Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

top

2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

top

3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

top

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

top

5. Two types of changes?

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

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

top

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

top

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

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

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

top

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

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

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

top

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

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

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

top

NFI5
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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	727	39	775	34	802	37	1070	32	1323	32	1833	30	10569	11	7134	12	8538	14	4250	16	255	83	406	46	788	31	80	.	572	42	1944	30	1862	27	1861	32	128	72	1687	32	4460	17	3615	22	3962	19	9943	14	10901	12	4332	15	2160	27	4563	22	4148	20	430	46	1729	25	1106	45	744	38	1968	26	1756	48	3113	30	2380	30	1656	30	1426	40	2156	24	884	32	1391	26	1216	28	1055	37	370	40	1008	50	686	40	1503	28	1978	27	911	27	1220	30	1092	37	783	35	850	38	3319	32	1674	26	217	59	0	.	29	61	1810	26	621	34	628	43	1341	33	794	46	2009	32	1208	35	1080	29	1896	40	615	38	261	56	1640	32	470	47	569	55	1968	24	850	37	1185	36	1022	33	365	57	467	43	214	80	584	72	9905	14	7345	17	6118	15	714	41	414	39	791	37	1586	29	1479	37	172	50	526	70	428	37	0	.	184413	2
	broadleaves	2364	21	1540	23	840	32	1550	24	151	55	833	34	4275	16	3879	17	5866	13	4278	15	799	41	1300	35	1462	27	0	.	339	43	961	29	2599	24	1574	27	316	59	1025	27	896	30	731	31	1440	35	3101	28	946	45	4422	15	1570	24	1045	29	1764	21	838	40	724	30	609	42	698	34	467	34	922	38	147	48	1190	31	984	32	1156	35	1655	31	1385	35	1721	25	2379	22	1832	31	1155	33	1164	35	1563	32	1171	29	312	42	940	39	1077	30	1010	30	1252	27	834	58	1507	37	374	42	3004	27	2607	29	5766	19	3368	24	2920	28	2928	25	1226	36	88	82	281	47	954	35	1005	36	908	40	987	42	525	49	1251	29	179	55	904	50	766	28	1304	36	887	27	1315	46	981	37	249	47	258	67	506	59	1432	30	1809	27	3329	21	374	41	709	33	842	31	1218	25	1380	30	1151	35	588	32	1077	29	0	.	130005	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	3091	21	2315	23	1641	28	2619	21	1474	31	2666	26	14845	10	11013	11	14404	11	8528	14	1053	40	1706	33	2250	24	80	.	911	37	2905	23	4461	21	3435	23	444	56	2712	24	5357	16	4346	20	5402	17	13045	13	11847	11	8754	12	3730	21	5608	20	5912	18	1268	32	2453	23	1715	37	1442	31	2436	24	2678	35	3260	29	3569	23	2640	27	2582	32	3811	23	2269	27	3112	21	3595	20	2887	27	1525	30	2172	31	2249	28	2674	24	2290	26	1851	27	2297	25	2102	27	2035	25	1684	37	4826	29	2048	23	3221	26	2607	29	5795	19	5178	20	3541	25	3556	23	2567	29	882	45	2290	30	2162	26	2085	28	2805	34	1603	33	786	45	2891	24	649	46	1473	41	2734	22	2154	28	2072	26	2337	33	1347	34	716	42	472	57	1090	49	11336	13	9154	15	9447	13	1088	34	1123	29	1632	28	2804	24	2858	25	1323	32	1114	39	1504	26	0	.	