Selection from the list of topics

back

total number of stems #3
Number of stems of all living and dead trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm. The total number of stems is the sum of the number of stems and the number of deadwood stems.

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 NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was accessible on foot.

1.4 km grid, subgrids 1-5 #1746
Sub-grids 1, 2, 3, 4 and 5 of the field surveys on the sampling grid with a mesh size of 1.4 km (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

NFI4
total number of stems
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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
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	.	189	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	232	64	170	28	75	26	122	20	203	24	183	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	650	58	278	47	442	47	0	.	0	.	190	37	0	.	550	.	0	.	0	.	0	.	0	.	0	.	25	71	0	.	0	.	0	.	0	.	0	.	98	56	141	45	177	22	0	.	0	.	0	.	184	35	126	70	0	.	0	.	50	0	334	54	0	.	38	41	50	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	200	.	0	.	0	.	0	.	0	.	164	15	208	18	175	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	177	6
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	126	14	53	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	82	75	27	125	36	90	19	128	22	111	12	0	.	0	.	0	.	50	.	0	.	0	.	0	.	0	.	0	.	386	35	198	76	256	39	0	.	0	.	79	53	0	.	350	.	0	.	0	.	0	.	0	.	0	.	25	71	0	.	0	.	0	.	0	.	0	.	0	.	159	27	173	23	200	.	0	.	0	.	125	37	50	0	0	.	100	.	150	0	125	41	100	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	.	0	.	0	.	0	.	0	.	99	18	127	21	112	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	113	6
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	100	14	95	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	16	78	18	114	30	94	21	74	18	65	11	0	.	0	.	0	.	50	.	0	.	0	.	0	.	0	.	0	.	126	49	107	58	120	54	0	.	0	.	89	51	0	.	90	.	0	.	0	.	0	.	0	.	0	.	45	39	0	.	0	.	0	.	0	.	0	.	62	57	91	24	52	20	20	.	0	.	0	.	42	39	35	30	0	.	0	.	136	29	40	47	90	.	67	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	200	.	0	.	0	.	0	.	0	.	51	20	85	16	95	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	5
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	66	13	61	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	33	49	14	47	13	72	20	42	16	43	11	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	56	51	39	50	39	0	.	40	.	45	39	0	.	20	.	0	.	0	.	0	.	0	.	0	.	60	71	0	.	0	.	0	.	0	.	0	.	25	52	51	27	34	43	0	.	0	.	0	.	36	33	30	23	0	.	0	.	61	46	13	82	0	.	36	53	40	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	.	0	.	0	.	0	.	0	.	44	18	37	18	49	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	5
