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 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

NFI1
total number of stems NFI1
altitudinal vegetation belts (NaiS, 3 classes) · diameter at breast height (10 classes)
regional demarcation: forest district (2024)
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 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)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	.	2334	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	946	41	439	52	804	29	1717	31	4953	16	0	.	0	.	0	.	22	.	0	.	0	.	0	.	0	.	0	.	530	91	373	89	549	68	0	.	0	.	371	58	0	.	241	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	16	100	485	35	310	40	65	.	0	.	0	.	288	56	0	.	0	.	0	.	73	80	184	71	53	.	23	100	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	84	.	0	.	0	.	0	.	0	.	2068	23	1934	28	844	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19747	8
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	2135	24	113	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	778	30	281	65	613	38	1093	30	2769	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	153	100	109	69	344	67	0	.	0	.	141	58	0	.	153	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	0	.	0	.	0	.	0	.	36	71	512	45	209	45	0	.	0	.	0	.	126	55	18	100	0	.	17	.	55	100	70	79	0	.	112	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	.	0	.	0	.	0	.	0	.	1401	22	1364	28	952	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13624	8
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	1151	25	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	71	396	29	101	54	598	33	759	28	2086	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	75	71	79	76	72	0	.	0	.	84	65	0	.	26	.	0	.	0	.	0	.	0	.	0	.	70	78	0	.	0	.	0	.	0	.	0	.	57	88	194	43	152	53	0	.	0	.	0	.	75	54	25	77	0	.	0	.	44	100	104	100	0	.	38	73	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	.	0	.	0	.	0	.	0	.	1063	20	606	27	465	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8448	8
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	553	26	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	58	290	28	63	53	358	28	396	25	846	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	71	83	17	71	0	.	7	.	58	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	69	59	202	35	43	53	0	.	0	.	0	.	50	74	14	71	0	.	7	.	59	89	28	71	7	.	14	71	26	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	.	0	.	0	.	0	.	0	.	548	20	385	30	355	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4651	7
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	134	31	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	75	160	33	42	57	139	36	139	31	432	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	14	100	9	100	0	.	7	.	15	100	0	.	9	.	0	.	0	.	0	.	0	.	0	.	29	100	0	.	0	.	0	.	0	.	0	.	28	79	86	51	14	70	0	.	0	.	0	.	36	66	7	100	0	.	7	.	21	74	35	100	14	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	0	.	0	.	0	.	0	.	297	23	186	31	142	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2065	9
