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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.

NFI3–NFI4
increment (gross increment)
tree species (56 classes)
regional demarcation: economic region
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI3/NFI4
grid: 1.4-km grid
change 2004/06–2009/17
	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
tree species (56 classes)	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%
n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
Abies alba	405	7	35	25	80	18	273	10	194	11	126	17	349	9	178	12	33	29	37	23	23	29	46	26	44	24	42	24	1934	4
Larix decidua, Larix kaempferi	2	71	2	120	17	32	13	44	14	33	2	65	3	78	2	40	4	69	0	100	9	53	190	11	109	15	108	16	448	7
Picea abies	489	7	77	20	195	13	355	10	520	8	312	11	456	7	389	9	277	11	131	16	135	16	325	9	650	7	280	10	4606	2
Pinus cembra	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	100	1	280	1	100	0	.	17	28	29	21	0	53	46	16
Pinus mugo subsp. uncinata	0	.	0	100	0	.	0	.	0	.	0	0	5	72	0	101	1	48	0	345	0	.	1	52	6	52	0	73	13	40
Pinus mugo subsp. mugo	0	.	0	.	0	.	0	.	0	.	0	0	0	100	0	110	1	90	0	100	0	100	0	134	1	36	0	100	3	41
Pinus nigra	-1	82	1	74	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	100	0	.	2	95
Pinus strobus	0	71	0	.	0	100	2	59	1	95	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	46
Pinus sylvestris	21	32	5	101	6	36	7	37	20	27	0	.	2	80	-1	161	2	86	4	77	-1	363	49	23	22	38	9	47	143	13
Pseudotsuga menziesii	3	95	9	59	14	46	11	38	11	49	0	.	0	71	0	.	0	.	0	.	0	.	0	.	0	.	1	100	53	23
Taxus baccata	0	434	1	46	0	.	1	67	-3	189	0	.	1	56	0	425	0	100	0	100	0	71	0	100	0	.	0	.	0	1224
other conifers	0	0	1	100	0	.	0	100	1	79	0	.	0	.	0	100	0	77	0	.	0	.	0	.	0	0	0	.	3	51
Acer campestre	6	55	1	44	1	52	0	503	0	85	0	.	0	73	0	100	0	77	0	100	0	100	1	55	0	71	1	66	11	34
Acer opalus	2	64	0	.	0	.	0	82	0	.	0	.	0	.	0	.	1	120	0	.	0	.	1	46	0	.	0	.	3	44
Acer platanoides	2	75	4	37	4	72	0	76	5	138	0	100	0	86	4	51	0	270	1	92	0	55	1	90	0	111	2	66	24	37
Acer pseudoplatanus	37	21	17	34	14	36	27	26	59	18	21	26	26	25	17	53	18	33	9	44	12	35	8	42	7	65	18	42	295	8
Aesculus hippocastanum	0	100	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	1	72
Alnus glutinosa	4	53	0	78	8	47	12	78	13	43	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	4	105	42	30
Alnus incana	0	245	0	100	2	85	0	57	2	62	2	57	5	39	4	59	4	65	3	87	7	68	8	58	29	39	5	46	67	20
Alnus viridis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	0	0	.	0	.	0	.	0	100
Betula pendula	0	83	1	53	0	59	1	83	9	58	0	100	1	62	1	52	1	132	0	72	1	53	12	27	8	39	56	19	86	14
Betula pubescens	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	100	0	100	0	.	0	100	0	100	0	0	1	68
Carpinus betulus	7	51	4	52	1	47	2	93	11	38	0	.	0	.	1	83	0	0	0	.	0	.	-1	119	0	.	1	104	28	24
Castanea sativa	0	.	0	.	4	94	0	100	0	100	0	.	0	.	0	.	0	100	0	.	0	.	1	346	0	100	111	21	106	20
Fagus sylvatica	380	7	137	13	154	15	236	10	302	8	58	22	162	12	142	14	42	40	27	30	50	18	36	28	20	38	118	20	1932	3
Fraxinus excelsior	55	15	39	22	54	24	43	23	97	15	13	42	28	23	34	28	13	59	7	58	6	67	13	60	6	63	43	24	464	7