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	21	17	28	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	27	29	20	33	34	40	15	21	21	23	13	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	84	6	85	9	76	0	.	20	.	12	59	0	.	20	.	0	.	0	.	0	.	0	.	0	.	30	24	0	.	0	.	0	.	0	.	0	.	41	38	37	26	15	52	0	.	0	.	0	.	30	46	0	.	0	.	40	.	60	0	7	82	20	.	10	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	.	0	.	0	.	0	.	0	.	20	18	27	24	45	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	6
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	7	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	81	12	31	13	29	15	33	6	36	8	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	84	0	.	9	76	0	.	0	.	12	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	24	0	.	0	.	0	.	0	.	0	.	8	52	8	52	8	45	0	.	0	.	0	.	7	92	20	0	0	.	20	.	20	0	0	.	40	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	11	28	7	30	10	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	10
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	3	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	81	12	34	0	.	3	53	3	47	3	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.	4	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	71	0	.	0	.	0	.	0	.	0	.	8	54	2	96	0	.	0	.	0	.	0	.	10	41	10	70	0	.	20	.	0	.	0	.	0	.	13	41	10	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	25	1	68	4	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	13
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	82	1	98	3	91	5	47	2	55	2	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	37	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	20
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	82	1	98	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	56	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	35
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	67	1	98	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	41
	total	0	.	0	.	0	.	0	.	0	.	0	.	512	10	237	12	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	462	48	427	14	411	19	444	12	480	14	438	8	0	.	0	.	0	.	100	.	0	.	0	.	0	.	0	.	0	.	1265	40	640	51	885	35	0	.	80	.	434	33	0	.	1030	.	0	.	0	.	0	.	0	.	0	.	235	29	0	.	0	.	0	.	0	.	0	.	242	28	490	16	459	17	220	.	0	.	0	.	434	24	271	34	0	.	180	.	477	14	518	45	250	.	164	31	110	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	770	.	0	.	0	.	0	.	0	.	401	11	494	14	491	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	453	4