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	69	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	49	38	0	.	49	43	93	39	100	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	7	100	8	100	0	.	7	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	6	100	14	100	0	.	0	.	0	.	0	.	14	71	14	100	0	.	7	.	7	100	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	167	34	0	.	71	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	736	13
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	13	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	29	61	7	100	23	58	21	58	49	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	121	33	21	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	320	17
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	7	100	0	.	14	71	7	100	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	28
	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	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	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	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	45
	>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	.	7	100	7	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	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	45
	total	0	.	0	.	0	.	0	.	0	.	0	.	6389	20	164	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	157	61	2656	25	932	50	2613	27	4232	23	11256	13	0	.	0	.	0	.	22	.	0	.	0	.	0	.	0	.	0	.	781	87	646	71	1003	64	0	.	21	.	698	49	0	.	429	.	0	.	0	.	0	.	0	.	0	.	184	74	0	.	0	.	0	.	0	.	0	.	219	62	1500	33	728	41	65	.	0	.	0	.	595	47	78	73	0	.	45	.	266	83	420	68	74	.	194	76	83	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	264	.	0	.	0	.	0	.	0	.	5701	17	4511	24	2830	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49757	6
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	365	52	50	.	0	.	516	45	969	43	2007	30	6969	14	6656	14	1723	26	7389	14	19	100	226	72	795	46	0	.	835	57	2379	27	3414	24	1615	38	0	.	2678	25	1611	23	2118	28	2312	26	5443	17	2435	23	5121	19	1225	30	1852	29	4964	20	519	56	2066	36	270	60	1052	41	1591	30	1409	34	1409	31	1948	30	1396	42	1168	34	1965	34	1351	43	2225	24	1441	40	693	42	1167	38	552	58	616	45	1282	32	2031	39	1403	37	1054	47	0	.	202	65	540	41	944	33	408	47	1122	42	1325	38	947	42	2283	30	1883	34	1110	34	1013	36	844	57	1444	35	983	39	495	38	1764	41	465	48	17	.	2034	28	83	.	331	43	1639	27	379	37	1257	39	630	42	186	60	489	67	102	80	207	67	4806	21	3349	23	4801	20	739	48	661	51	0	.	1480	34	823	40	0	.	149	.	50	.	0	.	136274	3
	21-30 cm	243	49	0	.	0	.	274	45	1019	30	2138	29	4141	13	3646	14	1213	26	4174	15	35	100	139	73	312	46	0	.	231	53	1171	35	1735	23	982	33	0	.	1968	26	1386	24	1469	26	1249	29	3190	17	1888	24	2592	18	627	31	474	33	2451	22	233	48	757	31	391	46	480	37	954	27	1001	32	697	34	1288	26	1038	29	831	37	1349	23	536	40	2147	22	712	37	490	51	878	33	105	69	772	34	685	28	1365	31	671	30	423	39	0	.	122	75	105	63	249	46	444	47	300	33	625	35	448	42	956	29	819	36	425	43	668	35	331	50	937	33	380	42	379	38	748	35	331	62	86	.	998	26	83	.	391	40	923	27	370	36	666	32	459	42	165	80	212	57	205	52	144	64	1988	21	1555	23	2630	18	148	48	490	50	149	53	1161	29	787	32	0	.	99	.	33	.	0	.	78559	3
