	161	42	1283	17	961	22	548	24	247	40	273	31	897	17	1426	14	1051	17	10071	6
	81-90 cm	162	41	140	51	124	50	414	33	290	32	16	100	481	26	165	45	105	47	102	53	200	38	314	26	432	24	315	27	3261	10
	91-100 cm	125	51	0	.	58	74	39	100	130	51	105	60	94	71	21	100	18	100	50	72	121	55	94	51	238	42	203	34	1296	16
	>100 cm	37	100	54	100	0	.	0	.	36	100	32	100	0	0	20	100	90	81	0	.	52	100	47	74	90	51	835	28	1292	21
	total	49936	3	14262	7	17994	7	30702	5	42312	4	16378	8	40075	5	30845	6	16828	7	7652	11	10644	10	23499	5	37275	4	22677	4	361078	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. 1321135. Birmensdorf, Swiss Federal Research Institute WSL

back