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	110	61	0	.	0	.	136	46	110	34	169	22	186	10	157	8	241	21	224	10	0	.	137	27	272	14	0	.	265	24	220	21	195	15	137	31	0	.	196	16	154	17	178	16	276	19	227	11	199	20	204	12	320	23	181	18	294	12	241	36	216	22	125	20	181	19	161	18	208	17	272	21	174	23	163	29	164	33	224	21	222	30	117	18	129	32	269	23	183	31	204	42	114	33	202	19	187	22	159	34	165	20	0	.	156	36	387	31	192	30	121	34	320	20	226	28	310	15	310	21	313	26	207	24	320	22	297	38	328	20	266	22	173	26	359	22	236	33	100	.	204	17	200	.	115	54	176	15	230	37	228	25	127	22	347	53	203	37	40	85	101	44	229	13	303	14	279	12	138	29	231	46	113	24	151	27	116	38	0	.	100	.	100	.	0	.	210	2
	21-30 cm	110	41	100	.	0	.	118	29	102	28	151	16	107	9	97	12	100	18	154	11	24	74	100	18	134	24	0	.	170	45	159	29	140	15	119	33	0	.	149	18	108	20	116	20	113	19	125	12	122	15	94	13	110	18	87	19	164	13	115	48	94	14	83	50	64	30	93	24	190	18	156	28	131	21	67	25	156	26	126	22	120	35	111	15	120	32	112	25	97	28	109	49	110	22	102	25	125	32	100	30	137	26	0	.	183	60	81	34	130	19	87	28	154	29	191	23	148	21	180	21	173	21	121	32	175	24	119	34	186	16	64	24	58	27	210	40	117	47	0	.	125	21	150	.	42	60	100	19	131	41	128	30	66	44	240	48	101	54	99	74	81	83	132	14	164	18	172	14	121	26	112	35	21	53	114	29	99	27	0	.	100	.	50	.	0	.	123	3
	31-40 cm	58	19	0	.	0	.	35	51	107	16	119	15	77	9	61	11	55	24	94	11	43	74	60	42	69	30	0	.	124	35	91	25	74	22	91	16	0	.	53	21	76	14	71	14	41	16	81	12	96	15	77	13	68	21	71	30	79	15	62	36	76	28	68	35	62	31	58	16	137	18	62	27	69	19	60	23	127	25	71	15	97	22	80	17	96	19	98	31	108	25	22	45	64	24	66	22	75	19	76	23	123	30	0	.	67	35	24	49	50	30	87	42	31	39	75	23	80	30	52	22	48	23	79	29	77	24	110	39	101	25	61	22	59	29	100	24	82	38	0	.	88	17	250	.	34	49	65	17	58	17	73	23	51	43	92	44	42	53	157	29	36	59	79	21	58	16	87	12	44	22	77	17	27	46	68	20	79	19	0	.	120	.	70	.	0	.	74	2
	41-50 cm	44	32	20	.	0	.	55	16	95	12	70	14	48	10	40	9	42	17	58	9	68	52	30	87	56	36	0	.	64	38	58	19	43	23	64	18	0	.	55	15	59	16	40	11	30	19	42	11	54	13	54	11	38	19	52	19	60	11	40	26	42	20	47	25	31	28	53	11	46	22	29	30	53	16	55	21	69	28	31	19	45	32	55	16	44	35	41	22	41	21	29	46	63	13	51	20	41	20	48	21	40	24	0	.	34	24	7	53	33	33	89	21	12	44	23	20	16	38	30	30	25	24	36	21	38	38	68	48	65	27	46	36	48	29	58	26	26	43	60	.	45	17	80	.	26	35	41	14	24	23	58	17	52	20	53	48	44	40	61	46	34	48	28	19	40	19	46	12	26	45	46	29	42	43	40	16	59	19	0	.	140	.	40	.	0	.	46	2
	51-60 cm	44	50	0	.	0	.	40	29	47	19	42	23	34	10	31	10	27	18	25	14	10	74	5	87	33	28	0	.	17	40	26	22	31	21	40	22	0	.	30	19	24	22	29	15	25	23	28	14	21	21	22	13	20	22	34	21	32	17	15	45	23	31	19	58	17	32	28	17	20	38	15	42	29	24	29	27	14	35	21	23	45	22	35	14	16	53	29	31	8	52	36	46	21	25	23	23	31	24	35	21	29	38	0	.	15	53	12	46	30	33	26	30	11	34	8	60	5	61	11	22	15	32	20	28	18	38	22	49	29	21	30	33	26	31	36	27	21	39	20	.	20	20	80	.	17	28	15	21	37	20	23	40	37	36	33	54	14	47	45	10	34	48	16	22	23	20	29	15	22	32	19	31	19	58	26	24	22	32	0	.	0	.	80	.	0	.	26	3
