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
total number of stems
degree of damage · conifers and broadleaves
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		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
degree of damage	conifers and broadleaves	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	.
	conifers	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	.
	broadleaves	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	.
	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	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 damage	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	6075	18	1608	31	2726	28	37259	6	1869	32	10531	13	258	81	2478	32	35520	6	5780	18	12561	12	6391	14	1645	47	5130	21	8797	13	2560	26	3445	18	5382	17	5885	21	7669	11	4236	21	16377	10	23282	9	1251	40	9263	13	217976	2
	broadleaves	7610	12	325	44	1120	30	20525	8	3749	19	6037	16	1192	50	1645	29	7584	16	5310	16	5185	16	3318	16	1315	46	1769	31	9768	14	2844	23	5612	15	2783	23	3631	16	25749	8	1581	29	14585	10	5524	15	368	43	7210	12	146338	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	13685	11	1932	31	3846	26	57784	5	5618	18	16568	11	1449	48	4123	24	43104	6	11090	13	17747	11	9709	12	2960	34	6899	18	18565	11	5404	19	9056	13	8165	15	9516	15	33418	6	5817	19	30962	8	28806	8	1618	34	16473	10	364314	2
slight damage	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	282	23	160	43	260	28	4009	10	89	40	1355	20	0	.	216	36	3260	9	668	27	914	16	418	22	138	42	271	30	757	22	77	47	402	26	631	25	376	29	641	20	601	25	1146	16	3490	12	17	100	726	32	20903	4
	broadleaves	255	31	21	77	82	47	1414	22	202	43	581	36	7	100	213	35	742	21	413	34	701	35	181	35	36	100	189	36	638	26	80	53	339	29	129	42	157	37	1856	12	291	33	751	18	817	30	38	74	323	23	10457	6
	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	537	19	181	39	342	26	5423	10	291	33	1936	20	7	100	429	26	4002	9	1082	22	1614	18	599	19	174	39	460	24	1394	18	157	36	741	20	761	22	533	24	2498	10	892	21	1898	12	4306	11	55	71	1049	24	31360	3
moderate damage	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	169	35	187	39	314	33	4131	10	147	39	756	21	0	.	350	32	3297	10	572	22	1633	15	638	26	145	51	338	27	865	29	129	48	459	26	549	27	343	29	797	25	560	28	920	16	2232	11	255	63	679	19	20465	4
	broadleaves	356	24	39	69	78	56	2989	11	704	29	536	24	24	76	556	30	1013	17	615	24	420	28	339	30	252	47	308	37	1318	27	306	39	1275	21	313	28	357	25	2303	10	330	38	961	16	878	20	88	52	659	21	17015	5
	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	525	20	226	37	392	33	7120	8	851	26	1292	16	24	76	906	23	4310	9	1187	17	2053	14	977	21	397	38	646	25	2183	20	434	32	1734	19	861	21	700	20	3100	10	890	24	1882	12	3110	10	342	49	1338	15	37479	3
severe damage	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	243	25	107	40	166	31	3080	9	67	45	1066	17	0	.	453	31	3111	9	398	21	1166	16	746	21	123	48	226	29	718	24	59	51	265	28	673	43	209	31	514	20	412	26	884	15	2825	10	56	58	461	19	18027	4
	broadleaves	536	24	37	72	62	51	1750	16	297	28	306	26	0	.	465	32	988	20	468	21	462	30	234	29	83	59	349	41	596	21	42	61	696	22	326	30	216	33	3308	13	175	39	948	19	634	22	48	58	491	22	13517	5
	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	779	19	144	37	228	28	4830	8	363	24	1372	15	0	.	918	23	4100	9	866	17	1628	15	980	18	206	45	574	29	1314	17	101	45	961	18	999	30	425	27	3822	11	587	22	1832	13	3459	10	105	41	952	16	31544	3
very severe damage	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	48	38	40	61	16	71	668	18	0	.	292	35	0	.	170	34	1031	16	119	35	254	29	121	36	29	71	60	52	199	33	31	64	93	37	170	40	71	46	212	35	111	47	237	25	1032	17	40	61	66	46	5111	7
	broadleaves	94	50	0	.	27	75	433	23	28	50	156	35	0	.	330	34	537	28	209	34	197	35	54	58	78	46	126	40	294	30	56	52	189	34	97	44	262	54	1697	17	34	71	301	27	256	34	18	100	85	44	5557	8
	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	142	35	40	61	43	61	1101	14	28	50	448	26	0	.	499	27	1567	14	328	25	450	23	175	34	107	39	186	36	493	23	87	40	282	28	267	31	333	44	1909	16	145	39	538	19	1288	15	58	59	152	32	10668	5
dead	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	362	28	107	54	203	35	2624	15	142	37	512	38	0	.	521	37	4166	12	641	33	576	27	131	41	220	42	477	31	1189	20	35	70	505	30	743	34	53	58	601	23	549	26	1476	20	4409	14	36	100	243	29	20522	5
	broadleaves	235	38	48	58	71	61	855	21	148	35	400	33	54	73	298	45	978	24	406	33	348	33	65	77	15	100	113	63	430	26	70	50	387	33	196	53	182	59	3362	13	70	50	979	25	368	26	0	.	418	30	10496	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	597	24	156	52	274	33	3478	13	290	29	912	26	54	73	819	29	5144	11	1047	24	925	22	197	37	235	42	590	28	1619	17	104	40	893	24	938	36	235	47	3963	12	619	24	2454	16	4777	13	36	100	661	23	31018	4
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	7178	16	2209	30	3684	26	51770	6	2314	28	14513	13	258	81	4188	28	50385	6	8179	16	17104	11	8446	14	2300	41	6502	20	12525	12	2891	25	5169	17	8147	17	6938	20	10434	11	6470	20	21040	9	37270	8	1654	40	11438	13	303004	2
	broadleaves	9087	12	469	44	1441	29	27967	7	5127	19	8015	15	1277	50	3507	26	11842	14	7421	15	7313	14	4191	16	1779	39	2853	28	13044	13	3397	22	8498	14	3844	21	4804	16	38275	7	2480	27	18525	10	8476	14	560	39	9186	11	203379	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	16265	11	2678	30	5125	24	79737	5	7441	17	22528	11	1535	48	7695	21	62227	6	15600	12	24417	10	12637	12	4079	30	9355	17	25568	10	6287	19	13666	12	11991	15	11742	14	48709	6	8950	17	39565	8	45746	7	2213	33	20625	10	506383	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. 1258473. Birmensdorf, Swiss Federal Research Institute WSL

back