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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NFI4
total number of stems
degree of damage · conifers and broadleaves
regional demarcation: forest district (2024)
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
degree of damage	conifers and broadleaves	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	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	.	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	broadleaves	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
no damage	n/a	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	1085	36	1231	37	944	43	1092	26	1767	29	2487	26	14066	10	7876	12	8281	13	5734	15	59	52	423	43	751	32	66	.	608	42	2947	27	2839	26	2084	31	114	80	2744	23	6200	15	4132	19	6860	17	12774	12	15303	11	4786	15	2734	28	3960	19	5179	18	933	42	2001	27	805	32	796	37	2133	26	1770	47	2893	29	2947	27	1370	30	1743	38	2838	24	1119	31	2493	21	1967	26	1151	34	617	38	384	41	708	37	2109	23	2320	25	1245	26	1740	35	1604	35	1867	33	1151	31	2911	25	2454	24	552	58	8	100	55	66	3006	21	826	32	1185	36	1507	33	1140	36	2189	30	1476	29	1234	27	2343	37	945	45	413	51	2011	29	501	48	539	46	2643	22	1276	43	1574	40	1041	31	941	58	668	41	270	65	523	66	11517	13	8934	16	7992	14	1138	35	1007	49	783	35	1621	30	1607	31	587	48	625	53	805	35	0	.	230679	2
	broadleaves	2068	22	1414	25	1344	34	1965	24	278	41	1339	32	5701	16	4041	16	6194	12	5234	14	748	37	1217	32	2249	25	0	.	618	39	1512	32	2407	26	1332	27	550	45	1973	24	1077	29	1509	28	2107	29	3474	25	1264	41	4891	16	1772	24	1288	31	2415	22	877	42	1027	31	631	39	682	38	685	29	1256	35	191	41	1407	35	1072	30	1767	30	2221	27	1568	30	2413	22	2891	23	1660	28	1376	28	1096	31	1772	25	1594	29	803	48	1289	34	1434	27	1118	29	1430	25	1040	55	1479	32	574	39	3685	24	3745	25	6677	19	4896	23	3506	26	4073	22	1321	35	607	54	375	43	1165	36	1223	35	1272	34	997	41	437	54	1576	27	293	45	750	53	793	30	1374	37	1432	27	887	34	1172	38	554	49	211	62	403	58	1787	29	2182	25	4162	19	514	45	833	30	887	30	1492	25	1425	24	1194	32	788	33	1255	34	0	.	157278	2
	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3154	22	2645	25	2288	29	3057	20	2045	28	3826	23	19768	10	11916	11	14475	10	10968	13	806	36	1640	31	3000	23	66	.	1225	34	4459	22	5246	21	3416	23	664	46	4717	19	7277	14	5641	17	8967	15	16248	11	16567	10	9676	12	4506	21	5248	18	7594	16	1810	31	3028	23	1435	31	1479	31	2819	23	3026	32	3085	28	4354	23	2442	27	3511	28	5059	21	2687	25	4906	18	4858	19	2811	25	1993	26	1479	28	2480	25	3703	22	3124	24	2534	26	3174	26	2722	27	3297	24	2191	33	4391	20	3027	21	4237	23	3752	25	6732	19	7902	17	4331	23	5259	19	2828	30	1747	33	2564	28	2641	25	2456	27	3615	30	1942	35	850	48	3587	24	794	46	1289	42	3436	21	2650	29	3006	27	1928	26	2114	37	1223	38	481	57	926	47	13304	12	11116	14	12153	12	1652	30	1841	32	1670	28	3113	24	3032	21	1781	28	1413	31	2060	29	0	.	387957	2