314419	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	159	44	241	42	144	71	112	50	174	35	231	32	2650	13	710	20	926	27	483	22	95	72	42	61	28	61	17	.	114	45	315	61	506	30	275	37	0	.	471	30	1686	19	723	21	1358	20	2298	14	2395	19	305	24	214	36	372	32	563	24	126	75	139	33	66	62	66	55	116	46	146	41	332	39	384	39	122	42	145	57	368	34	90	38	200	30	171	42	139	50	142	68	24	77	23	71	215	38	317	43	75	41	297	57	184	45	184	36	204	50	413	26	463	33	6	100	15	71	0	.	199	45	68	78	212	66	329	40	280	51	363	34	254	36	174	35	256	49	125	53	39	82	125	44	163	71	59	66	292	30	56	59	119	43	61	53	23	100	71	66	0	.	30	100	1804	20	1689	17	1530	20	78	55	76	63	44	54	128	36	231	46	24	100	92	86	126	46	0	.	31899	4
	broadleaves	67	41	53	53	40	61	87	45	25	77	108	47	632	21	449	28	196	31	701	30	0	.	56	49	141	37	0	.	46	54	137	62	214	52	72	49	29	80	226	33	87	45	132	53	193	41	331	27	145	60	485	25	152	41	148	40	280	35	66	53	72	61	58	58	90	45	49	63	119	47	87	65	128	36	115	50	57	49	143	34	165	46	332	31	210	32	47	54	80	55	57	52	242	37	69	50	58	62	130	54	115	40	54	49	80	43	131	49	118	42	154	59	315	32	444	40	470	23	506	26	224	38	626	54	110	65	0	.	36	71	183	47	144	49	78	91	79	62	17	100	104	42	17	100	17	100	65	51	26	78	72	55	54	57	17	100	51	88	0	.	33	72	218	41	199	49	320	40	17	100	48	63	29	50	66	69	103	44	70	60	56	76	68	61	0	.	13141	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	226	33	294	35	185	57	199	35	199	36	340	29	3282	12	1159	17	1122	23	1184	21	95	72	97	44	170	33	17	.	160	40	452	47	720	28	346	32	29	80	697	25	1773	18	854	20	1551	19	2629	13	2540	18	790	18	366	29	520	27	843	21	191	52	210	34	124	43	156	38	165	38	265	33	419	35	512	34	237	34	202	44	510	28	256	33	532	24	381	28	186	45	222	49	81	52	265	34	284	32	375	38	206	39	412	43	238	37	265	31	336	38	532	22	616	29	322	31	458	39	470	23	705	23	292	37	838	44	439	36	280	51	399	33	437	32	318	30	334	43	204	42	57	64	229	39	180	73	76	55	357	30	82	47	191	39	116	39	40	71	122	66	0	.	63	61	2022	19	1888	16	1850	18	96	48	124	50	73	40	193	39	334	36	94	52	147	61	194	42	0	.	45040	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	99	61	26	78	58	58	23	77	114	59	283	43	1134	17	492	22	331	26	428	25	0	.	17	100	38	73	0	.	33	61	195	41	217	43	184	37	17	100	230	26	353	30	279	33	387	28	899	18	932	17	243	27	136	55	167	34	262	26	46	62	150	55	31	64	43	62	173	35	98	49	67	49	317	36	54	49	97	54	265	36	72	44	262	32	111	36	33	66	35	100	35	70	31	81	145	37	180	40	98	42	156	37	56	56	96	47	120	55	444	35	199	47	7	100	0	.	0	.	114	45	25	100	135	52	193	46	97	62	127	38	73	50	57	50	54	49	72	51	0	.	261	44	33	66	35	71	72	42	99	58	159	31	30	64	17	100	34	65	21	73	20	74	630	18	631	22	524	23	33	71	35	45	30	64	251	40	70	40	59	72	7	100	55	52	0	.	15049	4
	broadleaves	154	36	108	38	31	64	79	47	42	47	139	55	570	25	566	22	610	22	754	25	102	49	127	48	270	49	0	.	107	65	26	79	199	59	99	43	25	100	304	31	157	40	209	37	234	44	314	34	91	60	396	23	118	38	31	61	217	36	17	100	90	52	77	46	66	79	73	62	359	45	0	.	439	33	92	55	269	36	373	33	262	62	250	39	196	46	183	40	198	38	144	50	212	37	287	33	54	58	36	53	119	42	55	48	31	64	111	41	285	56	90	55	484	27	465	27	960	25	526	28	312	36	582	25	260	48	18	100	63	59	312	49	94	50	166	49	24	77	0	.	96	41	20	100	53	73	100	50	76	56	84	66	85	47	74	49	7	100	0	.	14	100	245	39	161	37	441	31	31	63	63	45	30	64	179	41	90	43	61	51	65	54	107	41	0	.	16796	4
