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 (“±”).

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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).

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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).

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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.

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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.

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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

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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.

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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.

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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.

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NFI1—NFI5
change: growing stock (stemwood)
protection forest (2022) · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
change 1983/85–2018/26
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
protection forest (2022)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	fir	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	larch	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	ash	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
inside	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	-27	667	-330	78	-2232	52	73	68	1244	58	.	.	-673	65	1533	86	-102	221	-33	938	-6	1843	397	92	375	96	-1040	79	-118	135	-198	71	-246	186	-46	307	2597	44	440	103	474	76	3037	26	-148	107	254	139	5225	52
	fir	.	.	5	924	433	60	616	92	-10	100	460	74	.	.	86	97	318	58	94	251	160	124	276	43	63	106	276	67	454	84	.	.	52	84	404	94	3	2689	380	57	193	102	951	38	593	44	-93	107	-49	144	5664	20
	pine	.	.	.	.	.	.	-35	333	-10	100	-28	100	.	.	.	.	-39	344	-27	171	18	100	45	74	.	.	29	100	-25	100	0	0	-93	67	.	.	-2	928	-109	72	37	233	-5	100	186	96	.	.	-60	71	-117	256
	larch	.	.	.	.	.	.	-133	67	.	.	.	.	.	.	.	.	1048	25	.	.	.	.	.	.	.	.	.	.	-14	642	.	.	.	.	18	100	14	100	1338	23	-63	100	-7	917	1887	29	-12	100	.	.	4076	17
	Arolla pine	.	.	.	.	.	.	5	100	.	.	.	.	.	.	.	.	146	49	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	238	47	.	.	.	.	389	34
	other conifers	.	.	.	.	6	100	40	95	.	.	.	.	.	.	2	100	8	71	10	73	.	.	.	.	.	.	8	100	-1	2572	.	.	.	.	3	100	2	100	82	98	3	175	6	100	-5	211	.	.	31	51	195	49
	beech	.	.	24	99	-169	77	1408	33	45	414	473	34	.	.	10	2232	319	56	160	107	191	86	31	149	-28	289	-267	117	879	39	34	170	24	73	597	36	193	52	2894	15	215	49	648	31	394	37	108	99	-327	60	7857	13
	maple	.	.	-6	276	-6	626	413	31	-3	1675	220	34	.	.	175	56	190	33	-25	280	119	45	71	41	23	108	60	189	476	39	.	.	78	73	116	51	33	149	215	40	25	120	212	43	228	36	33	88	-22	360	2625	14
	ash	.	.	.	.	61	91	55	217	-33	106	225	77	.	.	119	79	149	44	44	131	9	538	-39	103	14	155	70	90	25	611	.	.	20	169	228	52	26	163	498	45	67	52	191	93	197	40	18	151	-78	101	1866	24
	oak	.	.	.	.	.	.	52	89	-29	100	24	107	.	.	.	.	50	102	45	146	-3	100	.	.	.	.	.	.	-34	88	.	.	.	.	8	100	-16	157	283	34	14	97	84	59	93	61	.	.	21	114	593	28
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	150	63	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1369	29	.	.	.	.	14	77	.	.	.	.	1533	26
	other broadleaves	.	.	-2	100	45	92	150	54	-4	689	-68	91	.	.	34	106	745	37	70	72	53	79	-3	342	22	61	-24	98	-2	3965	.	.	65	96	-43	133	2	235	1265	23	-29	217	-33	129	310	31	-3	371	11	172	2562	18
	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	0	0
	total	.	.	-6	3572	40	1266	339	416	29	742	2551	36	.	.	-247	226	4618	31	269	178	513	94	375	41	490	81	528	109	717	147	-84	235	-53	430	1085	68	208	113	10813	14	902	64	2521	28	7171	16	-97	251	-218	231	32467	11
outside	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-3013	32	594	59	-613	98	-5400	28	-447	70	-2795	37	3	121	302	87	3483	25	-328	123	-2614	37	-97	464	265	55	-464	94	318	221	-165	214	-1110	54	-156	246	-862	74	97	82	483	41	-1187	86	-6	5738	220	66	-2262	41	-15754	20
	fir	-414	89	95	50	85	204	761	118	294	38	-135	289	4	100	18	240	283	61	1162	30	1108	49	1313	40	18	140	-107	265	436	45	129	67	-271	123	83	198	654	49	74	100	8	193	1510	37	92	54	107	60	-459	85	6849	24
	pine	-748	37	.	.	-8	100	-284	68	-5	1004	-297	61	22	201	.	.	-45	443	-23	271	57	63	-34	69	4	100	110	100	11	100	-73	138	-302	38	22	114	-110	77	17	100	.	.	-270	80	-64	177	.	.	-654	34	-2675	22
