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.

NFI2
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (10 classes)
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (10 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
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	.
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	115	45	246	27	43	43	404	21
	21-30 cm	0	.	0	.	0	.	316	32	446	23	63	47	826	18
	31-40 cm	0	.	0	.	0	.	310	32	676	25	78	52	1063	19
	41-50 cm	0	.	0	.	0	.	345	27	580	23	48	62	973	17
	51-60 cm	0	.	0	.	0	.	156	35	501	29	13	100	670	23
	61-70 cm	0	.	0	.	0	.	127	47	233	39	0	.	360	30
	71-80 cm	0	.	0	.	0	.	69	50	203	45	0	.	272	36
	81-90 cm	0	.	0	.	0	.	24	100	73	71	0	.	97	59
	91-100 cm	0	.	0	.	0	.	35	100	0	.	0	.	35	100
	>100 cm	0	.	0	.	0	.	37	100	85	72	0	.	121	59
	total	0	.	0	.	0	.	1535	22	3043	18	244	41	4822	13
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	10	100	353	23	56	55	472	20	527	22	191	27	1609	11
	21-30 cm	10	100	998	20	145	49	1053	18	1558	16	448	25	4212	9
	31-40 cm	30	100	1320	21	273	41	1487	20	2261	15	684	21	6056	9
	41-50 cm	44	100	1174	27	355	49	1815	18	2938	13	775	24	7102	9
	51-60 cm	29	100	720	24	300	41	1466	20	2230	16	632	24	5378	10
	61-70 cm	0	.	341	33	187	48	745	30	839	24	249	32	2360	14
	71-80 cm	0	.	53	71	134	50	411	37	392	32	149	50	1138	20
	81-90 cm	0	.	68	71	29	100	177	52	134	52	0	0	409	31
	91-100 cm	0	.	0	.	46	100	47	100	61	100	0	.	155	58
	>100 cm	0	.	0	.	0	.	0	.	56	100	0	.	56	100
	total	123	100	5026	17	1525	30	7675	14	10997	11	3129	18	28474	7
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	22	86	817	19	422	30	1185	16	1138	15	364	18	3948	8
	21-30 cm	57	86	2047	17	897	28	1844	14	2632	14	1114	17	8591	7
	31-40 cm	159	57	2912	15	1313	23	2357	15	4079	13	1637	18	12456	7
	41-50 cm	53	60	3367	14	1201	22	2292	14	4475	12	2050	17	13438	6
	51-60 cm	68	69	2678	16	581	29	1581	18	3201	14	1576	16	9685	8
	61-70 cm	70	73	814	21	346	53	719	25	1478	18	1192	22	4619	11
	71-80 cm	77	100	284	36	243	40	497	32	796	26	722	32	2619	15
	81-90 cm	49	100	78	75	118	58	111	59	85	71	137	63	578	28
	91-100 cm	0	.	0	.	192	52	88	71	0	.	60	100	340	39
	>100 cm	0	.	44	100	0	.	0	.	78	100	236	58	358	46
	total	555	45	13040	12	5314	19	10673	11	17962	9	9089	13	56633	5
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	5773	7	3932	8	2607	10	263	25	351	27	1213	16	14138	4
	21-30 cm	12779	6	7514	8	6274	10	452	28	481	24	1429	16	28929	4
	31-40 cm	20250	5	10820	8	8474	9	637	28	722	24	1020	20	41924	4
	41-50 cm	19880	5	13197	7	9744	9	386	34	813	26	869	18	44889	3
	51-60 cm	13276	7	10405	8	7272	10	253	38	850	26	691	24	32748	4
	61-70 cm	4774	11	5031	11	3689	14	19	100	332	35	344	32	14190	6
	71-80 cm	1959	16	1467	19	1369	21	71	71	186	54	186	46	5238	10
	81-90 cm	331	41	688	30	237	45	39	100	20	100	51	100	1366	20
	91-100 cm	241	50	75	100	267	50	0	.	0	.	125	75	709	31
	>100 cm	0	.	55	100	0	.	0	.	0	.	0	.	55	100
	total	79265	4	53184	6	39935	7	2119	23	3756	19	5928	12	184187	3
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	6661	7	414	25	359	29	63	62	98	42	0	.	7594	7
	21-30 cm	12446	5	814	27	669	27	79	52	173	53	0	.	14182	5
	31-40 cm	19277	5	1058	22	913	27	15	100	232	49	0	.	21496	5
	41-50 cm	22402	5	1463	27	666	30	16	132	272	44	0	.	24819	5
	51-60 cm	15396	6	1182	26	338	34	38	77	236	45	0	.	17191	6
	61-70 cm	6222	9	448	34	222	57	58	100	43	100	0	.	6992	9
	71-80 cm	2165	16	184	75	245	66	0	.	42	100	0	.	2636	16
	81-90 cm	1192	24	58	100	0	.	0	.	0	.	0	.	1250	24
	91-100 cm	314	50	0	.	0	.	0	.	0	.	0	.	314	50
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	86075	4	5622	20	3412	25	269	53	1097	38	0	.	96475	4
hyperinsubric and colline	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	333	36	0	.	0	.	248	30	2	100	2086	11	2668	10
	21-30 cm	380	37	0	.	0	.	302	32	7	100	2243	11	2931	10
	31-40 cm	198	56	0	.	0	.	275	30	17	100	1364	15	1854	13
	41-50 cm	195	47	0	.	0	.	114	49	45	100	723	21	1076	18
	51-60 cm	141	71	0	.	0	.	56	100	8	100	407	25	612	25
	61-70 cm	0	.	0	.	0	.	28	100	0	.	210	30	238	29
	71-80 cm	0	.	0	.	0	.	27	100	0	.	197	36	224	34
	81-90 cm	0	.	0	.	0	.	0	.	0	.	226	46	226	46
	91-100 cm	0	.	0	.	0	.	0	.	0	.	80	63	80	63
	>100 cm	0	.	0	.	0	.	0	.	0	.	485	49	485	49
	total	1246	32	0	.	0	.	1049	28	79	100	8020	9	10394	9
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	12797	5	5516	7	3444	9	2346	10	2362	10	3896	7	30362	3
	21-30 cm	25673	3	11373	6	7986	9	4045	9	5297	9	5298	7	59671	2
	31-40 cm	39914	3	16110	6	10972	8	5080	10	7988	8	4784	8	84848	2
	41-50 cm	42573	3	19201	6	11966	8	4969	10	9123	7	4466	9	92298	2
	51-60 cm	28910	4	14984	7	8492	9	3552	12	7026	9	3318	10	66283	3
	61-70 cm	11067	7	6634	9	4444	13	1695	17	2925	12	1995	15	28760	4
	71-80 cm	4202	11	1987	16	1991	17	1075	21	1620	17	1253	21	12128	7
	81-90 cm	1573	20	892	25	385	34	351	34	312	34	414	35	3926	12
	91-100 cm	556	36	75	100	506	34	171	50	61	100	266	46	1633	19
	>100 cm	0	.	99	71	0	.	37	100	218	52	721	38	1075	29
	total	167264	2	76871	4	50186	6	23321	7	36933	6	26410	5	380985	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. 1321158. Birmensdorf, Swiss Federal Research Institute WSL

back