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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.

NFI4
number of stems
development stage · conifers and broadleaves
regional demarcation: canton
unit: n/ha
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		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
development stage	conifers and broadleaves	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
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	.
	conifers	35	30	0	.	0	.	103	25	0	.	90	55	0	.	0	.	102	24	0	.	114	33	0	.	32	0	48	39	3	93	0	.	0	.	197	49	0	.	37	87	0	.	33	72	88	31	0	.	0	.	85	14
	broadleaves	59	48	0	.	0	.	14	52	0	.	49	31	20	0	0	.	7	86	0	.	53	43	0	.	0	.	13	86	51	49	0	.	0	.	63	54	0	.	10	87	0	.	17	60	0	.	0	.	20	0	20	20
	indeterminable	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	94	19	0	.	0	.	118	21	0	.	138	43	20	0	0	.	109	22	0	.	166	22	0	.	32	0	61	20	54	50	0	.	0	.	260	37	0	.	47	65	0	.	51	51	88	31	0	.	20	0	105	12
young growth/ thicket	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	.
	conifers	39	32	0	.	128	36	73	16	17	67	51	30	0	.	85	38	96	13	52	31	85	29	124	53	9	91	58	36	50	22	25	71	23	54	99	32	35	46	35	35	68	37	41	20	64	20	35	39	37	28	61	6
	broadleaves	68	20	0	.	62	68	42	18	46	40	50	32	0	.	92	38	18	50	21	46	65	31	60	84	40	64	46	43	54	23	55	71	28	40	34	53	46	28	58	32	32	51	53	26	41	27	17	87	59	22	45	7
	indeterminable	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	107	17	0	.	191	40	116	11	63	43	101	20	0	.	177	22	114	13	74	26	149	22	184	42	48	50	104	28	104	16	80	71	51	30	132	32	82	32	93	21	100	38	95	17	105	18	52	45	96	18	106	5
pole timber	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	.
	conifers	312	19	378	70	394	48	347	11	154	49	368	23	0	.	134	28	422	9	356	23	413	14	350	46	608	41	852	25	304	23	507	27	250	25	278	18	387	25	99	16	224	29	373	13	285	11	465	23	309	19	301	4
	broadleaves	406	16	671	61	89	55	218	12	388	17	264	21	97	73	213	28	195	14	307	18	126	21	318	20	192	73	115	40	263	19	270	47	356	18	338	20	325	15	498	6	287	40	301	13	223	10	127	38	255	13	295	4
	indeterminable	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	718	9	1048	13	484	37	565	6	542	13	631	12	97	73	347	18	617	6	664	13	539	10	669	20	800	27	967	20	568	11	777	14	606	11	616	9	712	11	597	5	510	23	674	6	508	6	592	15	565	9	596	2
young timber	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	.
	conifers	200	20	603	6	679	42	285	11	173	30	465	18	75	72	308	36	423	9	212	23	467	13	522	15	425	27	413	18	392	16	124	35	196	24	362	21	408	19	112	22	313	19	277	14	415	12	446	25	228	18	303	4
	broadleaves	278	18	165	10	353	62	283	9	372	21	294	24	415	11	336	34	127	17	322	16	86	27	244	35	238	58	378	37	333	18	332	18	401	10	266	23	114	32	461	7	402	26	323	11	139	21	47	38	264	14	280	4
	indeterminable	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	478	11	768	2	1032	10	568	6	545	9	760	10	490	7	644	15	549	6	534	10	553	11	765	7	663	13	791	13	725	10	456	11	597	7	628	10	522	11	573	6	715	14	600	7	554	8	494	22	492	9	584	2
medium timber	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	.
	conifers	81	19	382	18	305	24	262	8	80	20	314	13	0	.	303	28	393	5	241	12	240	17	192	18	60	31	274	33	225	14	119	17	94	17	306	12	187	19	192	16	626	11	248	14	421	8	258	49	171	12	258	3
	broadleaves	222	11	149	35	181	30	233	12	323	12	149	18	543	0	251	23	36	26	200	13	183	24	167	19	326	24	249	28	189	14	238	16	215	12	144	18	178	12	231	15	36	47	214	11	60	33	185	42	163	10	171	4
	indeterminable	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	303	8	531	12	486	16	496	6	403	10	463	8	543	0	553	14	429	5	440	7	423	9	359	8	386	21	523	16	414	8	357	11	310	8	450	10	365	8	423	7	663	9	462	9	481	7	444	21	334	5	429	2
old timber	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	.
	conifers	83	15	460	8	276	12	229	5	108	24	180	12	25	71	202	27	268	5	143	16	250	9	182	11	156	17	365	15	199	9	92	29	96	16	245	9	110	21	139	14	189	23	176	8	285	7	135	30	120	11	202	2
	broadleaves	181	11	65	50	145	25	120	8	237	13	157	16	365	20	93	29	28	23	139	19	145	15	128	12	197	26	120	34	163	12	207	16	148	12	97	19	201	17	120	16	100	43	125	9	27	30	156	23	150	8	117	3
	indeterminable	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	263	7	524	10	422	9	349	3	345	10	337	7	390	24	295	19	296	5	282	10	395	7	309	7	354	16	486	10	361	6	298	12	244	9	341	6	312	11	259	8	289	14	301	5	312	6	291	23	271	6	319	2
mixed	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	.
	conifers	127	28	182	39	154	40	296	5	75	23	241	16	0	.	276	20	356	5	197	13	327	9	292	8	265	34	382	16	292	14	0	.	216	22	243	14	78	22	129	11	431	11	212	8	351	5	372	16	108	17	275	2
	broadleaves	245	17	60	54	213	42	129	9	252	21	203	19	0	.	184	25	53	16	286	13	216	17	92	15	178	31	74	23	256	15	570	0	334	16	209	28	281	16	276	8	81	33	188	12	67	16	69	57	185	12	151	4
	indeterminable	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	372	11	242	34	367	34	425	4	327	13	445	10	0	.	459	15	409	4	483	8	542	8	385	7	443	18	456	12	548	11	570	0	550	13	452	16	359	13	404	5	512	9	400	7	417	4	441	17	293	9	426	2
total	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	.
	conifers	128	10	379	11	295	13	249	3	99	15	261	8	34	70	215	12	336	3	210	8	298	6	258	8	246	25	345	10	233	7	163	18	136	10	256	7	209	14	119	7	317	9	219	5	312	4	271	16	167	8	241	1
	broadleaves	221	7	148	33	159	17	151	5	272	8	177	9	309	20	182	13	72	9	223	8	149	9	141	9	193	21	131	17	201	7	263	11	241	7	174	11	210	8	336	4	140	20	191	5	98	8	105	21	179	6	173	2
	indeterminable	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	349	5	526	10	454	9	401	2	372	6	438	5	343	20	397	8	408	3	432	5	447	4	399	5	439	14	476	8	434	5	427	8	377	5	430	5	419	7	454	3	456	8	410	3	410	3	376	12	345	4	413	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. 1325250. Birmensdorf, Swiss Federal Research Institute WSL

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