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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NFI2
total number of stems
degree of damage · conifers and broadleaves
regional demarcation: forest district (2024)
unit: n/ha
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		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	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
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	119	26	179	37	149	41	145	17	318	18	310	20	229	8	237	7	215	11	212	11	37	55	138	64	120	27	490	.	336	31	289	18	227	15	305	19	145	68	178	29	225	11	248	13	198	13	238	10	292	8	193	15	207	20	275	15	342	12	189	41	225	18	198	19	114	24	256	19	274	39	382	22	223	25	231	23	150	32	161	18	139	24	221	18	205	20	120	24	124	35	137	33	95	22	140	14	360	18	147	20	234	30	242	26	256	31	93	38	185	18	216	15	21	54	1	98	3	58	110	22	63	30	48	38	172	24	338	32	365	18	117	27	111	23	283	37	178	38	210	46	187	23	302	19	135	23	252	17	146	34	197	33	203	19	110	63	280	37	151	38	292	29	262	11	271	12	198	12	264	30	95	27	240	18	252	26	198	23	176	31	124	38	157	23	0	.	208	2
	broadleaves	194	11	181	18	161	21	184	19	64	36	127	23	80	16	100	13	146	10	210	12	207	31	245	27	239	16	0	.	176	31	143	23	171	25	146	21	673	22	118	26	41	29	63	30	55	39	85	26	23	42	177	12	130	28	98	22	124	19	128	34	120	20	129	25	115	42	69	30	219	39	31	44	139	30	156	20	288	25	143	23	201	26	246	21	228	17	119	21	194	18	195	21	276	18	146	30	59	35	136	32	140	21	156	22	157	15	91	56	71	27	55	40	347	21	406	14	473	11	228	22	277	22	323	13	183	26	85	78	35	48	155	34	152	34	157	33	149	33	160	42	161	22	97	30	148	25	66	25	259	28	205	28	149	31	414	26	198	24	150	43	252	42	38	30	34	27	104	19	78	32	159	29	113	26	181	19	117	22	192	21	190	23	85	24	0	.	139	3
	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	313	11	360	19	310	22	329	10	382	18	437	17	309	7	337	6	361	7	421	8	244	25	383	26	360	14	490	.	512	22	432	12	397	13	451	12	818	16	296	19	265	10	311	12	253	12	323	8	315	7	371	8	338	14	373	12	466	10	316	23	345	11	327	11	229	26	324	14	493	22	413	20	362	16	387	13	437	17	304	16	341	13	467	14	433	11	240	15	318	17	332	14	371	16	286	15	419	16	283	16	374	18	398	18	412	17	184	30	256	12	271	11	368	19	407	14	476	11	338	14	340	18	371	10	355	18	422	23	401	17	272	18	263	21	440	29	326	26	370	26	348	16	399	17	282	13	318	16	406	18	402	18	352	14	525	19	478	24	301	26	543	15	300	10	305	10	302	9	342	21	254	19	352	12	433	16	315	14	369	15	315	20	242	17	0	.	347	1
