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.

NFI1
total number of stems NFI1
main tree species
regional demarcation: canton
unit: n/ha
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
	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
main tree species	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
spruce	109	13	365	17	333	16	216	4	42	26	277	8	9	68	240	17	238	4	108	16	228	9	160	12	261	23	284	11	218	8	161	23	84	16	239	10	172	14	45	11	293	12	165	6	181	6	172	18	153	8	182	2
fir	47	15	117	26	88	24	89	6	37	23	68	16	0	.	35	33	9	17	113	12	128	10	136	11	59	29	46	23	59	12	5	47	61	14	65	15	32	22	9	25	66	27	71	9	19	21	96	35	45	13	53	3
pine	17	21	2	98	1	99	8	19	31	24	8	35	91	81	0	.	42	13	15	32	8	53	7	46	0	.	17	70	7	40	29	41	26	23	12	45	15	26	5	43	38	37	4	36	63	14	2	98	24	15	20	6
larch	10	41	1	98	2	99	2	34	1	61	4	67	0	.	1	76	42	8	0	70	1	67	0	100	0	.	0	100	6	36	8	53	1	61	1	71	3	86	31	12	1	99	3	31	80	7	1	98	4	29	18	5
Arolla pine	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	16	15	0	.	0	.	0	.	0	.	5	99	0	100	0	.	0	.	0	.	0	.	0	.	3	99	0	.	16	19	0	.	0	.	4	12
other conifers	2	49	4	98	1	99	2	40	7	100	1	75	0	.	1	99	2	49	0	100	2	55	0	100	3	98	0	.	3	47	2	84	3	48	0	100	1	70	0	.	5	55	2	51	1	85	0	.	4	49	2	16
beech	144	8	81	29	102	23	102	6	184	11	118	11	6	94	117	22	17	16	140	10	97	12	81	12	126	21	110	22	117	11	258	12	199	8	78	15	80	14	92	10	57	31	96	7	17	22	59	33	106	9	89	2
maple	22	15	20	46	31	37	18	11	30	19	13	22	6	94	43	27	3	22	26	15	17	21	18	28	35	24	17	26	36	14	20	37	32	18	24	22	24	34	4	28	17	36	28	14	6	25	24	46	26	16	17	4
ash	24	17	24	53	60	31	12	14	20	35	28	21	19	66	30	47	4	37	35	19	14	24	8	50	64	54	10	47	35	16	22	38	22	28	19	27	32	21	14	18	15	46	25	14	5	26	68	43	29	15	17	5
oak	21	20	0	.	0	99	5	19	24	47	8	28	594	24	0	.	3	49	9	32	6	34	10	51	2	98	0	99	5	52	28	23	14	33	1	66	18	22	19	20	2	64	28	18	5	27	1	98	10	21	11	8
sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	38	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	104	10	0	.	1	86	1	53	0	.	0	.	11	9
other broadleaves	41	17	24	51	14	50	20	11	44	20	22	21	44	47	29	30	27	13	39	18	22	28	21	34	35	40	31	33	39	21	9	41	27	18	35	28	50	22	104	8	48	37	35	15	39	14	10	35	34	17	38	4
indeterminable	0	.	0	.	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	436	4	637	12	632	10	473	2	420	7	546	5	770	15	495	11	411	3	488	5	522	4	441	5	586	9	519	8	526	4	540	9	470	5	474	6	427	6	427	3	545	7	458	3	434	3	432	12	437	4	462	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1240957. Birmensdorf, Swiss Federal Research Institute WSL

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