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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.

NFI4
volume of bolewood (HG 2010)
timber assortment classes (HG 2010) · conifers and broadleaves
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		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)	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	.
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	.
	conifers	300	22	62	48	154	29	1370	9	47	26	599	17	0	70	58	22	555	6	933	12	305	18	1175	11	12	33	68	16	483	18	53	39	104	25	137	22	238	23	110	9	49	20	1247	11	421	11	55	50	310	17	8846	3
	broadleaves	28	10	2	36	4	30	64	7	18	14	19	14	2	42	8	21	24	13	22	11	19	14	11	15	5	31	7	23	29	11	9	18	28	11	10	17	11	14	99	5	5	24	49	9	20	14	2	27	25	10	518	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	0	0	0	0	0	0	0	.	0	0
	total	329	20	64	47	158	29	1434	8	65	20	617	16	2	43	66	20	579	5	955	12	324	17	1185	11	17	26	75	15	511	17	63	34	131	20	147	20	249	22	209	5	54	19	1296	11	440	10	57	48	335	16	9364	3
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	.
	conifers	1955	13	698	22	966	20	12941	5	576	17	3395	11	42	82	1083	19	16036	4	1565	12	3891	9	1098	15	545	29	1952	14	3905	9	656	21	1425	12	2282	11	1580	17	3497	8	1671	15	5647	7	10522	5	511	23	2916	10	81355	2
	broadleaves	2528	11	159	38	460	24	6182	6	1302	13	1865	12	148	38	647	18	2488	10	1898	11	1542	12	871	12	383	30	662	22	2897	10	777	18	1887	11	1087	14	1229	14	8759	4	512	24	4376	7	2034	10	162	27	2279	9	47133	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	0	0	0	0	0	0	0	.	0	0
	total	4483	9	857	21	1426	18	19123	4	1878	12	5260	9	191	39	1730	15	18523	4	3463	9	5433	8	1968	11	928	22	2615	12	6802	8	1432	15	3312	10	3369	10	2809	12	12256	4	2183	14	10023	6	12556	5	673	21	5194	8	128488	1
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	.
	conifers	3019	12	1192	23	2179	19	22009	5	1043	21	5097	11	29	60	1816	16	19638	4	3359	12	5985	10	4240	10	493	28	2159	15	7147	9	852	20	2107	14	3795	12	1762	18	4252	8	1738	15	10436	7	11312	5	828	28	4684	10	121171	1
	broadleaves	3815	10	234	34	629	30	7658	7	2137	14	2690	13	202	46	865	19	1231	13	3035	10	2082	16	1163	14	355	30	631	27	3845	10	1171	16	3258	10	1176	19	1627	14	4066	6	326	30	5418	8	663	16	349	30	3289	10	51912	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	0	0	0	0	0	0	0	.	0	0
	total	6834	8	1426	22	2807	18	29667	4	3181	13	7787	9	231	43	2681	14	20868	4	6395	9	8067	9	5402	9	848	23	2790	14	10992	7	2023	15	5365	9	4971	11	3390	13	8318	5	2064	14	15854	6	11975	5	1176	24	7973	8	173084	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	.
	conifers	858	20	221	36	653	29	7561	7	269	35	1326	16	13	100	916	23	7077	7	697	21	1692	14	1074	17	128	37	849	20	3146	13	209	30	485	22	1414	18	333	32	2192	11	711	24	3178	10	3513	9	212	33	1178	15	39905	3
	broadleaves	1647	13	40	47	150	39	2288	12	902	22	785	24	32	88	182	32	278	23	521	19	622	23	246	42	54	47	200	49	935	16	290	32	1053	18	388	32	798	32	1640	17	77	51	1320	14	136	33	101	48	1677	16	16363	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	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	2505	11	262	32	803	27	9849	6	1171	19	2111	14	45	69	1098	20	7354	7	1219	15	2314	12	1320	16	182	31	1049	19	4081	11	499	24	1538	15	1801	16	1131	26	3833	9	789	23	4498	9	3649	9	313	30	2855	12	56268	2
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	6132	11	2174	22	3950	19	43880	4	1936	19	10417	10	84	61	3873	15	43305	4	6555	11	11873	9	7586	10	1179	25	5028	13	14681	8	1770	18	4121	12	7628	11	3914	14	10051	7	4170	14	20508	6	25768	5	1606	25	9088	9	251277	1
	broadleaves	8019	9	435	30	1244	26	16193	6	4358	13	5359	12	384	42	1702	18	4020	10	5476	10	4265	13	2290	13	796	27	1500	22	7706	9	2247	15	6226	9	2661	15	3665	14	14565	4	920	24	11163	7	2852	11	614	28	7269	9	115926	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	0	0	0	0	0	0	0	.	0	0
	total	14151	7	2609	21	5194	17	60073	4	6294	12	15775	8	469	40	5575	13	47325	4	12031	8	16138	8	9876	9	1975	20	6528	12	22386	7	4016	14	10346	9	10289	10	7579	11	24616	3	5089	13	31671	5	28620	5	2219	22	16357	7	367203	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. 1301485. Birmensdorf, Swiss Federal Research Institute WSL

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