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	6	43	2	98	12	41	9	31	32	35	29	21	39	13	24	16	10	22	20	34	0	.	1	97	12	37	0	.	18	66	50	30	45	24	13	34	0	.	16	33	21	21	19	21	15	23	26	17	26	15	22	25	15	29	34	16	13	28	7	45	11	49	13	48	24	31	13	39	23	34	6	85	20	25	8	53	16	66	21	29	16	49	16	45	6	45	20	35	12	46	10	79	13	41	14	42	46	28	16	46	16	44	23	46	10	45	11	43	24	29	12	43	2	74	1	98	0	.	2	56	2	99	9	51	27	37	39	51	54	25	11	82	8	47	26	35	16	34	29	93	9	57	21	63	7	95	6	49	4	73	12	70	14	44	17	40	21	47	14	54	49	36	28	19	47	19	38	17	4	97	28	92	11	41	11	33	25	65	10	42	5	70	8	71	0	.	20	4
	broadleaves	8	40	5	51	0	.	9	53	4	73	9	43	8	57	6	31	8	25	15	30	21	76	4	97	14	49	0	.	14	60	4	59	22	59	16	37	4	90	15	32	3	51	10	48	4	66	7	27	3	65	14	33	20	36	8	38	20	63	3	97	2	98	6	73	10	80	8	50	6	97	0	.	17	32	20	49	5	57	9	39	19	40	15	63	6	50	6	59	17	58	3	97	21	28	0	.	5	57	12	50	11	43	1	98	7	62	22	48	17	28	10	62	8	56	17	40	30	19	22	28	23	41	19	30	35	34	6	94	10	63	15	42	12	45	9	53	24	57	0	.	6	49	0	.	0	.	7	54	12	51	13	36	7	68	6	95	0	.	0	.	10	93	4	50	2	57	20	36	8	69	6	77	4	74	6	44	7	43	8	71	1	96	12	38	0	.	10	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	14	27	6	44	12	41	18	29	36	30	39	18	47	14	30	15	17	16	35	25	21	76	5	75	26	30	0	.	32	48	55	28	67	30	30	25	4	90	31	21	24	19	28	24	19	22	33	14	29	14	36	19	34	23	42	14	33	39	11	40	14	42	19	36	34	28	21	32	29	30	6	85	37	18	28	38	21	52	30	24	34	30	31	37	13	33	26	27	28	39	12	64	34	19	14	42	51	25	28	30	27	30	24	45	17	34	34	34	41	18	22	33	10	45	17	38	30	19	24	25	25	39	28	24	62	20	45	43	64	22	26	39	20	30	35	30	40	32	29	93	15	37	21	63	7	95	13	34	16	43	25	34	20	34	23	34	21	47	14	54	59	28	32	17	49	18	58	15	12	66	34	76	15	32	18	27	32	51	19	40	6	57	20	35	0	.	30	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	3	83	1	68	3	52	7	45	37	31	36	28	32	16	27	16	12	16	29	24	2	95	6	58	16	44	0	.	29	34	26	35	20	35	10	30	0	.	25	29	22	23	19	36	18	24	26	19	25	15	19	19	27	24	42	22	39	19	15	52	28	59	18	48	10	47	24	29	24	46	19	36	18	29	4	57	29	77	30	38	6	45	15	42	10	45	7	50	25	44	13	41	19	38	21	34	28	47	17	34	13	41	13	36	17	49	6	51	19	35	32	55	0	.	0	.	1	99	11	52	3	60	5	74	37	38	7	46	52	30	17	55	7	44	14	49	14	66	7	93	8	41	25	43	6	67	10	42	0	.	21	37	12	39	2	95	12	65	10	50	6	57	19	15	23	18	29	15	54	57	3	66	10	38	23	31	18	37	6	78	15	38	13	36	0	.	19	4
	broadleaves	11	36	9	46	4	71	8	41	8	64	9	53	9	30	13	25	21	16	39	18	42	49	18	45	65	33	0	.	13	69	6	47	23	31	10	35	14	63	40	26	5	42	11	34	10	36	10	26	2	60	21	22	12	38	5	50	12	44	12	69	19	50	15	42	10	58	2	98	42	41	1	97	27	34	14	41	35	42	25	22	58	65	15	60	24	36	30	44	41	35	54	43	54	20	11	38	7	69	21	36	17	34	12	38	15	41	29	32	13	41	5	57	31	25	33	21	37	20	24	24	19	40	21	30	22	38	13	94	27	61	5	49	7	65	15	42	16	49	7	93	12	38	14	72	6	49	3	68	17	52	36	30	6	56	14	50	2	96	9	88	0	.	