Selection from the list of topics

back

total number of stems NFI1 #243
Number of stems of all living and dead trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm according to the method used in NFI1. In NFI1, dead trees were only included if their wood was still usable. The total number of stems NFI1 is the sum of the number of stems and the number of deadwood stems NFI1.

altitudinal vegetation belts (NaiS, 3 classes) #2668
Altitudinal vegetation belts in the system used in the guidelines for monitoring the sustainability and performance of protection forests (NaiS; Frehner et al. 2005), reduced to three classes. The variable represents a simplification of the NaiS altititudinal vegetation belts in six classes (NAISHSTKOMB6KL) in which the class «hyperinsubric and colline» is mearged with «submontane» to form the class «hyperinsubric, colline, submontane», the class «lower and upper montane» with «high montane» to form the class «montane» and the class «subalpine» with «upper subalpine» to form the class «subalpine». The information is based on the altitudinal vegetation belts determined by experts (accessible forest sample plots of NFI4 on the 1.4-km network; Arge Frehner et al. 2020), as well as on the altitudinal vegetation belts modelled for the period 1981-2010 (other sample plots; Zischg et al. 2021).

diameter at breast height (10 classes) #1317
Diameter at breast height (dbh) of the trees and shrubs ≥12 cm dbh – in ten classes. Reference: Field Survey (MID 60: Brusthöhendurchmesser, MID 62: Umfang)

forest district (2024) #2777
Regional demarcation with the forest districts as a unit. This variable was implemented in the year 2024 and is based on a survey with the cantonal forest services in the years 2022/2023.

accessible forest without shrub forest #434
Forest of which less than two-thirds is covered with shrubs that can be accessed on foot.

1.4-km grid #410
NFI's sampling grid with a mesh size of 1.4 km. The 1.4-km grid is the grid size covering all the previous terrestrial Inventories, which is why it is also called the base grid.

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

NFI1
total number of stems NFI1
altitudinal vegetation belts (NaiS, 3 classes) · diameter at breast height (10 classes)
regional demarcation: forest district (2024)
unit: n/ha
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		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
altitudinal vegetation belts (NaiS, 3 classes)	diameter at breast height (10 classes)	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	450	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	450	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	550	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	550	.
subalpine	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	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	147	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	58	120	25	153	39	136	14	149	15	192	10	0	.	0	.	0	.	300	64	0	.	0	.	0	.	0	.	0	.	594	38	264	76	254	41	0	.	300	71	164	29	0	.	550	.	0	.	0	.	0	.	0	.	0	.	33	41	0	.	0	.	0	.	0	.	0	.	30	54	87	22	109	25	200	.	0	.	0	.	118	34	33	82	350	.	0	.	84	33	167	32	201	18	33	37	83	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	250	.	0	.	0	.	0	.	0	.	118	13	174	14	140	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	5
