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 (10 classes) · conifers and broadleaves
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-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
diameter at breast height (10 classes)	conifers and broadleaves	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	.
	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	.
	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	.
12-20 cm	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	.
	conifers	336	27	58	57	98	67	2460	10	122	36	500	33	8	86	132	34	2333	10	313	20	862	18	375	21	127	58	445	33	477	20	80	46	225	30	231	27	259	30	563	18	295	28	1061	16	1731	13	37	61	424	25	13556	4
	broadleaves	510	17	7	72	59	36	1696	11	372	24	435	20	12	68	98	35	778	17	311	25	416	18	346	20	95	43	135	37	569	20	217	28	533	22	291	33	302	25	2720	9	129	30	1124	13	756	14	22	50	626	17	12559	4
	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	846	16	66	53	157	44	4157	8	494	22	935	21	20	59	231	26	3111	9	625	17	1278	15	721	15	223	38	580	27	1046	16	297	27	757	19	522	25	561	21	3283	8	424	23	2185	11	2487	10	59	43	1051	15	26115	3
21-30 cm	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	.
	conifers	622	25	218	40	361	40	4519	9	203	35	964	25	0	.	203	48	4933	9	494	23	1626	16	1045	21	236	40	783	25	1252	20	249	36	432	25	571	26	761	25	1374	17	749	22	2146	14	3682	11	175	43	963	24	28561	3
	broadleaves	890	18	39	72	270	38	3111	10	530	23	1273	19	95	75	273	40	1209	18	708	21	704	20	474	19	349	47	304	37	1494	17	373	32	858	19	426	34	512	20	4497	9	425	31	1786	14	903	17	72	60	1069	18	22642	4
	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	1512	15	257	40	631	32	7630	7	733	20	2237	16	95	75	475	32	6142	8	1201	16	2329	14	1518	16	584	36	1087	21	2746	14	621	24	1290	16	998	22	1274	18	5872	8	1174	20	3933	11	4586	10	247	37	2032	15	51203	2
31-40 cm	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	.
	conifers	592	27	690	32	636	29	6828	8	247	32	1864	18	43	100	283	36	8150	8	892	25	2061	15	1199	21	492	43	1064	25	1888	18	440	33	703	28	1140	22	799	30	2159	18	1059	24	3264	12	4997	10	235	37	1397	18	43123	3
	broadleaves	1111	19	126	47	316	33	3995	9	876	22	1384	19	89	77	296	34	961	20	1318	18	1127	20	437	27	361	51	471	36	2085	16	633	27	1359	20	550	25	792	24	3328	10	391	42	2470	12	537	23	99	48	1397	15	26509	3
	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
	total	1703	16	817	31	952	26	10823	7	1123	20	3248	14	132	65	579	26	9111	7	2210	16	3189	13	1636	17	853	34	1536	20	3972	13	1073	24	2062	17	1690	18	1591	20	5486	10	1449	22	5734	9	5534	9	334	34	2795	12	69632	2
41-50 cm	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	.
	conifers	825	24	730	30	759	30	9323	8	308	35	2313	17	23	100	720	26	10357	7	1505	21	2417	14	1786	16	381	41	1149	23	2123	17	436	31	772	21	1475	21	861	23	2439	16	1248	28	4083	12	5626	10	215	49	2219	15	54094	3
	broadleaves	1609	18	136	64	231	42	4021	11	727	26	1285	21	71	62	484	31	823	22	1520	18	1136	20	616	23	279	42	339	32	2186	17	747	26	1429	17	717	29	759	22	2421	11	269	33	2756	13	311	33	179	52	1439	15	26491	4
	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	2434	15	866	30	990	26	13344	7	1036	23	3598	14	95	64	1204	23	11180	7	3025	15	3554	12	2403	14	660	33	1488	21	4309	13	1183	22	2201	15	2192	17	1620	19	4859	10	1517	25	6839	9	5937	10	394	39	3658	12	80585	2
51-60 cm	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	.
	conifers	864	23	820	34	1226	31	9578	8	440	40	2402	17	0	.	790	27	9407	8	1557	21	3213	15	2125	16	201	53	883	26	3089	15	314	34	670	28	1794	19	530	30	2532	18	865	25	3529	12	5299	11	175	50	1606	18	53908	3
	broadleaves	1547	19	0	.	116	45	3763	12	591	28	1070	23	52	100	397	35	507	26	818	24	1339	26	206	35	103	66	305	46	1347	19	348	36	1213	25	630	32	962	21	1342	16	114	60	2090	15	163	48	102	45	1850	15	20975	4
	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	2411	15	820	34	1342	30	13341	7	1030	24	3472	14	52	100	1187	23	9914	7	2375	17	4551	13	2331	15	304	42	1187	24	4436	13	663	26	1883	19	2424	17	1492	20	3874	13	980	24	5619	10	5462	10	276	38	3456	13	74883	2
61-70 cm	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	.