slight damage	n/a	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	24	77	108	53	108	36	40	71	139	40	188	42	1918	13	483	23	667	26	283	27	26	77	73	61	68	56	7	.	111	55	334	36	397	28	147	60	0	.	399	30	831	19	640	21	856	20	1902	15	1892	14	293	29	179	35	288	33	306	30	41	85	141	56	31	64	14	71	121	66	254	45	112	54	235	35	17	100	151	47	462	28	46	53	131	36	308	47	110	64	27	61	42	61	45	62	240	38	157	38	51	54	47	63	122	47	129	50	99	51	260	39	272	33	23	76	7	100	0	.	181	46	33	63	106	87	259	35	273	48	268	33	193	42	121	38	325	62	89	59	14	100	76	50	57	67	86	51	106	38	15	71	130	40	67	49	22	75	24	100	14	100	7	100	1177	21	1191	19	749	19	120	54	35	73	87	47	202	75	212	31	61	62	145	62	85	54	0	.	22929	4
	broadleaves	125	38	17	100	0	.	17	100	0	.	67	53	409	39	228	35	233	30	409	33	44	85	52	58	137	38	0	.	7	100	82	45	192	56	39	61	31	71	265	33	88	52	124	52	262	46	382	30	44	72	186	34	138	44	136	49	99	48	94	45	17	100	31	63	17	100	17	100	87	52	0	.	154	35	96	45	46	49	228	34	325	46	220	38	108	45	51	54	117	42	55	49	67	60	158	43	149	56	153	73	155	56	0	.	41	61	116	59	195	49	62	67	255	37	178	50	348	26	434	29	171	36	301	38	126	59	125	74	23	100	223	53	110	46	77	66	90	83	36	71	69	48	9	100	0	.	39	70	7	100	69	54	82	52	33	71	35	71	0	.	14	71	124	38	165	38	396	35	7	100	54	58	32	62	119	42	0	.	21	58	23	77	0	.	0	.	10369	6
	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	149	34	126	47	108	36	56	58	139	40	255	34	2327	13	711	19	900	21	692	25	70	80	124	48	206	41	7	.	118	52	416	30	589	27	186	49	31	71	663	23	919	18	764	20	1119	19	2284	14	1936	14	479	23	317	29	424	29	405	27	135	40	158	59	62	54	31	64	138	59	341	36	112	54	389	30	113	47	197	39	690	24	372	42	351	30	416	37	161	47	144	38	96	38	112	44	398	32	306	35	203	57	202	48	122	47	170	41	216	41	455	30	334	30	278	34	185	48	348	26	616	24	204	32	406	36	385	32	398	40	291	31	416	39	230	30	402	51	179	52	49	58	144	38	65	59	86	51	145	33	22	74	200	32	149	39	55	52	59	58	14	100	20	74	1301	20	1356	17	1145	19	127	51	89	45	119	41	321	52	212	31	82	49	168	55	85	54	0	.	33298	3
moderate damage	n/a	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	24	77	19	100	0	.	91	42	85	40	116	39	1169	15	508	22	419	24	389	26	0	.	0	.	77	56	0	.	35	54	73	57	301	44	98	43	0	.	242	30	346	24	276	27	272	34	772	20	866	18	329	26	232	37	156	34	132	34	61	57	65	54	42	71	44	61	25	75	120	49	89	53	195	35	69	48	124	68	174	38	44	55	135	34	55	49	73	49	73	63	17	100	24	77	64	43	159	43	68	51	40	61	17	100	55	49	45	60	173	43	137	53	7	100	0	.	0	.	98	49	34	71	64	77	174	51	140	73	163	44	89	54	69	58	70	71	50	67	0	.	88	40	0	.	0	.	99	53	21	75	87	43	43	60	71	70	21	73	0	.	7	100	571	22	410	25	438	26	73	55	69	50	31	64	106	57	44	54	0	.	13	100	17	100	0	.	12384	4
	broadleaves	160	41	128	49	7	100	160	42	36	54	75	49	627	23	314	39	251	30	875	27	77	46	114	35	418	50	0	.	35	71	126	52	193	37	159	39	7	100	244	34	106	45	126	38	265	38	345	29	175	49	332	29	144	44	94	53	167	42	87	51	35	71	27	100	88	52	17	100	266	44	0	.	340	43	160	43	250	38	337	34	340	57	212	51	223	35	191	42	226	38	132	57	160	42	209	43	119	47	60	73	106	49	17	100	94	45	137	49	284	40	94	45	259	32	378	31	904	27	475	31	356	37	439	28	219	45	186	69	18	100	177	48	84	48	198	48	52	58	8	100	88	44	7	100	17	100	17	100	62	59	0	.	74	57	87	65	0	.	7	100	0	.	145	43	166	39	486	25	17	100	137	35	94	40	51	75	54	63	99	72	101	53	15	71	0	.	15449	5