	61-70 cm	8	89	0	.	0	.	10	41	18	27	17	31	12	16	13	16	10	28	5	27	0	.	5	86	9	51	0	.	8	54	13	36	14	28	13	35	0	.	10	30	8	29	12	22	9	25	11	16	5	46	7	26	18	36	20	27	12	24	13	39	6	42	13	58	4	70	16	20	8	40	6	56	10	33	9	31	9	45	10	31	14	46	19	23	2	95	6	55	4	52	6	59	9	62	18	31	6	45	12	35	4	64	0	.	14	57	9	77	7	45	18	37	7	61	5	51	7	94	8	33	1	97	14	50	9	40	14	49	9	44	11	36	12	38	22	42	9	38	0	.	14	39	20	.	12	69	5	33	13	46	1	97	9	43	0	.	21	36	0	.	6	83	5	60	8	30	8	27	2	96	26	45	4	90	11	27	5	56	0	.	0	.	20	.	0	.	10	4
	71-80 cm	0	.	0	.	0	.	0	.	4	69	2	71	4	27	6	19	7	31	1	49	0	.	5	86	0	.	0	.	0	.	4	58	7	44	5	56	0	.	7	40	5	33	4	34	3	38	4	28	3	44	4	33	7	52	6	42	4	40	7	65	2	68	0	.	6	53	2	68	0	.	5	60	7	38	8	44	3	66	6	41	15	37	10	29	0	.	3	96	0	.	3	91	0	.	8	39	1	99	4	75	2	96	0	.	5	88	7	53	7	54	11	44	1	97	2	96	0	.	3	54	2	67	4	51	5	44	6	92	7	73	4	52	4	51	1	97	2	94	0	.	2	71	0	.	3	93	2	68	4	95	1	96	5	50	0	.	0	.	11	85	28	59	4	40	1	56	4	34	2	96	4	63	4	90	1	68	2	66	0	.	0	.	0	.	0	.	4	6
	81-90 cm	0	.	0	.	0	.	3	91	0	.	0	.	2	39	2	32	3	41	1	75	0	.	0	.	0	.	0	.	0	.	1	98	1	70	0	.	0	.	2	48	1	69	2	56	1	56	3	28	2	73	1	70	0	.	0	.	0	.	3	93	1	98	0	.	0	.	0	.	0	.	0	.	0	.	2	97	0	.	1	98	3	68	3	99	0	.	0	.	1	97	0	.	0	.	1	98	1	98	2	67	2	96	0	.	0	.	0	.	4	66	2	95	3	66	2	95	0	.	0	.	0	.	0	.	0	.	0	.	2	96	3	67	3	65	0	.	4	63	0	.	0	.	0	.	0	.	1	98	0	.	0	.	2	95	0	.	0	.	5	85	0	.	2	99	2	58	1	56	0	.	5	62	0	.	1	98	0	.	0	.	0	.	0	.	0	.	1	11
	91-100 cm	0	.	0	.	0	.	3	92	0	.	0	.	0	.	0	.	1	98	1	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	55	0	.	0	.	0	99	0	99	0	.	0	99	1	97	0	.	0	.	0	.	1	98	3	92	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	1	99	0	.	0	.	0	.	0	.	0	.	1	97	0	.	1	97	0	.	0	.	0	.	2	96	0	.	0	.	0	.	2	95	0	.	1	98	0	.	0	.	0	.	4	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	93	0	.	0	.	0	99	1	69	0	.	0	.	0	.	4	90	0	.	0	.	0	.	0	.	0	.	0	.	0	20
	>100 cm	0	.	20	.	0	.	0	.	0	.	0	.	0	.	0	100	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	1	70	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	3	96	2	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	96	0	.	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	.	0	.	0	28
	total	374	23	140	.	0	.	400	24	483	15	569	10	469	6	407	6	486	11	562	7	144	64	342	11	573	11	0	.	647	21	571	20	505	11	468	16	0	.	505	11	434	9	451	11	498	13	521	7	501	10	462	8	582	14	451	9	644	7	495	18	461	11	360	16	366	17	411	11	608	10	543	17	472	14	391	14	543	22	490	15	563	18	431	9	408	13	558	13	442	17	409	31	381	15	472	12	467	15	438	17	502	17	0	.	475	34	532	24	456	20	440	17	538	16	533	19	565	13	595	14	579	18	482	16	643	17	640	21	726	10	485	14	383	10	787	19	497	25	180	.	498	14	780	.	249	35	404	11	499	24	513	17	351	19	765	45	429	33	417	32	320	43	496	9	600	11	626	8	355	18	518	24	234	19	413	16	382	18	0	.	460	.	360	.	0	.	495	2
