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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)
site quality · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
site quality	main tree species	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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
low	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1552	22	8872	13	5909	17	1705	32	5435	17	830	34	24303	8
	fir	2185	21	894	39	324	51	67	85	0	.	0	.	3469	17
	pine	637	26	269	68	109	58	28	74	404	31	22	100	1470	19
	larch	1	100	0	0	99	100	1715	24	2985	18	1029	21	5828	12
	Arolla pine	0	.	0	.	0	.	804	27	1103	22	0	100	1908	17
	other conifers	64	53	19	68	15	61	0	.	29	41	0	.	127	31
	beech	5435	14	2018	26	918	40	203	73	0	.	0	.	8575	12
	maple	364	24	570	25	168	34	119	47	0	.	0	.	1220	15
	ash	219	32	130	67	14	68	93	87	0	.	0	.	455	30
	oak	319	36	81	73	52	96	17	71	0	.	0	.	468	29
	sweet chestnut	0	.	0	.	0	.	60	71	0	.	0	.	60	71
	other broadleaves	225	36	164	31	48	40	165	51	6	59	4	72	611	21
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	11001	11	13017	11	7655	16	4975	17	9962	12	1886	21	48495	6
moderate	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4883	14	17906	10	7544	16	12968	10	19862	9	8775	12	71938	4
	fir	3774	18	4025	18	1696	28	1255	30	498	40	1765	26	13013	10
	pine	21	100	32	55	146	81	1890	19	2313	19	142	54	4544	13
	larch	0	.	243	43	74	97	7755	13	4089	14	5035	13	17197	8
	Arolla pine	0	.	12	100	0	.	243	36	665	30	8	100	929	23
	other conifers	7	100	35	98	7	100	13	58	13	64	0	.	74	50
	beech	2091	17	1671	23	1656	24	348	43	838	26	2304	19	8907	9
	maple	543	21	856	20	726	27	246	38	221	32	177	39	2770	11
	ash	63	51	442	30	405	40	315	37	131	46	83	59	1439	17
	oak	0	.	14	72	0	.	195	42	64	100	194	41	467	28
	sweet chestnut	0	.	0	.	0	.	11	100	5	100	698	34	714	34
	other broadleaves	79	33	254	23	156	37	924	21	720	26	955	27	3088	12
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	11460	11	25491	9	12410	14	26162	8	29418	7	20138	7	125080	3
good	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13578	9	13651	9	15034	11	686	49	5396	18	2991	40	51335	5
	fir	11695	10	9951	11	5811	14	320	62	899	31	303	61	28978	6
	pine	1749	21	500	54	166	52	0	.	0	.	0	.	2415	19
	larch	393	32	30	74	101	71	0	.	196	71	725	39	1446	24
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	695	43	84	96	-9	225	0	.	0	.	82	98	853	37
	beech	13640	8	6482	14	5003	13	316	56	473	58	4126	15	30039	6
	maple	2695	13	1204	17	1819	16	103	57	211	52	302	33	6334	8
	ash	2631	18	1036	35	1433	22	45	71	142	65	799	33	6087	12
	oak	1861	18	129	49	80	54	20	100	85	100	781	24	2955	13
	sweet chestnut	0	.	6	100	0	.	26	100	18	100	4714	14	4763	14
	other broadleaves	1349	17	726	23	830	22	22	53	382	39	3999	13	7308	9
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	50287	5	33799	7	30267	8	1537	35	7802	15	18821	9	142513	3
very good	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21241	7	2472	24	1411	33	0	.	0	.	0	.	25124	7
	fir	14089	9	2711	21	1880	29	0	.	0	.	0	.	18679	8
	pine	2020	15	0	.	82	71	0	.	0	.	0	.	2102	15
	larch	836	22	189	58	192	48	0	.	0	.	0	.	1217	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	1138	41	0	.	19	57	0	.	0	.	0	.	1157	40
	beech	21166	6	2080	25	2712	22	53	71	0	.	208	78	26220	6
	maple	3505	10	396	30	446	34	8	100	0	.	0	.	4355	9
	ash	5471	11	422	45	657	34	11	89	0	.	30	74	6591	10
	oak	3754	16	12	100	56	84	45	78	0	.	42	80	3909	16
	sweet chestnut	0	.	0	.	0	.	68	100	0	.	0	0	68	100
	other broadleaves	3733	12	427	68	270	33	65	76	0	.	46	72	4542	12
	indeterminable	0	0	0	.	0	0	0	.	0	.	0	.	0	0
	total	76952	4	8710	15	7724	17	249	60	0	.	327	62	93963	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	41253	5	42901	5	29898	7	15359	9	30693	7	12596	12	172699	2
	fir	31742	5	17581	8	9710	11	1642	26	1397	24	2068	24	64140	4
	pine	4427	11	801	41	503	34	1918	18	2717	17	165	49	10531	8
	larch	1230	18	463	33	466	36	9470	12	7270	11	6789	11	25688	6
	Arolla pine	0	.	12	100	0	.	1047	22	1768	18	9	94	2836	13
	other conifers	1904	29	138	64	32	81	13	58	42	35	82	98	2211	25
	beech	42332	4	12251	10	10289	10	920	30	1310	27	6638	11	73741	3
	maple	7106	7	3026	11	3159	12	475	26	432	30	479	25	14678	5
	ash	8385	9	2030	21	2509	16	463	31	274	40	913	30	14573	7
	oak	5934	12	236	37	187	43	276	33	149	71	1017	20	7799	10
	sweet chestnut	0	.	6	100	0	.	165	51	23	82	5412	13	5605	12
	other broadleaves	5386	9	1571	22	1304	16	1175	19	1108	21	5004	11	15548	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	149699	2	81017	4	58057	6	32923	7	47182	5	41172	4	410050	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. 1195160. Birmensdorf, Swiss Federal Research Institute WSL

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