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 deadwood stems
deadwood: mechanical strength of wood (3 classes) · deadwood: regeneration (3 classes)
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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
deadwood: mechanical strength of wood (3 classes)	deadwood: regeneration (3 classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	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	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
	no plant	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	.
	1 plant	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	.
	2 or more plants	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	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	0	0	0	0	0
fresh/firm wood	n/a	313	27	195	46	177	34	2716	12	421	26	860	20	24	100	271	39	3693	11	994	22	1139	22	402	25	214	47	505	38	1358	18	266	41	497	27	802	28	358	38	5049	13	409	27	1938	16	2821	13	135	45	505	27	26060	4
	no plant	340	32	61	79	139	46	1728	16	326	34	317	27	54	73	529	52	3027	16	269	36	545	28	263	31	51	58	236	30	495	20	83	52	327	43	190	41	94	41	1973	16	180	48	704	17	2439	12	0	.	360	26	14729	6
	1 plant	0	.	0	.	0	.	28	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	8	100	7	100	0	.	0	.	0	.	0	.	54	51
	2 or more plants	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	24	77	0	.	0	.	0	.	0	.	11	100	17	100	0	.	0	.	0	.	0	.	36	63	0	.	0	.	13	71	0	.	7	100	123	32
	total	654	23	256	45	316	31	4487	11	746	24	1177	20	78	59	799	39	6744	11	1263	22	1684	21	665	22	265	41	762	32	1870	16	348	35	824	24	991	29	452	31	7065	11	596	26	2642	14	5273	11	135	45	872	23	40966	4
rotten wood	n/a	49	54	28	63	83	45	549	19	14	71	136	47	0	.	161	44	454	20	155	38	366	41	79	58	7	100	116	51	265	27	0	.	84	47	228	28	17	100	482	21	295	31	306	22	389	22	53	66	148	40	4465	7
	no plant	270	27	15	100	167	49	1588	16	120	46	580	22	17	100	192	38	1706	15	368	27	461	24	126	37	83	47	243	38	428	20	87	53	297	31	285	38	110	78	857	19	205	38	897	20	1694	14	18	100	393	47	11208	5
	1 plant	0	.	0	.	22	100	65	48	7	100	15	100	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	50	55	7	100	0	.	192	28
	2 or more plants	14	71	0	.	18	100	98	42	0	.	0	.	0	.	0	.	42	41	0	.	17	71	0	.	6	100	15	100	14	71	0	.	0	.	7	100	0	.	0	.	0	.	32	64	74	41	0	.	17	100	354	19
	total	333	25	43	73	290	39	2300	13	140	43	731	22	17	100	354	33	2208	13	523	22	844	23	205	37	96	43	374	31	707	17	87	53	382	28	520	26	127	69	1359	14	500	32	1234	17	2208	14	78	53	557	38	16219	5
mouldering/mull wood	n/a	0	.	0	.	36	100	182	38	0	.	40	52	0	.	39	61	147	41	35	70	95	56	0	.	6	100	48	52	75	53	7	100	17	100	6	100	0	.	119	50	73	50	68	60	79	45	0	.	7	100	1078	15
	no plant	123	40	0	.	51	58	1198	19	104	47	630	30	0	.	114	43	1215	16	289	30	509	24	124	43	42	62	126	42	327	26	24	77	185	32	210	42	0	.	778	19	126	39	588	20	846	18	80	59	303	31	7992	6
	1 plant	17	100	0	.	0	.	32	50	0	.	18	100	0	.	0	.	52	54	0	.	22	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	29	100	9	100	37	71	50	54	0	.	0	.	271	25
	2 or more plants	0	.	0	.	43	71	210	33	7	100	24	79	0	.	25	79	125	34	0	.	16	71	0	.	12	71	7	100	7	100	0	.	0	.	0	.	0	.	8	100	0	.	18	100	121	40	0	.	0	.	622	17
	total	139	41	0	.	129	51	1622	16	111	44	712	28	0	.	177	40	1539	14	324	31	641	25	124	43	60	51	181	35	416	25	31	64	202	31	216	41	0	.	933	18	207	36	710	20	1097	16	80	59	310	31	9964	6
total	n/a	362	24	224	43	296	34	3447	11	435	26	1036	19	24	100	471	35	4295	10	1184	22	1600	22	481	23	227	45	669	31	1697	17	273	40	598	26	1036	24	375	37	5649	12	777	23	2312	15	3289	13	187	37	659	23	31603	4
	no plant	734	21	76	66	358	36	4514	12	549	28	1527	21	71	61	835	36	5947	11	927	22	1514	18	513	23	176	39	604	30	1250	15	194	36	810	25	684	31	204	46	3608	12	511	29	2189	14	4979	11	98	52	1056	30	33928	4
	1 plant	17	100	0	.	22	100	125	34	7	100	33	71	0	.	0	.	58	50	0	.	22	100	0	.	0	.	11	100	7	100	0	.	0	.	0	.	0	.	57	63	16	71	37	71	100	38	7	100	0	.	517	17
	2 or more plants	14	71	0	.	61	76	322	25	7	100	24	79	0	.	25	79	192	27	0	.	33	50	0	.	18	74	33	60	39	55	0	.	0	.	7	100	0	.	44	54	0	.	50	54	208	29	0	.	23	77	1099	12
	total	1126	18	299	42	736	28	8409	9	998	23	2620	17	95	52	1330	29	10492	9	2110	19	3169	17	994	19	421	34	1317	27	2993	14	467	30	1408	20	1727	25	579	32	9357	9	1304	23	4587	11	8577	10	293	35	1739	23	67148	3

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. 1413637. Birmensdorf, Swiss Federal Research Institute WSL

back