Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

top

2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

top

3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

top

4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

top

5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

top

6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

top

7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

top

8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

top

9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

top

NFI3
number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21826	6	14680	6	8716	9	7738	9	15069	8	4087	11	72116	3
	fir	10508	6	5350	8	2603	12	848	22	327	24	710	23	20347	4
	pine	696	28	625	36	202	38	2304	18	2680	17	38	79	6547	11
	larch	305	29	206	42	185	45	3538	12	1768	16	2652	13	8655	7
	Arolla pine	0	.	11	100	22	100	609	22	911	19	10	100	1562	14
	other conifers	1096	22	172	53	235	33	67	49	709	31	10	100	2289	15
	beech	17398	4	6013	10	4572	10	1148	24	752	20	6799	10	36683	3
	maple	5902	8	2951	13	1945	15	824	19	368	30	922	20	12911	6
	ash	4543	10	1257	15	1407	18	531	24	488	29	1770	16	9996	6
	oak	1896	18	233	45	22	71	738	24	42	50	1196	24	4126	12
	sweet chestnut	79	71	0	.	0	.	84	71	0	.	5742	13	5905	13
	other broadleaves	7366	7	3506	11	3076	16	4218	13	3653	17	10936	8	32755	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	71616	3	35004	4	22986	6	22646	6	26768	6	34872	4	213891	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12751	6	10560	6	6883	9	5026	9	8432	7	2752	11	46404	3
	fir	5570	7	3087	9	1548	12	428	24	197	30	365	26	11195	5
	pine	1015	17	237	54	335	30	1724	15	1170	18	96	47	4577	9
	larch	347	28	73	48	91	47	2303	13	1153	14	1869	12	5834	7
	Arolla pine	0	.	63	86	42	71	694	19	919	19	20	71	1738	13
	other conifers	518	22	72	62	40	50	23	71	81	43	0	.	733	18
	beech	12858	5	4153	10	3141	10	661	23	538	25	3430	11	24781	4
	maple	3398	8	1806	11	1523	13	238	32	147	35	335	26	7447	5
	ash	2837	9	993	18	1537	19	318	33	263	38	733	24	6680	7
	oak	1892	14	296	33	31	75	180	34	19	71	765	21	3184	10
	sweet chestnut	19	100	0	.	0	.	32	73	9	100	3206	12	3266	12
	other broadleaves	2707	9	1049	16	936	17	1068	15	674	26	4276	9	10710	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	43912	3	22388	4	16105	6	12696	6	13602	5	17846	4	126550	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7346	5	7541	6	4402	8	3203	9	5437	6	1778	12	29706	3
	fir	3972	7	1883	9	1066	11	199	28	143	30	247	31	7511	5
	pine	1338	12	196	29	203	33	726	16	800	18	190	56	3453	8
	larch	214	23	21	67	121	40	1498	12	1034	13	1132	13	4020	7
	Arolla pine	0	.	43	80	0	.	335	20	442	21	0	.	820	14
	other conifers	141	37	10	100	0	.	0	.	0	.	0	.	151	36
	beech	9139	4	2336	12	2029	10	175	30	239	24	1273	12	15191	4
	maple	1548	9	563	15	546	15	76	38	65	46	151	31	2949	6
	ash	1977	10	444	26	536	18	133	49	63	56	205	24	3358	8
	oak	1218	13	90	44	47	44	37	46	26	100	331	23	1749	10
	sweet chestnut	22	71	0	.	0	.	42	61	4	100	1155	13	1223	12
	other broadleaves	1138	12	284	26	187	24	241	25	159	38	1212	12	3223	7
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	.	0	0
	total	28052	2	13412	5	9138	5	6665	6	8413	5	7675	5	73354	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5522	5	5228	5	3014	7	1649	8	3428	7	1125	11	19965	2
	fir	3062	5	1476	8	850	11	168	25	97	23	150	24	5803	4
	pine	1037	10	135	41	87	29	225	24	411	17	80	40	1975	8
	larch	190	24	48	41	53	46	1182	10	848	10	686	12	3007	6
	Arolla pine	0	.	4	100	4	100	150	24	246	18	0	.	405	14
	other conifers	81	36	0	.	0	.	0	.	0	.	4	100	85	35
	beech	5415	4	1229	10	1322	10	55	36	140	30	597	12	8758	3
	maple	615	11	172	18	256	16	12	58	17	61	17	80	1089	8
	ash	1014	9	148	22	300	26	16	61	16	59	42	42	1537	8
	oak	699	11	46	43	22	53	16	78	35	60	120	33	938	10
	sweet chestnut	12	100	4	100	0	.	4	100	0	.	455	16	474	15
	other broadleaves	413	15	65	33	66	33	35	40	24	47	252	15	856	10
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	.	0	0
	total	18059	2	8555	4	5974	5	3513	6	5262	5	3529	5	44892	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3478	5	3179	6	2064	8	833	10	1808	7	682	11	12045	3
	fir	2090	7	1158	8	510	12	78	32	120	24	105	26	4062	5
	pine	445	13	25	54	46	46	102	30	171	20	16	61	806	10
	larch	118	26	55	36	44	35	808	11	603	11	480	11	2109	6
	Arolla pine	0	.	0	0	0	.	33	36	99	24	4	100	136	20
	other conifers	53	37	0	.	0	.	0	.	0	.	4	100	57	35
	beech	2494	5	675	14	547	12	38	53	66	29	240	16	4060	4
	maple	143	20	46	38	75	26	0	.	11	74	4	100	281	14
	ash	401	13	43	34	77	29	4	100	10	73	16	60	551	11
	oak	433	13	18	50	8	71	4	100	16	50	47	40	526	12
	sweet chestnut	4	100	0	.	0	.	10	100	4	100	267	17	285	17
