NFI Results

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
number of stems
development stage · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	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	766	20	1598	19	589	26	244	40	594	47	30	71	3820	12
	fir	373	25	651	31	43	45	33	79	10	71	0	.	1109	20
	pine	191	43	0	.	20	100	10	100	0	.	0	.	221	38
	larch	20	100	0	.	0	.	170	80	157	63	33	59	380	45
	Arolla pine	0	.	0	.	0	.	31	88	27	100	0	.	58	66
	other conifers	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	beech	1160	18	372	40	207	47	0	.	24	67	147	93	1910	16
	maple	390	25	228	37	148	45	43	80	0	.	51	73	861	18
	ash	410	30	160	57	310	59	115	45	0	.	80	64	1074	23
	oak	242	37	0	.	0	.	34	89	0	.	14	100	290	33
	sweet chestnut	0	.	0	.	0	.	32	79	0	.	30	64	62	51
	other broadleaves	457	26	258	32	318	37	150	40	103	61	183	34	1469	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4012	13	3267	17	1635	26	862	28	914	40	568	32	11258	9
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1106	22	1117	22	713	28	238	32	522	31	259	34	3955	11
	fir	428	26	121	52	66	41	70	57	35	87	37	74	757	19
	pine	122	38	41	100	0	.	62	62	41	100	0	.	266	31
	larch	89	52	0	.	0	.	144	31	143	38	130	39	506	19
	Arolla pine	0	.	0	.	0	.	0	.	19	100	0	.	19	100
	other conifers	19	71	10	100	10	100	51	100	10	100	0	.	101	55
	beech	496	23	45	66	156	37	30	75	0	.	115	44	843	17
	maple	169	35	104	43	120	53	10	100	55	71	19	71	476	22
	ash	224	41	39	63	28	59	38	100	0	.	107	74	436	30
	oak	52	49	12	100	0	.	118	56	0	.	0	.	182	40
	sweet chestnut	0	.	0	.	0	.	0	.	4	100	101	49	105	47
	other broadleaves	432	28	229	38	76	58	265	36	309	48	315	27	1626	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3136	14	1718	18	1170	22	1027	21	1139	22	1083	19	9273	8
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15363	10	7063	16	5480	18	2347	24	2903	23	1176	37	34332	7
	fir	4044	16	1009	31	1268	27	503	52	48	100	83	95	6955	12
	pine	1150	24	368	69	240	63	2779	24	1197	35	9	100	5743	15
	larch	587	36	50	82	98	64	1452	24	588	32	1033	29	3808	14
	Arolla pine	0	.	0	.	0	.	357	50	271	66	0	.	628	40
	other conifers	419	39	0	.	81	55	0	.	10	100	0	.	510	33
	beech	10639	10	1936	26	2204	24	832	39	175	64	4325	18	20111	8
	maple	2368	17	608	26	955	22	192	32	94	57	303	36	4519	11
	ash	3450	13	915	32	934	23	107	48	387	56	1108	21	6900	10
	oak	3249	19	329	48	26	62	228	47	0	.	1555	24	5387	14
	sweet chestnut	110	100	0	.	20	100	82	68	0	.	7337	13	7550	13
	other broadleaves	3565	15	1178	27	1775	27	2335	23	1177	27	8516	10	18546	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	44944	6	13456	12	13080	13	11215	12	6850	15	25445	7	114989	4
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12212	8	11470	11	6518	15	3534	20	3541	19	1098	33	38375	6
	fir	6697	11	2443	18	1324	19	81	50	155	69	174	79	10875	8
	pine	1592	16	150	78	259	46	816	46	933	32	226	71	3976	15
	larch	256	46	30	71	38	71	1142	23	369	32	659	32	2494	15
	Arolla pine	0	.	0	.	0	.	409	47	166	55	0	.	575	37
	other conifers	163	38	9	100	54	52	0	.	0	.	0	.	225	30
	beech	14682	7	3336	16	3226	19	406	46	334	51	2103	22	24088	6
	maple	2303	12	1262	19	520	31	82	71	19	71	44	71	4230	10
	ash	1685	16	742	25	695	32	68	62	0	.	139	45	3328	12
	oak	1395	18	207	49	25	61	10	100	0	.	189	43	1826	16
	sweet chestnut	20	100	0	.	0	.	0	.	0	.	1293	26	1313	26
	other broadleaves	2151	18	1071	21	319	32	204	39	80	64	1029	26	4855	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	43157	5	20720	9	12978	11	6752	15	5598	16	6954	13	96160	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16185	6	12094	9	7203	11	3040	16	8812	11	2108	18	49442	4