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	152	23	148	20	215	28	95	22	0	.	0	.	184	22	163	17	156	13	0	.	174	22	197	33	149	22	0	.	139	32	189	30	0	.	191	43	99	15	0	.	195	58	224	14	0	.	451	37	131	40	143	17	117	30	225	22	320	25	314	24	0	.	238	34	150	.	0	.	0	.	0	.	725	51	216	56	441	39	500	64	321	19	0	.	206	22	156	36	141	53	83	33	261	19	477	59	0	.	0	.	274	27	220	27	166	27	450	.	374	32	250	15	571	20	278	23	509	13	353	29	238	32	457	21	289	16	0	.	205	17	321	18	456	33	0	.	271	43	260	39	83	43	183	48	448	51	0	.	204	43	175	30	176	27	254	44	38	55	0	.	280	72	447	32	293	22	247	29	156	22	135	29	174	25	184	7	112	27	123	27	143	49	171	28	0	.	220	4
	21-30 cm	53	21	96	32	83	22	64	37	0	.	0	.	95	31	92	22	86	13	100	.	50	35	126	29	140	28	50	.	91	30	143	23	0	.	96	23	166	28	0	.	79	53	94	25	0	.	265	27	100	0	89	17	80	23	83	18	167	52	34	46	0	.	67	42	150	.	0	.	0	.	0	.	100	36	254	23	200	22	200	18	220	50	0	.	143	23	40	60	90	36	71	35	205	29	70	76	0	.	50	71	135	22	91	33	114	26	250	.	122	29	156	30	128	39	195	16	224	13	163	26	163	33	218	15	425	32	0	.	195	18	244	27	157	33	0	.	206	27	157	40	83	43	106	38	62	17	0	.	220	29	122	24	84	49	157	24	150	20	0	.	52	53	162	29	129	26	88	40	100	0	92	29	82	30	239	50	51	23	111	28	76	34	114	25	0	.	116	4
	31-40 cm	43	26	38	26	60	23	41	24	0	.	0	.	82	31	69	24	41	11	0	.	28	36	72	33	103	22	40	.	72	43	98	31	0	.	76	21	105	36	0	.	20	36	31	62	0	.	133	23	36	29	72	23	62	23	71	31	74	3	32	44	0	.	44	54	20	.	0	.	0	.	0	.	75	71	56	63	89	37	115	3	56	25	0	.	71	17	39	21	74	31	67	21	127	20	59	12	0	.	150	19	65	36	61	21	54	28	50	.	23	39	91	39	50	30	86	28	69	15	41	41	67	29	79	32	162	23	0	.	94	45	90	29	75	66	0	.	54	34	59	45	70	41	10	42	60	34	0	.	56	24	79	12	76	38	63	16	47	42	0	.	53	34	54	31	62	33	12	44	56	63	36	58	63	30	70	59	73	26	61	24	77	30	41	29	0	.	60	4
	41-50 cm	32	20	16	29	27	28	42	17	0	.	0	.	108	31	27	25	37	11	0	.	14	40	48	39	34	23	40	.	61	33	43	40	0	.	30	24	51	37	0	.	39	56	22	32	0	.	34	29	48	44	56	14	59	27	39	20	44	6	40	36	0	.	44	33	40	.	0	.	0	.	0	.	0	.	15	41	35	44	70	10	32	40	0	.	50	18	18	43	72	39	43	27	46	33	28	76	0	.	60	24	24	51	31	27	26	25	40	.	23	37	43	57	8	51	32	19	28	31	15	29	20	33	27	26	45	43	0	.	42	34	43	52	51	40	0	.	69	45	38	31	13	81	30	55	20	50	0	.	17	29	45	38	49	24	54	29	50	46	0	.	40	46	20	43	17	50	14	40	91	8	34	42	45	21	13	84	35	30	27	23	46	28	31	23	0	.	35	4
	51-60 cm	13	27	15	22	14	34	30	22	0	.	0	.	38	37	24	24	22	12	0	.	23	35	11	49	18	33	60	.	22	48	28	29	0	.	24	22	12	90	0	.	15	57	13	36	0	.	11	48	18	76	29	20	37	45	22	27	9	76	23	43	0	.	24	62	0	.	0	.	0	.	0	.	0	.	42	82	23	46	30	71	32	18	0	.	15	26	37	34	20	57	15	64	10	50	28	76	0	.	20	0	9	33	26	28	21	20	0	.	3	94	0	.	7	51	11	38	8	51	8	51	8	95	15	32	34	77	0	.	0	.	5	85	46	66	0	.	16	61	3	91	33	43	6	93	5	86	0	.	6	60	30	44	33	43	17	43	10	51	0	.	36	37	16	65	0	.	2	96	9	79	37	35	22	26	0	.	27	32	28	31	24	31	23	28	0	.	19	5
