Articles containing the keyword 'forest inventory'

The purpose of this study was to test the benefits of a forest site quality map, when applying satellite image-based forest inventory. By combining field sample plot data from national forest inventories with satellite imagery and forest site quality data, it is possible to estimate forest stand characteristics with higher accuracy for smaller areas. The reliability of the estimates was evaluated using the data from a stand-wise survey for area sizes ranging from 0.06 ha to 300 ha. When the mean volume was estimated, a relative error of 14 per cent was obtained for areas of 50 ha; for areas of 30 ha the corresponding figure was below 20 per cent. The relative gain in interpretation accuracy, when including the forest site quality information, ranged between 1 and 6 per cent. The advantage increased according to the size of the target area. The forest site quality map had the effect of decreasing the relative error in Norway spruce (Picea abies) volume estimations, but it did not contribute to Scots pine (Pinus sylvestris) volume estimation procedure.

• Tokola, E-mail: tt@mm.unknown
• Heikkilä, E-mail: jh@mm.unknown

Theoretical and practical aspects of permanent sample plots are discussed in this paper. A study material of 6,871 permanent sample plots was generated using increment sample plots of the 7th National Forest Inventory of Finland. The effect of measurement errors and use of increment functions as ”a priori” information was studied via simulation experiments. The change in the growing stock volume between two consecutive measurement rounds was divided into the components drain, growth and mortality. Finally, a hypothetical inventory design using permanent sample plots was evaluated.

The PDF includes an abstract in Finnish.

• Päivinen, E-mail: rp@mm.unknown
• Yli-Kojola, E-mail: hy@mm.unknown

A method for using satellite data in forest inventories and updating is described and tested. The stand characteristics estimated by the method showed high correlation with the same characteristics measured in the field. The correlation coefficients for volume, age and mean height were about 0.85. It seems that the method is applicable to practical forestry. Extensive work in programming, however, is required.

The PDF includes an abstract in Finnish.

• Poso, E-mail: sp@mm.unknown
• Paananen, E-mail: rp@mm.unknown
• Similä, E-mail: ms@mm.unknown

At a joint meeting of the Finnish Statistical Society and the Society of Forestry in Finland on 17.10.1984, papers were presented on the history and mathematical foundations of statistical methods used in forest inventory in Finland. The advantages and applicability of Bayresian methods and methods of spatial statistics were also discussed. In two papers, forest inventories were examined as part of a forest information system and the information demands of the user were discussed.

This article includes eight presentation held in the meeting. The papers have each a summary in English.

• Suomen tilastoseura, E-mail: –
• Finnish Society of Forest Science, E-mail: –

The general requirements for forest information required in forest management include the availability of quantitative data concerning forest areas and timber volume, data describing that structure and quality of the forest by classes, data dealing with the forest dynamics such as increment and mortality, stand-wise data tied to on-the-ground locations, and the timelines of all this information.

A review of the present inventory systems reveals variations in the information used to manage forests. In many cases, there appears to be an inadequacy of information. There may be no inventory system, or sampling may concern only overall features of the forest. The general trend has been towards a more common use of delineation of stands and the estimation of stands characteristics. In European countries, survey techniques have been improved by, for instance, trying to avoid subjective features in stand-wise assessments and through the use of index sub-compartments which are remeasure. In North America, a new approach was recently introduced to generate stand tables which seems to have significant inventory capabilities. In some cases, the advanced inventory systems may simultaneously employ three kinds of inventories.

In designing an inventory and management information system experiences gained elsewhere should be utilized with studies of sampling methods, remote sensing techniques, new instrumentation and computer services. Improved decision making makes it possible to introduce cheaper methods for periodic inventories. The information system should be only as elaborate as is required to do the job. The costs of acquisition of inventory information correlates with a degree of sophistication of the system, but rarely exceeds one percent of the stumpage of the timber cut. Also, the increase in wood production more than compensates the costs of planning on the basis of inventories.

The PDF includes a summary in Finnish.