	larch	-29	332	.	.	.	.	-87	95	-65	97	-66	184	.	.	13	100	630	45	5	100	21	72	8	100	.	.	18	100	9	180	-8	288	20	100	.	.	34	82	400	35	.	.	321	42	978	34	.	.	-46	180	2156	24
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	141	128	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	8	100	.	.	.	.	73	76	.	.	.	.	222	85
	other conifers	166	55	.	.	5	100	274	79	222	100	-35	100	.	.	.	.	24	46	.	.	232	65	5	100	.	.	.	.	200	100	63	100	46	57	.	.	1	257	.	.	.	.	-56	288	4	62	.	.	4	421	1156	39
	beech	11	4781	67	64	78	121	-815	83	-340	116	349	101	.	.	-2	2701	-98	218	-431	89	384	80	144	128	108	94	166	63	568	40	673	36	136	281	45	172	287	81	159	56	294	58	902	42	-26	212	52	78	562	74	3273	43
	maple	-46	298	12	100	74	83	595	27	137	56	199	34	.	.	-1	1484	79	48	121	45	202	34	291	32	9	446	2	173	78	112	70	67	236	40	38	79	153	51	-6	235	72	64	351	27	54	79	.	.	604	29	3322	11
	ash	-157	116	.	.	-20	244	219	85	82	90	500	42	3	100	-59	103	-17	158	112	104	243	85	-33	299	263	77	1	848	459	45	53	86	217	40	36	75	252	52	113	71	96	81	124	127	9	285	-54	100	261	71	2705	23
	oak	138	154	.	.	.	.	76	133	-4	1019	25	225	3	5322	.	.	30	100	-31	100	84	116	-28	156	3	100	.	.	-6	280	-93	87	74	52	.	.	299	97	12	249	32	100	208	97	69	51	.	.	-144	101	748	68
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	79	94	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-49	316	.	.	6	100	.	.	.	.	.	.	36	483
	other broadleaves	18	736	-1	100	12	109	203	58	-37	108	248	49	5	150	18	83	91	186	10	686	82	92	-114	61	27	81	7	76	-76	119	2	1190	6	1676	42	80	1	10563	299	63	10	65	386	86	68	130	-9	100	197	70	1497	36
	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	.	.	0	0	0	0
	total	-4072	31	766	51	-387	174	-4457	46	-162	349	-2006	62	40	427	288	104	4680	22	596	113	-200	591	1454	52	696	45	-267	223	1997	47	653	75	-949	88	110	396	708	113	1124	26	995	39	2294	60	1252	45	316	62	-1937	64	3534	115
total	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-3013	32	567	69	-943	69	-7632	25	-374	85	-1552	80	3	121	-371	138	5017	31	-430	108	-2647	39	-102	449	662	59	-89	636	-722	150	-283	136	-1308	47	-402	148	-908	72	2694	42	924	53	-713	151	3031	29	71	301	-2008	50	-10529	39
	fir	-414	89	100	67	519	60	1377	77	284	39	326	159	4	100	104	90	602	42	1256	34	1268	45	1589	34	81	88	169	201	890	48	129	67	-220	154	487	85	657	51	454	50	201	98	2461	27	685	39	14	842	-508	78	12513	16
	pine	-748	37	.	.	-8	100	-319	71	-15	340	-325	56	22	201	.	.	-84	288	-51	156	75	53	11	379	4	100	140	81	-14	191	-73	138	-394	33	22	114	-112	78	-92	88	37	233	-275	78	122	174	.	.	-714	32	-2792	23
	larch	-29	332	.	.	.	.	-220	55	-65	97	-66	184	.	.	13	100	1677	23	5	100	21	72	8	100	.	.	18	100	-5	1821	-8	288	20	100	18	100	48	65	1739	19	-63	100	314	48	2865	22	-12	100	-46	180	6232	14
	Arolla pine	.	.	.	.	.	.	5	100	.	.	.	.	.	.	.	.	287	67	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	8	100	.	.	.	.	311	40	.	.	.	.	611	38
	other conifers	166	55	.	.	11	71	314	70	222	100	-35	100	.	.	2	100	33	39	10	73	232	65	5	100	.	.	8	100	199	101	63	100	46	57	3	100	3	120	82	98	3	175	-50	322	-1	946	.	.	35	66	1351	34
	beech	11	4781	90	54	-91	176	593	138	-295	147	823	47	.	.	8	3062	221	126	-271	156	575	60	175	109	80	162	-101	327	1446	28	707	35	160	239	642	35	480	53	3053	14	509	39	1550	28	368	42	161	71	236	195	11130	15
	maple	-46	298	6	345	68	108	1008	20	134	66	419	24	.	.	173	58	269	27	95	94	321	27	362	26	31	146	62	183	554	37	70	67	313	35	154	43	185	50	209	41	97	56	563	23	282	33	33	88	583	33	5947	9
	ash	-157	116	.	.	42	178	275	81	49	167	726	38	3	100	61	184	132	54	156	83	252	84	-72	149	277	73	70	89	484	53	53	86	237	39	264	46	278	50	611	39	162	52	315	75	206	40	-36	167	183	110	4571	17
	oak	138	154	.	.	.	.	128	87	-33	157	49	126	3	5322	.	.	80	74	14	516	81	121	-28	156	3	100	.	.	-39	86	-93	87	74	52	8	100	283	103	295	34	45	75	293	71	162	41	.	.	-123	120	1341	40
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	229	52	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1319	32	.	.	6	100	14	77	.	.	.	.	1568	28
	other broadleaves	18	736	-3	72	57	76	353	41	-41	120	180	76	5	150	52	75	836	39	81	108	135	64	-118	60	49	52	-16	145	-78	154	2	1190	70	163	0	14115	3	3626	1564	22	-18	339	353	95	378	35	-11	113	208	67	4058	18
	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	0	0	0	0	.	.	0	0	0	0
	total	-4072	31	760	58	-346	244	-4118	60	-133	455	545	282	40	427	41	1539	9298	19	866	96	313	406	1830	42	1187	43	261	318	2714	52	569	93	-1001	86	1195	72	917	91	11937	12	1896	37	4816	32	8423	15	220	141	-2155	62	36000	15

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. 1409356. Birmensdorf, Swiss Federal Research Institute WSL

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