Selection from the list of topics

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
growing stock (stemwood)
tree species (56 classes)
regional demarcation: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
	biogeographical region
	Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
tree species (56 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
Abies alba	16709	6	18527	6	23533	5	1073	25	1986	17	1838	18	63668	3
Larix decidua, Larix kaempferi	171	31	1461	15	847	28	8951	8	6776	9	7085	8	25291	4
Picea abies	21167	5	31211	5	73024	3	12544	7	31605	5	11716	8	181268	2
Pinus cembra	0	.	0	.	125	51	1102	15	1589	14	45	69	2861	10
Pinus mugo subsp. uncinata	4	100	0	.	405	30	109	36	506	19	53	71	1077	15
Pinus mugo subsp. mugo	0	.	0	.	43	55	5	67	64	30	6	71	117	26
Pinus nigra	141	54	50	100	62	100	0	.	0	.	0	.	254	44
Pinus strobus	36	58	92	45	0	.	0	.	0	.	0	.	128	36
Pinus sylvestris	1956	14	2959	10	701	31	1939	16	2402	15	374	44	10332	7
Pseudotsuga menziesii	226	57	1086	27	1	100	0	.	0	.	27	100	1340	24
Taxus baccata	73	55	93	35	37	55	0	.	0	0	0	.	203	27
other conifers	14	100	31	76	20	63	0	.	0	.	0	.	65	46
Acer campestre	111	28	49	32	23	59	27	67	4	77	11	48	226	19
Acer opalus	108	32	4	100	93	30	18	62	0	.	0	.	223	20
Acer platanoides	245	28	179	37	230	26	35	75	21	89	26	60	735	16
Acer pseudoplatanus	2623	8	3298	8	4701	7	103	31	407	31	404	20	11537	4
Aesculus hippocastanum	5	100	-1	100	0	.	0	.	0	.	1	100	5	97
Alnus glutinosa	73	63	708	24	3	100	0	.	0	.	229	35	1013	19
Alnus incana	29	62	75	33	446	19	181	34	337	28	252	31	1321	12
Alnus viridis	0	.	0	.	6	90	0	.	0	.	0	.	6	90
Betula pendula	18	62	195	37	184	30	248	23	238	28	1910	11	2794	9
Betula pubescens	0	.	0	.	30	69	0	.	1	100	0	.	31	66
Carpinus betulus	200	24	564	18	2	74	0	.	0	.	25	74	792	14
Castanea sativa	1	100	76	93	85	69	36	63	0	.	5363	10	5559	9
Fagus sylvatica	23045	4	25676	4	19286	5	508	30	1491	18	6323	9	76329	2
Fraxinus excelsior	2532	11	7872	7	3807	9	269	28	275	25	1144	17	15899	5
Fraxinus ornus	0	.	0	.	0	.	0	.	0	.	29	59	29	59
Ilex aquifolium	7	56	5	72	9	40	0	.	0	.	32	41	54	28
Juglans regia	-1	474	57	34	17	60	1	100	16	66	10	60	101	25
Laburnum anagyroides	5	75	0	.	3	100	2	72	0	.	3	59	12	41
Malus sylvestris	3	100	0	.	2	71	0	.	0	.	0	.	5	68
Ostrya carpinifolia	0	.	0	.	0	.	0	.	0	.	477	26	477	26
Populus alba, Populus x canescens	60	100	54	100	153	100	49	71	0	.	0	.	316	56
Populus nigra s.l.	0	.	559	51	0	.	17	75	1	100	103	62	680	43
Populus (others)	126	100	226	62	0	.	0	.	3	100	0	0	356	53
Populus tremula	80	76	59	42	28	55	128	32	93	45	239	34	629	19
Prunus avium	134	25	418	16	162	22	70	43	9	63	165	24	959	10
Prunus padus	0	.	52	42	4	100	0	.	2	100	0	.	59	38
Pyrus communis, Pyrus pyraster	0	.	1	100	0	.	-22	100	0	.	0	.	-21	105
Quercus cerris	0	.	0	.	0	.	0	.	0	.	47	56	47	56
Quercus petraea	1565	16	1994	15	178	31	48	36	146	53	684	20	4615	9
Quercus pubescens	28	71	16	87	0	.	126	35	0	.	151	27	321	20
Quercus robur	200	36	2790	13	285	31	0	0	0	.	118	42	3392	11
Quercus rubra	0	.	149	61	0	.	0	.	0	.	0	.	149	61
Robinia pseudoacacia	8	100	291	79	9	100	12	72	0	.	204	39	523	47
Salix (others)	27	55	124	38	129	42	95	31	58	38	173	28	606	16
Sorbus aria	314	16	15	62	189	19	49	25	36	40	85	19	689	10
Sorbus aucuparia	52	24	8	51	169	21	44	30	42	29	27	37	342	13
Sorbus torminalis	26	54	9	93	0	.	0	.	0	.	0	.	35	47
Tilia cordata	56	36	377	30	538	23	1	100	23	78	716	19	1711	13
Tilia platyphyllos	249	31	171	39	385	38	129	43	14	72	43	46	990	19
Ulmus glabra	130	30	182	25	317	18	0	.	39	65	60	49	728	12
Ulmus minor	2	100	9	73	0	.	0	.	0	.	0	.	11	62
other broadleaves	26	100	0	.	13	100	0	.	0	.	15	86	53	59
other shrubs	5	52	25	31	24	39	3	50	8	50	47	24	112	15
indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
total	72589	2	101798	2	130306	2	27900	5	48196	4	40265	3	421054	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. 1217666. Birmensdorf, Swiss Federal Research Institute WSL

back