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 NFI4/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	1080	31	1501	30	1833	30	11807	10	7184	12	8538	14	4401	16	255	83	406	46	806	31	80	.	572	42	2445	28	2443	29	1861	32	128	72	1835	30	5498	16	3824	21	5366	16	11950	12	13229	11	4332	15	2189	27	4563	22	4301	20	430	46	1729	25	1106	45	744	38	1968	26	1791	47	3217	29	2683	29	1656	30	1603	37	2406	23	884	32	1580	24	1216	28	1055	37	370	40	1008	50	693	40	1828	24	2072	26	1059	26	1220	30	1092	37	783	35	1009	34	3658	30	1941	24	328	49	64	100	29	61	3045	21	715	31	1233	35	1367	33	1035	39	2063	31	1497	31	1204	27	2309	36	615	38	261	56	1835	31	470	47	569	55	2065	23	876	36	1220	35	1022	33	412	52	467	43	214	80	584	72	10995	13	7678	16	6685	14	823	37	431	37	791	37	1619	28	1479	37	172	50	526	70	428	37	0	.	202656	2
	broadleaves	2364	21	1540	23	840	32	1685	23	151	55	833	34	5362	16	3879	17	5883	13	4322	15	799	41	1300	35	1648	26	0	.	339	43	961	29	2684	23	1574	27	414	51	1082	25	1024	29	833	28	1744	30	3255	27	1177	39	4422	15	1613	23	1045	29	1781	21	838	40	731	30	609	42	698	34	467	34	1017	36	147	48	1243	30	984	32	1190	34	1774	29	1385	35	1972	23	2379	22	1832	31	1155	33	1164	35	1625	30	1297	27	615	36	1033	37	1077	30	1010	30	1272	27	914	54	1754	33	470	36	3398	25	3100	26	5802	19	4020	22	3226	26	3261	23	1226	36	361	51	303	44	954	35	1056	35	1143	36	987	42	525	49	1543	30	226	48	904	50	785	28	1304	36	930	27	1315	46	981	37	249	47	258	67	506	59	1514	29	2438	24	4019	19	382	41	725	33	842	31	1405	25	1380	30	1151	35	643	31	1077	29	0	.	139142	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	2765	21	1652	30	2666	26	17168	10	11064	11	14420	11	8723	13	1053	40	1706	33	2454	23	80	.	911	37	3406	23	5127	21	3435	23	541	49	2917	22	6521	15	4657	18	7109	15	15205	11	14406	11	8754	12	3802	21	5608	20	6082	18	1268	32	2461	23	1715	37	1442	31	2436	24	2808	34	3364	29	3925	22	2640	27	2794	30	4179	21	2269	27	3552	19	3595	20	2887	27	1525	30	2172	31	2319	28	3125	22	2688	24	2092	26	2297	25	2102	27	2055	25	1923	33	5412	27	2411	21	3726	23	3164	26	5831	19	7065	16	3941	23	4493	20	2592	29	1396	34	2367	29	2451	25	2260	27	3452	30	1603	33	786	45	3378	25	696	43	1473	41	2850	21	2180	28	2150	26	2337	33	1394	33	716	42	472	57	1090	49	12509	12	10116	14	10704	12	1204	31	1156	29	1632	28	3024	23	2858	25	1323	32	1169	38	1504	26	0	.	341798	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	189	34	231	32	3291	13	710	20	926	27	483	22	95	72	42	61	38	52	17	.	114	45	489	49	686	33	275	37	0	.	501	29	1908	17	881	21	1920	18	2793	13	3013	17	305	24	229	34	372	32	622	23	126	75	139	33	66	62	66	55	116	46	181	37	332	39	505	33	122	42	178	49	436	31	90	38	282	27	171	42	139	50	142	68	24	77	23	71	364	43	331	41	75	41	297	57	184	45	184	36	204	50	511	27	553	30	207	94	15	71	0	.	385	29	117	62	238	59	391	37	302	47	389	32	275	34	182	34	274	46	125	53	39	82	125	44	163	71	59	66	299	29	56	59	119	43	61	53	23	100	71	66	0	.	30	100	2025	19	1877	16	1786	18	126	45	76	63	44	54	128	36	231	46	24	100	92	86	126	46	0	.	37105	3
