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.

NFI5
growing stock (stemwood)
main tree species
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
main tree species	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	.
spruce	2592	21	2564	31	2096	30	26972	7	751	36	7664	15	4	75	3152	24	32662	7	2239	23	6657	14	4483	15	1573	37	3932	21	9791	14	1388	28	1717	27	5119	17	2572	21	6765	21	4090	22	11378	11	14470	10	699	43	5757	14	161087	3
fir	1398	28	609	41	1551	36	16155	9	541	38	2259	24	4	100	501	48	2105	23	3889	20	6273	14	4592	17	368	56	1259	34	3634	18	184	59	1306	22	2031	25	1377	32	1298	29	862	33	7026	13	1621	26	269	44	2054	20	63168	4
pine	143	58	0	.	0	.	1324	28	297	35	331	55	67	100	0	.	2623	17	383	37	246	54	123	59	4	100	217	83	95	71	330	39	320	32	65	88	404	42	124	61	224	60	181	46	1891	19	0	.	840	22	10232	8
larch	286	41	0	.	0	.	248	48	27	71	97	69	0	.	50	100	7615	11	22	100	28	59	41	100	0	.	18	100	218	49	36	59	20	100	57	100	70	60	3914	15	0	.	492	31	8549	12	40	100	285	37	22114	6
Arolla pine	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	1499	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	874	24	0	.	0	.	2393	15
other conifers	198	50	0	.	14	74	525	43	252	100	0	.	0	.	3	100	35	40	11	74	352	71	8	100	0	.	8	100	344	98	66	100	57	58	4	100	5	72	82	98	7	100	105	57	13	58	0	.	75	49	2163	26
beech	4567	16	323	48	494	36	13391	9	2317	20	3366	19	0	.	935	31	1847	22	3416	17	3640	17	1304	18	486	42	1366	30	5257	15	2159	21	4431	15	1826	25	1734	20	5839	12	1001	33	6720	12	886	31	314	43	4304	14	71921	3
maple	636	22	25	75	266	42	2336	12	428	29	566	24	0	.	370	39	432	24	508	24	549	22	566	28	136	46	199	57	1746	18	101	53	727	22	336	30	473	39	246	38	170	42	1192	15	376	31	100	50	1276	18	13760	5
ash	743	29	0	.	254	55	1285	24	309	45	1343	28	9	100	260	44	301	39	770	29	575	49	99	46	511	59	116	76	1347	25	231	46	462	27	576	39	779	27	750	35	211	58	1310	23	342	35	115	60	1261	22	13959	7
oak	688	35	0	.	0	.	628	22	189	52	231	43	384	58	0	.	149	71	332	45	305	48	87	60	5	100	0	.	97	57	210	47	212	42	19	100	963	45	939	21	86	72	1153	24	231	38	0	.	558	26	7465	10
sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	515	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4838	14	0	.	6	100	97	52	0	.	0	.	5456	12
other broadleaves	684	24	0	.	75	69	1350	16	198	33	492	32	8	86	155	46	1563	22	393	34	292	29	59	37	81	55	43	43	466	27	39	48	339	37	228	40	469	34	3542	13	108	38	1169	32	906	17	7	63	700	26	13369	6
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	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0
total	11935	11	3520	29	4750	24	64227	5	5310	17	16349	11	475	55	5426	19	51347	5	11963	12	18918	10	11363	11	3164	27	7159	18	22996	10	4744	18	9590	12	10261	14	8847	14	28344	7	6758	18	30732	7	30257	7	1544	33	17110	9	387089	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. 1371156. Birmensdorf, Swiss Federal Research Institute WSL

back