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.

NFI4
growing stock (stemwood)
tree species (56 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 2009/17
	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
tree species (56 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	.
Abies alba	1887	25	557	38	1541	36	15576	9	455	40	2442	24	0	.	525	49	1991	23	3568	20	5854	15	4123	17	331	54	1068	34	3196	18	139	57	1316	24	1841	23	1299	30	1088	30	952	32	6417	13	1287	28	204	44	2135	19	59792	4
Larix decidua, Larix kaempferi	300	43	0	.	0	.	306	41	25	72	71	74	0	.	67	87	7281	11	20	100	18	62	43	100	0	.	14	100	210	47	39	66	17	100	52	100	55	85	3352	16	0	.	345	29	8161	12	55	100	287	38	20718	7
Picea abies	3200	21	2322	31	2875	27	30395	7	722	40	7736	15	0	.	3817	24	31534	7	2773	22	8167	15	4622	15	1342	35	3317	20	9686	14	1888	25	2066	27	5459	17	3174	19	5883	16	3979	22	11947	10	13654	10	506	43	6900	14	167961	2
Pinus cembra	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	1342	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	823	24	0	.	0	.	2164	15
Pinus mugo subsp. uncinata	0	.	0	.	0	.	39	59	0	.	0	.	0	.	0	.	414	29	0	.	0	.	0	.	0	.	163	91	0	.	0	.	0	.	45	85	0	.	58	100	17	82	0	.	158	41	0	.	0	.	894	24
Pinus mugo subsp. mugo	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	39	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	15	58	0	.	0	.	61	30
Pinus nigra	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	100	0	.	0	.	0	.	92	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	210	63
Pinus strobus	0	.	0	.	0	.	105	56	0	.	0	.	0	.	0	.	0	.	44	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	168	41
Pinus sylvestris	212	44	0	.	17	100	1243	33	217	41	495	46	110	79	0	.	2141	21	325	41	186	52	51	71	0	.	0	.	74	72	268	35	330	31	0	.	441	35	80	66	166	65	207	51	1837	21	0	.	978	22	9379	9
Pseudotsuga menziesii	80	65	0	.	0	.	349	48	177	100	0	.	0	.	0	.	0	.	0	.	258	72	0	.	0	.	0	.	291	100	57	100	0	.	0	.	3	100	50	100	0	.	290	73	0	.	0	.	10	100	1566	31
Taxus baccata	15	74	0	.	8	78	15	87	0	.	0	.	0	.	2	100	0	.	-5	145	1	100	7	100	0	.	4	100	-19	119	0	.	12	226	3	100	1	100	0	.	0	.	30	47	0	.	0	.	42	60	117	43
other conifers	26	100	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	80
Acer campestre	12	89	0	.	0	.	1	100	10	71	0	.	0	.	10	100	8	77	34	50	0	.	0	.	7	100	0	.	0	.	7	100	8	78	0	.	0	.	7	83	0	.	33	54	3	73	0	.	-10	100	129	27
Acer opalus	0	.	0	.	0	.	42	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	83	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	33	9	75	0	.	0	.	225	27
Acer platanoides	71	62	0	.	0	.	164	45	105	62	13	100	0	.	5	100	58	68	56	65	15	71	11	114	14	100	0	.	121	50	6	100	9	100	48	73	11	100	0	.	0	.	45	71	54	87	0	.	16	362	821	20
Acer pseudoplatanus	500	22	18	71	245	52	1686	14	328	32	471	27	0	.	302	32	273	33	355	24	511	23	391	31	109	39	249	57	1245	19	73	52	507	22	263	29	429	37	190	36	152	42	713	21	196	34	105	53	1145	20	10457	6
Aesculus hippocastanum	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	8	100
Alnus glutinosa	81	57	0	.	0	.	60	80	0	.	0	.	0	.	0	.	57	100	69	100	43	63	0	.	0	.	0	.	0	.	0	.	47	100	0	0	181	69	167	60	0	.	228	58	0	.	0	.	147	68	1079	24
Alnus incana	0	.	3	100	12	100	106	45	1	100	46	62	0	.	29	68	401	40	30	100	27	70	4	100	0	.	6	86	47	58	0	.	13	88	74	75	5	81	101	43	38	100	11	71	148	44	0	0	0	0	1102	19
Alnus viridis	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	.
