NFI Results

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
volume of bolewood (HG 2010)
timber assortment classes (HG 2010) · tree species (5 classes)
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
timber assortment classes (HG 2010)	tree species (5 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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
smallwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3208	8	689	16	382	17	293	18	289	10	97	15	4958	6
	fir	2492	8	267	14	198	34	92	68	8	29	13	23	3070	7
	other conifers	414	25	37	26	27	32	147	12	131	12	84	17	840	13
	beech	141	5	42	11	37	11	4	36	8	28	35	14	267	4
	other broadleaves	87	7	28	12	27	13	15	18	9	23	86	9	252	4
	indeterminable	0	72	0	0	0	100	0	0	0	0	0	0	0	68
	total	6342	6	1064	12	672	16	551	16	444	8	315	7	9387	4
classes 1 and 2	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10940	6	13246	6	8281	8	5622	10	10049	7	3431	12	51570	3
	fir	6316	7	4345	9	2022	13	385	25	219	27	440	26	13728	5
	other conifers	1987	10	503	29	419	26	4137	9	3459	10	2154	11	12659	5
	beech	10613	4	3521	9	2793	11	521	28	380	27	2749	13	20577	4
	other broadleaves	9354	5	3437	10	3037	11	1553	13	1160	18	7423	7	25963	3
	indeterminable	14	46	12	53	2	77	1	100	13	100	20	78	63	35
	total	39226	3	25064	4	16554	6	12220	7	15280	5	16216	4	124560	2
classes 3 and 4	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18701	5	18991	6	13365	8	6358	9	11925	7	5037	14	74377	3
	fir	14239	6	7745	8	4401	12	770	30	583	25	853	26	28590	4
	other conifers	3302	10	602	40	343	27	4462	10	4200	9	2338	11	15248	5
	beech	19935	4	5428	11	5319	10	297	36	662	31	2044	12	33685	3
	other broadleaves	9969	6	2001	13	2513	12	427	26	390	27	2718	10	18018	4
	indeterminable	11	100	27	71	0	0	0	0	0	0	0	0	37	58
	total	66158	2	34794	4	25940	6	12314	7	17761	6	12989	6	169956	1
classes 5 and 6	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5658	9	6874	8	5737	11	2085	15	4132	11	2512	18	26997	4
	fir	4715	10	3776	11	1935	17	216	41	414	30	466	30	11522	6
	other conifers	1080	27	93	43	85	40	1383	14	1336	14	903	20	4879	9
	beech	7650	7	1766	18	1396	18	97	53	209	45	471	19	11589	6
	other broadleaves	3094	14	397	47	493	26	74	42	102	51	989	25	5150	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	22196	4	12906	6	9646	8	3855	10	6193	9	5342	10	60138	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	38508	4	39801	5	27764	7	14358	9	26395	6	11077	12	157902	2
	fir	27762	5	16133	8	8556	11	1462	26	1224	24	1772	24	56910	4
	other conifers	6783	10	1236	26	875	23	10129	9	9126	8	5479	10	33627	4
	beech	38339	4	10757	9	9544	10	920	28	1259	27	5299	11	66118	3
	other broadleaves	22504	5	5863	11	6070	10	2070	14	1660	18	11216	7	49384	3
	indeterminable	25	51	39	52	2	77	1	100	13	100	20	78	101	31
	total	133921	2	73828	4	52812	5	28940	6	39678	5	34862	4	364041	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. 1360087. Birmensdorf, Swiss Federal Research Institute WSL

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