	broadleaves	67	41	53	53	40	61	111	41	25	77	108	47	835	21	449	28	219	30	701	30	0	.	56	49	187	32	0	.	46	54	137	62	214	52	72	49	29	80	252	30	105	41	330	54	231	36	381	25	188	49	485	25	195	39	148	40	280	35	66	53	72	61	58	58	90	45	49	63	147	43	87	65	252	39	115	50	57	49	178	31	165	46	424	32	210	32	47	54	80	55	57	52	249	36	90	45	58	62	130	54	115	40	54	49	80	43	167	44	216	51	154	59	418	26	462	38	488	22	674	23	240	36	707	48	110	65	43	60	36	71	183	47	144	49	131	62	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	241	38	295	38	437	33	17	100	48	63	29	50	66	69	103	44	70	60	56	76	68	61	0	.	15027	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	223	33	214	34	340	29	4126	12	1159	17	1145	23	1184	21	95	72	97	44	225	29	17	.	160	40	626	41	900	29	346	32	29	80	753	23	2012	17	1211	21	2151	16	3173	12	3201	16	790	18	423	28	520	27	902	20	191	52	210	34	124	43	156	38	165	38	329	32	419	35	757	30	237	34	235	39	615	25	256	33	706	23	381	28	186	45	222	49	81	52	272	34	454	40	389	36	206	39	412	43	238	37	265	31	371	36	726	24	706	27	625	36	477	37	488	22	1059	19	357	34	945	40	501	34	345	42	425	31	458	30	326	30	405	37	204	42	57	64	229	39	180	73	76	55	364	30	82	47	191	39	116	39	40	71	122	66	0	.	63	61	2266	17	2172	15	2223	17	144	41	124	50	73	40	193	39	334	36	94	52	147	61	194	42	0	.	52131	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	1284	16	492	22	331	26	436	25	0	.	17	100	55	59	0	.	33	61	195	41	232	40	184	37	17	100	254	26	421	27	286	33	573	26	1018	16	1232	15	243	27	136	55	167	34	308	27	46	62	150	55	31	64	43	62	173	35	155	48	67	49	317	36	54	49	139	48	369	32	72	44	300	30	111	36	33	66	35	100	35	70	31	81	164	35	180	40	98	42	156	37	56	56	96	47	135	49	498	33	217	44	112	88	0	.	0	.	127	41	25	100	171	46	193	46	97	62	145	35	80	46	57	50	106	55	72	51	0	.	261	44	33	66	35	71	86	39	113	52	159	31	30	64	17	100	34	65	21	73	20	74	708	17	655	21	566	22	77	65	35	45	30	64	251	40	70	40	59	72	7	100	55	52	0	.	16784	4
	broadleaves	154	36	108	38	31	64	79	47	42	47	139	55	694	23	566	22	627	21	772	25	102	49	127	48	303	44	0	.	107	65	26	79	199	59	99	43	44	71	357	29	157	40	300	33	234	44	417	30	117	52	396	23	118	38	31	61	235	34	17	100	90	52	77	46	66	79	73	62	359	45	0	.	439	33	92	55	269	36	557	30	262	62	259	38	196	46	183	40	198	38	144	50	229	35	387	33	77	50	129	74	119	42	55	48	31	64	111	41	317	52	90	55	546	25	516	25	976	25	646	25	368	33	637	24	260	48	107	62	86	51	312	49	94	50	219	41	24	77	0	.	96	41	36	71	53	73	100	50	76	56	84	66	85	47	74	49	7	100	0	.	14	100	272	37	233	35	514	28	31	63	63	45	30	64	216	38	90	43	61	51	65	54	107	41	0	.	18507	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	1978	14	1058	16	957	17	1208	20	102	49	144	44	359	43	0	.	