	61-70 cm	7	32	5	35	6	38	15	22	0	.	0	.	14	64	12	33	10	17	0	.	16	40	7	73	4	96	0	.	6	92	6	49	0	.	5	43	4	90	0	.	0	.	0	.	0	.	6	60	11	65	9	29	11	35	12	29	25	65	10	92	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	55	0	.	12	33	16	35	0	.	8	44	0	.	9	76	0	.	10	71	3	65	6	41	6	59	20	.	3	94	0	.	2	96	4	51	4	41	2	95	2	95	3	53	11	77	0	.	0	.	4	90	4	90	0	.	6	82	0	.	7	81	4	90	0	.	0	.	0	.	0	.	5	92	6	59	10	87	0	.	25	68	0	.	0	.	2	96	27	79	20	34	3	96	0	.	3	67	5	48	5	69	8	50	0	.	7	7
	71-80 cm	3	47	2	55	5	55	6	35	0	.	0	.	0	.	4	57	3	30	0	.	3	95	2	95	4	64	20	.	0	.	4	63	0	.	2	97	0	.	0	.	4	90	0	.	0	.	0	.	0	.	2	68	0	.	1	98	0	.	0	.	0	.	7	58	0	.	0	.	0	.	0	.	0	.	8	82	8	59	0	.	16	59	0	.	3	55	8	39	0	.	0	.	0	.	0	.	0	.	0	.	2	96	5	59	3	72	0	.	2	95	11	85	0	.	3	66	1	99	4	95	4	95	4	52	11	77	0	.	11	64	5	85	4	90	0	.	5	89	0	.	0	.	4	90	0	.	0	.	0	.	5	89	0	.	0	.	5	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	67	3	65	1	97	0	.	3	11
	81-90 cm	1	98	1	98	2	67	2	72	0	.	0	.	0	.	0	.	1	49	0	.	2	95	2	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	90	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	89	0	.	0	.	2	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	98	4	56	0	.	5	94	5	85	0	.	0	.	1	99	2	95	0	.	3	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	88	0	.	0	.	2	95	0	.	0	.	1	96	0	.	2	97	2	67	3	65	0	.	0	.	1	17
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	1	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	98	0	.	0	.	5	94	0	.	0	.	0	.	1	99	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	40
	>100 cm	0	.	0	.	0	.	1	98	0	.	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	.	0	.	0	.	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	5	96	0	.	3	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	38
	total	303	14	321	17	413	15	295	12	0	.	0	.	520	22	393	12	356	7	100	.	310	17	465	19	451	17	210	.	392	21	510	19	0	.	425	20	436	21	0	.	352	31	389	14	0	.	900	19	344	10	401	9	366	13	452	15	640	1	453	17	0	.	422	15	360	.	0	.	0	.	0	.	900	32	592	21	795	24	915	35	699	16	0	.	500	11	316	17	396	26	287	17	648	15	672	26	0	.	290	29	512	17	445	14	394	15	810	.	560	24	556	13	766	19	611	13	845	8	594	20	502	23	810	14	978	16	0	.	547	4	713	15	794	25	0	.	627	25	518	24	290	24	348	34	596	33	0	.	503	28	456	15	423	15	551	22	314	23	0	.	491	28	700	21	502	21	366	24	438	11	353	20	390	15	506	30	304	9	360	16	376	17	389	16	0	.	460	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	146	21	144	20	215	28	101	20	110	34	169	22	187	8	156	8	178	11	221	10	141	26	181	27	204	14	0	.	194	22	209	18	195	15	164	28	99	15	199	16	162	15	169	13	225	17	235	10	187	11	185	10	229	20	203	15	287	12	272	22	216	22	180	24	178	18	161	18	262	27	273	19	235	23	170	26	243	30	232	19	243	22	132	19	185	19	220	20	173	27	129	30	173	20	208	21	187	22	143	35	223	20	220	27	164	24	311	28	217	21	161	17	416	16	254	18	468	11	302	16	275	20	351	18	298	21	236	40	315	18	264	18	206	24	313	24	247	27	237	38	191	17	186	41	230	44	176	15	220	28	215	21	149	17	289	34	145	38	40	85	203	60	215	11	266	10	251	11	141	24	192	35	157	21	154	24	114	24	123	27	140	47	167	28	0	.	208	2
