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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 bolewood (HG 2010)
timber assortment classes (HG 2010) · tree species (5 classes)
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
timber assortment classes (HG 2010)	tree species (5 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	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	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	.
smallwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2093	11	98	44	420	24	257	26	350	20	355	30	206	12	272	23	131	23	72	24	41	18	293	18	297	10	97	15	4983	6
	fir	1896	9	40	57	211	28	217	21	136	25	87	33	157	16	111	33	23	24	79	73	9	29	92	68	8	29	13	23	3079	7
	other conifers	201	42	22	40	122	45	41	57	28	26	6	75	16	45	6	49	16	32	7	44	14	53	159	12	136	12	86	16	860	13
	beech	54	8	21	13	12	19	25	13	29	11	7	27	27	15	23	14	9	20	7	27	9	21	4	36	8	28	36	14	271	4
	other broadleaves	25	12	12	18	13	21	13	16	25	12	4	24	14	20	15	17	10	19	5	28	8	27	16	17	9	23	86	9	254	4
	indeterminable	0	72	0	0	0	0	0	0	0	0	0	0	0	0	0	100	0	0	0	0	0	0	0	0	0	0	0	0	0	68
	total	4270	7	194	30	779	18	554	17	568	15	460	25	419	10	428	19	189	17	169	41	81	16	564	15	458	8	318	7	9448	4
classes 1 and 2	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2699	10	702	23	1089	20	2690	14	3813	11	3377	13	5987	9	4764	11	4062	11	1959	17	1675	17	5668	10	10235	7	3442	12	52163	3
	fir	2450	10	364	32	333	27	2134	14	1098	16	914	24	2911	10	1251	16	521	22	633	26	202	26	385	25	219	27	440	26	13853	5
	other conifers	537	23	358	25	180	39	300	27	613	16	12	100	243	53	76	41	261	27	181	41	162	44	4420	10	3648	9	2184	11	13175	5
	beech	4247	8	1513	14	1022	17	1552	11	2324	9	561	25	2266	12	1638	15	711	20	611	25	618	25	521	28	380	27	2800	13	20766	4
	other broadleaves	2973	9	1123	14	1282	17	1277	13	2733	9	843	17	1406	18	1755	15	1289	16	623	22	741	19	1591	13	1161	18	7473	7	26270	3
	indeterminable	8	50	0	100	0	0	0	0	6	87	1	100	2	71	2	77	9	67	0	0	0	0	1	100	17	81	20	78	67	34
	total	12914	5	4060	10	3906	11	7953	8	10587	6	5707	11	12816	6	9485	8	6853	8	4007	13	3398	13	12587	7	15660	5	16359	4	126293	1
classes 3 and 4	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7945	7	1082	20	2042	22	3248	15	4724	10	5110	12	8138	9	8256	10	6150	11	2691	18	2681	16	6392	9	12011	7	5094	14	75564	3
	fir	7078	9	530	28	1012	25	3331	14	2341	14	1493	19	5163	10	3091	14	1092	22	930	27	550	30	770	30	583	25	853	26	28817	4
	other conifers	566	19	521	32	388	33	694	23	1133	18	3	100	409	56	107	45	189	36	136	48	100	44	4507	10	4252	9	2345	11	15352	5
	beech	7260	7	3184	13	2042	19	3700	11	3944	10	769	29	3627	14	3707	13	1125	22	721	28	982	22	297	36	662	31	2081	11	34102	3
	other broadleaves	2581	12	1130	19	1519	18	1538	19	3293	10	351	27	887	20	1455	14	795	22	401	35	663	26	432	26	390	27	2722	10	18156	4
	indeterminable	0	0	11	100	0	0	0	0	0	0	0	0	0	0	0	0	27	71	0	0	0	0	0	0	14	100	0	0	51	50
	total	25430	4	6458	10	7004	12	12512	8	15435	6	7727	10	18224	6	16615	7	9378	9	4878	14	4976	13	12397	7	17913	6	13095	6	172042	1
classes 5 and 6	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2280	15	466	36	465	38	1087	19	1411	18	1785	16	2945	12	3306	14	2283	14	1165	22	1449	23	2085	15	4156	11	2575	17	27456	4
	fir	2007	16	125	39	452	38	1190	18	1054	20	610	29	2412	14	1258	18	769	27	443	46	311	42	216	41	414	30	466	30	11727	6
	other conifers	84	53	123	76	35	76	410	39	428	51	0	0	53	64	55	53	41	55	9	100	21	72	1389	14	1336	14	922	20	4904	9
	beech	1552	15	1029	21	703	23	1856	18	2710	14	214	48	1252	23	831	24	300	34	237	38	328	36	97	53	209	45	471	19	11790	6
	other broadleaves	260	26	320	29	506	30	464	30	1549	24	45	62	58	61	325	34	300	61	92	59	76	46	74	42	102	51	989	25	5162	11
	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
	total	6183	9	2063	15	2161	17	5007	11	7151	10	2653	14	6721	9	5775	11	3693	13	1945	20	2185	18	3861	10	6217	9	5423	10	61039	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15018	6	2348	18	4017	18	7282	12	10298	9	10627	11	17275	8	16598	9	12626	10	5886	16	5846	16	14437	9	26698	6	11209	12	160166	2
	fir	13431	7	1058	27	2008	23	6873	12	4630	13	3104	19	10644	9	5711	13	2405	21	2084	27	1072	29	1462	26	1224	24	1772	24	57477	4
	other conifers	1388	19	1024	31	725	30	1444	23	2202	20	21	83	721	42	244	35	506	25	333	41	298	38	10475	9	9372	8	5537	10	34291	4
	beech	13113	6	5748	12	3781	17	7134	11	9007	9	1551	24	7172	12	6198	12	2145	19	1576	24	1936	20	920	28	1259	27	5388	11	66928	3
	other broadleaves	5839	10	2585	14	3320	16	3292	15	7599	9	1244	18	2365	17	3549	13	2394	19	1121	24	1488	21	2113	14	1662	18	11270	7	49842	3
	indeterminable	8	50	11	100	0	0	0	0	6	87	1	100	2	71	2	77	36	57	0	0	0	0	1	100	31	64	20	78	118	29
	total	48797	3	12775	9	13850	11	26026	7	33741	5	16547	10	38180	6	32303	7	20113	8	10999	13	10641	12	29409	6	40247	5	35195	4	368822	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. 1323779. Birmensdorf, Swiss Federal Research Institute WSL

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