	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	184	37	147	44	7	100	251	35	121	38	191	30	1796	14	822	21	669	19	1264	22	77	46	114	35	495	43	0	.	70	49	200	39	495	31	257	29	7	100	487	25	452	21	401	24	537	27	1117	17	1042	17	660	20	376	28	250	29	299	27	149	39	100	43	69	59	132	42	42	84	386	36	89	53	535	33	228	42	374	34	511	29	385	51	347	36	278	30	265	35	299	32	149	61	184	37	273	35	278	34	128	43	146	40	34	71	149	37	183	43	457	30	231	36	266	31	378	31	904	27	573	27	390	34	503	26	393	37	326	51	182	42	267	38	153	37	268	40	103	47	8	100	176	34	7	100	17	100	116	47	83	60	87	43	118	50	158	48	21	73	7	100	7	100	716	20	576	21	924	20	90	48	206	29	125	34	157	45	98	43	99	72	114	53	32	63	0	.	27833	3
severe damage	n/a	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	47	83	36	71	54	49	89	47	102	45	100	47	972	18	396	23	358	21	457	26	0	.	59	77	14	70	0	.	62	58	123	49	225	37	156	38	0	.	120	34	368	23	94	37	248	28	1022	17	928	19	296	28	47	54	152	37	350	29	34	82	76	44	41	56	86	43	186	39	41	54	91	59	139	36	83	42	84	77	223	33	103	57	141	41	35	71	123	39	101	68	35	70	14	70	109	50	263	33	76	58	34	71	24	77	7	100	18	100	249	37	88	44	0	.	0	.	0	.	142	38	43	61	57	52	82	47	41	55	205	36	43	77	115	43	34	61	7	100	7	100	220	43	23	77	0	.	112	41	55	56	62	56	74	47	0	.	0	.	85	91	13	100	716	18	628	20	498	19	98	60	24	77	37	55	148	42	57	66	34	100	24	77	17	100	0	.	13082	4
	broadleaves	158	39	38	67	53	65	122	40	25	100	0	.	380	24	389	31	447	24	443	28	7	100	121	44	124	38	0	.	52	76	48	53	112	49	14	71	41	100	358	37	178	52	137	43	365	35	709	36	253	41	597	22	87	42	144	34	243	39	78	58	54	73	8	100	72	50	45	60	140	52	0	.	286	46	141	50	102	45	224	30	378	64	189	36	38	55	71	56	186	46	188	41	219	44	128	41	9	100	165	55	185	53	74	57	72	46	145	47	293	32	144	61	841	35	635	30	940	24	436	26	264	35	1104	34	134	52	150	65	93	75	110	50	131	40	220	50	32	100	0	.	159	51	61	59	7	100	19	100	24	77	92	54	41	61	108	66	7	100	0	.	7	100	196	33	228	41	601	24	26	58	49	58	26	78	120	37	30	60	34	71	41	61	47	55	0	.	16292	5
	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	205	36	74	49	108	43	211	32	127	41	100	47	1352	15	785	23	805	17	900	20	7	100	180	39	138	37	0	.	114	47	171	38	337	31	169	36	41	100	478	32	546	23	231	31	613	23	1732	18	1181	18	893	18	134	38	296	26	594	24	112	47	130	40	49	50	159	38	231	34	181	47	91	59	425	34	224	36	186	43	447	25	481	55	330	30	73	44	194	35	287	38	223	40	233	42	237	36	272	32	242	42	218	46	98	47	79	43	163	44	542	25	231	41	841	35	635	30	940	24	577	22	308	31	1161	32	216	39	191	52	298	39	153	43	245	30	255	47	39	84	7	100	379	34	84	51	7	100	132	43	79	45	154	41	116	39	108	66	7	100	85	91	20	100	911	16	856	19	1099	17	124	50	74	51	63	45	268	30	87	48	69	61	65	61	64	48	0	.	29374	3
very severe damage	n/a	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	0	.	7	100	0	.	32	71	0	.	0	.	280	23	115	36	79	41	115	49	0	.	0	.	0	.	0	.	29	62	28	80	33	78	0	.	0	.	124	36	264	64	76	45	66	51	440	40	442	27	59	52	67	58	48	57	79	56	0	.	53	54	0	.	17	100	48	54	32	81	17	100	82	66	0	.	33	64	46	64	17	72	7	100	14	70	0	.	0	.	17	100	42	85	77	74	33	62	21	77	17	100	0	.	0	.	34	45	82	70	49	54	0	.	0	.	8	100	32	64	31	64	14	71	23	75	16	71	116	44	20	76	62	52	6	100	0	.	7	100	0	.	0	.	0	.	8	100	0	.	7	100	7	100	0	.	0	.	0	.	0	.	518	22	242	36	244	30	42	100	14	100	7	100	47	87	0	.	17	100	0	.	0	.	0	.	4609	8
