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NFI4
volume of dead wood
mechanical strength of wood · remaining bark on dead wood (3 classes)
unit of reference: economic region
unit: 1000 m³
unit of evaluation: accessible forest without shrub forest NFI3/NFI4
grid: 1.4 x 1.4 km grid
state2009/17
		mechanical strength of wood
		n/a		fresh wood		dead wood		rotten wood		mouldering wood		mull wood		total
economic region	remaining bark on dead wood (3 classes)	1000 m³	± %	1000 m³	± %	1000 m³	± %	1000 m³	± %	1000 m³	± %	1000 m³	± %	1000 m³	± %
Western Jura	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	0	.	409	15	407	15	339	18	47	41	1202	10
	31-60%	0	.	0	.	192	23	97	31	39	53	3	77	330	18
	over 60%	0	.	24	47	774	13	149	22	89	44	0	.	1036	11
	total	0	.	24	47	1375	10	653	12	467	18	50	39	2569	8
Eastern Jura	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	0	.	114	24	62	29	118	27	6	71	299	17
	31-60%	0	.	0	.	119	27	51	59	8	100	0	.	178	28
	over 60%	0	.	7	100	255	24	36	45	2	100	0	.	300	21
	total	0	.	7	100	487	16	150	26	128	26	6	71	778	14
Western Plateau	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	0	.	144	28	173	29	143	31	21	59	481	21
	31-60%	0	.	0	.	51	38	76	42	38	48	1	100	167	24
	over 60%	0	.	11	90	324	21	88	32	4	63	8	67	435	18
	total	0	.	11	90	520	16	338	20	185	30	30	46	1083	13
Central Plateau	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	0	.	134	30	119	29	199	23	31	62	483	18
	31-60%	0	.	0	.	54	38	79	41	45	58	0	.	177	28
	over 60%	0	.	17	100	311	19	41	51	14	65	2	100	385	17
	total	0	.	17	100	499	15	239	22	258	22	32	58	1045	11
Eastern Plateau	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	0	.	93	25	356	18	287	21	26	41	763	13
	31-60%	0	.	1	100	63	40	55	55	10	58	0	.	130	31
	over 60%	0	.	16	53	338	16	94	29	32	59	0	.	480	14
	total	0	.	17	50	494	14	506	16	329	19	26	41	1372	10
Western Pre-Alps	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	0	.	446	40	349	22	233	24	15	87	1043	21
	31-60%	0	.	0	.	181	30	141	33	44	57	0	.	365	22
	over 60%	0	.	12	92	495	17	175	38	15	66	8	90	705	15
	total	0	.	12	92	1121	18	665	20	292	21	22	65	2113	14
Central Pre-Alps	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	0	.	305	20	508	17	243	20	120	34	1177	12
	31-60%	0	.	0	.	234	29	206	24	31	52	0	.	471	19
	over 60%	0	.	18	63	895	14	276	22	122	26	4	71	1316	12
	total	0	.	18	63	1434	13	991	14	396	17	125	33	2964	9
Eastern Pre-Alps	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	0	.	264	24	429	20	268	23	8	92	970	15
	31-60%	0	.	0	.	130	25	60	36	39	56	5	100	234	21
	over 60%	0	.	22	48	503	16	61	33	16	48	0	.	601	15
	total	0	.	22	48	897	14	550	17	322	22	13	95	1804	11
Northwestern Alps	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	0	.	224	25	496	20	359	22	54	45	1133	14
	31-60%	0	.	0	.	215	26	165	30	28	57	4	100	411	21
	over 60%	0	.	14	61	609	15	170	26	28	48	3	100	824	13
	total	0	.	14	61	1048	14	831	16	415	19	60	41	2369	11
Central Alps	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	2	100	129	27	170	29	80	32	57	47	437	19
	31-60%	0	.	0	.	73	33	54	49	57	55	0	.	184	28
	over 60%	0	.	0	.	149	20	22	45	3	100	0	.	174	18
	total	0	.	2	100	351	17	246	28	140	29	57	47	795	15
Northeastern Alps	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	0	.	130	37	185	28	214	24	20	51	549	19
	31-60%	0	.	0	.	88	40	10	46	22	46	0	.	120	32
	over 60%	0	.	13	55	317	24	56	47	2	100	0	.	387	21
	total	0	.	13	55	536	22	250	24	238	23	20	51	1057	15
Southwestern Alps	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	0	.	404	15	590	13	386	13	144	22	1525	9
	31-60%	0	.	1	100	331	16	125	25	33	37	0	.	489	14
	over 60%	0	.	17	39	1454	10	148	23	49	38	9	73	1676	9
	total	0	.	18	39	2189	9	863	10	468	13	153	21	3690	7
Southeastern Alps	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	4	100	359	18	506	14	603	14	110	25	1582	9
	31-60%	0	.	0	.	249	19	112	30	78	34	0	.	438	15
	over 60%	0	.	20	72	1367	10	217	20	28	40	2	100	1634	9
	total	0	.	25	62	1974	9	835	12	709	13	112	25	3654	7
Southern Alps	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	0	.	622	13	408	13	356	17	62	35	1448	9
	31-60%	0	.	1	100	291	16	75	22	54	42	3	71	424	13
	over 60%	0	.	56	45	1229	11	230	17	51	30	22	91	1588	9
	total	0	.	57	44	2142	9	714	10	461	15	87	34	3461	7
Switzerland	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	0-30%	0	.	6	76	3777	7	4760	5	3829	5	720	11	13093	4
	31-60%	0	.	4	59	2270	7	1305	10	524	14	16	44	4119	6
	over 60%	0	.	246	18	9020	4	1763	8	454	14	58	41	11541	3
	total	0	.	256	18	15068	3	7829	4	4806	5	794	11	28753	3

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. 812182. Birmensdorf, Swiss Federal Research Institute WSL
https://doi.org/10.21258/1755013

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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