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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.

NFI5
volume of deadwood (stemwood)
development stage
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	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
development stage	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	0	.	0	.	29	100	174	61	0	.	0	0	0	.	19	100	535	39	0	0	44	100	0	.	0	.	0	.	72	100	0	.	20	100	0	0	73	100	97	64	0	.	92	100	80	89	0	.	11	100	1247	24
young growth/ thicket	130	48	20	100	102	75	763	33	69	100	169	76	3	71	18	82	463	36	38	100	280	48	0	0	0	.	44	100	93	49	0	0	23	100	106	100	0	0	15	84	7	100	53	59	219	61	92	74	83	91	2791	15
pole timber	217	45	0	.	27	69	460	29	136	57	308	43	0	.	12	77	296	30	138	68	312	45	69	40	15	100	11	84	12	79	7	75	9	92	121	40	53	53	389	20	44	67	176	29	235	25	6	77	109	44	3163	10
young timber	30	49	8	100	0	0	490	36	20	71	82	46	0	.	72	75	235	27	212	56	94	62	21	100	16	100	220	63	142	44	9	100	129	47	0	0	26	100	657	20	66	49	209	29	565	24	5	100	125	49	3433	10
medium timber	165	42	45	56	18	68	986	24	86	42	107	44	15	100	134	56	1121	28	211	42	66	42	47	46	69	69	23	59	338	31	225	75	238	37	160	46	1	100	385	32	117	68	395	30	423	33	1	100	266	38	5642	10
old timber	321	40	21	80	206	47	1262	16	143	51	469	35	12	100	210	51	1464	17	266	35	932	32	195	70	134	56	194	41	587	30	91	85	146	58	498	32	177	51	611	32	223	45	809	29	1024	21	15	81	298	50	10309	7
mixed	28	75	0	0	136	95	1723	16	125	62	464	50	0	.	147	47	1302	16	391	42	365	31	182	37	39	71	184	49	516	29	40	71	168	39	76	54	21	88	872	23	262	38	701	27	1472	15	31	76	125	54	9371	7
total	891	21	94	39	519	35	5858	9	579	26	1599	21	31	64	612	26	5415	10	1256	20	2094	18	514	31	273	35	677	28	1761	15	372	50	733	21	962	22	351	35	3025	10	719	23	2435	14	4020	10	149	49	1016	22	35956	3

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. 1346058. Birmensdorf, Swiss Federal Research Institute WSL

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