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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: production region
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI3/NFI4
grid: 1.4-km grid
change 2004/06–2009/17
	production region
	Jura		Plateau		Pre-Alps		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	±%
n/a	0	.	0	.	0	.	0	.	0	.	0	.
Abies alba	445	6	547	7	656	6	185	11	42	24	1934	4
Larix decidua, Larix kaempferi	4	68	45	21	8	40	304	8	108	16	448	7
Picea abies	570	7	1068	5	1139	5	1532	4	280	10	4606	2
Pinus cembra	0	.	0	.	1	71	47	17	0	53	46	16
Pinus mugo subsp. uncinata	0	100	0	.	5	71	8	47	0	73	13	40
Pinus mugo subsp. mugo	0	.	0	.	0	91	2	45	0	100	3	41
Pinus nigra	0	306	0	0	0	.	2	94	0	.	2	95
Pinus strobus	0	71	3	49	0	100	0	.	0	.	4	46
Pinus sylvestris	26	32	33	19	2	118	75	20	9	47	143	13
Pseudotsuga menziesii	12	50	37	26	0	71	0	.	1	100	53	23
Taxus baccata	1	222	-2	267	1	168	0	70	0	.	0	1224
other conifers	1	100	1	63	0	100	0	77	0	.	3	51
Acer campestre	7	48	1	49	1	59	2	44	1	66	11	34
Acer opalus	2	64	0	82	0	.	1	70	0	.	3	44
Acer platanoides	6	36	10	78	4	46	2	75	2	66	24	37
Acer pseudoplatanus	53	18	100	13	63	20	55	18	18	42	295	8
Aesculus hippocastanum	0	100	1	100	0	.	0	.	0	100	1	72
Alnus glutinosa	4	49	32	35	0	0	0	.	4	105	42	30
Alnus incana	0	164	3	48	11	30	51	27	5	46	67	20
Alnus viridis	0	.	0	.	0	.	0	100	0	.	0	100
Betula pendula	1	47	10	51	2	39	22	22	56	19	86	14
Betula pubescens	0	.	0	.	1	100	0	61	0	0	1	68
Carpinus betulus	11	38	15	32	1	83	-1	119	1	104	28	24
Castanea sativa	0	.	4	88	0	.	1	325	111	21	106	20
Fagus sylvatica	515	6	693	5	365	8	176	13	118	20	1932	3
Fraxinus excelsior	93	12	195	11	75	17	45	28	43	24	464	7
Fraxinus ornus	0	.	0	.	0	.	0	.	4	80	4	80
Ilex aquifolium	0	55	0	.	0	63	0	70	1	34	2	25
Juglans regia	0	355	2	39	0	126	2	56	2	84	6	35
Laburnum anagyroides	0	83	0	.	0	.	0	53	0	71	0	40
Malus sylvestris	0	100	0	.	0	100	0	100	0	0	0	78
Ostrya carpinifolia	0	.	0	.	0	.	0	.	15	32	14	32
Populus alba, Populus x canescens	2	100	3	100	0	.	2	71	0	.	7	56
Populus nigra s.l.	1	100	10	52	3	100	1	87	5	60	20	35
Populus (others)	0	.	8	71	0	.	0	100	0	0	8	70
Populus tremula	4	93	5	43	1	65	10	31	10	38	29	22
Prunus avium	4	34	19	23	0	309	3	34	5	53	31	18
Prunus padus	0	.	3	41	0	100	0	76	0	.	4	38
Pyrus communis, Pyrus pyraster	0	0	0	100	0	.	0	100	0	0	0	100
Quercus cerris	0	.	0	.	0	.	0	.	1	65	1	65
Quercus petraea	33	23	33	23	1	58	5	49	19	28	93	14
Quercus pubescens	1	100	0	72	0	.	2	71	5	83	7	55
Quercus robur	7	37	55	18	3	100	2	90	1	121	72	16
Quercus rubra	2	100	3	74	0	.	0	.	0	.	6	60
Robinia pseudoacacia	4	65	2	70	0	.	1	68	9	44	16	31
Salix (others)	0	683	4	37	2	66	13	25	11	33	28	19
Sorbus aria	12	38	0	139	2	28	6	26	2	44	23	22
Sorbus aucuparia	2	35	1	78	2	60	7	26	1	34	13	19
Sorbus torminalis	0	54	-1	112	0	.	0	.	0	.	-1	171
Tilia cordata	6	70	15	30	2	53	14	33	20	42	56	20
Tilia platyphyllos	8	34	5	48	1	66	4	62	0	425	18	25
Ulmus glabra	6	31	11	34	1	225	7	41	3	73	29	22
Ulmus minor	1	91	0	74	0	100	0	.	0	0	1	84
other broadleaves	1	73	0	0	0	.	1	93	1	71	2	46
other shrubs	1	46	1	35	1	41	1	44	3	25	7	16
indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
total	1845	3	2974	3	2354	3	2590	3	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. 1281929. Birmensdorf, Swiss Federal Research Institute WSL

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