2	49	6	39	21	23	19	44	13	40	4	76	16	46	15	43	6	70	20	54	19	42	0	.	16	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	14	32	10	40	8	49	16	29	45	29	45	27	41	14	40	14	33	12	68	14	44	47	24	39	80	27	0	.	42	29	32	27	43	21	20	25	14	63	65	18	27	19	29	28	28	18	36	14	27	15	40	14	39	19	47	20	50	17	27	37	47	41	33	37	20	38	26	27	66	30	21	33	45	24	18	33	64	40	55	24	64	59	30	35	35	28	36	37	67	27	67	38	72	22	32	23	35	39	39	26	30	24	24	23	32	30	35	25	32	24	36	47	31	25	33	21	38	19	35	20	22	36	26	26	59	26	20	56	78	27	23	42	15	37	29	33	31	39	14	93	20	27	39	33	11	49	14	39	17	52	57	24	18	31	16	44	13	56	19	35	6	57	21	14	29	15	50	13	72	41	15	38	14	31	39	24	33	30	11	49	35	33	31	36	0	.	36	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	2	72	2	98	4	73	15	24	21	29	19	24	22	13	22	14	11	19	21	18	2	95	3	66	7	56	0	.	22	62	35	26	27	28	23	21	0	.	33	27	25	20	19	30	23	22	22	15	20	17	13	19	26	26	25	23	27	20	8	56	15	43	21	56	31	48	38	23	21	42	15	42	11	32	9	49	24	69	17	34	19	33	9	36	5	49	9	58	11	55	12	34	5	57	8	45	61	50	10	40	12	45	7	58	7	50	13	65	14	28	11	37	1	98	0	.	0	.	6	50	5	44	1	99	20	43	21	36	38	27	10	44	18	38	8	53	17	65	7	93	19	28	13	61	6	70	5	59	2	67	9	48	7	48	8	94	6	69	17	58	19	58	30	14	39	15	21	16	12	49	12	67	14	35	18	28	5	37	4	44	5	70	3	53	0	.	17	4
	broadleaves	21	30	7	72	8	53	12	41	7	67	7	47	8	28	13	39	10	21	14	22	15	48	17	46	22	34	0	.	7	96	12	42	5	54	9	37	0	.	33	29	11	40	8	48	12	41	8	32	1	70	16	19	11	36	6	56	15	45	10	56	6	58	9	65	20	44	1	98	14	55	13	81	23	44	5	58	16	38	14	35	21	44	8	44	3	59	27	43	23	36	29	29	17	39	19	42	3	71	19	35	14	42	4	73	8	52	6	67	5	67	7	70	31	43	64	22	58	24	17	27	31	42	72	26	11	38	13	94	11	67	28	52	3	76	15	48	0	.	0	.	21	29	3	91	4	96	6	66	9	73	12	46	6	50	7	94	6	71	0	.	43	57	4	38	6	52	11	28	10	52	9	48	13	50	11	47	10	36	2	97	25	49	4	74	0	.	13	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	23	28	9	61	11	50	26	21	28	26	26	21	29	12	35	16	21	14	34	14	17	41	19	39	28	27	0	.	29	52	47	20	32	24	32	18	0	.	66	18	36	17	28	25	34	19	30	13	21	16	29	14	36	23	31	20	42	21	18	35	21	33	30	40	51	33	39	23	34	39	28	45	34	31	14	40	40	44	31	23	40	31	17	27	8	42	35	33	34	28	41	25	22	32	27	29	64	47	29	23	26	33	11	54	15	45	19	47	19	25	18	32	31	42	64	22	58	24	23	22	37	36	73	26	31	31	34	34	50	22	38	39	21	35	23	33	17	65	7	93	40	21	17	48	10	53	11	43	11	59	20	40	12	36	16	94	12	44	17	58	62	35	34	13	45	15	32	14	22	30	21	42	27	27	29	26	16	28	7	52	30	39	7	43	0	.	30	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	1	99	1	98	0	.	3	42	8	55	2	68	7	25	4	39	2	40	2	42	0	.	0	.	0	.	0	.	16	55	4	60	10	59	5	65	0	.	12	31	7	61	7	39	5	37	9	21	7	30	4	34	0	.	11	34	5	46	7	66	2	67	0	.	1	97	7	48	5	67	0	.	5	49	1	97	7	74	6	41	1	98	4	69	3	62	3	46	2	67	5	67	2	98	9	71	4	49	8	54	4	68	2	98	3	75	8	60	2	70	12	55	0	.	0	.	0	.	1	99	0	.	1	69	2	97	0	.	17	45	5	49	1	98	2	67	10	56	0	.	8	40	0	.	0	.	4	72	0	.	1	98	1	97	0	.	0	.	0	.	3	91	15	23	7	38	10	27	8	54	0	.	3	96	3	60	0	.	1	97	0	.	1	97	0	.	5	7