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	128	15	173	10	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	72	115	20	109	27	103	21	111	16	98	9	0	.	0	.	0	.	68	77	0	.	0	.	0	.	0	.	0	.	141	64	75	50	175	37	0	.	50	71	78	33	0	.	350	.	0	.	0	.	0	.	0	.	0	.	50	48	0	.	0	.	0	.	0	.	0	.	55	44	78	34	87	26	0	.	0	.	0	.	54	32	49	48	200	.	50	.	84	43	129	45	26	69	102	48	57	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	.	0	.	0	.	0	.	0	.	85	14	139	14	152	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	106	5
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	87	14	32	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	54	48	21	42	21	82	16	80	12	65	9	0	.	0	.	0	.	54	77	0	.	0	.	0	.	0	.	0	.	39	46	48	62	58	28	0	.	25	71	69	41	0	.	60	.	0	.	0	.	0	.	0	.	0	.	64	52	0	.	0	.	0	.	0	.	0	.	25	59	30	31	47	35	0	.	0	.	0	.	49	31	46	26	0	.	0	.	64	44	101	61	102	69	39	42	29	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	260	.	0	.	0	.	0	.	0	.	56	11	66	14	63	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	4
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	34	17	37	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	25	39	19	21	33	46	14	44	12	34	9	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	86	48	66	13	38	0	.	10	71	29	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	48	0	.	0	.	0	.	0	.	0	.	30	31	39	18	13	37	0	.	0	.	0	.	29	38	20	0	0	.	20	.	55	66	25	32	81	52	16	22	62	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	.	0	.	0	.	0	.	0	.	28	10	36	15	50	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	4
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	12	20	9	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	50	20	21	15	25	21	20	16	16	13	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	86	15	55	8	65	0	.	20	0	7	66	0	.	20	.	0	.	0	.	0	.	0	.	0	.	27	81	0	.	0	.	0	.	0	.	0	.	14	40	19	30	9	59	0	.	0	.	0	.	12	42	7	82	0	.	20	.	20	46	26	86	60	23	8	55	15	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	.	0	.	0	.	0	.	0	.	14	14	20	17	15	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	6
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	8	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	66	5	31	4	75	6	29	7	22	3	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	86	10	51	3	93	0	.	10	71	4	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	48	0	.	0	.	0	.	0	.	0	.	8	53	4	57	2	96	0	.	0	.	0	.	6	49	14	80	0	.	20	.	7	82	0	.	0	.	0	.	19	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	22	1	56	5	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	9
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	1	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	61	3	50	1	98	4	36	1	49	2	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	86	5	93	0	.	0	.	2	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	48	4	57	2	96	0	.	0	.	0	.	4	64	0	.	0	.	20	.	7	82	0	.	20	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	24	1	56	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	12
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	62	1	99	1	97	1	56	0	99	1	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	95	2	67	2	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	44	1	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	19