	other broadleaves	118	25	90	50	61	44	49	38	10	72	53	35	381	17
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	.	0	0
	total	9777	3	5290	5	3435	6	1960	7	2920	6	1919	6	25300	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1278	8	1108	8	783	10	358	19	716	10	285	15	4529	4
	fir	795	9	451	12	250	17	51	36	31	35	39	34	1617	6
	pine	105	23	14	59	8	71	8	71	58	31	4	100	198	16
	larch	35	36	20	60	18	51	368	14	157	18	151	18	748	9
	Arolla pine	0	.	8	71	4	100	23	41	55	32	0	.	90	23
	other conifers	45	46	0	.	0	.	0	.	0	.	0	.	45	46
	beech	672	10	229	19	151	21	4	100	8	71	97	22	1160	8
	maple	27	41	25	47	0	.	0	.	0	.	0	.	51	31
	ash	132	22	4	100	18	51	0	.	4	100	6	100	164	19
	oak	179	22	6	100	5	100	6	100	8	71	15	72	218	20
	sweet chestnut	0	.	0	.	0	.	9	71	0	.	89	25	98	24
	other broadleaves	35	36	11	58	0	.	0	.	0	.	12	58	59	27
	indeterminable	0	.	0	0	0	.	0	.	0	.	0	.	0	0
	total	3302	5	1876	6	1237	8	827	11	1037	8	698	9	8976	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	399	12	463	11	388	14	154	22	296	14	171	20	1871	6
	fir	307	13	182	16	97	23	8	71	27	38	25	40	646	9
	pine	8	71	0	.	0	.	0	.	0	.	0	.	8	71
	larch	8	71	4	100	9	71	171	20	73	26	67	25	333	13
	Arolla pine	0	.	0	.	0	.	27	43	24	41	0	.	50	30
	other conifers	13	58	4	100	0	.	0	.	0	.	0	.	17	50
	beech	238	16	28	42	29	43	4	100	11	74	32	35	343	13
	maple	6	100	8	71	5	100	0	.	0	.	0	.	19	51
	ash	35	39	4	100	8	71	0	.	0	.	0	.	48	32
	oak	71	29	7	100	24	45	0	.	0	.	0	.	102	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	84	27	84	27
	other broadleaves	19	51	0	.	0	.	4	100	0	.	8	71	31	38
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1102	7	701	9	562	11	368	14	431	11	388	12	3551	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	122	33	126	19	106	31	54	36	71	26	73	26	552	12
	fir	93	22	41	32	21	46	4	100	12	58	15	58	187	16
	pine	4	100	0	.	0	.	0	.	4	100	0	.	8	71
	larch	0	.	0	.	0	.	78	25	35	40	38	41	151	19
	Arolla pine	0	.	0	.	0	.	8	71	12	58	0	.	20	45
	other conifers	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	beech	52	37	24	47	14	58	4	100	0	.	4	100	97	25
	maple	0	.	0	.	4	100	0	.	0	.	4	100	8	71
	ash	10	72	0	.	0	.	0	.	0	.	0	.	10	72
	oak	26	42	0	.	0	.	0	.	0	.	0	.	26	42
	sweet chestnut	0	.	0	.	0	.	4	100	0	.	57	32	62	30
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	311	17	191	16	145	24	153	19	134	19	190	17	1124	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12	58	45	37	46	33	8	71	32	35	27	41	170	17
	fir	12	58	17	50	0	.	0	.	0	.	13	59	43	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	16	50	8	71	9	72	33	36
	Arolla pine	0	.	0	.	0	.	4	100	4	100	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	22	45	0	.	0	.	0	.	0	.	8	71	31	38
	maple	0	.	0	.	4	100	0	.	0	.	0	.	4	100
	ash	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	oak	16	63	0	.	0	.	0	.	0	.	0	.	16	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	20	59	20	59
	other broadleaves	21	64	0	.	0	.	4	100	0	.	4	100	29	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	88	25	62	30	49	33	32	35	45	30	82	25	358	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12	58	8	71	20	45	4	100	21	45	17	61	82	24
	fir	7	100	8	71	4	100	0	.	4	100	4	100	27	42
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	4	100	12	58	15	61	32	39
	Arolla pine	0	.	0	.	0	.	8	71	4	100	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	19	100	19	100
	maple	0	.	8	100	4	100	0	.	0	.	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	70	38	70	38
	other broadleaves	5	100	0	.	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	27	42	24	47	28	38	16	50	41	32	125	29	262	17
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	52745	4	42938	5	26422	6	19028	7	35310	5	10997	8	187441	2
	fir	26416	4	13653	6	6951	8	1784	19	960	17	1673	18	51437	3
	pine	4648	9	1233	30	881	26	5090	14	5294	13	424	43	17570	7
	larch	1216	16	426	27	521	29	9966	8	5692	9	7100	9	24922	5
	Arolla pine	0	.	129	80	73	75	1891	16	2717	15	33	66	4843	11
	other conifers	1950	17	258	50	275	30	90	53	790	31	18	71	3381	13
	beech	48289	3	14688	8	11805	8	2089	21	1754	18	12498	9	91123	3
	maple	11638	6	5579	10	4361	11	1149	17	608	25	1434	18	24770	4
	ash	10953	6	2893	14	3883	14	1002	25	844	29	2772	15	22346	5
	oak	6433	10	696	31	158	28	981	23	146	51	2473	18	10889	8
	sweet chestnut	135	70	4	100	0	.	186	58	17	100	11145	10	11487	9
	other broadleaves	11822	6	5006	10	4327	13	5620	12	4520	17	16754	7	48050	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	176246	2	87504	3	59658	4	48875	5	58652	4	67323	3	498258	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. 1326990. Birmensdorf, Swiss Federal Research Institute WSL

back