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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
deadwood volume (merch. wood without piles of branches), lying
development stage
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
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	.	12	100	469	63	0	.	88	100	0	.	37	100	668	41	13	100	91	100	0	.	0	.	0	.	21	100	11	100	94	79	8	100	17	77	61	89	0	.	36	68	81	59	0	.	2	100	1710	26
young growth/ thicket	110	52	7	100	37	100	1435	37	77	81	238	65	16	100	45	75	910	33	27	57	209	59	14	100	0	.	19	94	145	44	6	88	77	57	140	89	0	.	13	74	87	79	232	55	218	43	3	100	101	66	4166	16
pole timber	169	37	0	.	18	71	737	24	110	57	452	42	0	.	16	66	356	26	18	54	418	68	84	43	63	76	43	56	96	69	10	63	19	51	139	43	31	43	479	32	81	54	285	32	476	31	10	100	220	32	4329	11
young timber	46	49	11	100	0	.	285	31	26	89	129	41	0	.	250	67	276	26	211	41	61	61	22	65	0	.	171	65	100	50	78	60	129	44	5	100	10	70	468	22	30	47	312	31	543	27	4	100	192	47	3357	10
medium timber	135	38	23	68	14	82	595	33	79	42	122	34	0	.	244	53	1203	24	211	40	101	37	73	40	62	84	35	71	342	30	45	46	196	35	100	45	13	61	553	36	33	82	330	26	365	39	17	81	430	43	5320	10
old timber	483	36	83	87	181	46	1455	17	39	55	381	36	1	100	144	59	1633	22	206	35	522	35	75	38	138	51	97	47	507	34	69	50	222	37	412	37	273	38	415	30	229	62	894	24	952	23	22	58	160	32	9591	7
mixed	47	50	1	100	5	100	1275	18	120	58	605	59	0	.	330	53	1143	20	320	41	298	33	151	38	10	91	172	73	466	37	1	100	192	53	138	60	61	63	756	23	27	55	726	30	1586	20	8	100	156	44	8593	8
total	989	20	126	60	265	35	6252	12	451	27	2014	23	18	94	1065	27	6189	11	1007	19	1700	23	418	19	272	37	538	33	1677	17	219	29	928	19	942	24	405	28	2745	12	486	34	2814	13	4222	11	64	36	1260	19	37066	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. 1347354. Birmensdorf, Swiss Federal Research Institute WSL

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