• Nyyssönen, E-mail: an@mm.unknown

18 permanent sample subplots of the Swedish and the Hassian Forestry Institute, each measured in equal intervals for several decades, were divided into subplots of different size. An analysis of variance was calculated for every set of subplot size. The development of intraclass-correlations over years and over different sizes of subplots could be explained if three different correlations were assumed: soil-correlation, correlations from irregular distribution of the trees, and correlation resulting from competition. Intraclass-correlations were positive or negative depending on dominance of one or two of these correlations.

An explanatory simultation study of competitional variance showed the effect of the degree of competitional correlations on the variance of means of subplots of different sizes. If this coefficient was small, all variances of subplots means within the range investigated became larger than expected in experiments without competition, with larger coefficients the variances of means of the smaller subplots became smaller, those of larger subplots larger than expected.

Plots of medium or large size are probably optimal for long term experiments with forest trees, if all sources of costs in such experiments are taken into account.

The PDF includes a summary in English and Finnish.

• Stern, E-mail: ks@mm.unknown

In connection to the Third National Forest Inventory of Finland, two survey strips in the northernmost Finland were photographed on scale 1:15,000. Infrared films and a yellow filter were used. For the present experiment a total length of 66 km of the strips was photographed. The strips were surveyed visually from the ground by stands. Sample plots were measured at kilometre intervals. The aerial photographs were surveyed the distances covered in the ground. The work was aided by stereograms which showed 16 large-size sample plots localised on aerial photographs.

The main groups of land identified along the survey line were productive and poorly productive forest land, wasteland and another land, in addition, peatland and firm land were distinguished. Although some differences were noted, the two survey methods provided fairly similar results. For an estimation of the tree species composition the material is one-sided since the district is mainly Scots pine. The principal tree species was successfully distinguished on aerial photographs in 78 out of 82 comparable pairs.

The mean of ground observations of dominant height of the stands was 10.9 m, that of observations on aerial photographs 11.2 m. The result of stand volume estimates reveals a distinct correlation between the various methods of estimation.

In an earlier study it was shown that it is possible, using a stand volume table based on characteristics revealed in aerial photography, to create a general idea of stand volume on the southern half of the country. A few additional factors, of interest for the stratification necessary in forest inventories, were also studied. A distinct correlation was observed between the results of aerial and ground survey for all the characteristics discussed. The present experiment showed that the prerequisites for stratification through aerial photographs do exist. Further investigation is needed into the most appropriate methods for stratification in each situation.

The PDF includes a summary in English.

• Nyyssönen, E-mail: an@mm.unknown
• Poso, E-mail: sp@mm.unknown

Silva Fennica issue 46 includes presentations held in professional development courses, arranged for foresters working in public administration in 1937. The presentations focus on practical issues in forest management and administration, especially in regional level. The education was arranged by Forest Service. 

This presentation describes execution of settlement in compliance to the Land Settlement Act in the state lands, and the role of a forest officer in managing the settlement. 

• Jokinen, E-mail: pj@mm.unknown

Silva Fennica Issue 39 includes presentations held in professional development courses in 1935 that were arranged for foresters working in public administration. The presentations focus on practical issues in forest management and administration, especially in regional level.

This presentation lists statistics available on forestry.

• Saari, E-mail: es@mm.unknown

Silva Fennica Issue 39 includes presentations held in professional development courses in 1935 that were arranged for foresters working in public administration. The presentations focus on practical issues in forest management and administration, especially in regional level. The education was arranged by Forest Service.

This presentation describes different forest inventory methods.

• Ilvessalo, E-mail: yi@mm.unknown

The aim of this investigation is to study, for north European conditions, some overall standards of accuracy attainable in the estimation in a number of forest characteristics from aerial photographs. Field data was acquired in three areas, comprising whole stands, fixed and variable size sample plots and sections of survey strips.  

The results show that land use classes could be estimated to a rather high degree of precision from aerial photographs. The accuracy of determination of the main tree species (Scots pine, Norway spruce and deciduous trees) was more moderate; three quarters of all stands were interpreted correctly from the present photographs. The estimate of pure stands was noticeable better than those for mixed stands. In general, the agreement between treatment class stratification in the field and from aerial photographs was poor, as only one-third of all cases the class was same. The dominant height was estimated with relative lack of bias for small stands, but systematic underestimation of nearly 2 m existed for high stands.  