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	69	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	33
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	94	0	.	0	.	0	.	0	99	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	70	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	35
	total	0	.	0	.	0	.	0	.	0	.	0	.	417	10	251	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	287	42	350	13	346	23	400	11	409	9	409	6	0	.	0	.	0	.	422	68	0	.	0	.	0	.	0	.	0	.	825	37	465	50	516	32	0	.	415	61	360	21	0	.	980	.	0	.	0	.	0	.	0	.	0	.	214	23	0	.	0	.	0	.	0	.	0	.	172	23	263	16	271	20	200	.	0	.	0	.	271	20	169	30	550	.	130	.	321	34	448	25	491	40	199	34	272	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	790	.	0	.	0	.	0	.	0	.	316	8	437	10	426	13	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	380	3
montane	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	.
	12-20 cm	158	27	125	14	203	65	182	24	229	21	226	17	195	7	180	8	152	16	269	8	26	68	126	41	213	20	0	.	277	46	300	15	256	13	247	20	0	.	233	16	164	17	190	15	157	15	195	9	157	13	214	9	216	19	173	17	296	11	142	31	211	22	160	28	197	23	178	15	253	17	221	16	232	15	202	28	226	16	217	18	190	26	192	12	387	21	137	24	226	19	294	39	207	22	210	16	226	18	201	21	207	31	0	.	123	47	138	24	200	20	121	26	234	17	222	20	249	24	319	14	262	21	276	18	228	17	300	33	256	18	210	22	154	27	296	18	217	28	24	74	197	17	318	23	149	21	166	13	119	16	192	21	124	23	248	25	201	42	84	36	149	32	263	11	224	12	252	10	186	24	304	36	65	81	164	21	161	22	0	.	239	66	76	69	0	.	211	2
	21-30 cm	146	15	75	71	128	31	119	31	218	16	203	16	112	7	106	8	106	15	157	8	48	73	135	27	119	19	0	.	135	28	146	23	143	12	150	17	0	.	147	16	99	15	132	13	81	18	104	10	122	13	131	9	136	16	67	17	148	12	70	25	94	18	211	20	84	21	119	15	174	17	135	17	112	15	161	14	173	21	138	11	112	18	161	11	187	24	92	33	145	16	56	47	166	15	110	16	136	16	105	18	108	16	0	.	100	43	44	45	78	20	76	28	90	19	136	16	149	21	96	18	110	23	106	18	139	18	100	30	177	16	79	26	70	24	160	15	113	27	119	74	119	15	181	38	140	20	89	16	87	15	122	18	102	25	104	46	61	45	101	20	161	35	130	11	97	13	123	10	80	26	145	30	78	26	120	18	146	15	0	.	159	66	51	69	0	.	122	2
	31-40 cm	118	19	0	.	36	57	83	22	121	14	108	12	82	7	78	8	82	12	88	8	78	55	117	29	77	17	0	.	58	34	97	20	91	13	106	21	0	.	79	13	73	14	57	14	51	15	61	10	94	12	80	9	85	17	105	15	84	12	64	30	63	14	111	32	41	21	76	15	86	13	92	17	76	15	97	17	96	20	87	13	94	21	84	12	83	28	79	22	90	16	54	35	101	15	64	18	87	13	74	20	73	23	0	.	52	35	37	46	49	28	66	21	26	25	58	17	36	32	25	19	30	27	47	29	73	20	82	38	80	18	47	28	64	24	84	16	65	24	236	55	69	14	77	40	59	20	68	14	54	15	87	14	78	17	69	35	104	41	62	23	123	38	58	13	61	14	63	9	79	19	70	42	99	20	93	13	93	14	0	.	207	37	31	69	0	.	75	2
	41-50 cm	33	24	0	.	0	.	34	20	54	17	65	14	50	7	54	7	53	12	37	8	21	68	35	45	65	11	0	.	66	32	42	17	44	13	40	19	0	.	40	13	37	12	41	11	44	14	36	9	35	12	39	9	45	17	55	13	50	12	49	20	40	14	54	20	26	20	50	10	39	22	42	18	43	17	48	20	39	20	48	15	56	14	58	9	21	40	44	18	36	20	78	28	43	15	40	16	49	14	36	16	51	22	0	.	30	42	23	32	25	20	31	16	9	39	22	35	15	32	19	21	17	29	16	21	33	22	34	37	30	20	29	30	34	21	43	15	52	20	30	24	32	19	69	27	44	21	30	14	34	20	41	17	53	17	47	47	29	31	41	25	66	28	31	14	23	16	33	10	51	19	44	22	79	16	54	16	40	23	0	.	49	14	51	69	0	.	41	2