	31-40 cm	171	61	0	.	0	.	294	46	679	29	989	25	3138	13	2586	15	1076	23	2493	15	56	88	83	85	231	43	0	.	42	74	628	35	1010	24	839	35	0	.	1076	22	985	23	554	23	721	25	1801	18	1321	22	1902	17	385	33	875	29	1312	21	212	56	381	31	166	54	193	40	750	28	410	31	451	37	912	26	605	32	507	40	525	26	526	42	1038	24	355	43	411	37	432	31	123	55	430	32	336	29	731	26	606	32	274	45	0	.	89	58	87	66	194	62	488	35	103	56	208	39	139	51	146	37	197	47	264	41	378	38	238	64	327	37	254	44	386	39	398	40	252	41	148	.	683	26	30	.	140	45	471	27	147	41	582	31	373	35	56	73	184	51	131	53	174	92	790	23	982	23	1326	17	253	45	128	38	189	49	828	24	493	30	0	.	103	.	20	.	0	.	48000	3
	41-50 cm	70	58	0	.	0	.	62	48	371	31	559	28	2175	12	1798	13	697	22	903	15	15	100	21	100	215	36	0	.	20	58	292	31	541	23	251	34	0	.	568	22	450	23	456	23	651	24	1161	17	463	22	824	18	215	34	346	32	804	20	142	48	274	30	118	47	94	41	406	23	187	37	171	37	525	29	256	33	188	38	413	28	366	31	671	21	90	53	222	35	230	37	221	48	235	32	224	31	502	27	213	31	158	41	0	.	52	63	133	43	138	40	207	32	50	55	58	49	43	58	132	38	103	51	102	39	173	37	82	48	164	37	179	46	183	34	182	35	137	50	14	.	287	29	33	.	111	51	292	27	110	36	179	38	222	38	76	84	50	55	53	66	71	58	404	28	414	25	736	18	170	43	145	42	129	50	466	29	171	39	0	.	13	.	33	.	0	.	26131	3
	51-60 cm	14	100	0	.	0	.	33	66	90	43	213	31	1077	14	746	16	248	23	336	22	30	100	14	71	34	53	0	.	14	100	97	51	227	30	40	48	0	.	217	31	256	25	291	25	282	30	500	17	178	27	269	22	215	37	173	30	264	29	37	54	74	46	50	54	81	43	207	34	54	43	147	37	257	31	138	37	82	59	230	29	146	41	313	28	35	53	90	47	44	48	22	58	24	100	141	37	157	34	188	38	23	76	0	.	37	82	82	54	90	41	52	52	50	55	14	100	21	100	84	37	22	74	71	60	84	48	30	63	69	42	121	40	95	56	94	39	84	43	0	.	195	37	0	.	83	53	124	33	90	43	42	62	111	50	29	71	23	100	8	100	36	60	139	34	185	27	297	25	57	56	55	49	21	58	138	31	40	57	0	.	0	.	0	.	0	.	11170	4
	61-70 cm	0	.	13	.	0	.	7	100	20	76	8	100	204	23	231	20	68	45	65	37	15	100	7	100	7	100	0	.	0	.	14	71	129	32	7	100	0	.	113	50	81	37	67	39	91	35	199	27	54	51	93	33	43	61	15	71	69	36	22	58	9	100	0	.	22	58	32	50	7	100	66	50	69	54	57	47	45	60	42	41	66	43	92	46	14	100	34	82	14	71	0	.	0	.	53	50	7	100	43	47	18	73	0	.	15	71	7	100	46	76	45	53	21	58	15	71	7	100	22	58	15	71	14	71	13	71	28	100	22	74	26	50	7	100	0	.	22	58	0	.	36	45	0	.	15	70	7	100	38	60	8	100	14	71	0	.	38	67	8	100	0	.	55	53	19	58	94	30	14	71	49	51	0	.	20	58	15	71	0	.	0	.	0	.	0	.	3087	6
	71-80 cm	0	.	0	.	0	.	0	.	13	71	0	.	65	40	126	27	15	71	30	62	0	.	0	.	0	.	0	.	0	.	13	71	14	71	0	.	0	.	49	53	18	57	28	61	20	58	94	32	21	58	14	71	0	.	0	.	18	70	15	71	15	71	7	100	30	60	15	71	0	.	0	.	14	71	0	.	38	75	25	59	27	50	77	58	7	100	0	.	0	.	0	.	0	.	15	100	11	100	29	50	0	.	0	.	0	.	7	100	25	100	22	75	0	.	7	100	0	.	7	100	8	100	7	100	0	.	0	.	0	.	13	71	20	76	0	.	13	100	0	.	16	100	0	.	0	.	22	58	7	100	0	.	16	100	0	.	0	.	16	100	0	.	45	41	14	71	34	51	7	100	7	100	0	.	15	71	7	100	0	.	0	.	0	.	0	.	1196	9
	81-90 cm	0	.	0	.	0	.	7	100	0	.	0	.	13	71	39	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	61	0	.	6	100	19	58	14	71	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	8	100	0	.	0	.	6	100	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	8	100	7	100	7	100	7	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	249	18