	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	253	37	134	34	89	44	102	42	156	49	422	37	1704	15	1058	16	940	17	1182	20	102	49	144	44	308	49	0	.	141	54	221	38	416	36	283	29	42	100	533	22	511	24	488	26	622	24	1213	16	1023	16	639	19	254	35	198	32	479	23	63	52	240	44	108	40	109	53	246	35	457	40	67	49	756	26	145	39	366	33	638	27	334	49	512	28	307	33	217	38	233	38	178	47	243	36	432	27	234	34	134	34	275	29	110	37	127	44	231	40	728	31	289	37	491	27	465	27	960	25	639	25	337	34	717	22	453	39	116	55	191	34	385	41	150	36	221	42	96	52	0	.	357	36	53	64	88	52	173	34	175	43	243	33	115	41	91	44	41	56	21	73	34	60	875	20	792	19	965	19	64	48	97	36	61	54	431	33	161	30	119	47	71	49	162	32	0	.	31845	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	17	100	36	71	14	71	33	45	101	46	106	44	750	17	259	23	276	25	208	30	7	100	7	100	0	.	0	.	50	59	160	42	157	33	73	45	0	.	162	31	330	25	177	36	272	27	622	21	900	17	263	27	58	50	194	30	372	26	34	64	177	55	8	100	60	63	101	38	78	55	198	61	280	35	45	50	30	60	150	38	105	54	146	35	14	71	122	51	68	60	17	100	50	53	34	62	86	43	25	77	40	61	15	71	30	64	141	52	278	46	130	38	16	100	0	.	8	100	149	63	29	50	58	66	110	54	26	77	199	53	62	54	72	57	43	46	67	57	0	.	116	40	20	100	7	100	173	40	68	52	50	73	42	47	7	100	8	100	0	.	0	.	552	20	530	23	365	26	51	54	14	71	45	48	106	41	54	49	24	77	7	100	17	100	0	.	11160	4
	broadleaves	237	30	139	43	40	61	133	40	32	81	91	45	429	25	213	30	375	23	371	27	167	53	72	43	208	55	0	.	66	63	213	69	126	44	87	41	0	.	234	35	149	39	107	66	330	35	577	43	114	51	553	26	112	42	124	47	328	36	52	55	20	100	7	100	0	.	18	100	445	59	0	.	266	43	151	39	118	48	160	38	171	55	128	38	155	36	32	63	170	46	90	44	201	36	105	47	108	49	85	44	273	36	30	64	161	40	122	52	362	49	170	53	727	38	602	28	1022	24	356	37	313	31	993	29	60	59	147	79	36	66	130	46	164	41	53	54	136	58	42	60	244	40	33	71	36	71	0	.	52	54	188	46	118	57	153	58	38	71	0	.	33	66	281	57	159	50	501	28	99	59	118	37	112	62	70	44	104	55	70	44	85	60	28	50	0	.	16534	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	254	29	175	37	54	49	167	33	134	40	197	39	1179	14	472	19	651	18	578	21	174	51	79	40	208	55	0	.	115	44	373	44	282	30	160	30	0	.	396	26	479	21	284	34	602	24	1199	24	1014	16	816	20	169	35	317	33	701	23	86	46	197	50	15	71	60	63	119	36	523	51	198	61	546	28	195	33	148	40	309	28	276	41	274	28	169	34	153	44	237	38	107	43	251	33	139	46	195	39	111	46	313	34	45	48	192	35	263	37	639	35	300	34	744	37	602	28	1030	24	505	32	343	29	1051	28	169	47	173	68	236	46	193	36	236	39	96	38	203	48	42	60	361	33	53	58	43	62	173	40	120	39	237	43	160	46	160	55	46	61	0	.	33	66	834	23	689	21	867	20	150	43	132	36	158	51	177	36	158	43	94	38	92	56	45	49	0	.	27693	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	17	100	0	.	7	100	0	.	27	71	21	57	370	24	105	37	101	31	60	43	25	76	0	.	49	54	0	.	22	75	15	100	39	71	33	66	0	.	113	43	301	51	6	100	81	42	333	34	413	26	83	41	38	61	50	62	45	57	0	.	14	100	0	.	0	.	39	54	13	71	0	.	161	70	17	100	69	53	97	48	37	65	24	74	24	77	0	.	31	71	0	.	7	100	20	58	37	83	0	.	7	100	14	71	13	100	14	71	47	47	30	63	0	.	0	.	8	100	23	76	7	100	29	50	14	71	90	91	58	67	13	71	33	64	7	100	0	.	0	.	108	47	0	.	8	100	15	100	33	62	7	100	20	75	8	100	0	.	14	100	7	100	438	27	204	48	275	34	21	100	7	100	31	51	62	53	23	76	0	.	7	100	34	71	0	.	4674	8