141	54	221	38	431	35	283	29	62	75	610	21	579	22	586	24	807	22	1435	15	1349	15	639	19	254	35	198	32	543	22	63	52	240	44	108	40	109	53	246	35	514	37	67	49	756	26	145	39	408	31	926	23	334	49	559	27	307	33	217	38	233	38	178	47	260	35	551	26	257	32	227	46	275	29	110	37	127	44	245	38	815	28	307	35	657	26	516	25	976	25	773	22	393	31	807	21	453	39	204	44	231	31	392	40	150	36	326	35	96	52	0	.	357	36	69	54	88	52	186	33	190	40	243	33	115	41	91	44	41	56	21	73	34	60	980	18	888	18	1079	18	108	49	97	36	61	54	467	31	161	30	119	47	71	49	162	32	0	.	35290	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	897	16	259	23	276	25	208	30	7	100	7	100	0	.	0	.	50	59	174	40	172	31	73	45	0	.	169	30	351	24	177	36	328	26	805	18	1161	16	263	27	58	50	194	30	390	25	34	64	177	55	8	100	60	63	101	38	107	48	198	61	280	35	45	50	55	56	171	36	105	54	159	33	14	71	122	51	68	60	17	100	57	48	88	52	93	41	25	77	40	61	15	71	30	64	175	46	332	40	191	38	16	100	0	.	8	100	197	50	29	50	65	60	128	49	62	52	217	50	62	54	97	47	61	43	67	57	0	.	116	40	20	100	7	100	180	39	68	52	50	73	42	47	7	100	8	100	0	.	0	.	610	19	555	22	455	26	90	47	14	71	45	48	106	41	54	49	24	77	7	100	17	100	0	.	12576	4
	broadleaves	237	30	139	43	40	61	133	40	32	81	91	45	534	23	213	30	375	23	398	26	167	53	72	43	229	51	0	.	66	63	213	69	211	48	87	41	0	.	252	33	178	37	107	66	364	33	627	40	184	44	553	26	112	42	124	47	328	36	52	55	20	100	7	100	0	.	18	100	445	59	0	.	266	43	151	39	125	45	227	36	171	55	149	34	155	36	32	63	170	46	90	44	201	36	173	41	198	42	85	44	273	36	30	64	161	40	122	52	394	46	193	48	768	36	620	27	1040	24	432	31	320	30	1050	28	60	59	209	60	36	66	130	46	208	39	106	46	136	58	42	60	244	40	33	71	36	71	0	.	52	54	188	46	118	57	153	58	38	71	0	.	33	66	320	51	216	40	617	24	115	53	135	35	112	62	89	41	104	55	70	44	85	60	28	50	0	.	17918	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	1431	13	472	19	651	18	606	20	174	51	79	40	229	51	0	.	115	44	387	42	383	32	160	30	0	.	420	25	529	20	284	34	692	23	1432	21	1345	15	816	20	169	35	317	33	719	23	86	46	197	50	15	71	60	63	119	36	552	49	198	61	546	28	195	33	180	36	399	26	276	41	308	26	169	34	153	44	237	38	107	43	258	32	260	37	291	34	111	46	313	34	45	48	192	35	297	35	726	31	384	31	785	36	620	27	1048	23	629	27	350	28	1115	26	187	44	270	49	254	44	193	36	305	34	166	34	203	48	42	60	361	33	53	58	43	62	180	39	120	39	237	43	160	46	160	55	46	61	0	.	33	66	930	22	771	20	1072	18	205	37	149	34	158	51	195	34	158	43	94	38	92	56	45	49	0	.	30493	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	457	25	105	37	101	31	60	43	25	76	0	.	49	54	0	.	22	75	15	100	55	58	33	66	0	.	113	43	333	47	6	100	98	39	479	28	637	22	83	41	38	61	50	62	63	50	0	.	14	100	0	.	0	.	39	54	13	71	0	.	161	70	17	100	69	53	136	45	37	65	51	64	24	77	0	.	31	71	0	.	7	100	96	81	44	72	0	.	7	100	14	71	13	100	14	71	47	47	56	49	7	100	0	.	8	100	114	48	7	100	29	50	14	71	90	91	76	56	13	71	33	64	7	100	0	.	0	.	108	47	0	.	8	100	22	76	33	62	7	100	20	75	8	100	0	.	14	100	7	100	546	24	300	37	313	31	42	70	7	100	31	51	62	53	23	76	0	.	7	100	34	71	0	.	5775	7
