Selection from the list of topics

back

NFI4
volume of bole wood (HG 2010)
timber assortment classes (HG 2010) · conifers and broadleaves
unit of reference: canton
unit: 1000 m³
unit of evaluation: accessible forest without shrub forest NFI3/NFI4
grid: 1.4 x 1.4 km grid
state2009/17
		timber assortment classes (HG 2010)
		small diameter timber		classes 1 and 2		classes 3 and 4		classes 5 and 6		total
canton	conifers and broadleaves	1000 m³	± %	1000 m³	± %	1000 m³	± %	1000 m³	± %	1000 m³	± %
AG	conifers	294	20	2001	11	3033	10	854	19	6182	9
	broadleaves	29	8	2597	9	3858	7	1665	11	8149	6
	total	323	18	4598	6	6891	5	2520	9	14332	4
AI	conifers	50	39	692	17	1088	15	182	28	2013	15
	broadleaves	2	34	154	37	218	30	43	46	417	25
	total	52	38	847	14	1307	13	224	24	2430	12
AR	conifers	131	25	925	16	1989	11	590	24	3635	11
	broadleaves	3	28	387	23	478	25	109	34	977	21
	total	134	24	1312	14	2467	9	699	22	4612	9
BE	conifers	1388	8	12730	4	21950	4	7532	6	43601	3
	broadleaves	65	6	6145	5	7516	6	2241	12	15966	5
	total	1453	8	18875	3	29466	3	9773	5	59567	2
BL/BS	conifers	52	24	645	14	1164	20	321	33	2183	17
	broadleaves	18	10	1428	10	2311	9	940	20	4698	9
	total	71	17	2074	8	3475	8	1262	16	6881	7
FR	conifers	599	15	3395	9	4953	8	1310	14	10257	7
	broadleaves	19	11	1937	9	2651	10	769	22	5376	8
	total	618	15	5332	6	7605	6	2079	11	15633	5
GE	conifers	1	66	85	80	57	53	26	100	169	54
	broadleaves	4	28	285	24	402	34	65	87	756	30
	total	5	30	371	25	459	30	90	65	925	26
GL	conifers	54	20	1034	17	1746	12	870	22	3705	12
	broadleaves	8	18	630	15	865	15	183	30	1685	13
	total	62	17	1664	11	2611	9	1053	19	5390	8
GR	conifers	514	5	15282	3	18800	3	6606	6	41201	3
	broadleaves	23	12	2359	9	1148	14	250	23	3781	9
	total	537	5	17641	3	19948	3	6856	6	44982	2
JU	conifers	920	10	1532	10	3254	10	690	20	6396	8
	broadleaves	22	9	1855	9	2921	7	508	18	5305	7
	total	941	10	3387	6	6174	5	1197	14	11700	4
LU	conifers	293	17	3852	7	5890	7	1635	11	11669	6
	broadleaves	18	13	1474	10	1985	15	582	21	4060	11
	total	311	15	5326	5	7875	5	2217	10	15729	4
NE	conifers	1113	9	984	14	4045	8	1054	16	7198	7
	broadleaves	10	14	808	10	1089	12	151	36	2058	10
	total	1123	9	1792	8	5134	6	1206	14	9255	5
NW	conifers	16	29	674	23	594	20	161	35	1445	18
	broadleaves	5	31	400	27	324	19	47	40	776	19
	total	20	23	1074	16	918	14	209	30	2221	12
OW	conifers	58	14	1785	10	2221	12	896	18	4960	10
	broadleaves	7	20	652	20	619	23	219	48	1497	18
	total	65	12	2437	8	2840	9	1116	16	6457	7
SG	conifers	430	16	3927	8	6815	6	2961	11	14132	6
	broadleaves	28	9	2680	8	3532	8	849	15	7089	7
	total	458	15	6607	6	10347	5	3809	9	21221	4
SH	conifers	57	35	728	18	870	14	216	26	1871	13
	broadleaves	10	14	836	14	1211	9	304	30	2361	9
	total	67	29	1564	9	2081	7	520	21	4233	6
SO	conifers	106	25	1487	10	1988	11	435	20	4015	9
	broadleaves	27	8	1879	9	3077	7	954	16	5936	6
	total	133	20	3366	6	5064	6	1388	13	9951	5
SZ	conifers	139	20	2136	8	3716	9	1427	16	7418	8
	broadleaves	11	16	1028	12	1214	16	405	29	2657	12
	total	150	19	3164	6	4931	6	1831	13	10076	5
TG	conifers	221	23	1273	14	1670	15	346	30	3510	11
	broadleaves	9	12	1030	11	1529	10	577	22	3145	9
	total	230	22	2303	8	3198	8	923	19	6655	6
TI	conifers	102	9	3351	7	4212	7	2191	11	9856	7
	broadleaves	98	5	8619	4	4102	6	1614	17	14433	4
	total	200	5	11970	3	8314	4	3805	9	24288	3
UR	conifers	50	18	1652	11	1725	10	680	21	4107	10
	broadleaves	5	21	506	21	316	26	78	47	904	19
	total	54	16	2158	9	2040	9	759	20	5011	7
VD	conifers	1246	11	5472	6	10132	5	3114	9	19963	5
	broadleaves	45	7	4147	6	5084	6	1225	13	10500	5
	total	1291	10	9618	4	15216	3	4339	8	30463	3
VS	conifers	412	10	10085	4	10799	4	3363	8	24658	3
	broadleaves	18	13	1908	9	592	15	125	32	2644	9
	total	430	10	11993	3	11390	3	3488	8	27302	3
ZG	conifers	35	53	472	16	757	24	194	29	1458	19
	broadleaves	2	21	140	24	301	22	89	43	532	21
	total	37	50	611	13	1058	18	283	26	1989	14
ZH	conifers	321	16	2874	8	4910	7	1242	14	9347	6
	broadleaves	25	8	2277	7	3324	7	1655	16	7281	6
	total	346	15	5151	5	8234	4	2897	10	16628	3
Switzerland	conifers	8601	3	79073	2	118378	1	38896	3	244947	1
	broadleaves	510	2	46160	2	50666	2	15649	4	112985	2
	total	9111	3	125233	1	169043	1	54545	2	357932	1

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.

Table citation

Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold, A.; Meile, R.; Rösler, E.; Speich, S.; Traub, B., 2020: Swiss national forest inventory - Result table No. 806799. Birmensdorf, Swiss Federal Research Institute WSL
https://doi.org/10.21258/1675853

back

Selection from the list of topics

Estimates and standard error of the estimates

Confidence interval of the estimate

Significance of the estimate

Dealing with missing values

Changes

Table citation