Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

top

2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

top

3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

top

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

top

5. Two types of changes?

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

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

top

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

top

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

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

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

top

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

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

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

top

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

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

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

top

NFI5
number of stems
development stage · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	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	.
	spruce	103	87	132	47	28	79	0	0	380	38	99	66	742	26
	fir	7	100	67	78	0	.	7	100	0	0	0	.	81	65
	pine	55	66	0	.	0	.	7	100	14	71	0	.	76	50
	larch	0	.	0	.	0	.	14	71	89	86	14	100	118	67
	Arolla pine	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	166	73	30	100	61	72	0	.	0	.	0	.	257	52
	maple	17	100	9	100	139	88	0	.	0	.	0	.	165	75
	ash	7	100	0	.	0	.	0	.	0	.	0	.	7	100
	oak	16	72	0	.	0	.	0	.	0	.	0	0	16	72
	sweet chestnut	0	.	7	100	0	.	0	.	0	.	0	0	7	100
	other broadleaves	7	100	0	.	0	.	0	.	0	.	0	0	7	100
	indeterminable	0	.	0	0	0	.	0	.	0	.	0	.	0	0
	total	378	51	245	42	229	59	29	50	483	39	113	59	1477	22
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	750	29	1522	22	469	34	104	57	791	27	310	53	3947	13
	fir	897	44	220	36	112	44	0	0	0	0	0	.	1229	33
	pine	66	45	0	0	7	100	0	.	82	73	64	100	219	42
	larch	111	79	44	86	18	100	69	62	199	54	59	47	501	31
	Arolla pine	0	.	0	.	0	.	0	.	126	100	0	.	126	100
	other conifers	47	61	15	100	36	100	0	.	55	100	0	.	152	48
	beech	596	19	486	34	206	36	0	.	14	100	26	100	1328	16
	maple	167	36	181	43	170	52	17	100	105	85	0	.	641	25
	ash	63	67	42	100	67	60	43	100	0	.	0	.	215	39
	oak	86	47	0	.	9	100	0	.	0	.	0	.	95	44
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	277	38	347	41	95	62	87	82	115	67	266	46	1187	21
	indeterminable	0	.	0	0	0	.	0	.	0	0	0	.	0	0
	total	3061	20	2857	18	1189	26	321	38	1488	23	734	31	9649	10
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	11465	13	6093	20	3971	23	1474	27	6528	20	1698	39	31230	8
	fir	4559	19	1748	41	861	44	154	73	24	77	167	46	7513	16
	pine	430	40	104	100	151	90	1089	50	2330	31	0	.	4105	23
	larch	1050	45	453	44	0	.	1337	31	957	37	2215	27	6012	16
	Arolla pine	0	.	0	.	0	.	49	87	935	38	0	.	984	36
	other conifers	224	38	256	78	58	100	156	62	451	42	0	.	1145	27
	beech	8743	12	1585	34	647	57	534	53	105	71	2908	28	14523	11
	maple	4611	16	1912	22	980	32	1269	34	20	100	1126	39	9918	11
	ash	2303	17	711	34	675	36	916	39	137	79	1058	31	5800	12
	oak	1213	40	128	85	0	.	687	43	0	.	953	47	2980	24
	sweet chestnut	0	.	0	.	0	.	15	100	0	.	3858	28	3873	28
	other broadleaves	3117	17	2037	25	1236	39	2810	22	3080	28	7513	18	19793	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	37715	7	15027	13	8579	18	10490	15	14569	14	21496	11	107877	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8276	13	6976	21	3047	25	4417	28	4708	22	1285	37	28709	9
	fir	2679	19	1453	33	865	38	7	100	144	63	299	69	5447	15
	pine	624	32	124	71	0	.	1964	37	799	50	181	87	3692	24
	larch	160	40	70	100	261	73	1497	26	594	36	1050	43	3632	18
	Arolla pine	0	.	0	.	0	.	325	61	692	52	49	100	1066	39
	other conifers	639	41	0	.	57	58	17	100	17	100	0	.	731	36
	beech	6123	15	1303	35	1899	33	291	70	179	49	4666	25	14461	12
	maple	2144	16	1421	32	970	30	0	.	255	63	324	47	5114	13
	ash	916	23	815	46	574	39	128	50	139	68	864	49	3437	19