Fraxinus ornus	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	80	4	80
Ilex aquifolium	0	100	0	62	0	.	0	.	0	.	0	.	0	72	0	78	0	100	0	71	0	.	0	100	0	.	1	34	2	25
Juglans regia	0	100	0	722	0	100	2	47	1	94	0	.	0	126	0	.	0	.	0	100	0	.	0	71	1	79	2	84	6	35
Laburnum anagyroides	0	83	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	53	0	.	0	71	0	40
Malus sylvestris	0	100	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	100	0	.	0	.	0	.	0	.	0	0	0	78
Ostrya carpinifolia	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	32	14	32
Populus alba, Populus x canescens	0	.	2	100	3	100	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	1	87	0	.	0	.	7	56
Populus nigra s.l.	0	0	1	100	9	59	0	100	1	100	0	.	0	.	2	100	0	.	0	.	0	.	1	100	0	100	5	60	20	35
Populus (others)	0	.	0	.	8	70	0	448	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	0	8	70
Populus tremula	4	93	0	.	3	56	0	.	2	66	1	102	1	83	0	.	0	100	0	.	0	100	6	40	4	49	10	38	29	22
Prunus avium	2	42	2	54	2	61	8	35	9	37	0	180	0	77	-1	142	0	76	0	837	1	51	2	40	0	550	5	53	31	18
Prunus padus	0	.	0	.	1	90	0	0	2	45	0	.	0	.	0	100	0	.	0	.	0	.	0	100	0	100	0	.	4	38
Pyrus communis, Pyrus pyraster	0	0	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	0	0	100
Quercus cerris	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	65	1	65
Quercus petraea	19	31	15	34	14	39	9	46	9	31	0	.	1	78	0	51	2	79	0	373	1	95	2	37	0	389	19	28	93	14
Quercus pubescens	1	100	0	.	0	.	0	100	0	76	0	.	0	.	0	.	0	.	0	.	0	.	2	71	0	.	5	83	7	55
Quercus robur	4	54	3	46	10	48	13	28	31	26	2	56	2	66	-1	244	2	98	0	71	0	.	0	621	0	.	1	121	72	16
Quercus rubra	0	0	3	100	0	.	0	.	3	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	60
Robinia pseudoacacia	1	63	3	82	0	100	0	.	2	73	0	.	0	.	0	.	1	100	0	.	0	.	0	82	0	.	9	44	16	31
Salix (others)	1	62	-2	100	1	41	1	41	1	108	1	203	1	48	0	67	2	77	2	85	0	78	7	38	3	33	11	33	28	19
Sorbus aria	10	41	1	66	0	340	0	67	0	239	1	41	1	39	0	57	1	42	0	104	0	57	4	36	1	56	2	44	23	22
Sorbus aucuparia	2	36	0	100	0	396	0	100	1	84	2	46	0	323	0	202	2	43	0	156	0	322	3	37	2	40	1	34	13	19
Sorbus torminalis	0	99	0	65	0	.	0	100	-1	108	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-1	171
Tilia cordata	1	46	5	84	0	110	7	48	8	38	0	100	2	55	1	128	1	82	4	61	0	75	5	57	2	79	20	42	56	20
Tilia platyphyllos	5	39	3	64	3	75	1	78	1	48	0	100	1	66	0	371	-1	197	0	94	1	74	4	64	0	100	0	425	18	25
Ulmus glabra	2	33	5	44	4	50	0	535	6	35	0	68	3	68	-2	106	3	76	1	36	3	57	0	0	0	262	3	73	29	22
Ulmus minor	1	91	0	.	0	100	0	.	0	100	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	1	84
other broadleaves	1	73	0	0	0	.	0	.	0	0	0	.	0	.	0	.	1	93	0	.	0	.	0	.	0	.	1	71	2	46
other shrubs	0	56	0	68	0	52	0	62	1	58	0	100	0	58	0	69	0	76	0	.	0	100	0	59	0	87	3	25	7	16
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
total	1468	4	375	9	612	8	1027	6	1335	5	543	9	1052	5	773	7	415	10	228	13	249	12	747	6	943	6	913	5	10810	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. 1281925. Birmensdorf, Swiss Federal Research Institute WSL

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