The emphasis in this investigation was laid on determination of the volume of growing stock. Stand volumes in the small and medium volume classes were overestimated, against clear under-estimate for high volume stands. The standard error of difference, including bias, was ±43 both in m3 and as a percentage. 

The variance data available provide a basis for the conclusion that under some conditions photo stratification within the forest land seems to improve the efficiency of volume estimation, whereas in some other cases the stratification is hardly an economic proposition. Alternative computations made from the data of an experimental survey indicated the likelihood that no gain was deprived from the use of aerial photographs for volume class estimation. 

The PDF includes a summary in Finnish.

• Nyyssönen, E-mail: an@mm.unknown
• Poso, E-mail: sp@mm.unknown
• Keil, E-mail: ck@mm.unknown

This paper reports on tests made for the study of alternative methods in forest survey. Data were acquired by measurements in five areas in Finland and in Mexico, varying in size from 20 to 900 ha. The principal characteristics used in the analysis was the entire volume. By the combination of neighbouring plots, the variation could be studied for different plot sizes and survey strips. Variable (relascope) plots could be compared.

A starting point for the comparison of different sampling methods, calculations were made of the coefficients of variation for each plot type; total and within the strata. The amount of decrease of variation with an increasing plot size could be established. Comparisons have been made of the following sampling methods: simple random, stratified random, simple systematic, and stratified systematic sampling.

On comparisons of the standard error of sample mean it was found that in both stratified sampling and different types of systematic sampling there is, with increasing size and diminishing interval of sample plots, an increase in the relative improvement of the result against simple random sampling. Only in exceptional cases did systematic surveys give results which were less precise than those derived by other methods.

In discussion of some methods for determination of the precision of systematic sampling, possibilities of theoretical determination of the degree of precision was considered. An empirical study was made of the behaviour of some equations based on the sample itself. The larger the plot size and the shorter the plot interval, the more the equations overestimated in general the variance of sample mean.

As none of the equations studied gave reliable results, regression equations were calculated for the relative standard error on the basis of the data measured. The independent variables were plot size, plot or strip interval, area of survey unit and mean volume. The results arrived at are based mainly on the complete measurement of one area only. To enable extension of the scope of application, more material is needed with a complete enumeration of trees.

The PDF includes a summary in Finnish.

• Nyyssönen, E-mail: an@mm.unknown
• Kilkki, E-mail: pk@mm.unknown
• Mikkola, E-mail: em@mm.unknown

Highest degree of precision in determining the areas of different strata in forest survey is achieved when the areas are measured from a map. However, in practice the stratum-areas usually need to be determined on the basis of samples taken in the field or from aerial photographs. The goal of the present investigation was to determine the precision in stratum-area estimation on the application of different sampling methods.

Three sampling methods were used: 1. sampling with random plots, 2. uniform systematic plot sampling, and 3. sampling with equidistant lines.

The dependence of the standard error of stratum-areas in systematic line and plot sampling was examined by regression analysis. The models for regression equations were derived from random sampling formulae. It appears that the characteristics of these formulae were applicable as variables in the regression equations for systematic samples. Also, some characteristics of the distribution of the stratum was found, which seem to influence the error in sampling with equidistant lines.

The results as regards uniform systematic plot sampling indicate that the use of random sampling formulae leads to considerable over-estimation of the standard error. Nonetheless, unless relatively short intervals between sample plots are used in the forest survey made on the ground, it is of advantage to study the division of the area into strata by measuring the distribution of the survey lines in these strata.

The results can be used in two ways: for estimation of the precision in a survey already made, or to predetermine the sample size in a survey to be made. The results may be applicable to areas ranging from 100 to 1,000 ha in size, as well as to larger areas.

• Nyyssönen, E-mail: an@mm.unknown
• Kilkki, E-mail: pk@mm.unknown

The purpose of this paper is to review tests made on the basis of Finnish material with regard to the efficiency of the 10-point cluster in sampling a stand in forest inventory. Currently, this system is applied in field work in the national forest surveys in the United States of America. The paper reports on tests, made on the basis of Finnish material, for comparison of the 10-point cluster of variable plots with 13 other designs in sampling a stand in forest survey. The research material consists of 12 stands, with Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) H. Karst.) as the main species.

