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.

NFI5
growing stock (stemwood)
tree species (56 classes)
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
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	31349	5	17464	8	9330	12	1621	26	1397	24	2006	24	63168	4
Larix decidua, Larix kaempferi	1230	18	440	34	365	37	8549	12	6131	12	5398	12	22114	6
Picea abies	40757	5	38892	6	27528	8	14470	10	27821	7	11606	13	161073	3
Pinus cembra	0	.	12	100	0	.	874	24	1499	20	8	100	2393	15
Pinus mugo subsp. uncinata	0	.	275	67	85	70	162	43	434	26	66	100	1023	24
Pinus mugo subsp. mugo	0	.	1	100	7	91	13	60	35	40	0	.	56	31
Pinus nigra	157	60	0	.	0	.	0	.	0	.	0	.	157	60
Pinus strobus	208	48	37	100	0	.	0	.	0	.	0	.	246	44
Pinus sylvestris	4062	12	463	58	308	47	1728	20	2175	20	72	54	8807	9
Pseudotsuga menziesii	1674	33	81	100	0	.	0	.	0	.	81	100	1836	30
Taxus baccata	201	27	20	66	19	126	1	100	0	.	1	100	243	25
other conifers	28	100	13	100	0	.	0	.	0	.	0	.	41	75
Acer campestre	139	25	33	60	9	100	6	78	7	76	7	84	201	21
Acer opalus	103	42	91	41	0	.	11	72	0	.	0	.	206	28
Acer platanoides	434	28	106	45	214	37	82	91	51	100	15	100	903	20
Acer pseudoplatanus	6289	7	2631	11	2657	13	277	32	284	29	313	32	12451	5
Aesculus hippocastanum	11	100	0	.	0	.	0	.	0	.	0	.	11	100
Alnus glutinosa	922	24	0	.	2	100	0	.	0	.	302	42	1226	21
Alnus incana	77	40	138	39	154	46	145	49	405	43	102	54	1022	21
Alnus viridis	0	.	0	.	0	.	0	.	0	.	0	.	0	.
Betula pendula	194	41	50	76	73	50	177	29	135	40	1416	19	2046	14
Betula pubescens	0	.	8	100	2	100	47	100	5	100	5	100	66	72
Carpinus betulus	770	20	0	.	16	100	7	100	0	.	8	100	801	19
Castanea sativa	0	.	6	100	0	.	97	52	23	82	5330	13	5456	12
Fagus sylvatica	41671	4	11778	9	9901	10	886	31	1290	27	6395	12	71921	3
Fraxinus excelsior	8225	9	1898	22	2444	17	342	35	219	47	789	34	13916	7
Fraxinus ornus	0	.	0	.	0	.	0	.	0	.	43	70	43	70
Ilex aquifolium	10	62	8	80	5	60	2	100	0	.	60	50	84	38
Juglans regia	26	46	4	71	0	.	1	100	37	52	8	100	77	32
Laburnum anagyroides	8	81	0	0	0	.	2	100	0	.	2	100	12	57
Malus sylvestris	0	.	0	.	0	.	0	.	0	.	0	.	0	.
Ostrya carpinifolia	0	.	0	.	0	.	0	.	0	.	540	38	540	38
Populus alba, Populus x canescens	151	100	283	100	0	.	0	0	0	.	0	.	435	74
Populus nigra s.l.	213	56	0	.	0	.	0	.	0	.	0	.	213	56
Populus (others)	185	61	0	.	0	.	0	.	0	.	0	.	185	61
Populus tremula	97	48	38	49	11	100	69	42	4	100	271	72	491	41
Prunus avium	494	19	45	39	82	35	51	39	22	62	249	30	943	13
Prunus padus	51	49	0	.	0	.	0	.	1	100	0	.	52	47
Pyrus communis, Pyrus pyraster	2	100	0	.	0	.	0	.	0	.	0	.	2	100
Quercus cerris	0	.	0	.	0	.	0	.	0	.	97	66	97	66
Quercus petraea	2739	15	113	57	99	63	37	58	149	71	612	28	3748	13
Quercus pubescens	58	67	0	.	0	.	162	49	0	.	107	45	327	31
Quercus robur	2655	20	107	54	74	67	33	100	0	.	122	51	2992	18
Quercus rubra	301	59	0	.	0	.	0	.	0	.	0	.	301	59
Robinia pseudoacacia	38	86	0	.	0	.	14	97	0	.	426	50	478	45
Salix (others)	178	26	73	35	25	83	83	47	96	48	73	39	528	17
Sorbus aria	230	19	57	34	60	59	48	32	15	67	97	25	507	13
Sorbus aucuparia	64	31	136	30	28	44	46	38	23	38	14	58	311	17
Sorbus torminalis	38	60	0	.	0	.	0	.	0	.	0	.	38	60
Tilia cordata	367	23	257	52	252	45	6	78	27	79	575	30	1483	18
Tilia platyphyllos	300	42	22	93	185	40	201	47	19	100	9	87	736	24
Ulmus glabra	379	24	271	25	154	29	0	.	4	71	39	64	846	15
Ulmus minor	27	100	0	0	0	.	0	.	0	.	0	.	27	100
other broadleaves	57	97	0	0	0	.	0	.	0	.	25	100	82	74
other shrubs	23	33	7	83	2	100	8	61	13	59	76	34	129	22
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. 1217677. Birmensdorf, Swiss Federal Research Institute WSL

back