	21-30 cm	61	20	96	31	83	22	73	28	102	28	151	16	111	8	95	11	90	10	153	11	45	33	119	23	137	19	50	.	125	31	154	21	140	15	107	21	166	28	144	17	94	16	115	16	105	15	135	10	115	9	92	11	96	15	85	13	161	13	81	43	94	14	75	34	70	27	93	24	214	18	163	27	142	18	92	23	162	19	123	19	142	29	120	15	137	19	81	25	96	23	85	30	148	20	93	24	125	32	95	29	136	17	91	33	124	24	69	33	139	15	127	17	144	22	193	14	208	12	167	15	161	18	177	15	199	22	126	24	176	14	100	23	68	26	179	42	145	30	134	43	120	20	112	32	49	35	100	19	165	26	126	24	77	30	188	27	118	32	99	74	65	52	118	11	147	13	147	12	118	22	104	25	65	30	128	25	76	21	111	28	77	31	110	25	0	.	120	2
	31-40 cm	45	22	36	26	60	23	40	22	107	16	119	15	83	8	63	10	44	11	93	11	31	33	69	27	88	18	40	.	95	28	93	20	74	22	83	13	105	36	52	20	73	11	72	13	58	15	83	9	75	9	75	11	65	15	71	21	78	14	49	30	76	28	56	31	59	31	58	16	136	17	69	25	74	18	60	21	109	21	75	15	88	20	81	16	78	13	73	26	100	21	50	22	89	17	64	19	75	19	83	20	93	24	61	21	56	24	36	36	58	19	77	26	39	24	81	19	71	14	47	18	53	18	79	22	82	22	116	29	90	22	68	17	63	24	85	27	73	30	50	48	86	16	42	72	43	31	65	17	58	14	75	17	68	27	74	23	44	37	157	29	46	31	63	14	68	11	78	11	46	22	61	21	53	27	68	19	76	16	61	24	80	27	43	27	0	.	70	2
	41-50 cm	34	18	16	28	27	28	44	14	95	12	70	14	55	8	38	9	38	9	57	9	24	42	43	36	44	23	40	.	62	25	53	17	43	23	47	16	51	37	54	14	54	11	39	10	44	15	41	9	47	8	54	9	47	18	45	14	58	11	40	21	42	20	46	20	32	25	53	11	48	20	33	25	49	16	49	21	57	24	34	16	42	28	54	16	49	16	31	21	48	20	38	24	56	15	50	19	41	20	49	19	31	25	31	27	27	21	14	37	37	19	63	20	10	34	28	14	25	28	27	21	24	18	31	17	37	33	66	38	54	25	43	31	47	22	55	23	39	34	41	26	42	18	36	43	24	29	41	14	21	19	55	16	55	16	54	26	46	30	61	46	38	34	35	13	37	13	42	11	36	34	41	24	44	19	37	16	47	17	27	23	52	26	32	22	0	.	43	2
	51-60 cm	17	27	15	22	14	34	32	18	47	19	42	23	31	9	30	9	24	10	24	14	21	34	9	44	25	23	60	.	20	34	27	17	31	21	32	16	12	90	32	17	25	15	28	14	30	15	25	11	22	11	24	11	27	29	28	17	30	17	18	32	23	31	21	43	16	32	28	17	21	35	13	40	25	24	31	27	17	27	20	21	43	19	34	14	16	24	33	23	10	40	23	39	17	24	24	20	31	24	33	20	19	31	26	28	21	19	19	34	26	22	18	28	9	29	10	33	7	44	14	23	12	33	17	21	21	31	31	32	22	24	25	32	27	30	31	29	20	33	6	60	21	18	16	66	13	30	15	21	25	24	25	31	37	26	23	37	13	37	45	10	35	29	18	14	22	16	29	14	20	31	26	26	21	24	24	24	24	23	28	31	23	32	26	26	0	.	24	2