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	8	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	8	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	26
	>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	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	41
	total	862	47	63	.	0	.	1194	44	3162	29	5915	24	17788	11	15826	11	5039	20	15397	13	171	71	489	60	1595	39	0	.	1143	52	4593	26	7071	19	3734	28	0	.	6704	20	4789	19	4988	21	5345	22	12419	14	6360	19	10820	15	2717	26	3736	23	9882	17	1179	41	3575	28	1010	43	1951	35	3978	23	3067	28	2941	29	5013	23	3498	27	2858	31	4556	24	3024	32	6572	20	2653	35	1940	34	2771	29	1022	45	2077	31	2735	26	4803	28	3169	26	1950	35	0	.	517	61	976	35	1686	31	1667	35	1645	34	2260	33	1604	40	3652	27	3047	31	1993	31	2328	30	1561	46	2977	32	1963	31	1580	31	3187	35	1311	43	266	.	4255	23	229	.	1071	39	3478	23	1141	33	2734	28	1825	34	513	53	997	53	522	52	632	60	8233	19	6533	19	9931	16	1388	36	1541	41	488	45	4107	24	2335	29	0	.	364	.	136	.	0	.	304819	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	1414	27	2026	29	1182	40	1516	31	0	.	0	.	672	49	1370	30	7106	16	0	.	372	52	875	46	834	46	192	.	309	68	581	59	0	.	750	36	1241	51	0	.	389	68	273	60	0	.	1333	44	31	100	1974	31	939	50	1672	30	213	75	218	63	0	.	710	52	156	.	0	.	0	.	0	.	121	100	146	71	882	56	278	77	464	60	0	.	2066	29	241	49	408	49	854	40	696	46	220	77	0	.	18	100	916	43	1505	30	1442	38	124	.	331	45	441	57	1775	39	2613	30	5573	22	901	55	1214	51	2906	28	542	89	0	.	313	71	370	59	523	67	0	.	296	59	379	84	87	83	698	47	172	74	0	.	1082	47	729	53	527	55	1012	47	602	68	0	.	283	59	1200	41	559	44	1071	38	116	77	103	71	997	42	224	72	954	34	952	33	750	38	1014	36	0	.	70039	5
	21-30 cm	1177	28	909	29	490	41	1207	23	0	.	0	.	388	52	824	35	4145	14	0	.	231	50	534	42	661	37	17	.	284	56	529	43	0	.	809	31	177	47	0	.	200	61	204	60	0	.	456	51	15	100	1267	25	785	34	1162	26	88	82	183	45	0	.	282	49	87	.	0	.	0	.	0	.	35	100	145	73	499	52	170	71	149	71	0	.	1253	32	238	46	372	47	325	40	729	41	31	100	0	.	125	71	255	46	646	33	576	33	35	.	120	70	253	63	316	45	739	31	1511	24	190	51	212	37	1164	33	210	76	0	.	231	71	139	58	249	64	0	.	188	56	34	100	156	90	495	53	190	92	0	.	349	41	399	48	553	47	304	44	149	71	0	.	313	72	493	44	264	51	193	47	120	87	120	66	459	35	59	100	492	40	559	34	614	42	503	37	0	.	35038	4
	31-40 cm	777	24	620	26	302	38	1083	23	0	.	0	.	358	42	467	31	2567	13	0	.	202	47	220	50	370	38	0	.	312	43	246	42	0	.	545	32	39	84	0	.	156	65	130	55	0	.	88	59	28	100	874	25	505	27	880	26	96	81	224	53	0	.	108	57	31	.	0	.	0	.	0	.	59	100	73	79	251	63	44	70	79	62	0	.	598	27	130	54	205	66	402	39	303	41	46	100	0	.	104	74	192	51	382	33	316	30	0	.	22	100	86	64	120	58	216	42	394	34	49	47	83	62	304	31	22	100	0	.	47	82	84	76	82	64	0	.	134	54	40	100	42	75	237	46	89	61	0	.	88	71	75	64	273	46	175	55	50	64	0	.	154	76	131	48	73	55	66	53	79	73	100	57	435	40	85	92	309	35	574	34	273	35	462	33	0	.	19861	4
	41-50 cm	446	28	480	29	117	34	715	24	0	.	0	.	243	47	325	35	1558	15	0	.	103	54	73	50	97	57	14	.	113	53	129	53	0	.	304	34	50	87	0	.	68	63	93	62	0	.	40	52	34	73	417	24	252	32	399	26	59	89	118	59	0	.	54	63	0	.	0	.	0	.	0	.	14	100	42	85	94	56	29	100	52	83	0	.	289	28	270	41	47	65	232	45	103	48	18	100	0	.	29	71	173	49	322	30	216	30	0	.	7	100	16	70	16	100	51	47	101	45	60	48	58	71	109	40	25	100	0	.	10	100	36	61	23	73	0	.	83	52	0	.	42	100	52	56	48	87	0	.	72	49	22	73	62	58	20	74	41	71	0	.	109	55	44	85	0	.	53	46	13	100	102	53	231	40	23	100	272	45	259	33	238	34	259	32	0	.	10786	5
