Selection from the list of topics

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
growing stock (stemwood)
forest type (17 classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
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
forest type (17 classes)	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
inaccessible forest	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	.
shrub forest	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	.
permanently unstocked forest area	810	45	48	100	172	100	698	43	210	100	917	41	0	.	0	.	221	49	172	100	1354	55	147	100	0	.	839	48	313	74	0	.	77	100	0	.	109	76	118	82	0	.	1004	50	355	63	0	.	254	73	7818	16
temporarily unstocked forest area	51	100	0	.	0	.	470	43	0	0	359	56	0	.	150	95	433	52	132	100	293	54	0	.	0	.	83	100	38	100	11	100	0	.	192	71	30	100	0	.	130	100	168	98	-4	100	24	100	65	100	2626	20
aisle, embankment	0	.	0	.	0	.	-15	100	49	100	65	100	0	.	164	100	241	61	62	100	0	.	0	.	0	.	11	88	0	.	0	.	271	79	0	.	0	.	126	88	0	.	68	100	79	100	0	.	0	.	1122	32
permanently open stand	0	.	0	.	66	100	1362	39	8	100	96	100	0	.	0	.	2437	22	207	69	117	85	442	65	18	100	218	72	373	62	0	.	0	.	492	53	0	.	430	40	71	100	1306	30	882	36	0	.	0	.	8525	12
(nut) orchard, plantation	0	.	0	.	0	.	0	.	0	.	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	522	58	0	.	245	100	0	.	0	.	0	.	833	48
coppice forest	0	.	0	.	0	.	203	60	209	100	31	100	0	.	59	82	475	50	18	100	26	100	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3577	17	0	.	109	78	160	64	0	.	0	.	4916	14
coppice with standards	275	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	181	71	60	100	0	.	0	.	0	.	0	.	0	.	160	100	0	.	14	100	0	.	1654	28	115	100	409	62	0	.	0	.	75	100	2944	21
selection type high forest	0	.	0	.	0	.	7151	14	128	100	873	49	0	.	257	67	6379	13	324	71	471	56	3271	22	0	.	1783	30	413	54	0	.	0	.	404	55	152	100	1725	26	703	44	1996	32	3140	19	175	100	45	100	29389	7
non-uniform high forest	1640	50	0	.	486	71	4356	22	591	45	709	47	0	.	458	61	1968	25	122	100	1320	37	397	60	0	.	0	.	745	43	94	100	465	51	0	.	523	61	3393	20	301	68	1413	29	3120	25	0	.	1030	39	23131	9
young growth/ thicket	775	36	0	.	13	100	1275	31	371	57	147	69	0	.	134	96	645	37	0	0	302	58	43	100	0	.	0	.	209	71	208	63	163	71	7	100	62	100	164	56	169	72	353	52	388	39	16	100	847	36	6292	12
poletimber	1211	28	0	.	193	75	2349	22	131	82	1231	31	0	.	162	68	2742	18	563	56	1428	30	330	57	125	100	138	82	949	36	386	49	518	38	669	42	1628	33	2245	18	182	61	1194	29	2147	24	157	80	1971	26	22650	7
young timber	698	38	0	.	570	71	6917	15	908	41	2259	29	355	59	2238	51	2877	21	1861	31	1814	34	446	53	625	51	176	100	3939	23	1788	33	1483	29	334	63	899	38	2305	22	584	49	3008	22	3647	18	147	100	1873	26	41751	6
medium timber	3286	23	649	45	966	47	15950	11	2387	31	4082	22	148	100	1261	40	10946	13	4668	20	3290	25	1681	27	1305	38	124	100	5290	22	1509	34	4186	19	2097	30	2069	31	3301	21	1890	37	9065	14	5794	17	267	60	5759	18	91970	4
old timber	5912	19	2417	34	2889	32	26609	9	1032	39	4513	23	258	100	2113	37	18315	11	4114	22	10566	16	3832	23	334	73	2824	31	10256	16	955	48	3235	28	5970	19	4477	22	3197	22	1626	34	9597	15	7069	16	623	59	6884	17	139616	3
total	14658	11	3114	28	5355	22	67325	5	6026	17	15347	11	760	48	6997	22	47860	5	12303	12	20980	11	10637	11	2407	27	6196	18	22527	10	5111	18	10400	13	10179	14	9948	14	22758	6	5770	18	29935	8	26778	7	1409	34	18803	9	383583	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. 1232805. Birmensdorf, Swiss Federal Research Institute WSL

back