	51-60 cm	12	42	0	.	0	.	16	44	12	33	29	17	26	8	22	9	26	12	14	14	42	68	17	43	12	26	0	.	12	49	15	28	16	18	15	30	0	.	14	22	23	13	21	16	19	18	18	10	14	17	14	12	36	20	23	16	17	19	11	30	12	27	17	39	17	26	27	16	12	24	23	22	22	21	23	21	17	33	20	19	16	31	31	15	8	39	16	34	6	35	7	47	7	48	15	21	19	18	25	24	10	50	0	.	40	47	16	35	17	22	11	29	5	47	4	49	4	97	10	24	4	49	13	32	14	39	13	37	13	24	24	25	19	31	19	20	22	28	31	67	16	30	19	84	27	28	11	22	18	24	15	30	20	35	12	59	12	54	10	49	33	29	18	18	12	18	18	18	23	26	18	32	33	46	18	19	12	32	0	.	9	75	0	.	0	.	18	3
	61-70 cm	0	.	20	71	0	.	2	66	2	74	1	70	6	15	7	13	7	26	3	23	21	68	3	93	4	45	0	.	7	94	3	50	8	23	5	47	0	.	7	38	7	29	7	22	8	22	8	17	4	37	4	22	6	45	3	44	4	29	8	32	3	43	0	.	6	42	4	30	4	50	13	28	5	45	6	39	9	36	5	29	7	36	9	25	3	96	5	80	2	55	2	94	0	.	5	36	4	39	9	28	3	69	0	.	9	53	3	72	7	46	4	50	2	47	2	68	1	97	3	41	3	47	3	41	3	54	16	64	4	56	5	33	4	40	4	52	10	39	0	.	4	34	0	.	11	35	1	69	10	36	5	38	4	46	0	.	12	49	5	60	0	.	3	33	3	38	5	23	3	58	11	39	6	92	2	47	2	54	0	.	9	75	0	.	0	.	5	4
	71-80 cm	0	.	0	.	0	.	0	.	1	68	1	99	2	27	3	21	2	43	1	47	0	.	0	.	1	98	0	.	0	.	1	69	1	60	1	99	0	.	3	42	2	39	3	38	2	43	3	23	2	43	0	57	0	.	0	.	1	57	5	56	2	65	2	96	5	48	3	68	0	.	1	98	2	48	0	.	4	71	1	57	3	47	5	44	1	96	0	.	0	.	0	.	1	98	2	51	1	58	6	39	0	.	0	.	0	.	1	98	3	65	4	51	0	.	1	98	0	.	1	56	3	77	2	74	0	.	6	95	2	98	2	54	6	49	0	.	2	98	0	.	1	99	0	.	1	96	1	56	1	98	2	68	3	72	0	.	0	.	5	93	0	.	2	29	1	57	2	42	1	98	3	66	0	.	2	56	1	98	0	.	0	.	0	.	0	.	2	7
	81-90 cm	0	.	0	.	0	.	2	66	0	.	0	.	0	44	1	32	0	99	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	60	0	.	1	70	1	57	1	49	1	70	0	100	1	98	0	99	0	.	0	.	0	.	0	.	0	.	1	99	1	99	1	98	0	.	1	98	0	.	1	74	1	99	1	70	0	.	0	.	1	98	0	.	0	.	0	99	0	.	1	98	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	2	58	0	.	0	.	0	.	2	95	2	68	2	68	1	98	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	70	1	56	1	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	12
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	71	0	100	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	1	70	0	.	0	99	0	.	0	72	0	99	0	100	0	.	0	.	0	.	0	.	0	.	2	96	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	1	98	1	98	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	2	68	0	.	0	.	0	.	2	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	20
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	1	98	0	.	1	99	1	99	0	.	0	.	1	71	0	.	2	74	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	27
	total	467	11	220	10	368	36	438	19	637	12	632	10	474	4	452	4	428	8	570	5	235	2	433	17	491	12	0	.	554	23	605	12	559	8	564	11	0	.	525	11	404	9	451	9	363	10	427	6	427	8	483	6	523	11	427	8	598	7	348	15	425	12	556	13	377	16	460	9	568	9	528	12	493	10	538	11	564	10	518	10	478	14	541	8	690	15	371	15	505	11	491	21	524	10	447	10	522	11	458	10	451	14	0	.	354	32	264	18	382	14	314	16	365	13	446	12	455	17	477	11	428	17	465	12	490	11	552	19	566	13	399	15	353	15	605	12	483	15	439	51	439	10	663	20	431	12	365	8	322	10	465	9	383	14	481	14	418	26	307	15	531	15	506	8	421	9	496	7	423	13	596	20	360	17	454	11	455	10	0	.	673	48	209	69	0	.	475	1
hyperinsubric, colline, submontane	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	.
