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 NFI4/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	15761	9	457	40	2442	24	0	.	525	49	1991	23	3568	20	5854	15	4123	17	331	54	1068	34	3619	17	139	57	1316	24	1953	23	1299	30	1121	29	977	31	6417	13	1311	28	371	51	2135	19	60763	4
Larix decidua, Larix kaempferi	300	43	0	.	0	.	311	40	25	72	71	74	0	.	67	87	8395	10	20	100	18	62	43	100	0	.	14	100	306	44	39	66	17	100	52	100	55	85	4139	13	0	.	345	29	8917	11	55	100	287	38	23475	6
Picea abies	3229	21	2507	29	2875	27	32377	7	744	39	8189	14	0	.	4047	22	33907	6	2773	22	8628	14	4622	15	1454	33	3537	20	10588	13	1888	25	2092	26	5764	16	3174	19	6186	15	4098	21	12945	10	14247	9	816	38	6910	14	177599	2
Pinus cembra	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	1532	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	958	22	0	.	0	.	2489	13
Pinus mugo subsp. uncinata	0	.	0	.	0	.	63	47	0	.	0	.	0	.	0	.	443	27	0	.	0	.	0	.	0	.	163	91	0	.	0	.	0	.	55	71	0	.	85	75	17	82	0	.	175	39	0	.	0	.	1002	22
Pinus mugo subsp. mugo	0	.	0	.	0	.	38	96	0	.	0	.	0	.	0	.	45	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	15	58	0	.	0	.	104	39
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	.	2189	21	325	41	186	52	51	71	0	.	0	.	179	52	268	35	330	31	0	.	441	35	80	66	221	55	207	51	1845	20	0	.	978	22	9594	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	20	100	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	63
Acer campestre	12	89	0	.	0	.	1	100	2	449	0	.	0	.	10	100	8	77	34	50	0	.	0	.	7	100	0	.	0	.	7	100	8	78	0	.	0	.	9	71	0	.	33	54	8	52	0	.	-10	100	127	28
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	26	70	0	.	0	.	241	26
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	1818	14	380	30	471	27	0	.	350	29	424	41	355	24	511	23	395	31	125	36	284	51	1305	19	73	52	539	21	301	26	429	37	254	30	185	39	758	20	234	30	122	47	1152	20	11227	5
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	.	5	100	0	.	47	100	0	0	181	69	169	59	0	.	356	52	0	.	0	.	147	68	1214	24
Alnus incana	0	.	3	100	12	100	107	45	1	100	51	57	0	.	29	68	428	38	30	100	27	70	4	100	6	100	6	86	58	51	0	.	13	88	135	62	5	81	160	37	121	64	45	78	148	44	0	0	0	0	1392	17
Alnus viridis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	11	90
Betula pendula	1	100	0	.	32	89	73	59	0	.	4	100	0	.	0	.	359	25	0	.	3	100	16	100	0	.	2	100	25	91	5	100	8	100	1	100	1	100	1659	19	12	64	8	100	301	30	0	.	32	100	2542	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	41	76	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	831	19
Castanea sativa	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	449	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4911	13	0	.	9	84	194	59	0	.	0	.	5569	12
Fagus sylvatica	5027	16	258	46	557	33	13742	9	3295	20	3303	19	0	.	961	30	1869	22	3594	16	3441	18	1538	19	436	42	1539	30	5263	15	2108	21	4936	15	1745	25	1723	19	5004	13	875	34	6645	12	958	29	273	43	4199	14	73286	3
Fraxinus excelsior	1017	24	0	.	335	48	1520	21	512	35	1288	25	28	100	297	42	314	34	922	27	929	50	128	42	439	57	73	65	1516	20	215	42	503	29	712	36	1065	24	638	28	182	51	1672	21	437	31	171	70	1453	19	16367	6
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	.	432	93	0	.	0	.	173	100	0	.	0	.	0	.	805	56
Populus (others)	0	.	0	.	0	.	85	100	0	.	303	77	11	100	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	404	62
Populus tremula	0	.	0	.	0	.	9	77	0	.	37	80	7	100	0	.	240	49	0	0	19	100	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	64	82	0	.	25	71	105	39	0	.	0	.	522	27
Prunus avium	62	61	0	.	0	.	244	32	18	76	7	75	0	0	14	73	51	69	7	100	31	63	0	.	0	.	0	.	62	50	0	.	46	58	0	0	27	63	158	29	14	100	54	38	32	36	0	.	70	49	895	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	.	85	56	0	.	0	.	0	.	0	.	0	.	85	56
Quercus petraea	258	58	0	.	0	.	194	37	103	62	0	.	473	55	0	.	151	63	122	57	65	100	84	60	0	.	0	.	7	90	212	57	184	41	0	.	152	64	584	27	75	63	858	28	47	41	0	.	409	34	3978	12
Quercus pubescens	4	100	0	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	46	0	.	24	100	137	52	0	.	0	.	284	32
Quercus robur	179	64	0	.	0	.	475	25	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	19	100	314	49	21	100	10	100	259	36	3014	16
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	.	18	100	23	100	0	.	0	0	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	226	57	0	.	0	.	21	72	0	.	0	.	301	44
Salix (others)	0	.	0	.	17	100	93	66	-13	100	17	78	0	.	0	.	178	34	0	0	0	0	15	84	0	.	0	0	4	100	0	.	17	79	0	.	57	89	38	59	0	.	123	74	95	40	0	.	21	81	661	22
Sorbus aria	3	100	0	.	0	.	65	30	44	49	9	90	0	.	44	73	29	55	48	51	4	100	25	47	0	.	9	63	19	83	10	100	30	54	4	100	0	.	74	28	26	47	99	44	49	34	0	.	29	58	620	13
Sorbus aucuparia	0	.	0	0	0	.	91	33	4	100	0	0	0	.	0	.	39	41	7	100	15	78	20	57	0	.	8	73	17	51	0	.	0	.	13	71	0	.	29	54	14	72	72	39	43	35	0	.	0	0	371	15
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	.	233	39	25	60	13	100	18	100	0	.	0	.	150	62	6	82	4	100	37	100	9	89	479	36	44	61	96	52	17	92	0	.	48	66	1535	18
Tilia platyphyllos	192	58	0	.	0	.	271	76	0	.	14	100	0	.	67	72	10	100	0	.	0	.	3	100	0	.	0	.	50	49	0	.	37	100	30	76	5	100	17	100	2	100	30	100	166	52	0	.	0	.	894	29
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	.	27	86	0	.	23	100	0	.	0	.	0	.	50	66
other shrubs	5	58	0	.	0	.	18	58	0	.	1	100	0	.	0	.	21	40	2	100	1	100	3	100	0	.	0	.	8	83	0	.	0	.	0	.	4	100	45	31	0	.	13	71	5	50	0	.	0	.	128	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	13685	11	3362	28	5641	22	69962	5	6320	16	16966	11	632	51	6452	18	53598	5	12284	12	20415	10	11241	11	2890	26	6720	17	23858	9	5169	18	10254	12	10892	13	9960	14	27082	5	6908	17	32328	7	30572	7	1826	31	18446	9	407462	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. 1217646. Birmensdorf, Swiss Federal Research Institute WSL

back