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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
growing stock (stemwood)
diameter at breast height (10 classes) · conifers and broadleaves
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 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 (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	±%
n/a	n/a	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	.
	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	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	1660	10	243	27	490	32	1338	16	1020	15	472	28	1757	12	624	18	661	15	359	24	304	25	1731	13	2182	10	714	15	13556	4
	broadleaves	1849	9	754	15	620	19	950	14	1215	11	310	21	844	15	601	16	596	20	246	32	321	28	756	14	515	19	2983	8	12559	4
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	3509	7	997	14	1110	18	2288	12	2235	10	782	19	2600	10	1225	13	1258	13	605	22	625	21	2487	10	2697	9	3697	7	26115	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	3488	9	569	21	831	24	2185	16	2265	14	1220	21	3155	10	2028	15	1350	15	839	20	642	29	3682	11	4404	9	1903	13	28561	3
	broadleaves	3644	9	1148	15	1148	18	1231	14	2417	11	937	22	1898	14	1778	15	770	20	518	27	545	27	903	17	673	22	5033	9	22642	4
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	7132	6	1717	13	1979	15	3416	12	4681	9	2157	16	5053	8	3806	12	2120	12	1356	18	1186	22	4586	10	5077	9	6937	7	51203	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	5417	9	813	22	1085	21	2483	14	2743	13	2374	16	4710	10	3741	12	2279	13	1334	20	838	26	4997	10	6825	8	3483	14	43123	3
	broadleaves	5366	8	1740	15	1626	17	1978	14	3131	11	786	24	2227	14	2556	13	1029	20	645	29	599	24	537	23	443	19	3846	9	26509	3
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	0
	total	10783	6	2553	13	2711	14	4462	11	5874	9	3160	14	6938	8	6296	9	3308	11	1980	17	1437	19	5534	9	7268	8	7329	8	69632	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	6932	7	963	20	1402	19	3100	14	4113	11	2947	14	5628	9	4032	12	3498	14	1858	22	1200	19	5626	10	8850	8	3945	12	54094	3
	broadleaves	5589	8	2267	14	1838	18	2158	13	3242	10	584	27	2167	15	2753	14	964	23	500	28	873	21	311	33	442	30	2801	10	26491	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12520	5	3230	13	3240	14	5258	10	7355	8	3531	13	7795	8	6784	10	4462	13	2358	19	2073	16	5937	10	9293	7	6746	7	80585	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	6645	8	869	25	1513	21	3369	13	3434	12	2966	14	6347	10	5505	11	3073	14	1473	20	1476	20	5299	11	7868	8	4072	12	53908	3
	broadleaves	3148	11	1701	20	1622	19	2827	15	4067	10	473	30	1843	18	1502	18	764	26	328	38	691	27	163	48	375	30	1474	15	20975	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	9793	6	2570	15	3134	15	6196	10	7501	8	3439	13	8190	9	7007	10	3837	13	1801	18	2167	17	5462	10	8243	8	5545	10	74883	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	4314	12	619	27	878	23	2694	16	2358	15	1985	20	3980	13	3812	15	2328	17	998	24	803	27	2971	14	4884	11	2214	13	34838	4
	broadleaves	1146	20	840	25	803	22	1261	19	2192	16	79	71	685	31	694	27	412	42	190	54	303	51	108	53	219	36	788	21	9721	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5460	10	1459	19	1682	17	3956	13	4550	11	2064	19	4665	12	4505	14	2741	16	1187	23	1106	24	3079	14	5103	11	3002	11	44559	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	2157	15	379	47	476	36	1270	21	1620	21	723	30	2270	16	2118	18	1596	19	1044	31	925	30	1904	20	2456	15	1583	21	20521	5
	broadleaves	254	48	432	32	294	36	622	36	1013	22	169	47	405	39	236	70	175	50	38	100	41	100	30	100	161	56	365	27	4235	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2411	15	812	28	770	26	1892	18	2633	16	892	26	2674	15	2354	18	1770	18	1083	30	966	30	1934	19	2617	14	1948	17	24755	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	737	31	61	100	406	42	646	31	1038	27	320	46	1032	27	632	32	282	42	135	62	291	42	723	29	1287	23	654	38	8243	9
	broadleaves	48	100	66	100	70	100	0	.	459	36	0	.	44	100	251	50	0	.	134	58	27	100	0	0	0	.	325	32	1423	19
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	785	30	126	71	476	39	646	31	1497	22	320	46	1075	26	883	27	282	42	269	43	318	39	723	29	1287	23	978	27	9667	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	340	45	0	.	0	.	122	71	172	58	57	100	109	71	58	100	233	50	68	100	288	46	382	34	422	35	357	45	2608	15
	broadleaves	0	.	0	.	0	.	111	72	379	43	0	.	0	.	55	100	0	.	0	.	25	100	29	100	0	.	145	59	744	28
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	340	45	0	.	0	.	233	50	551	35	57	100	109	71	113	71	233	50	68	100	312	43	412	32	422	35	502	36	3352	13
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	255	59	0	.	71	100	84	100	0	.	36	100	263	61	129	71	49	100	0	.	90	71	103	73	312	46	314	59	1705	22
	broadleaves	0	.	0	.	0	.	66	100	194	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	374	60	633	48
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	255	59	0	.	71	100	150	71	194	100	36	100	263	61	129	71	49	100	0	.	90	71	103	73	312	46	687	42	2338	20
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	31943	5	4516	16	7151	15	17292	9	18764	8	13100	12	29249	7	22678	9	15349	11	8108	16	6856	16	27418	7	39492	6	19239	9	261157	2
	broadleaves	21045	5	8948	10	8022	13	11204	10	18308	7	3338	19	10112	11	10425	11	4709	16	2599	23	3424	17	2838	15	2828	17	18132	6	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
	total	52988	4	13463	10	15173	11	28496	7	37072	6	16438	11	39361	6	33103	7	20058	9	10707	15	10280	13	30257	7	42320	6	37372	5	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. 1353904. Birmensdorf, Swiss Federal Research Institute WSL

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