	broadleaves	163	40	120	38	31	63	98	52	13	71	157	42	273	29	181	31	194	29	195	34	40	54	42	85	34	71	0	.	24	77	17	100	127	57	41	71	0	.	400	32	82	60	98	49	74	57	254	37	71	63	305	27	86	45	31	64	136	45	0	.	0	.	0	.	17	100	19	100	207	59	0	.	105	48	112	55	67	54	116	41	42	71	137	46	108	51	43	62	65	73	100	58	189	38	68	48	10	100	21	58	160	54	24	77	75	51	34	71	276	47	212	55	315	42	414	29	698	26	193	42	248	37	374	36	46	100	18	100	22	100	17	71	57	48	62	51	0	.	0	.	17	100	23	59	0	.	0	.	26	78	31	81	0	.	17	100	7	100	14	100	37	71	78	50	142	50	468	33	7	100	114	46	0	.	104	40	96	47	13	100	17	100	54	57	0	.	9222	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	180	38	120	38	37	55	98	52	40	53	179	39	644	19	286	26	295	24	255	28	65	50	42	85	83	47	0	.	46	54	32	71	166	47	73	49	0	.	513	27	384	43	104	47	155	35	588	25	485	24	388	23	124	36	81	51	181	37	0	.	14	100	0	.	17	100	59	49	220	57	0	.	266	47	128	50	136	37	213	33	79	61	161	41	132	44	43	62	97	55	100	58	196	37	87	41	47	69	21	58	167	53	38	55	88	51	47	54	323	41	242	48	315	42	414	29	706	26	216	38	256	36	403	34	60	79	108	77	80	56	30	50	89	38	69	47	0	.	0	.	125	43	23	59	8	100	15	100	59	49	38	68	20	75	25	76	7	100	27	100	43	62	516	24	346	35	743	28	28	79	121	44	31	51	166	32	120	40	13	100	23	77	88	44	0	.	13896	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	116	46	98	62	23	76	193	29	266	43	437	32	3833	15	1091	20	623	20	1022	23	42	61	76	65	253	47	0	.	143	54	433	44	766	34	316	38	7	100	454	37	981	24	620	26	1324	26	2447	17	2761	20	940	28	432	38	270	30	1407	30	104	53	83	46	59	52	52	58	320	36	275	44	341	34	624	48	129	49	319	53	592	29	233	57	434	38	224	44	130	45	152	61	114	62	181	40	224	35	680	48	248	51	140	56	76	63	147	83	89	44	445	29	395	33	7	100	0	.	0	.	151	57	80	46	79	73	198	38	94	59	651	34	246	38	210	49	567	38	165	63	0	.	370	33	17	100	47	50	138	44	128	66	163	38	173	66	50	74	68	65	79	83	87	55	2454	17	2073	21	2389	22	251	37	460	69	150	41	95	46	48	55	64	48	17	100	34	71	0	.	39981	4
	broadleaves	367	38	150	48	17	100	162	40	33	71	299	38	671	25	361	30	394	29	400	33	244	48	101	55	264	46	0	.	237	52	303	50	171	41	251	56	89	52	876	37	265	42	125	64	426	36	1234	26	293	54	1171	23	190	35	239	42	615	30	214	45	90	64	34	100	0	.	39	100	146	52	76	55	275	56	70	42	229	59	377	27	322	43	290	33	243	37	24	77	264	54	240	48	271	35	198	58	71	71	307	58	79	52	114	89	24	77	343	47	363	64	207	56	1049	36	1019	31	2267	22	747	28	661	36	1455	29	196	42	147	100	17	100	368	45	245	45	82	45	146	48	86	72	280	48	50	58	53	58	56	76	180	47	103	47	200	53	173	58	17	100	39	100	0	.	326	29	365	37	951	26	42	85	24	77	57	52	380	41	218	64	37	71	92	62	47	58	0	.	27030	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	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	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	137	40
