NFI Results

Estimates and standard error of the estimates

The results published in the NFI tables are estimates for variables in the Swiss forest (population parameters) whose true values are not known and must therefore be extrapolated (estimated) from the NFI sample data.

The extrapolations carry uncertainties. However, the accuracy of the extrapolations can be reliably estimated from the NFI sample, which is designed to be a random sample. To this end, all NFI tables include a second figure in addition to the estimate itself, namely the standard error of the estimate.

In most tables the percent standard error is reported (±%), but the absolute standard error (±) is also reported occasionally, especially for estimated percentages. The relationship between absolute and percent standard error is as follows:

percent standard error = absolute standard error / estimate × 100

absolute standard error = percent standard error × estimate / 100

Information on the forest road network is based on a full survey of all forest roads. In this case, there is no need to specify a standard error because there is no uncertainty caused by sampling.

Confidence interval of the estimate

With the estimate and the standard error of the estimate, the limits of the so-called confidence interval of the estimate can be calculated as:

lower limit = estimate – tQ × absolute standard error

upper limit = estimate + tQ × absolute standard error

If the simple standard error is used for the calculation (tQ = 1), then the 68% confidence interval is formed. In this case, the true value of the population parameter has a 68% probability of lying within this confidence interval of the estimate. If the double standard error is used (tQ = 2), then there is a 95% probability that the true value lies within this 95% confidence interval.

Significance of the estimate

The confidence interval can be used to statistically test whether the estimated population parameter is larger or smaller than a specific reference or target value, or whether two estimated population parameters truly differ (in the real population). For practical applications, proceed as follows: if a reference value lies outside the confidence interval, the estimated population value can be interpreted as being significantly different from it; if it lies within the confidence interval, the difference between the estimated population parameter and the reference value can be interpreted as random or not significant. When two population parameters are compared, they can be interpreted as differing significantly if their confidence intervals do not overlap.

Dealing with missing values

When calculating a results table, data are not always available for all combinations of characteristics^* of the classification variables and regional demarcation applied. In most cases, this indicates that the attribute estimated with the respective target variable does not occur or occurs only very rarely. Usually, the value 0 is used (imputed). However, as this value is not based on direct measurements, the associated standard error is represented with a full stop [.]. If the imputed value of 0 is referred to in the calculation, e.g. in the case of percentages or certain change estimates, no value can be entered. In this case, the estimated value and standard error are both represented with a full stop [.].

For example, no Arolla pines have been found and measured in the Swiss Plateau to date (growing stock of Arolla pines by production region). The values can be assumed to be missing because Arolla pine does not occur in the Swiss Plateau and therefore the growing stock there must be 0.

^* e.g. ‘Arolla pine’ characteristic of the classification variable ‘tree species’ and ‘Swiss plateau’ characteristic of the ‘regional demarcation’

Changes

There are two types of changes in the NFI:

The first type involves specific target variables relating to components of change, such as increment, fellings, mortality and losses. These target variables are only available for two consecutive inventory cycles, e.g. NFI3–NFI4. In the evaluation of components of change, the classification variable characteristic of the second inventory cycle is assigned to the first inventory cycle. These evaluations therefore do not take into account potential changes in a classification variable characteristic from the earlier to the later inventory (e.g. from private to public ownership).

With the second type of change, the difference in target variables, such as number of stems, growing stock or forest area, is used to assess the change between two inventory cycles. Target variables are generally used to represent states, such as that in NFI4, but can also indicate the net change between any two inventory cycles, e.g. NFI1–NFI4. In evaluations of change using these target variables, the change in a classification variable characteristic is considered in the analysis. Consequently, one can detect, for example, that the forest area without shrub forest increased between the two inventory cycles. This only has an effect on classification variables whose characteristic (class) can actually change, e.g. the affiliation with a forest area or the tree condition.

NFI2–NFI3
bolewood harvested
type of last silvicultural treatment
regional demarcation: canton
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
change 1993/95–2004/06
	canton
	AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
type of last silvicultural treatment	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%
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	.
no treatment	14	58	2	100	1	100	59	27	7	76	10	55	0	0	16	93	40	38	20	62	39	42	8	48	0	0	7	71	12	68	3	100	4	65	4	61	2	55	15	85	0	0	21	40	6	63	0	0	10	48	289	13
tending	116	40	0	.	3	100	133	29	16	89	57	70	0	.	0	0	48	49	9	100	38	83	21	55	0	.	21	100	38	73	2	70	58	61	3	88	29	56	0	100	1	100	55	41	6	75	10	81	65	44	725	14
thinning	74	25	0	0	4	86	159	19	17	51	57	30	2	100	10	84	32	41	54	43	73	37	33	34	4	58	6	90	23	51	22	43	54	34	6	61	37	36	10	64	8	89	59	28	12	46	0	.	94	26	846	8
secondary felling	10	66	0	.	3	100	104	33	17	80	60	51	4	100	0	0	32	47	27	45	21	65	23	72	3	100	0	.	18	93	10	100	21	68	8	71	32	89	0	.	7	71	56	41	31	50	0	.	38	54	523	14
final felling	17	61	4	100	3	100	90	42	21	100	14	71	0	.	1	100	27	59	44	46	27	100	16	75	0	.	0	.	0	0	7	100	32	63	30	100	0	.	23	68	7	100	40	56	11	90	0	.	11	100	416	18
selection felling	0	.	0	.	0	.	75	39	0	.	0	.	0	.	0	.	0	0	0	.	4	100	39	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	21	47	0	100	0	.	0	.	142	24
thinning of mountain forest	0	.	0	.	0	.	9	72	0	.	6	73	0	.	0	.	63	34	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	0	0	.	0	.	0	.	0	.	36	44	0	.	0	.	121	24
coppice cut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	80	0	.	3	100	0	.	0	.	0	.	5	64
conversion	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	16	80	0	.	0	.	0	.	0	.	0	100	0	.	0	0	8	94	27	59
transformation	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	3	78
sanitary felling	270	25	0	0	19	52	649	13	33	81	226	27	0	.	5	78	46	40	76	33	192	29	35	33	1	100	79	45	42	41	11	60	36	49	66	52	103	42	4	100	13	93	103	31	18	39	18	60	216	27	2242	7
thinning of continuous cover forest	21	47	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	71	1	100	0	.	0	.	0	.	0	.	54	42	90	29
tending of forest edge	11	100	0	.	23	100	12	71	0	.	2	100	0	.	0	.	0	0	0	0	8	100	0	.	0	.	0	.	39	100	0	.	0	.	0	.	8	100	0	0	0	.	0	0	7	100	0	.	0	0	103	43
planting	12	100	0	.	0	.	21	62	0	.	0	.	0	.	0	0	3	100	0	.	6	100	4	100	0	.	0	.	0	.	9	100	35	100	0	.	0	.	0	.	0	.	69	70	0	.	12	100	37	71	210	33
agrosilvicultural treatments	2	100	6	71	1	100	24	69	0	.	5	100	0	0	0	.	0	100	6	100	2	100	4	95	0	.	0	0	1	100	0	.	0	100	0	.	1	100	8	59	0	.	11	66	5	52	0	.	0	.	78	28
total	544	16	11	52	58	45	1335	8	112	36	434	18	6	74	33	55	291	16	237	19	413	19	182	17	8	48	113	36	178	31	63	30	258	25	116	39	222	25	68	33	36	46	429	16	133	21	42	45	531	15	5715	4

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. 1399088. Birmensdorf, Swiss Federal Research Institute WSL

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