	broadleaves	163	40	120	38	31	63	98	52	13	71	157	42	300	28	181	31	194	29	195	34	40	54	42	85	34	71	0	.	24	77	17	100	127	57	41	71	0	.	469	31	100	52	118	44	178	43	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	152	36	42	71	165	42	108	51	43	62	65	73	100	58	189	38	68	48	57	85	21	58	160	54	24	77	82	47	74	50	349	39	230	51	333	40	414	29	698	26	433	33	265	35	399	34	46	100	248	70	22	100	17	71	57	48	79	45	0	.	0	.	17	100	36	52	0	.	0	.	26	78	31	81	0	.	17	100	7	100	14	100	37	71	194	63	142	50	764	34	7	100	114	46	0	.	122	37	96	47	13	100	17	100	54	57	0	.	10699	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	757	19	286	26	295	24	255	28	65	50	42	85	83	47	0	.	46	54	32	71	182	44	73	49	0	.	582	26	433	39	124	42	276	34	733	22	709	21	388	23	124	36	81	51	199	35	0	.	14	100	0	.	17	100	59	49	220	57	0	.	266	47	128	50	136	37	287	32	79	61	216	40	132	44	43	62	97	55	100	58	196	37	164	52	101	57	21	58	167	53	38	55	95	48	87	44	396	35	286	42	340	39	414	29	706	26	546	28	272	34	428	32	60	79	337	57	98	49	30	50	89	38	87	42	0	.	0	.	125	43	36	52	8	100	22	76	59	49	38	68	20	75	25	76	7	100	27	100	43	62	740	24	442	30	1077	28	49	62	121	44	31	51	184	30	120	40	13	100	23	77	88	44	0	.	16474	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	284	41	437	32	4222	15	1091	20	623	20	1022	23	42	61	76	65	253	47	0	.	143	54	450	42	766	34	316	38	7	100	575	36	1133	22	653	25	1518	23	2920	15	3245	18	940	28	432	38	270	30	1425	29	104	53	83	46	59	52	52	58	320	36	282	43	341	34	642	47	129	49	354	49	760	26	233	57	442	38	224	44	130	45	152	61	114	62	181	40	277	32	687	47	248	51	140	56	76	63	147	83	122	39	463	28	420	31	7	100	0	.	0	.	280	36	114	39	176	54	198	38	111	52	651	34	253	37	261	44	602	37	165	63	0	.	370	33	17	100	47	50	138	44	128	66	171	37	173	66	50	74	68	65	79	83	87	55	2911	15	2149	20	2682	20	251	37	460	69	150	41	110	42	48	55	64	48	17	100	34	71	0	.	43473	4
	broadleaves	367	38	150	48	17	100	162	40	33	71	299	38	782	23	361	30	461	29	419	32	244	48	101	55	264	46	0	.	237	52	321	48	171	41	251	56	143	50	893	36	283	40	125	64	479	33	1326	25	319	50	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	419	26	322	43	290	33	243	37	24	77	264	54	240	48	271	35	296	46	94	59	400	50	79	52	114	89	24	77	360	45	399	59	258	48	1085	34	1019	31	2283	22	994	25	695	34	1523	28	196	42	288	71	17	100	368	45	263	42	118	38	146	48	86	72	280	48	57	52	53	58	56	76	180	47	111	44	200	53	173	58	17	100	39	100	0	.	365	28	383	35	1164	22	42	85	24	77	57	52	380	41	218	64	37	71	92	62	47	58	0	.	28748	4