	total	483	31	248	38	40	85	356	25	299	42	736	28	4518	14	1452	19	1017	19	1421	20	305	42	176	42	517	35	0	.	379	43	736	34	938	29	567	37	95	52	1330	29	1261	21	745	24	1750	22	3682	14	3054	19	2110	19	622	34	508	30	2039	23	318	38	173	40	93	61	52	58	359	34	421	34	417	31	900	37	198	39	549	41	969	23	554	35	723	28	467	30	153	45	450	41	353	44	452	31	421	34	751	45	555	39	219	40	189	59	171	72	432	39	808	36	602	31	1056	35	1019	31	2267	22	898	25	741	33	1534	28	395	32	241	65	668	34	614	36	455	34	649	36	311	47	86	72	650	32	67	61	100	38	194	43	308	48	267	32	373	43	223	55	85	55	117	71	87	55	2799	15	2439	19	3339	18	293	35	484	66	207	33	474	37	266	53	118	42	109	55	81	45	0	.	67148	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	1135	33	1177	35	1047	37	1432	28	2006	30	2912	25	19305	10	9790	12	10794	13	6450	14	424	65	548	48	1157	30	98	.	934	40	3062	31	3548	21	2741	29	152	68	3116	25	8111	16	5420	19	7384	16	16542	12	18303	11	6167	15	3037	25	5615	20	6797	18	739	41	2292	25	1269	41	966	35	2717	24	2366	43	4051	28	4145	30	2021	28	2087	37	3627	23	1421	31	2457	24	1760	26	1479	32	797	48	1198	43	979	34	2141	25	3278	26	1358	28	1861	32	1436	35	1255	30	1418	32	4945	29	2892	25	254	59	15	71	45	53	2446	26	830	33	1142	41	2185	31	1380	41	3408	28	1855	31	1625	27	2824	38	1045	36	300	56	2620	27	701	47	724	50	2659	22	1233	35	1683	30	1348	32	470	53	648	42	327	81	727	65	15783	12	12471	15	11201	15	1148	37	1005	45	1091	32	2228	29	1905	31	342	49	654	68	693	36	0	.	287176	2
	broadleaves	3351	21	2110	22	998	30	2109	24	296	47	1627	27	6851	14	5650	15	7635	12	6698	15	1352	39	1697	32	2379	27	0	.	818	43	1657	33	3436	23	2124	25	458	51	3066	23	1637	28	1402	27	2697	33	5813	22	1660	42	7331	14	2229	22	1617	25	3340	20	1188	38	995	27	785	42	870	35	666	31	2198	35	311	42	2403	29	1523	27	1895	33	2823	25	2346	37	2858	23	3291	21	2160	28	1931	28	1794	31	2677	28	1897	30	613	36	1519	40	1823	28	1286	30	1623	26	1575	47	2911	35	1207	43	5894	24	5551	22	11182	17	5695	22	4679	25	6958	22	1898	35	420	80	455	43	1965	32	1708	34	1350	38	1373	38	671	45	1993	27	322	51	1063	49	987	28	1664	34	1366	29	1773	38	1416	37	369	42	311	65	623	60	2580	25	2835	23	6010	18	571	41	1075	31	1070	29	2016	25	1992	29	1402	34	902	33	1381	27	0	.	212728	2
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	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	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	137	40
	total	4487	20	3287	23	2045	27	3541	20	2302	29	4540	22	26172	9	15440	10	18429	10	13149	13	1795	35	2245	31	3536	24	98	.	1752	34	4719	24	6983	18	4865	22	610	52	6182	19	9764	14	6822	17	10081	15	22355	11	19963	11	13497	12	5265	20	7233	18	10155	16	1927	30	3287	23	2054	35	1836	30	3383	23	4564	29	4361	27	6549	22	3544	24	3982	29	6450	20	3768	28	5314	20	5050	19	3639	25	2763	27	2992	28	3656	26	4038	24	3892	24	2877	28	3684	24	2722	26	2878	23	2993	31	7856	27	4098	23	6149	23	5566	22	11227	17	8141	18	5509	23	8099	20	4083	28	1800	40	3863	27	3820	24	3334	26	4174	32	2417	32	971	44	4614	23	1023	46	1787	40	3645	21	2898	27	3049	24	3121	29	1887	34	1017	39	638	59	1350	47	18382	11	15306	14	17210	13	1719	31	2081	30	2161	26	4244	23	3897	23	1761	29	1557	36	2073	26	0	.	500040	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. 2277502. Birmensdorf, Swiss Federal Research Institute WSL

back