The main results are concerned with the ability of different designs to provide gross volume estimates. As a measure of efficiency, three alternative series of variances were used, adjusted by three alternatives of time. The results are applicable, for instance, in double-sampling with photo and field classifications. In the comparisons, no attention was paid to the possibility of systematic errors in various designs.

For inventory volume, the 10-point cluster proved to be about 10 per cent less efficient than the best design of each alternative. The use of a single circular plot of 1,000 m² can be recommended under the conditions of this test; furthermore, one or two 500 m² plots were more efficient than any combination of variable plots.

The reason for the use of the 10-point cluster in forest surveying has been the ability of the design to provide simultaneous information on area condition classes. Among the designs tested, the 10-point cluster seems to be the only one capable of application in the estimation of condition classes.

Most of the information obtained by means of the 10-point cluster can be gained through ocular estimation, and from the sample trees to be measured in any design, but a cluster of several points appears to offer good means of estimation, for instance, of the presence of clumps and gasps in a stand.

• Nyyssönen, E-mail: an@mm.unknown
• Kilkki, E-mail: pk@mm.unknown

A comparison was made between two alternative methods of continuous national forest inventory. In method 1, samples measured annually are taken throughout the country, in method 2 samples are confined to one section of the country each year. The figures are derived from national forest inventories carried out in the North-European countries Finland, Norway and Sweden. Systematic sampling on the ground has been employed without remeasured sample plots. Field work consisted of survey tracts.

For the whole country, method 1 gives results, which are continuously up-to-date, although detailed information requires observation over a period of several years. On an average, the results obtained from method 2 area at least n/2 years old (survey cycle n years). For a specific section of the country, method 1 gives preliminary results already in 1-2 years, but more accurate results are at least n/2 years old. Method 2 gives accurate results every n^th year. Thus, method 2 is not always to be recommended, even if the emphasis is laid on regional information and planning.

To gain knowledge of annual timber removals, often necessary in assessing the forest resource situation, stump measurements can be used, either exclusively or by way of control. The corresponding sampling must be affected throughout the whole country, and this can be done only when method 1 is used. Other information required annually, such as estimates of seed crops, occurrence of pests or annual variation of growth due to the climate, favour method 1.

It can be concluded that method 1 has important advantages, although these must be bought at higher costs. A comparison of inventory costs shows, assuming the same degree of accuracy, that the total expenditure for method 2 is 7-8% lower than that for method 1, owing to the difference in transport requirements. Also, other aspects may affect the choice of method, for example, the use of aerial photographs may be arranged more efficiently in method 2.

• Nyyssönen, E-mail: an@mm.unknown

It is necessary to study the problems related to the size, number and location of sample plots as well as to the effect of stratification. This paper, mainly concerned with the volume of the growing stock based on measurements, comprises a part of the research in progress related to the issue.

The study contains a discussion of certain problems relevant to the planning of a forest inventory on the basis of variation in the volume of the growing stock. In particular, the aim has been to give data for selection of the size and number of sample plots, and to illustrate the importance of stratification, here based principally on dominant height.

The research material comprised two forest areas of 10 ha each; measurement of the growing stock was made in units of 49 m². Based on the data was constructed a model area embracing 4 forested strata and one non-forested stratum. The mean and standard deviations of the volumes was calculated for the forest area as a whole and for the different strata. On studying stratification, two approaches were employed to distribute the sample plots among the strata: optimum allocation and proportional allocation. The latter is simple in application, and gave for forested areas number of sample plots which did not exceed by more than 10% those obtained by optimum allocation. It is obvious that the application of stratification is worthy of consideration within forest areas which comprise parts with distinct differences. The decrease in number of plots with increasing plot size is considerably more marked within a stratified area. Thus, larger sample plots should be used in the former case than in the later. The investigations give hints on how forest inventories should be carried out. However, it has some limitations, which are discussed in the paper.

The PDF includes a summary in English.

