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.

NFI5
number of stems
conifers and broadleaves · diameter at breast height (10 classes)
regional demarcation: biogeographical region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
conifers and broadleaves	diameter at breast height (10 classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
conifers	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	13354	8	17820	8	30757	6	12564	9	20863	8	9420	11	104779	3
	21-30 cm	7859	7	10380	8	19835	6	8789	10	12864	8	6495	10	66223	3
	31-40 cm	5421	8	5981	8	14070	5	5400	9	7981	7	3990	10	42843	3
	41-50 cm	3866	6	4465	6	9704	5	3212	10	5295	7	2519	10	29062	3
	51-60 cm	2404	7	2911	7	6300	5	1866	10	2969	7	1676	12	18127	3
	61-70 cm	1136	11	1371	9	3117	7	742	14	1208	10	648	12	8221	4
	71-80 cm	435	14	670	12	1455	9	333	21	502	13	333	18	3728	5
	81-90 cm	104	30	327	16	341	16	118	29	225	21	192	25	1305	8
	91-100 cm	37	45	34	45	127	24	66	33	73	35	65	36	401	14
	>100 cm	22	58	21	58	64	33	14	71	44	41	40	52	205	20
	total	34637	5	43981	5	85771	4	33103	8	52025	6	25378	8	274895	2
broadleaves	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	16774	7	21558	6	23064	6	6989	13	4788	15	24701	7	97874	3
	21-30 cm	9246	7	10735	6	13458	7	2563	16	2384	18	13860	7	52247	3
	31-40 cm	6186	7	6450	6	7135	7	578	22	904	17	5339	9	26592	3
	41-50 cm	3699	7	4192	6	3427	8	208	32	412	22	2000	9	13938	3
	51-60 cm	1548	9	2780	7	1721	9	30	50	224	24	765	14	7069	4
	61-70 cm	413	15	967	10	555	16	15	71	65	34	265	19	2280	7
	71-80 cm	90	31	387	15	202	21	0	.	32	52	124	27	834	10
	81-90 cm	0	.	95	28	50	41	0	0	8	100	82	32	235	18
	91-100 cm	0	.	70	34	13	71	7	100	0	.	38	60	128	27
	>100 cm	0	.	44	53	0	.	0	.	0	.	50	51	94	37
	total	37957	5	47279	4	49625	5	10390	13	8817	14	47224	6	201292	2
indeterminable	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	0	0	.	0	0	0	.	0	.	0	.	0	0
	21-30 cm	0	0	0	.	0	.	0	0	0	.	0	.	0	0
	31-40 cm	0	0	0	0	0	0	0	.	0	0	0	.	0	0
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	0	0	0	0	0	0	0	0	0	0	.	0	0
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	30128	5	39378	5	53821	5	19553	8	25652	7	34121	6	202653	2
	21-30 cm	17106	5	21116	5	33293	4	11351	9	15248	7	20356	5	118470	2
	31-40 cm	11608	5	12431	5	21204	4	5978	9	8885	7	9329	7	69435	2
	41-50 cm	7565	4	8658	4	13131	4	3420	9	5707	6	4519	6	43001	2
	51-60 cm	3952	5	5692	5	8021	4	1896	10	3193	7	2442	10	25195	2
	61-70 cm	1549	9	2338	6	3672	6	756	14	1273	10	913	10	10501	3
	71-80 cm	525	13	1057	9	1656	8	333	21	533	13	457	15	4562	5
	81-90 cm	104	30	422	14	391	15	118	29	233	20	273	20	1541	8
	91-100 cm	37	45	103	27	141	23	73	32	73	35	102	32	529	12
	>100 cm	22	58	65	40	64	33	14	71	44	41	90	37	299	18
	total	72595	3	91259	3	135395	3	43493	7	60843	5	72602	4	476187	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. 1348729. Birmensdorf, Swiss Federal Research Institute WSL

back