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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.

NFI3
number of deadwood stems
deadwood: mechanical strength of wood (3 classes) · deadwood: regeneration (3 classes)
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 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
deadwood: mechanical strength of wood (3 classes)	deadwood: regeneration (3 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	±%
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
	no plant	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	1 plant	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	2 or more plants	0	.	0	.	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
fresh/firm wood	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 plant	694	16	161	42	293	28	4643	9	530	21	1592	15	158	51	755	21	5528	7	709	16	1778	17	290	23	227	31	644	21	1936	13	174	32	668	19	1041	25	457	33	4942	9	565	25	2554	10	3867	9	112	38	665	16	34983	3
	1 plant	4	100	0	.	0	.	50	41	0	.	10	100	0	.	4	100	27	64	4	100	4	100	0	.	0	.	0	.	9	100	0	.	0	.	25	61	0	.	4	100	4	100	27	62	28	54	0	.	8	71	208	20
	2 or more plants	14	77	0	.	0	.	26	54	0	.	0	.	0	.	4	100	17	50	0	.	18	61	0	.	0	.	0	.	16	50	0	.	0	.	11	100	0	.	0	.	0	.	15	77	18	64	0	.	4	100	143	22
	total	712	16	161	42	293	28	4720	8	530	21	1603	15	158	51	763	21	5573	7	713	16	1800	17	290	23	227	31	644	21	1961	13	174	32	668	19	1077	25	457	33	4945	9	569	25	2595	10	3913	9	112	38	677	16	35334	3
rotten wood	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 plant	178	38	28	58	140	32	1200	14	56	47	413	22	10	100	114	37	1595	11	238	24	58	42	43	63	44	62	311	35	432	27	20	71	157	32	157	31	9	100	1416	11	101	35	512	19	1609	11	0	.	203	26	9042	5
	1 plant	0	.	0	.	4	100	36	48	0	.	19	71	0	.	10	100	29	44	0	.	0	.	0	.	0	.	9	100	18	64	0	.	0	.	18	71	0	.	0	.	10	100	10	100	26	50	0	.	10	100	199	20
	2 or more plants	0	.	0	.	0	.	69	35	4	100	0	.	0	.	14	77	64	40	0	.	9	100	0	.	0	.	10	100	25	59	0	.	0	.	28	86	0	.	23	70	0	.	0	.	67	30	0	.	20	71	332	17
	total	178	38	28	58	144	32	1305	14	60	44	433	22	10	100	138	33	1688	10	238	24	66	39	43	63	44	62	330	33	475	26	20	71	157	32	203	28	9	100	1440	11	111	33	522	18	1701	11	0	.	232	28	9574	4
mouldering/mull wood	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 plant	115	34	10	100	0	.	699	16	41	44	162	26	0	.	29	58	1253	13	182	38	94	33	19	71	35	61	59	37	162	31	0	.	89	31	193	32	23	61	606	15	69	41	248	22	967	14	31	74	61	39	5146	6
	1 plant	0	.	0	.	0	.	51	44	0	.	10	100	0	.	0	.	50	43	0	.	11	100	0	.	4	100	0	.	0	.	0	.	0	.	20	71	0	.	23	54	0	.	18	65	34	52	0	.	0	.	221	20
	2 or more plants	9	100	0	.	15	77	103	30	0	.	18	71	0	.	4	100	59	36	0	.	12	100	0	.	0	.	4	100	26	61	0	.	0	.	10	100	0	.	0	.	0	.	4	100	69	42	0	.	0	.	333	17
	total	124	32	10	100	15	77	852	15	41	44	190	26	0	.	33	52	1362	13	182	38	116	33	19	71	40	56	63	35	188	28	0	.	89	31	223	29	23	61	629	15	69	41	270	21	1070	14	31	74	61	39	5700	6
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
	no plant	987	14	199	37	433	26	6542	8	628	20	2167	14	168	53	898	19	8376	7	1129	15	1930	16	352	23	306	27	1014	22	2530	13	194	30	913	17	1392	21	489	31	6964	7	735	22	3313	10	6442	8	143	40	929	14	49171	3
	1 plant	4	100	0	.	4	100	137	30	0	.	40	81	0	.	14	77	106	29	4	100	15	77	0	.	4	100	9	100	27	54	0	.	0	.	62	39	0	.	27	48	14	77	55	41	88	30	0	.	18	65	628	13
	2 or more plants	23	61	0	.	15	77	198	22	4	100	18	71	0	.	22	55	140	25	0	.	38	48	0	.	0	.	15	77	67	34	0	.	0	.	49	74	0	.	23	70	0	.	19	71	154	28	0	.	24	61	808	11
	total	1014	14	199	37	451	25	6877	8	632	20	2225	14	168	53	934	19	8622	7	1133	15	1983	16	352	23	310	27	1038	22	2624	12	194	30	913	17	1503	20	489	31	7014	7	750	22	3387	10	6684	8	143	40	970	14	50607	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. 1322025. Birmensdorf, Swiss Federal Research Institute WSL

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