	12-20 cm	159	14	239	15	241	21	169	18	0	.	0	.	230	31	186	17	198	11	123	53	89	26	218	25	167	28	550	.	175	27	163	35	527	.	146	22	583	25	0	.	253	39	153	34	125	71	349	16	49	73	202	17	142	36	212	23	329	15	154	30	0	.	325	31	450	.	0	.	25	71	1126	17	231	37	121	30	445	35	313	33	322	29	419	.	236	15	81	25	236	27	199	23	261	20	300	21	0	.	80	58	216	25	221	17	164	24	174	71	93	23	230	27	312	24	434	14	436	10	225	27	368	23	372	15	642	43	0	.	383	20	311	19	422	33	150	.	252	18	167	49	77	50	299	21	252	43	0	.	329	33	272	27	287	22	325	29	353	37	0	.	197	37	312	19	322	27	257	25	272	41	54	41	131	28	168	30	137	20	204	20	155	18	226	20	0	.	230	3
	21-30 cm	108	13	94	15	112	21	98	15	0	.	0	.	150	33	144	22	108	9	137	47	74	30	130	21	144	19	50	.	111	27	178	19	151	.	135	17	120	13	100	.	140	31	120	32	49	71	99	29	24	73	146	13	127	23	136	16	133	31	102	17	0	.	159	24	250	.	0	.	225	24	25	71	130	43	128	29	174	32	238	24	112	32	84	.	134	18	88	20	180	18	86	22	232	16	171	59	0	.	159	11	112	24	115	21	91	19	79	19	33	38	147	33	91	27	134	16	122	13	68	29	81	19	147	19	320	15	0	.	282	17	166	24	151	39	0	.	137	19	67	45	136	57	134	27	208	38	0	.	120	21	209	12	162	22	138	15	102	33	0	.	189	20	108	26	93	26	73	31	111	37	82	40	97	19	110	38	81	27	91	19	116	23	106	19	0	.	117	3
	31-40 cm	67	12	78	14	55	16	83	14	0	.	0	.	114	22	62	20	69	8	33	36	55	23	56	24	98	17	0	.	119	14	85	20	209	.	87	19	39	42	40	.	93	47	90	29	0	.	26	29	44	73	90	13	84	10	102	16	125	31	94	29	0	.	84	41	90	.	0	.	180	35	90	71	67	43	80	21	76	55	81	31	75	40	0	.	74	18	64	19	125	29	71	22	97	16	142	45	0	.	106	36	68	26	61	24	69	19	0	.	7	57	45	37	26	40	44	21	25	25	30	30	33	33	42	18	29	57	0	.	75	30	58	38	51	38	140	.	103	22	57	44	48	33	101	16	72	25	0	.	45	39	91	22	85	24	75	29	40	24	0	.	97	26	44	23	23	37	18	38	55	42	72	33	86	18	93	37	72	16	86	19	90	18	95	19	0	.	68	3
	41-50 cm	36	15	42	15	24	17	57	13	0	.	0	.	71	31	45	25	49	8	31	68	38	24	35	26	33	26	40	.	61	20	44	28	100	.	51	19	20	70	80	.	40	43	50	38	20	71	13	32	50	14	41	13	48	16	49	15	71	34	46	37	0	.	26	43	0	.	0	.	30	71	20	71	28	47	45	24	30	42	39	52	31	40	0	.	28	17	65	16	24	33	43	24	43	23	19	83	0	.	46	11	40	25	47	22	26	18	0	.	6	57	11	38	5	56	13	38	9	29	18	35	14	47	16	34	26	84	0	.	9	81	20	29	13	57	40	.	51	25	28	55	51	52	35	22	25	59	0	.	28	27	33	32	24	27	15	45	29	38	0	.	33	25	14	42	3	96	13	31	23	39	52	21	59	15	55	25	57	23	36	19	50	15	52	18	0	.	36	3
	51-60 cm	14	21	20	20	14	22	25	18	0	.	0	.	26	31	19	28	21	10	0	.	21	23	9	34	12	36	20	.	25	25	10	47	33	.	18	35	8	66	20	.	23	61	21	46	30	23	6	47	0	.	18	24	21	27	16	29	28	36	11	58	0	.	11	60	0	.	0	.	10	71	10	71	4	90	18	39	4	95	10	87	16	29	0	.	14	23	25	23	0	.	14	30	6	64	0	.	0	.	19	82	20	33	15	30	14	23	0	.	4	44	7	56	2	68	7	42	3	37	6	59	6	46	7	38	13	84	0	.	9	81	12	49	5	89	0	.	12	30	3	94	10	87	15	24	7	65	0	.	6	95	9	39	7	40	4	63	9	44	0	.	12	41	2	67	2	96	9	34	10	62	18	46	14	27	10	45	26	23	13	26	23	27	15	23	0	.	14	4