	fir	9733	7	3859	13	2725	15	265	50	279	33	391	36	17252	6
	pine	1910	14	105	71	201	51	287	44	513	25	110	56	3125	11
	larch	263	24	57	52	203	44	1263	23	1030	20	478	29	3293	12
	Arolla pine	0	.	0	.	0	.	169	52	311	38	0	.	480	30
	other conifers	201	36	25	85	10	100	0	.	0	.	0	.	237	32
	beech	14452	6	4091	13	3443	14	81	63	699	30	364	36	23131	5
	maple	2015	12	729	19	675	20	10	100	55	49	19	100	3503	9
	ash	1683	13	251	29	679	25	20	71	17	100	33	74	2682	11
	oak	1404	14	57	53	27	74	0	.	40	90	55	53	1583	13
	sweet chestnut	0	.	0	.	0	.	0	.	17	100	669	36	685	35
	other broadleaves	1789	12	536	31	450	24	51	53	108	47	265	36	3200	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	49635	4	21804	7	15616	9	5187	14	11881	10	4492	13	108614	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5361	9	4427	13	3547	14	1284	21	3718	12	1510	19	19846	5
	fir	3643	11	1841	16	997	24	113	84	248	36	189	38	7031	8
	pine	538	20	0	.	9	71	73	79	270	49	47	65	938	20
	larch	71	41	35	78	14	72	865	24	571	24	457	24	2014	14
	Arolla pine	0	.	0	.	0	.	43	71	366	38	15	100	424	34
	other conifers	75	52	10	100	10	100	0	.	0	.	0	.	96	43
	beech	5619	8	1423	21	1124	20	132	83	278	38	696	32	9272	7
	maple	668	22	158	42	459	30	0	.	21	71	4	100	1311	16
	ash	683	20	39	56	117	39	0	.	0	.	0	.	839	17
	oak	758	18	4	100	24	63	10	100	15	100	0	.	812	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	571	40	571	40
	other broadleaves	1105	19	130	41	206	40	267	52	60	71	167	41	1936	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	18521	6	8068	11	6508	12	2788	17	5547	11	3656	15	45089	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6492	9	10851	9	5060	13	7114	12	10377	9	2559	16	42454	4
	fir	4623	11	4818	12	1250	17	725	30	264	30	267	41	11947	7
	pine	729	21	451	34	459	40	2103	20	2618	19	84	89	6443	11
	larch	47	69	74	37	9	100	2765	12	1661	14	1592	15	6148	8
	Arolla pine	0	.	98	95	59	72	610	23	1091	21	4	100	1862	15
	other conifers	345	44	0	.	11	100	10	100	337	51	0	.	703	33
	beech	7655	10	3450	14	2322	18	299	41	230	41	2558	19	16515	7
	maple	1462	14	945	19	831	23	252	46	113	56	139	39	3741	10
	ash	1963	16	666	24	615	27	220	53	62	58	96	42	3621	11
	oak	765	25	77	59	67	61	98	43	110	77	372	55	1487	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	1230	30	1230	30
	other broadleaves	2705	14	1293	17	913	24	658	29	489	32	1754	16	7812	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	26786	6	22722	7	11595	10	14854	9	17352	8	10654	9	103964	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	57486	3	48620	4	29110	6	17801	7	30468	5	8740	9	192225	2
	fir	29541	4	14742	6	7672	9	1790	21	1040	19	1141	21	55925	3
	pine	6232	8	1114	29	1187	24	6130	14	5572	13	476	40	20712	6
	larch	1331	19	247	27	364	31	7801	8	4518	9	4382	10	18643	5
	Arolla pine	0	.	98	95	59	72	1620	19	2251	16	19	82	4047	12
	other conifers	1225	20	55	50	177	32	62	85	357	48	0	.	1876	17
	beech	54704	3	14653	7	12681	8	1781	23	1741	19	10310	10	95870	2
	maple	9374	6	4035	9	3707	10	590	25	358	27	577	23	18641	4
	ash	10098	6	2811	14	3378	13	568	26	465	47	1561	17	18881	5
	oak	7865	9	685	29	169	31	498	27	165	57	2185	20	11567	8
	sweet chestnut	131	86	0	.	20	100	114	53	21	83	11231	10	11517	9
	other broadleaves	12205	7	4694	10	4057	14	3932	15	2325	17	12230	8	39444	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	190191	1	91755	3	62582	4	42686	5	49281	4	52852	3	489347	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. 1283682. Birmensdorf, Swiss Federal Research Institute WSL

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