	conifers	608	31	469	39	398	46	6445	10	245	44	1079	20	0	.	519	36	5712	10	959	27	2097	20	1332	23	412	45	374	40	2502	19	305	41	313	55	1071	27	529	30	1387	16	542	28	3273	16	2971	14	82	100	1215	22	34838	4
	broadleaves	691	28	39	75	69	71	1648	18	211	52	297	34	37	100	146	55	311	29	617	30	389	34	0	.	32	100	142	59	677	31	287	39	430	34	123	60	580	32	696	23	114	76	914	25	108	53	63	100	1099	24	9721	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	1299	20	509	37	468	42	8092	9	456	33	1376	18	37	100	665	30	6023	10	1576	20	2486	19	1332	23	444	42	516	38	3179	17	593	29	743	31	1194	25	1109	23	2083	13	656	27	4187	14	3079	14	145	71	2314	17	44559	3
71-80 cm	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	.
	conifers	477	31	138	58	182	51	3730	13	244	61	719	31	0	.	591	40	2976	13	468	42	984	26	801	24	97	61	590	42	1859	21	180	62	121	58	624	30	290	54	1063	27	402	48	1213	21	1904	20	89	71	779	30	20521	5
	broadleaves	632	29	0	.	30	100	535	37	133	58	184	43	45	100	0	.	188	50	126	58	250	53	37	100	0	.	29	100	159	59	134	58	350	50	192	82	75	74	338	28	0	.	361	34	30	100	0	.	407	34	4235	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	.
	total	1108	22	138	58	211	46	4266	12	377	44	903	27	45	100	591	40	3164	13	595	37	1234	23	838	24	97	61	619	40	2018	20	314	43	472	40	815	33	365	46	1401	21	402	48	1574	18	1934	19	89	71	1186	24	24755	5
81-90 cm	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	.
	conifers	230	60	49	100	0	.	1555	22	61	100	439	42	0	.	184	51	1672	22	104	71	238	50	449	39	0	.	145	77	680	32	0	.	114	71	235	52	289	53	269	46	23	100	380	40	723	29	0	.	406	38	8243	9
	broadleaves	221	50	0	.	0	.	44	100	0	.	70	100	0	.	0	.	31	100	0	.	0	.	0	.	0	.	0	.	283	47	0	.	0	.	0	.	192	58	293	34	134	58	48	100	0	0	0	.	107	74	1423	19
	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	451	39	49	100	0	.	1599	21	61	100	509	38	0	.	184	51	1703	21	104	71	238	50	449	39	0	.	145	77	963	27	0	.	114	71	235	52	481	43	563	28	157	52	428	37	723	29	0	.	513	34	9667	8
91-100 cm	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	.
	conifers	63	100	0	.	0	.	474	36	0	.	0	.	0	.	232	51	613	30	68	100	0	.	65	100	0	.	0	.	113	71	0	.	68	100	68	100	109	71	165	71	0	.	185	59	382	34	0	.	0	.	2608	15
	broadleaves	108	71	0	.	0	.	111	72	0	.	0	.	0	.	25	100	0	.	0	.	0	.	0	.	0	.	0	.	114	100	0	.	0	.	55	100	52	100	145	59	0	.	0	.	29	100	0	.	105	71	744	28
	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	171	58	0	.	0	.	585	32	0	.	0	.	0	.	257	47	613	30	68	100	0	.	65	100	0	.	0	.	227	61	0	.	68	100	123	71	161	58	310	47	0	.	185	59	412	32	0	.	105	71	3352	13
>100 cm	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	.
	conifers	0	.	0	.	0	.	324	54	0	.	71	100	0	.	51	100	386	42	185	71	58	100	70	100	0	.	0	.	99	73	0	.	0	.	68	100	0	.	240	71	0	.	49	100	103	73	0	.	0	.	1705	22
	broadleaves	0	.	0	.	0	.	66	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	194	100	374	60	0	.	0	.	0	.	0	.	0	.	633	48
	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	.	390	48	0	.	71	100	0	.	51	100	386	42	185	71	58	100	70	100	0	.	0	.	99	73	0	.	0	.	68	100	194	100	614	46	0	.	49	100	103	73	0	.	0	.	2338	20
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	.
	conifers	4617	17	3173	30	3661	26	45237	6	1869	28	10351	13	75	91	3706	23	46540	6	6544	16	13556	11	9247	12	1945	33	5434	20	14082	12	2004	24	3419	18	7277	16	4428	17	12192	13	5183	19	19183	9	27418	7	1007	38	9010	12	261157	2
	broadleaves	7318	13	347	47	1090	30	18989	8	3441	18	5998	16	401	58	1720	25	4808	14	5419	14	5362	16	2116	15	1219	35	1725	27	8914	13	2739	20	6171	14	2985	22	4419	17	16153	7	1575	30	11549	10	2838	15	537	41	8100	11	125932	2
	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
	total	11935	11	3520	29	4750	24	64227	5	5310	17	16349	11	475	55	5426	19	51347	5	11963	12	18918	10	11363	11	3164	27	7159	18	22996	10	4744	18	9590	12	10261	14	8847	14	28344	7	6758	18	30732	7	30257	7	1544	33	17110	9	387089	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. 1353902. Birmensdorf, Swiss Federal Research Institute WSL

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