	61-70 cm	6	28	4	35	2	60	3	45	0	.	0	.	9	55	7	45	6	16	0	.	8	36	2	98	1	97	20	.	4	52	4	45	33	.	8	56	0	.	40	.	4	90	0	.	0	.	1	97	10	69	4	48	2	68	4	61	7	85	0	.	0	.	3	93	0	.	0	.	0	.	0	.	0	.	0	.	13	58	5	87	8	95	0	.	3	38	10	39	2	95	2	68	3	69	0	.	0	.	10	82	4	46	3	58	7	32	10	71	1	97	0	.	0	.	0	.	1	70	4	59	1	98	2	55	0	.	0	.	9	81	0	.	0	.	0	.	13	49	2	94	0	.	9	48	5	61	0	.	0	.	0	.	1	98	0	.	9	63	0	.	1	97	1	97	0	.	3	54	0	.	2	96	5	45	3	92	6	39	3	50	7	38	6	32	0	.	4	7
	71-80 cm	1	56	1	73	1	69	4	42	0	.	0	.	0	.	6	46	2	23	0	.	2	67	0	.	0	.	0	.	1	97	2	67	0	.	2	68	0	.	0	.	0	.	3	92	0	.	0	.	0	.	1	99	2	68	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	69	0	.	1	98	0	.	0	.	0	.	0	.	1	98	1	98	2	47	0	.	10	61	0	.	0	.	1	98	2	51	2	98	0	.	3	56	6	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	66	0	.	0	.	2	95	0	.	0	.	0	.	3	92	0	.	1	97	0	.	0	.	1	98	0	.	0	.	2	98	0	.	2	71	2	55	1	98	1	99	0	.	1	12
	81-90 cm	0	99	1	71	1	99	0	.	0	.	0	.	0	.	3	54	1	42	0	.	1	97	1	98	0	.	0	.	0	.	1	98	0	.	2	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	4	54	0	.	0	.	0	.	1	70	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	1	68	0	.	1	98	0	.	0	18
	91-100 cm	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	2	67	0	.	0	.	0	.	1	73	0	.	0	.	2	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	31
	>100 cm	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	99	7	49	0	.	2	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	30
	total	391	7	480	8	451	13	440	8	0	.	0	.	600	22	472	9	454	5	324	38	287	15	451	14	455	14	680	.	497	15	487	16	1053	.	448	12	770	15	280	.	553	22	437	22	224	15	496	15	177	40	501	8	426	16	520	10	693	12	407	13	0	.	608	14	790	.	0	.	470	9	1270	7	460	37	392	14	741	23	686	16	564	22	502	.	488	10	338	10	567	7	416	13	642	12	633	19	0	.	420	7	461	12	463	11	373	14	262	56	161	14	440	26	436	20	634	12	599	8	360	20	504	17	594	12	1036	27	0	.	766	16	566	18	642	24	330	.	567	13	325	30	322	25	597	14	570	25	0	.	531	21	614	12	566	11	556	13	543	26	0	.	531	16	480	17	443	21	373	20	471	26	280	20	394	11	439	15	383	11	437	11	442	9	501	11	0	.	472	2
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	.
	12-20 cm	159	12	235	15	238	20	172	15	229	21	226	17	186	6	179	7	186	9	265	8	83	26	195	23	189	17	550	.	214	27	251	14	260	13	207	16	583	25	211	16	153	13	180	13	149	11	195	7	180	8	211	8	185	17	190	14	298	10	146	22	211	22	219	23	208	22	178	15	270	18	278	19	235	13	174	23	288	18	216	15	224	20	202	12	275	13	107	18	228	16	225	20	229	15	205	15	226	18	189	20	212	20	221	17	159	22	107	23	140	15	133	17	262	14	326	12	403	10	268	12	287	16	324	12	267	20	254	32	257	15	230	16	175	23	267	17	229	19	142	49	186	17	302	19	183	23	166	13	179	21	213	17	200	17	294	21	259	29	84	36	181	28	199	9	212	9	226	9	203	21	185	34	119	26	165	19	148	15	204	20	161	18	215	20	0	.	209	2