	broadleaves	56	49	87	44	7	100	41	85	15	100	18	100	369	34	192	37	131	36	54	43	31	71	31	64	73	50	0	.	7	100	52	58	144	76	58	44	0	.	235	43	79	74	91	82	109	45	315	49	53	100	163	27	114	94	95	43	64	54	41	61	24	77	0	.	0	.	19	100	134	56	85	52	105	43	65	53	58	65	150	37	78	63	71	49	7	100	14	71	48	87	28	50	124	47	44	61	22	100	14	71	116	51	0	.	17	100	81	82	245	47	234	45	435	36	639	34	295	35	172	36	250	32	392	38	32	70	35	100	0	.	82	63	79	52	36	71	18	100	17	100	79	56	0	.	17	100	0	.	33	65	44	62	17	100	7	100	0	.	0	.	17	100	158	62	71	50	574	36	0	.	7	100	19	100	116	44	23	77	0	.	34	71	22	58	0	.	8225	7
	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	56	49	94	43	7	100	73	57	15	100	18	100	648	22	307	27	210	27	168	36	31	71	31	64	73	50	0	.	36	54	80	47	176	63	58	44	0	.	359	31	343	52	168	49	175	35	755	31	495	26	223	24	181	63	143	38	143	39	41	61	78	44	0	.	17	100	67	48	166	48	102	52	187	38	65	53	91	47	197	35	95	53	78	45	21	58	14	71	48	87	45	60	166	51	121	53	55	65	35	54	133	46	0	.	17	100	115	59	327	39	283	39	435	36	639	34	303	35	204	32	281	29	406	36	55	51	51	72	116	44	102	53	141	37	42	62	18	100	24	77	79	56	0	.	17	100	8	100	33	65	51	55	23	76	7	100	0	.	0	.	17	100	676	22	313	32	818	29	42	100	21	75	26	78	163	40	23	77	17	100	34	71	22	58	0	.	12834	5
dead	n/a	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	103	41	61	68	16	100	143	35	177	42	437	31	4132	15	1029	20	612	22	635	22	25	76	69	79	149	43	0	.	189	53	663	38	952	33	219	43	7	100	656	31	939	22	563	31	986	26	2369	17	1949	16	788	27	449	41	359	28	1622	32	85	66	54	58	35	70	0	.	320	36	230	39	331	41	440	33	174	44	149	49	679	28	226	58	329	46	149	36	160	46	169	56	66	78	217	38	287	34	650	57	158	49	50	75	86	83	86	66	101	45	397	31	209	40	14	71	0	.	0	.	197	42	100	49	79	81	164	50	90	68	662	33	223	37	189	42	267	48	139	54	51	74	374	32	95	84	26	78	155	43	225	54	80	46	205	53	73	71	124	75	8	100	39	62	2380	16	1731	21	2389	21	160	43	128	52	101	42	145	34	40	61	91	52	17	100	90	49	0	.	36986	4
	broadleaves	124	56	160	46	0	.	175	42	30	100	167	48	682	22	250	32	280	29	184	33	78	78	82	67	195	46	0	.	113	71	40	61	78	45	268	46	150	53	391	33	267	36	179	58	418	38	1056	28	117	47	547	24	114	39	117	67	512	35	71	50	73	60	17	100	21	100	19	100	96	77	154	89	251	49	0	.	259	39	212	41	195	40	174	36	129	53	7	100	243	51	150	70	113	49	225	43	30	100	216	61	70	61	17	100	17	100	205	54	282	48	191	51	823	34	890	29	1881	21	693	31	428	45	1297	26	70	56	109	75	75	76	351	49	125	57	157	43	15	100	138	79	161	49	20	100	17	100	39	100	161	57	89	45	120	51	67	61	57	76	19	100	0	.	453	35	191	32	772	26	42	85	41	85	34	71	278	47	141	50	88	45	31	80	47	58	0	.	20132	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	18	100	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	.	7	100	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	77