	51-60 cm	158	34	274	30	65	42	221	30	0	.	0	.	77	52	128	37	601	15	0	.	73	44	34	60	56	56	7	.	56	53	34	53	0	.	92	38	14	100	0	.	39	73	44	60	0	.	24	57	0	.	112	34	130	42	135	38	28	79	14	70	0	.	28	71	0	.	0	.	0	.	0	.	0	.	17	100	12	100	0	.	27	58	0	.	110	37	89	53	0	.	69	49	7	100	0	.	0	.	0	.	70	48	138	37	114	34	0	.	0	.	8	100	7	100	30	60	32	50	0	.	44	51	34	51	13	100	0	.	0	.	28	62	8	100	0	.	16	71	0	.	0	.	38	54	7	100	0	.	22	100	16	71	0	.	14	71	14	71	0	.	18	73	7	100	7	100	65	46	0	.	14	100	48	43	7	100	118	39	75	41	114	47	75	33	0	.	3977	6
	61-70 cm	77	41	42	52	27	61	29	63	0	.	0	.	17	73	37	53	167	24	0	.	23	74	14	100	7	100	7	.	14	71	13	70	0	.	30	62	0	.	0	.	7	100	0	.	0	.	0	.	7	100	28	50	14	71	38	64	10	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	34	77	0	.	30	100	0	.	16	71	27	50	0	.	8	100	0	.	0	.	0	.	0	.	14	70	28	60	57	49	7	.	0	.	0	.	0	.	0	.	8	100	16	100	0	.	22	58	0	.	0	.	10	100	0	.	0	.	0	.	31	71	0	.	0	.	13	100	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	22	58	0	.	0	.	22	76	0	.	35	52	27	61	21	58	29	63	0	.	1106	10
	71-80 cm	7	100	7	100	7	100	28	60	0	.	0	.	0	.	31	50	72	32	0	.	8	100	0	.	0	.	0	.	0	.	13	70	0	.	15	71	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	14	71	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	8	100	27	50	0	.	41	100	0	.	0	.	0	.	24	57	0	.	0	.	28	59	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	17	72	7	100	7	100	7	100	0	.	419	16
	81-90 cm	0	.	0	.	7	100	0	.	0	.	0	.	0	.	14	71	31	70	0	.	8	100	7	100	0	.	0	.	0	.	7	100	0	.	16	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	10	100	0	.	0	.	0	.	16	71	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	158	24
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	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	.	7	100	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	19	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	48	42
	>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	.	10	100	0	.	0	.	0	.	0	.	19	100	0	.	22	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	49
	total	4056	21	4358	22	2196	30	4798	20	0	.	0	.	1755	43	3195	26	16248	11	0	.	1020	40	1757	36	2024	35	237	.	1087	47	1553	39	0	.	2567	29	1521	47	0	.	859	54	752	51	0	.	1939	42	115	79	4671	21	2646	31	4297	23	495	71	757	45	0	.	1188	44	274	.	0	.	0	.	0	.	229	100	423	70	1772	47	521	72	800	56	0	.	4338	25	995	36	1032	45	1890	31	1839	39	316	71	0	.	275	71	1627	33	3037	25	2757	28	166	.	543	42	805	58	2234	38	3649	29	7666	20	1235	47	1611	42	4616	26	818	80	0	.	611	71	656	60	885	59	0	.	750	53	454	72	327	67	1549	45	513	69	0	.	1613	42	1242	48	1415	43	1525	40	869	60	0	.	877	57	1875	39	903	44	1476	34	329	74	438	48	2193	30	398	59	2205	30	2459	28	2017	30	2348	28	0	.	141484	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	1779	24	2076	29	1182	40	2032	26	969	43	2007	30	9974	11	8026	12	8829	14	7389	14	391	50	1100	39	1629	32	192	.	