	61-70 cm	7	31	5	35	6	38	15	21	18	27	17	31	11	15	12	14	10	15	5	27	13	43	7	61	6	48	0	.	7	54	10	31	14	28	9	29	4	90	9	28	9	22	11	20	11	20	10	14	7	19	8	20	15	27	16	21	12	23	12	41	6	42	7	64	4	70	16	20	8	36	5	57	10	31	7	32	6	48	10	28	15	37	18	23	9	32	10	31	3	53	7	36	5	65	18	26	6	45	11	33	3	46	6	41	7	45	9	50	7	33	12	33	4	54	5	36	4	42	6	31	3	46	8	42	10	33	16	34	6	47	11	31	9	38	18	43	8	35	0	.	14	38	6	60	8	72	5	33	8	49	1	98	8	36	4	64	17	35	0	.	17	62	8	26	7	22	8	24	6	74	23	32	3	71	10	28	4	43	5	48	4	70	9	44	0	.	9	3
	71-80 cm	2	48	2	55	5	55	5	36	4	69	2	71	3	23	6	18	4	22	1	49	2	96	3	66	2	67	20	.	0	.	4	44	7	44	3	50	0	.	7	38	7	25	3	35	3	31	3	24	3	29	3	30	4	54	3	40	4	40	4	68	2	68	3	65	6	54	2	68	0	.	4	61	6	39	8	40	5	40	5	38	15	32	10	30	2	56	5	43	0	.	1	97	0	.	9	36	1	99	4	76	2	68	5	59	3	58	7	41	4	45	7	42	1	98	2	55	1	99	4	37	3	49	4	37	7	34	5	94	6	61	4	45	5	35	3	67	3	65	0	.	2	71	3	91	2	95	2	68	2	97	2	67	3	53	0	.	2	96	11	85	12	72	6	22	1	43	4	30	2	96	3	66	1	97	1	68	1	69	2	67	3	66	1	97	0	.	4	5
	81-90 cm	1	99	1	99	2	67	2	59	0	.	0	.	1	40	2	32	1	32	1	75	2	96	1	97	0	.	0	.	0	.	1	99	1	70	0	.	0	.	2	42	1	57	2	42	3	38	2	25	2	39	1	57	0	.	0	.	0	.	2	96	1	98	0	.	0	.	0	.	0	.	0	.	0	.	2	98	0	.	1	99	3	55	3	99	0	.	1	98	1	98	0	.	0	.	1	98	1	98	2	67	1	98	2	98	4	57	0	.	3	49	2	67	1	68	1	98	1	99	1	99	0	.	2	71	0	.	0	.	1	97	2	68	2	67	1	96	3	66	0	.	0	.	3	91	0	.	1	98	0	.	0	.	1	97	0	.	0	.	5	85	3	94	2	41	1	50	1	49	0	.	3	66	1	97	1	98	1	99	2	67	3	66	0	.	0	.	1	9
	91-100 cm	0	.	0	.	0	.	1	99	0	.	0	.	0	100	0	100	1	66	1	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	47	0	99	0	.	0	99	0	100	0	100	0	100	1	99	0	.	0	.	0	.	1	98	2	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	1	99	0	.	0	.	0	.	0	.	0	.	1	98	0	.	1	98	0	.	2	98	0	.	1	97	1	99	0	.	0	.	1	98	1	99	1	99	0	.	1	99	0	.	3	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	65	0	.	0	.	1	49	1	57	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	16
	>100 cm	0	.	1	99	0	.	1	98	0	.	0	.	0	.	0	70	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	1	99	0	.	0	.	1	70	1	57	0	100	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	2	97	1	99	0	.	0	.	0	.	1	99	1	99	0	.	2	55	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	20
	total	313	13	315	17	413	15	313	11	483	15	569	10	482	5	402	5	389	6	556	7	279	18	432	15	506	10	210	.	504	16	551	15	505	11	446	13	436	21	502	11	427	7	439	9	480	10	536	6	457	6	443	6	484	11	451	9	630	7	478	13	461	11	390	11	366	16	411	11	690	15	559	17	541	13	418	13	599	18	502	13	592	14	452	10	475	9	455	12	432	15	333	17	489	12	467	11	467	15	423	16	507	12	445	14	406	14	468	20	492	12	466	10	625	13	574	11	787	8	569	11	532	14	671	12	654	15	599	18	670	10	518	12	428	14	686	21	538	18	469	25	476	13	405	29	369	27	404	11	500	18	499	14	398	12	631	24	389	25	417	32	417	25	466	7	551	8	559	7	368	15	453	18	346	13	423	15	344	11	360	16	382	16	388	16	0	.	480	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. 2281357. Birmensdorf, Swiss Federal Research Institute WSL

back