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NFI4
stem number
altitude (200 m class) · number of damages
unit of reference: production region
unit: 1000 n
unit of evaluation: accessible forest without shrub forest NFI3/NFI4
grid: 1.4 x 1.4 km grid
state2009/17
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
altitude (200 m class)	number of damages	1000 n	± %	1000 n	± %	1000 n	± %	1000 n	± %	1000 n	± %	1000 n	± %
above 1800 m	no visible damage	0	.	0	.	671	49	19646	7	3371	14	23688	6
	one visible damage	0	.	0	.	81	47	4240	8	699	17	5019	7
	two visible damages	0	.	0	.	50	70	1319	11	379	21	1747	10
	dead or down tree	0	.	0	.	0	.	680	26	67	42	748	24
	total	0	.	0	.	801	46	25885	6	4516	13	31202	6
1601-1800 m	no visible damage	0	.	0	.	3221	22	22405	7	4692	14	30317	6
	one visible damage	0	.	0	.	628	23	4642	8	926	19	6196	7
	two visible damages	0	.	0	.	114	34	1588	10	399	22	2100	9
	dead or down tree	0	.	0	.	40	60	255	29	44	46	339	23
	total	0	.	0	.	4003	21	28890	6	6060	13	38953	6
1401-1600 m	no visible damage	718	27	367	42	7866	11	21651	7	6937	13	37539	5
	one visible damage	80	35	85	52	1573	13	4805	7	1494	14	8037	6
	two visible damages	53	44	4	100	517	19	2019	11	366	23	2960	8
	dead or down tree	0	.	0	.	45	51	308	33	71	43	424	25
	total	851	25	456	42	10002	11	28783	6	8868	12	48959	5
1201-1400 m	no visible damage	6452	11	378	45	12358	9	19116	8	6432	13	44736	5
	one visible damage	1052	15	23	61	2547	11	4391	8	1768	14	9781	6
	two visible damages	372	19	10	100	1014	16	1540	12	761	18	3697	8
	dead or down tree	27	86	4	100	62	42	251	26	103	47	447	20
	total	7902	11	415	46	15981	8	25298	7	9065	11	58660	4
1001-1200 m	no visible damage	14606	8	754	37	16662	7	14310	8	8187	12	54519	4
	one visible damage	2527	10	115	43	3387	9	3880	10	2059	13	11968	5
	two visible damages	733	14	13	75	1348	13	1867	12	766	17	4727	7
	dead or down tree	21	71	0	.	82	40	142	37	49	52	293	23
	total	17886	7	882	37	21479	7	20198	8	11061	11	71506	4
801-1000 m	no visible damage	13151	8	5942	15	19211	7	11084	9	6128	13	55515	4
	one visible damage	2992	10	921	23	3351	9	3152	11	2014	14	12431	5
	two visible damages	1279	15	243	31	1091	17	1260	14	806	18	4680	8
	dead or down tree	29	58	10	100	50	45	223	25	109	38	421	18
	total	17450	8	7116	15	23704	6	15720	9	9057	12	73047	4
601-800 m	no visible damage	15546	7	24175	6	11131	9	7137	12	5269	14	63258	4
	one visible damage	2691	10	3488	8	2082	12	1663	14	1755	17	11679	5
	two visible damages	1066	15	1041	13	702	16	598	17	1150	17	4556	7
	dead or down tree	32	52	58	46	32	58	113	38	75	62	310	24
	total	19334	7	28761	6	13947	8	9511	11	8249	13	79802	4
to 600 m	no visible damage	14433	8	40051	4	2836	20	3059	20	6945	12	67323	3
	one visible damage	2483	10	5351	6	621	43	728	27	2335	14	11518	5
	two visible damages	562	15	1411	10	233	42	303	34	1341	16	3849	8
	dead or down tree	28	55	99	34	12	100	10	100	65	47	214	24
	total	17506	7	46912	4	3702	20	4100	19	10685	11	82905	3
total	no visible damage	64905	2	71667	2	73955	3	118407	2	47961	3	376895	1
	one visible damage	11824	4	9983	4	14271	4	27502	3	13049	4	76629	2
	two visible damages	4066	7	2722	7	5069	6	10493	4	5967	6	28316	3
	dead or down tree	135	29	170	26	324	19	1983	12	583	17	3195	9
	total	80931	2	84542	2	93618	2	158385	2	67560	3	485035	1

number of stems #73
Anzahl Stämme der lebenden Bäume und Sträucher (stehende und liegende) ab 12 cm Brusthöhendurchmesser (BHD).
altitude (200 m class) #964
Höhe über Meer in Klassen zu 200 m. Grundlage: digitales Höhenmodell DHM 25 von Swisstopo
number of damages #653
Einteilung der Bäume und Sträucher ab 12 cm Brusthöhendurchmesser (BHD) nach der Anzahl sichtbarer Schäden bzw. Baumzustand (lebend/tot, stehend/liegend) in vier Klassen. Grundlage: Felderhebung (MID 1018: Baumzustand, MID 1027: Baumschadenart)
production region #49
Die Produktionsregionen sind Regionen mit ähnlichen forstlichen Wuchs- und Produktionsbedingungen, erstellt durch das damalige Bundesamt für Forstwesen für die forstliche Statistik lange vor dem ersten LFI (1983-85). Die Regionenzugehörigkeit wurde definiert über die damaligen Gemeindegrenzen nach unbekannten Kriterien. Das LFI arbeitet sei jeher mit diesen unveränderten Perimetern mit einer winzigen kleinen Ausnahme am Genfersee. In der heutigen Forststatistik (Bundesamt für Statistik, BFS) hat der Begriff Produktionsregionen in «Forstzonen» gewechselt. Diese Regionen werden vom BFS unterhalten und dabei fanden Änderungen statt. Dabei wurde vermehrt auf die Lage des Waldes innerhalb einer Gemeinde Rücksicht genommen. Für einige Gemeinden entlang des Jurabogens bedeutet dies beispielsweise, dass sie neu der Forstzone «Jura» angehören und nicht wie bisher (Produktionsregionen) dem Mittelland. Das LFI hat die alten Perimeter beibehalten, die sich heute mit diversen neuen Gemeindegrenzen nicht mehr decken.
accessible forest without shrub forest NFI3/NFI4 #1429
Wald, der sowohl im LFI3 (2004-2006) als auch im LFI4 (2009-2017) zu weniger als zwei Dritteln mit Sträuchern bedeckt war und zu Fuss aufgesucht werden konnte.
1.4 x 1.4 km grid #410
Stichprobennetz des LFI mit einer Maschenweite von 1,4 km. Beim 1,4-km-Netz handelt es sich um das gemeinsame Netz aller bisherigen terrestrischen Inventuren, weshalb es auch als Basisnetz bezeichnet wird.

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. 794426. Birmensdorf, Swiss Federal Research Institute WSL
https://doi.org/10.21258/1758381

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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