	21-30 cm	114	11	93	14	114	19	104	14	218	16	203	16	117	6	111	7	107	8	156	8	71	29	131	17	132	14	50	.	120	20	158	16	143	12	144	13	120	13	140	16	106	11	126	11	88	14	106	8	105	8	135	7	133	13	96	12	145	11	82	16	94	18	192	16	91	20	119	15	174	15	122	17	122	14	149	13	166	17	135	10	112	16	163	11	148	14	90	19	154	12	78	20	193	11	110	15	136	16	110	16	110	15	115	21	92	17	49	30	66	17	88	18	89	16	135	11	127	11	83	14	97	17	127	13	156	16	96	26	181	14	94	20	86	20	144	15	121	18	76	39	121	15	141	23	163	20	89	16	97	12	145	13	128	16	124	19	77	28	101	20	180	17	107	8	113	9	123	8	86	22	115	24	94	17	119	16	113	14	91	19	119	22	102	19	0	.	119	1
	31-40 cm	75	10	75	14	54	15	83	12	121	14	108	12	85	6	76	8	72	7	86	8	57	21	71	20	88	12	0	.	96	15	92	15	92	12	99	15	39	42	75	13	65	11	56	12	61	11	64	8	73	7	83	7	84	11	104	11	86	11	75	21	63	14	101	26	44	20	76	15	88	13	87	16	73	13	91	13	87	20	85	12	89	19	83	12	76	15	71	15	99	14	66	19	99	11	68	16	87	13	77	18	70	17	61	24	67	17	31	38	32	22	59	17	25	22	51	13	27	21	30	15	32	19	44	18	65	20	78	33	82	17	52	22	59	20	79	15	77	17	88	41	67	14	98	15	63	16	68	14	51	15	88	12	85	14	73	22	79	35	62	23	106	22	56	8	59	10	57	9	74	17	71	27	88	15	93	13	82	11	86	19	99	17	91	19	0	.	72	1
	41-50 cm	36	13	40	15	22	18	51	12	54	17	65	14	48	6	53	7	50	7	37	8	36	23	35	22	49	13	40	.	63	18	43	15	44	12	44	13	20	70	42	11	38	10	38	11	44	10	37	7	34	7	40	7	46	12	52	10	50	12	48	18	40	14	44	20	25	20	50	10	38	21	41	18	38	16	47	15	35	18	45	14	49	14	56	10	26	16	55	12	33	17	53	19	43	13	38	15	49	14	37	15	45	17	47	22	26	16	24	24	24	14	24	15	7	33	18	26	10	23	20	16	16	23	16	20	31	21	38	33	27	18	30	24	28	19	45	14	52	16	28	45	34	18	39	19	38	22	30	14	33	17	39	15	42	15	28	38	29	24	41	25	44	20	28	9	27	11	34	9	45	18	48	15	63	13	54	14	49	17	36	19	50	14	51	18	0	.	39	1
	51-60 cm	13	19	19	20	13	23	23	17	12	33	29	17	23	7	21	9	22	8	14	14	22	23	11	28	12	22	20	.	20	23	13	24	17	17	16	23	8	66	14	20	22	11	19	13	20	13	16	9	13	11	15	11	30	17	20	15	17	18	11	28	12	27	15	33	16	26	27	16	12	23	22	20	19	20	21	19	14	29	18	18	16	24	30	15	12	21	21	19	4	36	12	27	7	39	15	21	19	18	24	23	15	28	15	30	17	22	15	28	14	17	10	25	4	39	6	32	3	37	9	20	5	32	10	25	14	34	15	30	15	27	23	21	15	29	18	21	19	24	8	69	15	29	15	25	21	27	11	22	14	25	13	26	14	28	7	47	10	40	10	49	19	27	15	12	14	13	16	14	20	25	18	28	17	25	17	18	19	19	13	26	22	27	14	24	0	.	16	2
