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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 NFI1-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	1466	22	8055	14	5603	18	1450	37	4761	18	592	45	21928	8
	fir	2185	21	894	39	324	51	67	85	0	.	0	.	3469	17
	pine	637	26	250	73	61	93	28	74	384	32	0	.	1360	21
	larch	1	100	0	0	0	.	1583	26	2737	19	410	35	4731	14
	Arolla pine	0	.	0	.	0	.	665	30	932	24	0	.	1597	19
	other conifers	64	53	19	68	9	77	0	.	24	48	0	.	117	33
	beech	5435	14	2018	26	906	40	203	73	0	.	0	.	8562	12
	maple	364	24	553	26	113	41	101	54	0	.	0	.	1131	16
	ash	219	32	130	67	3	100	93	87	0	.	0	.	444	31
	oak	319	36	81	73	50	100	17	71	0	.	0	.	466	30
	sweet chestnut	0	.	0	.	0	.	60	71	0	.	0	.	60	71
	other broadleaves	225	36	157	32	33	37	148	57	4	71	2	100	569	22
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	10914	12	12157	12	7102	17	4416	19	8843	13	1004	32	44435	6
moderate	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4745	14	15836	10	6369	17	12479	10	17963	9	8122	13	65514	4
	fir	3774	18	3979	19	1637	29	1234	30	498	40	1704	27	12825	10
	pine	21	100	26	65	124	94	1862	19	2225	20	138	56	4396	13
	larch	0	.	220	46	72	99	6966	14	3331	15	4263	14	14853	8
	Arolla pine	0	.	12	100	0	.	209	39	567	34	8	100	797	26
	other conifers	7	100	0	.	7	100	13	58	11	72	0	.	38	39
	beech	2091	17	1659	23	1430	25	343	43	818	27	2213	20	8552	9
	maple	543	21	814	21	620	29	171	49	221	32	128	43	2497	12
	ash	63	51	385	31	369	43	194	42	102	52	83	59	1196	19
	oak	0	.	14	72	0	.	181	45	64	100	169	45	429	30
	sweet chestnut	0	.	0	.	0	.	11	100	5	100	698	34	714	34
	other broadleaves	79	33	233	24	128	40	689	18	549	31	780	33	2458	14
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	11322	12	23178	9	10756	15	24353	8	26354	8	18306	8	114268	3
good	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13412	9	12541	10	14179	11	541	59	5096	18	2892	42	48661	6
	fir	11602	10	9881	11	5516	15	320	62	899	31	303	61	28520	6
	pine	1749	21	500	54	166	52	0	.	0	.	0	.	2415	19
	larch	393	32	30	74	101	71	0	.	63	68	725	39	1313	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	695	43	82	98	-9	225	0	.	0	.	82	98	851	37
	beech	13321	8	6021	14	4885	13	316	56	473	58	3974	16	28990	5
	maple	2672	13	1098	17	1746	16	97	60	121	55	208	42	5941	8
	ash	2563	18	961	37	1415	22	45	71	117	76	719	36	5819	12
	oak	1834	18	113	54	68	61	0	.	85	100	727	25	2827	14
	sweet chestnut	0	.	6	100	0	.	26	100	18	100	4632	14	4682	14
	other broadleaves	1210	17	634	25	660	24	14	61	251	45	3472	14	6242	9
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	49452	5	31867	7	28726	9	1359	38	7124	16	17735	10	136262	3
very good	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21134	7	2472	24	1377	34	0	.	0	.	0	.	24983	7
	fir	13789	9	2711	21	1853	30	0	.	0	.	0	.	18353	8
	pine	2020	15	0	.	41	100	0	.	0	.	0	.	2061	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	20824	6	2080	25	2680	23	24	100	0	.	208	78	25817	6
	maple	3386	10	396	30	402	36	8	100	0	.	0	.	4192	9
	ash	5379	11	422	45	657	34	11	89	0	.	30	74	6500	10
	oak	3600	17	12	100	56	84	33	100	0	.	42	80	3743	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	0	0	0
	other broadleaves	3399	12	372	77	229	34	54	90	0	.	46	72	4101	12
	indeterminable	0	0	0	.	0	0	0	.	0	.	0	.	0	0
	total	75505	4	8655	15	7507	17	129	70	0	.	327	62	92123	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	40757	5	38905	6	27528	8	14470	10	27821	7	11606	13	161087	3
	fir	31349	5	17464	8	9330	12	1621	26	1397	24	2006	24	63168	4
	pine	4427	11	775	42	393	41	1891	19	2609	18	138	56	10232	8
	larch	1230	18	440	34	365	37	8549	12	6131	12	5398	12	22114	6
	Arolla pine	0	.	12	100	0	.	874	24	1499	20	8	100	2393	15
	other conifers	1904	29	102	80	27	95	13	58	35	40	82	98	2163	26
	beech	41671	4	11778	9	9901	10	886	31	1290	27	6395	12	71921	3
	maple	6965	7	2862	11	2880	13	376	31	342	28	336	30	13760	5
	ash	8225	9	1898	22	2444	17	342	35	219	47	832	32	13959	7
	oak	5753	12	220	39	173	46	231	38	149	71	939	21	7465	10
	sweet chestnut	0	.	6	100	0	.	97	52	23	82	5330	13	5456	12
	other broadleaves	4913	9	1396	24	1050	17	906	17	805	26	4300	12	13369	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	147193	2	75857	4	54090	6	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. 1195155. Birmensdorf, Swiss Federal Research Institute WSL

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