	oak	1050	42	107	56	0	0	140	56	0	.	633	36	1930	26
	sweet chestnut	0	.	0	.	0	.	68	84	18	100	2624	28	2709	27
	other broadleaves	2205	21	651	37	537	47	704	35	786	62	3748	22	8631	13
	indeterminable	0	0	0	0	0	0	0	.	0	.	0	0	0	0
	total	24815	9	12919	16	8211	19	9559	18	8331	18	15723	13	79558	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6342	12	5311	20	4601	18	2368	27	5661	17	1895	30	26178	8
	fir	4890	14	1296	30	936	24	342	52	77	91	204	62	7747	11
	pine	926	26	50	100	76	90	367	46	652	40	14	100	2085	19
	larch	48	38	0	0	0	.	636	37	606	31	1129	32	2419	19
	Arolla pine	0	.	0	.	0	.	24	100	181	49	0	.	206	45
	other conifers	317	49	0	.	59	76	0	.	0	.	0	.	376	43
	beech	10876	9	2292	22	2363	19	453	67	228	59	1583	34	17796	7
	maple	2029	15	517	31	720	26	18	100	33	71	114	47	3431	11
	ash	1690	19	423	44	522	31	80	74	0	.	56	64	2771	15
	oak	923	25	55	74	7	100	68	62	0	.	119	51	1172	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	647	45	647	45
	other broadleaves	1794	17	407	31	190	31	156	46	99	56	1310	29	3956	13
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	29834	7	10351	15	9476	14	4512	21	7539	15	7071	15	68783	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5442	10	8664	10	6662	13	4234	17	6774	14	3057	18	34833	5
	fir	7432	9	5035	13	2085	18	458	41	232	44	597	43	15839	7
	pine	448	22	90	100	157	78	25	77	250	37	0	.	970	21
	larch	101	39	35	72	62	44	1711	25	1263	20	688	25	3860	14
	Arolla pine	0	.	0	.	0	.	189	49	318	39	0	.	507	30
	other conifers	325	41	0	.	54	57	0	.	0	.	0	.	378	36
	beech	11231	7	4489	15	2841	16	18	100	542	34	1137	34	20258	6
	maple	2347	13	638	21	937	22	261	52	85	61	0	.	4268	10
	ash	1525	16	300	40	446	30	71	62	30	72	0	.	2371	13
	oak	755	18	8	100	6	100	16	100	35	100	46	61	866	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	1121	34	1121	34
	other broadleaves	2150	20	538	27	515	37	219	42	231	53	668	35	4321	13
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	.	0	0
	total	31755	5	19798	9	13763	10	7202	15	9761	12	7313	13	89593	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6456	11	13187	11	5499	18	6618	15	10646	13	5127	24	47533	6
	fir	6984	12	5147	14	1630	23	563	43	157	40	369	44	14851	8
	pine	522	27	730	67	362	52	1218	31	617	37	79	70	3527	20
	larch	72	61	76	61	120	84	3105	18	1887	20	3353	18	8614	11
	Arolla pine	0	.	10	100	0	.	1203	28	1695	34	16	100	2924	23
	other conifers	243	35	53	58	133	74	0	.	121	77	44	70	593	28
	beech	6188	11	3994	17	2777	22	1017	42	600	50	2404	23	16980	8
	maple	2085	15	1965	22	860	30	393	41	278	44	217	44	5797	11
	ash	1295	19	647	32	394	34	167	62	472	64	454	28	3429	14
	oak	1006	30	91	58	131	47	231	82	55	100	430	39	1945	21
	sweet chestnut	0	.	0	.	0	.	156	95	28	100	2190	26	2373	25
	other broadleaves	2635	16	1712	23	1413	27	1183	22	507	33	3234	23	10685	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	27485	7	27610	8	13320	13	15855	11	17064	11	17916	12	119249	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	38834	5	41884	6	24277	8	19216	9	35488	7	13471	12	173170	3
	fir	27447	5	14967	9	6490	12	1532	24	634	26	1637	24	52707	4
	pine	3070	13	1098	47	753	36	4670	21	4745	19	338	53	14674	10
	larch	1543	32	677	33	461	47	8371	11	5596	11	8507	12	25155	6
	Arolla pine	0	.	10	100	0	.	1791	22	3947	20	65	79	5813	15
	other conifers	1795	20	324	63	396	34	173	56	644	34	44	70	3376	15
	beech	43923	4	14179	9	10795	10	2313	27	1669	23	12725	12	85602	3
	maple	13400	7	6643	12	4776	12	1958	25	778	29	1781	26	29336	5
	ash	7799	8	2937	18	2678	15	1405	28	778	43	2433	22	18030	6
	oak	5049	15	389	36	154	41	1142	32	91	72	2180	24	9004	11
	sweet chestnut	0	.	7	100	0	.	238	67	45	72	10448	13	10739	13
	other broadleaves	12184	8	5692	12	3986	17	5160	14	4819	21	16738	10	48580	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	155043	2	88808	4	54766	6	47968	6	59235	5	70366	4	476187	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. 1283957. Birmensdorf, Swiss Federal Research Institute WSL

zurück