	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	317	40	736	28	5019	13	1452	19	1084	19	1441	20	305	42	176	42	517	35	0	.	379	43	771	33	938	29	567	37	150	49	1468	28	1432	19	778	24	1997	20	4247	13	3564	17	2110	19	622	34	508	30	2057	23	318	38	173	40	93	61	52	58	359	34	428	34	417	31	918	37	198	39	583	39	1179	21	554	35	732	28	467	30	153	45	450	41	353	44	452	31	573	29	782	43	648	37	219	40	189	59	171	72	482	35	862	34	678	28	1092	34	1019	31	2283	22	1273	21	809	31	1700	26	395	32	399	53	668	34	621	35	524	32	719	33	311	47	86	72	650	32	73	56	100	38	194	43	308	48	282	30	373	43	223	55	85	55	117	71	87	55	3294	14	2531	18	3846	16	293	35	484	66	207	33	489	36	266	53	118	42	109	55	81	45	0	.	72357	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	1442	28	2216	29	2912	25	21958	9	9841	12	10794	13	6609	14	424	65	548	48	1201	29	98	.	934	40	3768	28	4354	22	2741	29	152	68	3447	23	9644	14	5827	18	9804	14	19964	11	22518	10	6167	15	3081	25	5615	20	7110	17	739	41	2292	25	1269	41	966	35	2717	24	2528	41	4154	28	4588	28	2021	28	2399	34	4277	21	1421	31	2815	22	1760	26	1479	32	797	48	1198	43	993	34	2817	23	3408	25	1506	27	1861	32	1436	35	1255	30	1659	29	5508	27	3379	23	678	62	79	82	45	53	4147	20	1007	30	1912	33	2290	30	1696	35	3542	27	2179	29	1834	25	3359	34	1045	36	300	56	2815	26	701	47	724	50	2790	21	1274	34	1725	30	1348	32	517	49	648	42	327	81	727	65	17794	11	13212	14	12487	14	1410	33	1022	44	1091	32	2276	29	1905	31	342	49	654	68	693	36	0	.	318368	2
	broadleaves	3351	21	2110	22	998	30	2268	23	296	47	1627	27	8507	14	5650	15	7759	12	6807	14	1352	39	1697	32	2665	25	0	.	818	43	1674	32	3606	23	2124	25	631	42	3305	22	1847	26	1813	26	3229	28	6261	21	2057	37	7331	14	2314	21	1617	25	3375	20	1188	38	1003	27	785	42	870	35	666	31	2321	33	311	42	2580	28	1523	27	1937	32	3308	23	2346	37	3258	22	3291	21	2160	28	1931	28	1794	31	2764	27	2310	27	1100	35	1799	38	1823	28	1286	30	1650	25	1748	43	3429	31	1394	38	6548	22	6131	21	11286	17	7199	19	5115	23	7576	20	1898	35	1256	54	500	40	1965	32	1821	33	1795	33	1373	38	671	45	2286	26	405	45	1063	49	1006	28	1664	34	1416	28	1773	38	1416	37	369	42	311	65	623	60	2906	23	3708	21	7514	17	594	40	1109	31	1070	29	2276	25	1992	29	1402	34	957	31	1381	27	0	.	230039	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	3710	20	2513	28	4540	22	30480	9	15491	10	18553	10	13416	13	1795	35	2245	31	3866	22	98	.	1752	34	5443	23	7960	18	4865	22	782	44	6751	18	11506	13	7640	16	13033	13	26225	10	24574	10	13497	12	5395	20	7233	18	10502	16	1927	30	3295	23	2054	35	1836	30	3383	23	4850	28	4465	27	7168	21	3544	24	4336	28	7585	19	3768	28	6073	18	5050	19	3639	25	2763	27	2992	28	3757	25	5128	22	4508	22	3305	26	3684	24	2722	26	2905	23	3407	28	8937	25	4772	21	7226	21	6210	21	11332	17	11346	15	6122	21	9488	18	4188	27	2952	33	4042	26	4144	23	3655	24	5154	28	2417	32	971	44	5101	22	1107	43	1787	40	3795	20	2938	27	3141	23	3121	29	1933	33	1017	39	638	59	1350	47	20719	11	16920	13	20001	12	2003	29	2131	29	2161	26	4553	23	3897	23	1761	29	1612	35	2073	26	0	.	548544	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. 2277521. Birmensdorf, Swiss Federal Research Institute WSL

back