	61-70 cm	5	28	5	32	2	60	3	38	2	74	1	70	6	13	7	13	6	14	3	23	9	33	2	72	3	42	20	.	5	55	4	35	8	22	6	37	0	.	7	32	6	21	6	22	7	18	7	14	4	19	4	20	4	40	3	37	4	28	5	35	3	43	1	98	6	42	4	30	4	44	12	27	4	42	4	40	10	30	5	26	7	38	9	25	3	37	8	37	2	48	2	55	1	71	6	32	4	39	9	27	3	39	3	58	7	29	5	40	5	36	3	46	2	48	1	70	1	57	3	29	3	40	3	33	3	46	14	59	4	49	4	34	3	42	6	38	11	31	2	95	3	34	8	48	9	31	1	69	7	38	4	39	2	42	0	.	11	39	5	60	1	98	5	19	2	32	5	20	3	59	7	39	5	41	2	42	4	32	3	50	7	35	6	33	0	.	5	3
	71-80 cm	1	57	1	73	1	69	3	42	1	68	1	99	2	24	4	20	2	20	1	47	2	67	0	.	0	99	0	.	1	98	2	48	1	60	1	57	0	.	3	36	2	31	3	34	3	29	2	21	2	24	1	52	1	70	0	99	1	57	3	58	2	65	1	98	5	48	3	68	0	.	1	68	2	45	0	.	3	72	1	49	2	48	5	44	0	99	2	71	0	.	1	99	0	99	2	51	1	58	5	40	0	99	1	98	2	48	2	47	5	39	3	46	0	.	1	69	1	51	2	42	2	77	2	45	2	68	6	75	1	99	3	50	4	50	0	.	1	99	0	.	1	99	2	67	1	98	1	56	1	69	1	68	2	74	0	.	1	97	5	93	1	98	3	19	1	40	2	37	1	98	1	68	1	99	1	56	1	58	2	55	1	98	1	99	0	.	2	6
	81-90 cm	0	99	1	71	0	99	1	70	0	.	0	.	0	44	1	28	1	39	0	70	1	97	1	98	0	.	0	.	0	.	0	99	0	.	1	69	0	.	2	46	0	100	1	57	1	40	0	44	1	35	0	100	1	70	0	99	0	.	0	.	0	.	0	.	0	.	1	99	1	99	1	99	0	.	1	99	0	.	1	51	0	99	1	70	0	99	0	.	1	99	0	.	0	.	0	99	0	.	1	98	0	.	1	98	0	.	1	99	2	39	0	99	0	.	0	.	1	70	1	58	0	.	0	99	0	.	2	96	1	68	1	69	1	99	0	99	0	.	0	.	0	99	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	37	1	44	0	57	0	.	0	.	1	99	0	.	0	.	1	68	0	.	1	98	0	.	0	9
	91-100 cm	0	.	0	99	0	.	0	.	0	.	0	.	0	58	0	100	0	100	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	70	0	.	1	70	0	.	0	99	0	70	0	72	0	70	0	100	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	99	0	.	0	.	0	.	0	.	0	.	1	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	99	1	69	1	57	0	.	0	.	0	99	1	73	0	.	0	.	1	72	0	.	0	.	1	68	0	.	0	.	0	.	1	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	58	0	100	0	.	0	.	1	98	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	15
	>100 cm	0	.	0	99	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	99	0	.	0	.	0	51	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	1	99	0	99	1	99	0	99	0	.	0	100	2	43	0	.	2	46	0	.	0	.	0	.	0	.	1	99	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	57	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	18
	total	403	6	470	8	443	13	439	8	637	12	632	10	467	4	452	4	447	4	562	5	282	14	447	12	472	9	680	.	519	13	562	10	566	8	518	9	770	15	495	11	392	7	429	8	374	8	428	5	412	5	488	5	483	9	465	6	599	7	369	11	425	12	575	10	395	15	460	9	586	9	563	12	493	10	487	9	603	11	507	9	500	12	548	8	540	9	354	9	521	8	437	11	572	8	444	10	522	11	454	9	457	9	463	11	371	13	236	15	289	10	321	12	388	11	538	9	573	8	418	9	440	12	529	9	538	13	503	18	570	11	438	12	372	13	558	12	511	10	345	26	428	10	606	13	477	13	365	8	382	11	504	7	474	9	525	10	467	19	307	15	531	12	416	6	429	6	462	6	432	12	445	17	387	9	451	10	418	8	437	11	460	10	481	11	0	.	462	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. 2299573. Birmensdorf, Swiss Federal Research Institute WSL

back