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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)
type of assortment · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
type of assortment	main tree species	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	.
	spruce	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
	pine	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	larch	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	Arolla pine	0	.	0	.	0	.	0	0	0	0	0	.	0	0
	other conifers	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
	maple	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	ash	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	oak	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	sweet chestnut	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
	indeterminable	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
coniferous wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	49	74	696	56	385	55	259	59	448	56	28	100	1864	29
	fir	46	100	181	75	42	100	0	.	0	.	0	.	269	55
	pine	10	100	0	.	0	.	14	100	65	68	0	.	89	53
	larch	10	100	0	.	0	.	78	80	53	100	0	.	141	59
	Arolla pine	0	.	0	.	0	.	0	.	28	94	0	.	28	94
	other conifers	0	.	0	.	0	.	0	.	4	77	0	.	4	77
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	114	75	877	50	427	51	352	57	598	44	28	100	2395	25
coniferous shortwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21038	6	35788	6	25141	8	11607	10	25714	7	10969	12	130256	3
	fir	13208	8	12899	9	7427	12	743	34	1102	26	1749	24	37128	5
	pine	2730	12	542	40	399	36	1864	18	2241	17	220	43	7996	8
	larch	559	22	256	38	219	39	6920	11	5180	11	4931	10	18065	6
	Arolla pine	0	.	10	100	0	.	928	22	1423	18	8	92	2369	14
	other conifers	1472	33	62	58	65	36	23	62	70	36	75	99	1766	28
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	39008	5	49556	5	33250	7	22085	7	35729	5	17952	8	197580	2
coniferous mediumwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2168	21	2805	21	1423	32	1860	25	438	44	212	61	8906	11
	fir	1611	20	2469	21	441	40	616	43	123	65	23	100	5282	13
	pine	422	28	8	100	78	71	92	68	85	77	0	.	686	23
	larch	40	66	115	49	114	71	536	47	204	68	303	48	1312	26
	Arolla pine	0	.	0	.	0	.	2	100	7	100	0	.	10	81
	other conifers	6	100	41	98	1	100	0	.	0	.	0	.	48	85
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	0	0	.	0	0	0	.	0	.	0	0
	total	4246	15	5437	17	2057	27	3107	22	857	39	538	42	16242	9
coniferous longwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15708	8	1240	30	1382	36	711	52	99	100	0	.	19139	7
	fir	13135	8	603	38	956	44	104	91	0	.	0	.	14799	8
	pine	999	28	211	100	0	.	0	.	0	.	0	.	1210	29
	larch	275	33	4	100	0	.	16	75	12	100	0	.	307	30
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	261	75	0	.	0	.	0	.	0	.	0	.	261	75
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	0	0	0	0	0	0	.	0	.	0	.	0	0
	total	30378	6	2059	27	2338	34	831	47	110	100	0	.	35717	6
broadleaf roundwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	38783	4	10868	9	9709	10	920	28	1259	27	5388	11	66928	3
	maple	6336	6	2562	11	2639	11	417	25	348	30	422	24	12723	5
	ash	7178	9	1749	20	2142	15	417	28	281	45	792	25	12558	7
	oak	4600	12	191	31	159	40	202	34	108	71	883	20	6143	9
	sweet chestnut	0	.	4	100	0	.	148	54	20	84	4958	12	5129	11
	other broadleaves	4521	10	1499	22	1219	15	929	16	906	20	4215	10	13289	6
	indeterminable	25	51	39	52	2	77	1	100	31	64	20	78	118	29
	total	61443	3	16911	7	15870	8	3035	14	2952	16	16678	5	116888	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	38963	4	40528	5	28331	7	14437	9	26698	6	11209	12	160166	2
	fir	27999	5	16152	8	8867	11	1462	26	1224	24	1772	24	57477	4
	pine	4161	11	761	40	477	32	1971	17	2392	16	220	43	9981	8
	larch	883	18	375	30	333	35	7551	11	5448	10	5234	10	19824	6
	Arolla pine	0	.	10	100	0	.	930	22	1458	18	8	92	2407	13
	other conifers	1739	30	103	52	65	36	23	62	74	34	75	99	2079	26
	beech	38783	4	10868	9	9709	10	920	28	1259	27	5388	11	66928	3
	maple	6336	6	2562	11	2639	11	417	25	348	30	422	24	12723	5
	ash	7178	9	1749	20	2142	15	417	28	281	45	792	25	12558	7
	oak	4600	12	191	31	159	40	202	34	108	71	883	20	6143	9
	sweet chestnut	0	0	4	100	0	.	148	54	20	84	4958	12	5129	11
	other broadleaves	4521	10	1499	22	1219	15	929	16	906	20	4215	10	13289	6
	indeterminable	25	51	39	52	2	77	1	100	31	64	20	78	118	29
	total	135189	2	74840	4	53943	5	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. 1261832. Birmensdorf, Swiss Federal Research Institute WSL

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