Betula pendula	1	100	0	.	32	89	53	73	0	.	4	100	0	.	0	.	282	28	0	.	3	100	16	100	0	.	2	100	25	91	5	100	8	100	1	100	1	100	1221	19	12	64	8	100	198	30	0	.	32	100	1903	13
Betula pubescens	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	3	100	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	36	100	0	.	0	.	50	75
Carpinus betulus	122	43	0	.	0	.	79	58	40	79	10	100	3	100	0	.	0	.	90	45	75	85	0	.	0	.	0	.	21	78	9	100	44	89	0	.	93	56	9	81	0	.	69	87	3	100	0	.	163	47	830	19
Castanea sativa	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	449	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4838	13	0	.	9	84	90	53	0	.	0	.	5391	12
Fagus sylvatica	5014	16	258	46	557	33	13216	9	2719	19	3303	19	0	.	961	30	1786	23	3594	16	3441	18	1538	19	436	42	1539	30	4962	16	2108	21	4855	15	1633	27	1723	19	4923	13	850	35	6635	12	922	30	249	46	4174	14	71394	3
Fraxinus excelsior	1017	24	0	.	335	48	1445	22	452	39	1288	25	28	100	282	44	285	36	922	27	929	50	128	42	439	57	67	70	1434	21	215	42	503	29	592	40	1065	24	546	32	180	51	1611	22	263	34	171	70	1438	20	15636	7
Fraxinus ornus	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	77	0	.	0	.	0	.	0	.	0	.	40	77
Ilex aquifolium	2	100	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	42	57	2	100	1	100	0	.	0	.	0	.	56	44
Juglans regia	5	100	0	.	0	.	16	88	0	.	0	.	0	.	0	.	35	59	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	3	100	0	.	0	.	0	0	4	100	0	.	-1	100	68	39
Laburnum anagyroides	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	9	76	2	100	0	.	0	.	12	57
Malus sylvestris	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	81
Ostrya carpinifolia	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	120	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	400	40	0	.	0	.	0	.	0	.	0	.	520	38
Populus alba, Populus x canescens	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	348	77	2	100	0	.	0	.	350	77
Populus nigra s.l.	39	100	0	.	0	.	8	100	0	.	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	101	100	0	.	0	.	0	.	32	100	0	.	0	.	0	.	0	.	0	.	0	.	232	54
Populus (others)	0	.	0	.	0	.	85	100	0	.	303	77	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	393	63
Populus tremula	0	.	0	.	0	.	9	77	0	.	37	80	0	.	0	.	232	50	0	0	19	100	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	64	82	0	.	24	74	89	42	0	.	0	.	489	28
Prunus avium	62	61	0	.	0	.	233	33	0	0	7	75	0	0	14	73	51	69	7	100	31	63	0	.	0	.	0	.	62	50	0	.	46	58	0	0	27	63	150	31	14	100	54	38	24	43	0	.	70	49	850	14
Prunus padus	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	1	100	0	.	0	.	6	100	33	79
Pyrus communis, Pyrus pyraster	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-40	100	0	.	0	.	-38	104
Quercus cerris	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	61	0	.	0	.	0	.	0	.	0	.	78	61
Quercus petraea	258	58	0	.	0	.	194	37	103	62	0	.	473	55	0	.	130	71	122	57	65	100	84	60	0	.	0	.	6	100	212	57	184	41	0	.	152	64	552	28	75	63	858	28	34	51	0	.	409	34	3912	13
Quercus pubescens	4	100	0	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	91	47	0	.	24	100	121	57	0	.	0	.	265	33
Quercus robur	73	58	0	.	0	.	444	26	94	85	160	48	0	.	0	.	0	.	177	70	243	57	2	100	5	100	0	.	76	59	29	100	18	95	16	100	743	48	173	49	0	.	314	49	21	100	0	.	239	38	2827	17
Quercus rubra	254	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	254	61
Robinia pseudoacacia	12	100	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	226	57	0	.	0	.	21	72	0	.	0	.	283	47
Salix (others)	0	.	0	.	17	100	91	67	-13	100	17	78	0	.	0	.	156	38	0	0	0	0	15	84	0	.	0	0	4	100	0	.	17	79	0	.	19	178	18	112	0	.	102	88	78	46	0	.	21	81	542	26
Sorbus aria	3	100	0	.	0	.	53	30	33	56	9	90	0	.	44	73	26	60	48	51	4	100	25	47	0	.	6	81	4	100	10	100	30	54	0	.	0	.	70	29	18	51	82	50	33	36	0	.	29	58	526	14
Sorbus aucuparia	0	.	0	0	0	.	91	33	0	.	0	0	0	.	0	.	16	41	7	100	13	86	20	57	0	.	8	73	17	51	0	.	0	.	11	80	0	.	11	64	8	100	63	42	43	35	0	.	0	0	311	16
Sorbus torminalis	0	.	0	.	0	.	14	100	28	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	1	100	57	51
Tilia cordata	43	61	0	.	2	100	307	45	0	.	4	100	0	.	0	.	217	42	25	60	13	100	18	100	0	.	0	.	150	62	6	82	4	100	37	100	9	89	425	39	22	70	96	52	17	92	0	.	48	66	1443	18
Tilia platyphyllos	192	58	0	.	0	.	69	68	0	.	14	100	0	.	67	72	10	100	0	.	0	.	3	100	0	.	0	.	48	52	0	.	37	100	30	76	5	100	17	100	2	100	30	100	166	52	0	.	0	.	690	24
Ulmus glabra	40	52	0	.	0	.	143	34	62	56	19	68	0	.	33	96	14	64	13	87	17	92	0	.	72	59	13	100	68	49	0	.	46	81	20	110	9	100	27	63	19	68	31	53	0	.	7	100	72	52	725	15
Ulmus minor	0	.	0	.	0	.	2	100	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	71
other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	23	100	0	.	23	100	0	.	0	.	0	.	46	71
other shrubs	5	58	0	.	0	.	9	52	0	.	1	100	0	.	0	.	17	44	2	100	1	100	3	100	0	.	0	.	8	83	0	.	0	.	0	.	4	100	43	32	0	.	4	60	5	50	0	.	0	.	104	19
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	0	0
total	13536	11	3158	29	5641	22	66691	5	5581	16	16508	11	614	53	6159	19	49373	5	12284	12	19942	11	11237	11	2756	27	6455	18	21853	10	5169	18	10114	12	10129	14	9523	14	24967	5	6511	17	30820	7	28478	7	1297	33	18369	9	387165	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. 1217629. Birmensdorf, Swiss Federal Research Institute WSL

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