	broadleaves	6	59	0	.	0	.	2	75	0	.	3	72	2	48	2	48	3	41	4	40	2	96	3	66	1	98	0	.	13	67	3	69	4	51	2	98	0	.	24	31	8	45	3	74	9	59	3	46	1	78	7	32	4	61	2	74	7	45	0	.	0	.	4	96	0	.	4	68	13	39	4	98	9	39	5	81	8	73	3	59	8	75	8	42	4	50	2	68	8	74	5	60	12	41	2	98	3	98	7	48	29	58	1	98	3	69	3	97	7	57	4	68	20	50	23	64	29	32	9	35	9	53	43	31	3	98	0	.	3	97	8	72	2	97	6	67	2	97	0	.	6	66	0	.	0	.	0	.	4	74	7	71	3	97	4	63	2	94	0	.	4	94	2	60	1	70	5	48	4	96	4	97	0	.	2	75	2	98	3	97	0	.	1	97	0	.	5	8
	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	6	54	1	98	0	.	5	38	8	55	5	57	9	22	6	29	5	29	6	28	2	96	3	66	1	98	0	.	29	36	7	44	14	45	7	53	0	.	36	23	15	37	10	34	14	40	11	19	7	28	11	23	4	61	13	32	12	31	7	66	2	67	4	96	1	97	11	46	18	31	4	98	14	34	6	67	15	50	10	32	8	68	12	42	7	37	5	35	10	60	10	48	15	46	10	63	8	49	15	36	32	52	3	74	6	49	11	49	10	45	16	43	20	50	23	64	29	32	10	33	9	53	45	30	5	68	0	.	19	39	12	49	3	76	8	50	12	48	0	.	14	36	0	.	0	.	4	72	4	74	8	63	4	72	4	63	2	94	0	.	8	61	16	22	8	33	14	23	12	52	4	97	3	96	6	45	2	98	4	74	0	.	2	67	0	.	10	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	15	34	5	60	11	76	5	64	21	49	23	29	31	18	8	41	5	35	14	32	15	42	0	.	12	47	0	.	9	65	32	54	8	42	4	59	0	.	37	34	23	26	37	43	35	32	35	17	20	20	21	31	8	44	5	53	24	33	7	97	2	98	4	96	3	97	5	57	37	35	30	42	24	39	10	77	19	38	32	25	24	63	30	35	3	69	14	42	23	60	13	57	20	54	20	48	45	54	24	35	5	67	2	98	0	.	20	44	17	38	16	49	0	.	0	.	1	99	2	74	3	98	6	59	27	32	22	94	53	24	14	51	19	46	18	97	31	63	0	.	28	39	30	48	0	.	6	54	3	98	0	.	36	43	18	96	14	96	0	.	81	54	49	21	43	24	46	22	8	96	8	81	0	.	8	43	6	59	4	73	0	.	5	67	0	.	20	5
	broadleaves	9	49	5	72	0	.	5	77	10	53	8	58	9	31	2	45	2	40	7	33	5	95	7	66	13	39	0	.	40	66	10	49	3	69	11	40	31	58	21	42	1	70	6	75	13	50	13	29	1	71	14	31	9	63	5	61	9	44	13	72	0	.	0	.	0	.	0	.	3	98	7	98	6	79	5	67	16	47	9	45	15	53	4	69	6	47	6	54	21	58	10	75	16	49	5	69	10	97	9	71	19	67	2	98	2	98	6	66	7	57	5	81	37	46	44	24	90	21	17	30	27	38	52	31	3	98	6	94	6	66	16	62	11	47	11	67	12	51	65	93	12	34	0	.	0	.	0	.	19	71	15	67	13	79	13	96	4	96	0	.	3	93	4	39	4	51	3	47	0	.	3	96	0	.	5	55	13	62	14	56	11	96	12	45	0	.	10	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	24	31	9	45	11	76	10	50	31	46	31	27	41	16	10	34	7	26	20	25	20	46	7	66	26	31	0	.	