	total	227	37	220	39	16	100	319	29	207	46	622	30	4814	14	1279	20	892	19	820	20	103	62	151	51	344	35	0	.	302	51	703	38	1030	31	488	34	157	51	1047	24	1206	20	741	27	1404	22	3425	15	2066	16	1336	19	563	35	476	29	2134	26	156	43	126	50	52	58	21	100	339	34	326	35	485	40	691	28	174	44	408	33	898	24	421	39	503	33	278	32	166	44	412	38	216	61	330	34	512	30	680	56	374	41	120	61	103	71	103	57	306	39	679	27	400	35	837	33	890	29	1881	21	890	26	529	39	1376	25	234	41	199	51	737	31	574	39	314	35	425	40	154	50	189	61	536	31	114	87	43	62	194	40	387	47	169	33	325	39	140	54	182	57	27	100	39	62	2833	15	1922	19	3161	18	202	40	169	44	135	38	423	35	181	42	179	37	47	62	138	39	0	.	57142	3
total	n/a	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	1285	33	1462	37	1122	39	1487	23	2270	28	3328	24	22537	9	10407	11	10415	13	7613	14	110	42	624	47	1059	30	73	.	1034	40	4169	27	4747	21	2704	29	121	76	4285	22	8947	14	5780	18	9288	15	19280	11	21380	10	6551	15	3709	24	4964	18	7668	17	1155	40	2390	27	953	31	958	36	2832	24	2447	40	3534	28	4038	25	1713	27	2283	35	4422	22	1556	31	3236	21	2528	25	1617	31	986	43	560	36	1050	32	2886	22	3583	26	1619	27	1928	33	1852	34	2145	33	1448	30	4072	23	3209	23	596	55	15	71	63	59	3657	20	1067	30	1506	35	2210	31	1701	36	3604	27	2044	27	1788	26	3047	35	1230	40	491	50	2769	27	676	46	650	44	3123	21	1592	41	1941	36	1438	31	1107	55	838	40	376	70	588	63	16878	11	13136	14	12310	14	1631	34	1277	46	1045	34	2269	29	1961	29	791	45	823	50	1014	34	0	.	320669	2
	broadleaves	2690	20	1844	25	1411	33	2480	23	384	43	1667	30	8168	14	5413	14	7536	12	7199	14	985	36	1617	31	3197	24	0	.	831	36	1860	30	3126	25	1869	25	780	44	3466	21	1795	27	2165	26	3527	29	6281	21	1906	38	6716	15	2368	22	1874	27	3501	21	1249	37	1229	29	713	40	881	36	804	30	1979	33	430	51	2543	30	1533	28	2483	29	3373	23	2884	32	3279	22	3397	23	1994	27	2195	28	1648	30	2454	26	2357	27	1132	42	1898	37	2065	26	1227	28	1670	24	1725	41	2779	29	1298	37	6298	22	6464	21	11044	17	7106	20	4976	24	7606	21	1902	34	1212	56	585	42	2109	33	1751	32	1960	33	1205	40	636	52	2132	25	388	45	808	52	907	29	1662	34	1726	27	1221	33	1474	38	653	48	237	63	440	56	2862	25	3003	23	6991	16	607	42	1122	30	1092	29	2176	24	1672	24	1437	32	1017	33	1386	33	0	.	227745	2
	indeterminable	0	.	0	.	0	.	0	.	0	.	18	100	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	.	7	100	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	77
	total	3975	20	3306	24	2533	28	3968	19	2654	28	5012	22	30705	9	15820	10	17951	10	14812	13	1094	34	2240	30	4256	22	73	.	1866	33	6029	22	7873	18	4573	22	900	44	7751	17	10742	13	7945	16	12815	14	25561	10	23286	10	13267	12	6077	20	6837	17	11169	15	2404	29	3619	24	1667	31	1839	31	3636	23	4426	28	3964	27	6581	20	3246	25	4767	26	7802	19	4440	26	6515	18	5925	19	3611	24	3181	26	2208	28	3505	25	5244	21	4715	24	3516	27	3993	24	3079	26	3815	23	3173	29	6851	19	4507	21	6894	21	6479	20	11107	17	10763	16	6043	21	9112	19	4111	28	2912	33	4188	25	4153	23	3539	24	5007	28	2434	33	1128	45	4901	22	1064	45	1458	40	4029	21	3254	29	3666	26	2660	26	2582	36	1491	38	613	59	1029	46	19740	11	16140	13	19300	12	2237	29	2399	31	2137	27	4445	23	3632	21	2228	28	1840	31	2400	28	0	.	548438	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. 2295479. Birmensdorf, Swiss Federal Research Institute WSL

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