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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) · conifers and broadleaves
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/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
timber assortment classes (HG 2010)	conifers and broadleaves	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	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	.
	conifers	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	.
	broadleaves	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	.
	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	.
	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	.
smallwood	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	.
	conifers	146	37	59	59	92	50	1355	12	74	57	582	23	6	85	22	25	472	8	876	16	208	21	1467	15	23	40	161	44	278	23	75	53	101	49	55	17	148	29	150	14	52	20	1742	13	531	16	8	41	185	25	8868	5
	broadleaves	25	14	1	52	3	35	66	8	13	19	18	17	3	56	9	25	31	14	18	17	20	17	9	20	6	37	6	31	34	14	9	22	24	15	11	21	12	20	107	8	6	30	46	11	20	16	2	46	21	13	519	3
	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	171	32	61	58	95	49	1421	12	88	49	600	22	9	70	32	20	502	7	893	16	228	20	1475	15	29	33	167	42	312	20	84	48	125	40	66	16	160	27	257	9	58	18	1787	13	551	16	10	36	207	22	9387	4
classes 1 and 2	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	.
	conifers	1468	18	803	30	952	28	12651	6	549	22	3045	15	41	100	891	24	15768	5	1024	21	4221	11	782	18	757	36	2095	19	3368	12	593	28	1143	18	2109	15	1595	21	3984	11	2102	18	4732	11	10144	7	487	32	2657	15	77957	2
	broadleaves	1863	13	59	57	452	30	6207	7	1277	18	1870	15	103	47	604	23	2642	13	1554	16	1575	13	889	15	596	38	636	28	3018	13	746	24	1890	15	994	20	1114	17	9103	6	763	28	4031	10	2076	12	149	41	2392	11	46604	2
	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	3331	12	862	30	1404	24	18858	5	1826	16	4915	12	143	52	1495	18	18409	5	2577	14	5796	10	1671	13	1353	28	2730	17	6386	10	1339	20	3032	13	3103	13	2709	15	13087	6	2865	17	8763	8	12220	7	637	28	5049	10	124560	2
classes 3 and 4	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	.
	conifers	2005	18	1652	28	1867	26	21153	6	813	30	5051	14	44	78	1780	21	19701	5	3194	17	6354	12	4103	13	799	33	2197	20	6667	12	930	25	1607	18	3641	15	1645	18	5235	14	2300	20	9159	9	11590	8	669	37	4062	12	118215	2
	broadleaves	3176	14	203	48	446	32	8467	8	1681	20	2412	18	167	53	900	26	1549	17	2734	15	2378	18	962	20	483	38	734	31	4454	13	1266	21	2776	15	1303	24	1659	17	4265	8	556	34	5012	11	724	23	280	46	3153	12	51741	3
	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	5181	12	1856	27	2313	25	29620	5	2493	18	7463	12	211	52	2680	19	21250	5	5928	13	8732	10	5065	12	1282	28	2931	18	11121	10	2195	19	4383	13	4944	14	3304	15	9499	8	2856	18	14170	8	12314	7	949	32	7215	10	169956	1
classes 5 and 6	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	.
	conifers	806	26	428	40	375	33	8072	8	345	43	1677	19	0	0	1096	28	7399	8	994	27	2371	16	1476	21	282	42	784	30	3529	15	306	46	326	34	1445	22	665	30	2365	19	678	27	2865	14	3683	11	189	43	1242	18	43399	3
	broadleaves	1662	18	26	71	51	51	2699	14	498	31	555	23	65	100	190	38	417	28	597	27	744	27	84	49	33	100	216	44	1232	22	298	42	884	24	410	41	1150	32	1355	19	218	46	1557	20	172	37	64	69	1562	19	16739	5
	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	2468	15	453	38	425	31	10771	7	843	25	2232	16	65	100	1286	25	7816	8	1591	20	3115	14	1560	20	315	39	1000	26	4761	13	605	32	1210	22	1855	20	1816	24	3719	13	896	26	4422	12	3855	10	254	38	2804	14	60138	3
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	.
	conifers	4425	16	2942	28	3285	25	43230	6	1781	27	10354	12	90	82	3789	21	43339	5	6087	16	13154	11	7828	12	1860	31	5237	19	13841	11	1904	23	3177	16	7250	15	4053	17	11733	12	5132	18	18498	8	25949	7	1354	34	8146	12	248439	2
	broadleaves	6726	13	289	47	952	30	17439	7	3469	17	4855	16	338	55	1704	23	4639	13	4902	14	4717	15	1944	15	1118	35	1591	26	8738	12	2319	20	5573	13	2719	20	3935	17	14829	6	1544	28	10645	10	2991	14	496	41	7129	11	115602	2
	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	11151	11	3232	28	4237	23	60670	5	5249	16	15210	11	428	52	5492	18	47978	5	10989	12	17872	10	9771	11	2978	25	6829	17	22579	9	4223	18	8750	12	9968	13	7988	13	26563	6	6676	16	29143	7	28940	6	1849	30	15275	9	364041	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. 1301508. Birmensdorf, Swiss Federal Research Institute WSL

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