49	58	41	43	11	34	15	33	31	58	59	25	24	24	44	37	48	25	48	14	21	19	35	22	17	38	10	46	33	26	20	55	2	98	4	96	3	97	5	57	39	34	37	37	30	34	14	55	34	25	41	22	39	44	34	31	8	37	20	37	44	39	23	50	36	39	25	42	56	61	33	31	23	54	5	67	2	98	25	34	24	30	21	50	37	46	44	24	91	21	19	28	30	36	58	27	30	32	28	72	59	22	31	46	30	31	29	63	43	55	65	93	40	34	30	48	0	.	6	54	21	62	15	67	49	35	31	65	18	74	0	.	84	52	53	20	46	22	49	20	8	96	11	61	0	.	13	37	20	46	17	49	11	96	17	41	0	.	30	4
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	146	22	189	36	179	36	184	15	437	17	418	17	360	7	322	7	254	10	297	10	55	50	148	60	166	25	490	.	430	27	435	18	337	15	361	17	145	68	301	24	323	10	350	11	293	12	356	9	389	7	273	13	283	17	393	10	449	11	234	39	283	20	255	19	185	20	343	15	383	32	453	20	302	21	262	20	244	30	268	16	205	23	294	18	232	19	174	20	197	30	189	28	155	20	211	15	546	18	222	17	283	27	289	25	292	31	151	26	262	17	300	16	24	53	2	68	4	49	132	23	76	28	72	36	285	21	427	26	579	16	174	22	164	24	352	33	266	31	253	45	259	18	391	16	153	22	284	15	155	32	239	31	272	19	155	49	332	36	193	29	450	27	402	9	430	11	341	12	350	30	146	27	278	17	315	21	252	23	202	29	150	37	188	23	0	.	289	2
	broadleaves	249	11	206	18	173	21	220	18	93	37	164	22	116	15	136	12	190	9	288	10	291	29	293	24	355	17	0	.	263	31	178	22	228	23	194	19	721	22	252	21	69	29	101	26	103	34	125	22	31	42	248	11	185	20	123	21	186	16	165	31	147	20	162	23	154	41	84	25	297	31	57	53	220	26	204	21	368	24	203	19	322	27	296	21	272	16	190	23	304	17	297	19	396	13	183	28	87	30	205	29	229	20	176	21	193	14	157	40	119	23	85	41	474	18	586	11	718	9	317	19	387	20	529	13	256	26	123	83	91	42	227	28	188	33	212	30	203	31	232	51	218	18	114	32	158	23	83	23	320	23	287	24	183	30	459	24	212	23	158	39	312	42	54	27	53	25	165	16	118	28	193	25	134	28	221	19	164	20	225	19	247	22	132	24	0	.	194	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	395	9	395	18	351	21	404	9	529	17	582	14	476	6	458	5	444	6	585	6	346	23	441	22	521	13	490	.	693	19	614	11	565	12	555	10	867	15	553	14	392	9	451	10	396	10	481	7	420	6	521	7	469	10	516	8	636	8	399	21	430	13	417	10	339	23	427	11	680	16	510	18	522	14	467	12	611	15	470	14	527	16	590	13	504	10	363	14	501	13	486	12	551	11	394	13	633	17	427	12	512	14	466	16	485	16	308	19	381	10	385	12	498	17	588	11	722	9	449	12	463	17	601	11	541	15	549	19	671	13	401	13	352	19	565	26	469	21	485	27	477	13	505	16	310	12	367	14	475	15	526	15	455	13	614	17	545	24	351	24	762	12	456	8	482	9	506	8	468	19	339	17	412	11	537	13	416	13	427	13	397	17	320	18	0	.	483	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. 2295447. Birmensdorf, Swiss Federal Research Institute WSL

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