1144	45	2961	24	3414	24	2365	28	1241	51	2678	25	2946	20	2830	23	3116	21	8493	14	7419	13	7095	16	2164	27	3524	21	5199	19	737	44	2066	36	980	41	1208	38	1591	30	1939	35	1782	31	2619	27	1542	39	2050	31	2613	28	1815	36	2466	24	3507	23	934	33	1575	30	1406	33	1312	32	1520	30	2031	39	1420	36	1969	32	1505	30	1644	35	680	37	1760	22	1160	29	2962	28	3938	23	6520	19	3472	25	3097	28	4016	22	1555	39	917	53	1941	29	1405	31	1041	38	1786	40	761	37	397	80	2121	27	781	43	503	38	1639	27	1461	36	1986	31	1241	32	1199	41	1090	48	102	80	490	45	8074	15	5842	16	6716	16	855	43	763	45	997	42	1705	31	1776	26	952	33	899	35	1063	35	0	.	226060	2
	21-30 cm	1420	24	909	29	490	41	1481	20	1019	30	2138	29	6664	11	4582	13	5358	12	4174	15	266	45	673	36	972	29	17	.	515	39	1700	27	1735	23	1791	23	177	47	1968	26	2364	18	1954	22	1862	23	4739	14	4672	14	3859	14	1413	24	1636	21	2540	21	416	33	757	31	673	34	567	35	954	27	1154	31	807	31	1666	24	1183	27	1329	30	1660	21	684	35	2301	22	1965	24	728	37	1250	27	430	34	1501	26	753	26	1365	31	796	28	678	30	646	33	698	30	176	44	881	31	907	31	615	28	1364	23	1959	20	1272	24	1049	29	1589	26	894	32	386	45	1238	29	518	34	740	32	748	35	520	45	121	77	1154	26	578	48	582	40	923	27	719	27	1065	27	1046	31	469	40	361	44	205	52	457	53	3882	14	3183	17	3775	15	269	47	610	42	608	29	1220	28	1279	25	559	34	714	38	536	35	0	.	127221	2
	31-40 cm	948	22	620	26	302	38	1377	21	679	29	989	25	4647	11	3074	13	3642	11	2493	15	259	41	303	43	601	28	0	.	354	39	874	28	1010	24	1384	25	39	84	1121	21	1537	18	785	20	1319	20	2648	15	3435	13	2776	14	890	21	1755	19	1408	20	436	39	381	31	274	40	224	37	750	28	455	29	522	34	1047	24	678	30	758	34	654	23	605	37	1065	24	952	23	541	31	637	30	525	32	734	25	452	27	731	26	710	29	467	34	382	33	405	27	144	53	410	36	726	27	223	40	424	29	533	28	269	26	305	35	567	25	400	36	282	56	477	35	338	38	505	32	407	39	386	33	188	81	725	24	267	42	229	36	471	27	235	37	657	28	733	27	231	45	235	42	131	53	329	60	1984	14	1661	17	1857	17	332	39	227	33	624	32	913	24	802	23	574	34	376	37	481	32	0	.	76309	2
	41-50 cm	516	26	480	29	117	34	777	22	371	31	559	28	2970	11	2148	12	2255	12	903	15	118	48	94	44	313	31	14	.	133	45	421	27	541	23	555	24	50	87	638	21	809	17	611	20	1010	18	1596	14	1343	13	1241	14	466	23	745	20	863	19	260	37	274	30	172	38	94	41	406	23	216	35	242	36	556	27	298	31	289	31	500	26	417	29	671	21	379	25	492	27	277	33	453	33	338	27	257	28	502	27	242	28	332	32	322	30	269	27	202	35	347	26	265	27	65	48	108	34	143	36	242	28	176	39	211	28	205	33	141	46	202	32	222	39	220	30	208	32	220	37	14	100	328	28	85	52	159	44	292	27	182	29	201	35	331	31	96	68	91	44	53	66	180	40	995	16	799	19	1143	15	183	40	247	33	359	31	489	28	443	31	259	33	251	33	292	31	0	.	41568	2
	51-60 cm	172	32	274	30	65	42	254	28	90	43	213	31	1288	13	880	14	849	13	336	22	104	43	48	47	90	40	7	.	70	47	130	40	227	30	132	30	14	100	238	29	455	19	377	22	421	23	663	15	610	17	380	18	346	28	308	24	292	28	51	43	74	46	78	43	81	43	207	34	69	40	161	35	266	30	155	35	101	50	245	28	172	36	322	28	145	31	179	35	44	48	91	39	31	81	171	35	157	34	188	38	93	41	138	37	151	32	110	45	176	32	75	40	57	49	44	52	53	50	119	32	73	39	105	44	103	41	52	48	104	44	163	32	110	49	94	39	100	38	0	.	195	37	38	54	90	50	124	33	112	40	58	49	124	46	43	53	37	68	8	100	53	47	444	19	379	20	503	19	57	56	68	44	70	34	145	30	158	32	75	41	114	47	75	33	0	.	17212	3
	61-70 cm	77	41	56	46	27	61	36	54	20	76	8	100	290	19	267	19	236	21	65	37	38	60	21	74	14	71	7	.	14	71	27	50	129	32	37	54	0	.	120	48	137	26	67	39	140	27	292	22	161	27	120	28	57	49	53	50	79	34	22	58	9	100	7	100	22	58	32	50	14	71	73	46	77	50	57	47	86	45	56	35	96	43	92	46	30	60	61	51	14	71	8	100	0	.	68	45	7	100	43	47	32	51	28	60	72	41	21	58	60	62	45	53	21	58	15	71	15	71	52	43	30	61	36	45	20	58	35	82	32	60	26	50	7	100	9	100	53	48	0	.	36	45	13	100	22	58	7	100	38	60	8	100	14	71	0	.	45	59	8	100	0	.	222	28	19	58	186	25	14	71	49	51	22	76	20	58	49	42	27	61	21	58	29	63	0	.	4929	5
	71-80 cm	7	100	7	100	7	100	28	60	13	71	0	.	78	35	157	24	87	29	30	62	8	100	0	.	0	.	0	.	0	.	26	50	14	71	15	71	0	.	56	48	48	44	42	47	42	41	116	28	70	32	14	71	14	71	10	100	18	70	15	71	15	71	7	100	30	60	15	71	0	.	0	.	14	71	0	.	38	75	25	59	27	50	77	58	7	100	0	.	0	.	0	.	0	.	15	100	11	100	29	50	7	100	8	100	27	50	14	71	66	73	22	75	0	.	7	100	24	57	14	71	8	100	35	51	13	71	7	100	0	.	13	71	20	76	0	.	13	100	0	.	16	100	8	100	0	.	22	58	7	100	0	.	16	100	0	.	7	100	16	100	0	.	165	26	35	45	42	46	7	100	7	100	0	.	15	71	24	59	7	100	7	100	7	100	0	.	1934	7
	81-90 cm	0	.	0	.	7	100	7	100	0	.	0	.	13	71	52	38	31	70	0	.	8	100	7	100	0	.	0	.	0	.	7	100	0	.	16	71	0	.	36	53	7	100	6	100	33	45	21	58	14	71	0	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	8	100	0	.	7	100	6	100	7	100	7	100	0	.	7	100	0	.	0	.	0	.	0	.	9	100	0	.	8	100	0	.	0	.	17	72	0	.	0	.	0	.	16	71	11	100	0	.	7	100	0	.	8	100	7	100	7	100	7	100	9	100	0	.	0	.	7	100	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	15	71	14	71	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	500	13
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	14	71	8	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	8	100	0	.	0	.	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	7	100	0	.	0	.	0	.	8	100	8	100	0	.	0	.	19	73	0	.	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	7	100	0	.	0	.	7	100	0	.	0	.	8	100	0	.	0	.	0	.	0	.	191	20
	>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	.	14	71	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	10	100	0	.	0	.	0	.	0	.	31	73	0	.	22	58	0	.	0	.	0	.	0	.	7	100	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	137	26
	total	4918	19	4421	22	2196	30	5992	18	3162	29	5915	24	25932	9	19186	10	21287	10	15397	13	1191	36	2246	31	3619	26	237	.	2230	35	6146	22	7071	19	6301	20	1521	47	6862	20	8304	15	6672	18	7958	17	18590	11	17731	11	15492	12	5363	20	8032	16	10399	17	1936	31	3575	28	2198	31	2225	33	3978	23	3848	28	3587	27	6244	22	3921	25	4652	26	5774	21	3824	28	7001	20	6992	20	2935	25	3803	24	2912	26	3916	25	3234	23	4803	28	3444	25	3577	24	3037	25	3274	25	1362	29	3728	20	3200	25	3944	26	5909	22	9270	17	5482	21	4737	24	6609	20	3190	30	1827	41	4008	27	2693	27	2659	27	3270	34	2061	33	720	58	4582	22	1778	41	1584	34	3478	23	2754	28	3975	24	3504	26	2039	33	1866	40	522	52	1509	42	15809	12	11947	14	14237	13	1717	33	1979	34	2680	26	4506	22	4540	20	2459	28	2381	29	2484	27	0	.	496061	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. 2298480. Birmensdorf, Swiss Federal Research Institute WSL

back