• Nyyssönen, E-mail: an@mm.unknown
• Vuokila, E-mail: yv@mm.unknown

The purpose of the investigation was to study the characteristics of the site and the growing stock and the effect of their variation on the precision of forest inventory. The total area of the forest tract is considered to be already known or it can be measured with surveying. The sub-areas or strata and the mean timber volume of each wooded stratum are estimated by sampling. Thus, the total volume of each stratum is the product of the estimates of the area and mean volume. Only line method of sampling the areas will be examined. Field material was measured in the inventory of the Finnish State Forests with the method used in the third National Forest Inventory. The circular plots and the sample trees were measured in the forests of Inari district. In this systematic line-plot method the distance between the lines was 5 km.

The results show that the total area should be broken down into suitable strata, such as the full-stocked productive forest land and regeneration areas with mother trees. The mean volume for each stratum is estimated with the plot method. An advanced estimation of the approximate mean volume and the coefficient of variation in the strata are needed for calculating the optimum allocation of plots.

If the volume calculation is done with the diameter as variable the variation of the plot volumes is decreased. If the local volume table is used and the site and stock characteristics differ from the characteristics of the volume table stock, a significant systematic error is possible. Planning of efficient and economical forest inventories requires data concerning site and stock variation. It should be calculated and published systematically by geographical region.

The PDF includes a summary in English.

• Kuusela, E-mail: kk@mm.unknown

The distances between the lines in the line survey in the first two National Inventories of Finland were too long to supply data for every State Forest district. Consequently, the Third National Forest Inventory offered an opportunity to supplement the inventory for State Forests in 1954 and 1955, and to gather data on forest resources of the State Fforests. On the basis of the results, a management plan for the State Forests was drafted. The first part of the paper describes the inventory procedure and results of the inventory, the second deduces future cuttings and a forest management programme.
In 1955 the total land area of the State Forests was 9.49 million ha. Drained peatlands cover 126,000 ha, drainable peatlands 798,000 ha and undrainable peatlands 2,621,000 ha. The average volume of growing stock in all State Forests was 55.2 m³/ha, including productive and unproductive forest land. The average increment in all State Forests was 1,39 m³/ha on productive land and in all lands in average 1,14 m³/ha.
Cutting budgets for the progressive yield were prepared by checking the silvicultural cut and estimating the allowable cut. They were made by age classes, developmental stages and for each region. The stock development was forecasted for a period of 40 years. In average the allowable cut was larger during the first decade than during the second. Allowable cut was estimated by the tree species and by timber assortments. The management plan included future forest management work, such as intermediate fellings, regeneration fellings, site preparation, artificial regenereation, tending of seedling stands, and draining of peatland.
The PDF includes a summary in English.

• Linnamies, E-mail: ol@mm.unknown

The first estimates on the forest resources of Finland were presented in the middle of the 1900^th century. The first line survey was conducted in 1912 in Central Finland. In 1921-1923 a survey of the forests of the whole country was commenced. The method consisted in measurement of sample plots in conjunction with ocular estimation of all the stands within the range of the lines. The methods were further developed in the second National Forest Survey in 1936-1938, which payed special attention to the silvicultural condition of the forests, and the growth in the light of climatic variation. When 3.3 million ha of forests were ceded to the Soviet Union in the peace treaty of 1944, the results of the survey had to be recalculated. The next survey was conducted 1951-1953. In this survey, the recovery of stands on drained peatlands was studied. The results of the inventories show that forest resources of Finland had icreased since the 1936-1938 survey.

The first investigation of wood utilization in Finland was carried out in 1927, after the first National Forest Survey had provided information on the forest resources, and knowledge of the other side of the forest balance was desired. The most difficult part was to determine the domestic wood consumption of the rural population. This was accomplished by studying 1,337 sample farms. The second investigation was commenced in 1938, and third in 1954.

These two investigations have made it possible to determine the annual removal and annual growth, and by comparing these results, growth balance. A forest balance is an essential condition for judicious forestry.

The Acta Forestalia Fennica issue 61 was published in honour of professor Eino Saari’s 60th birthday.

• Ilvessalo, E-mail: yi@mm.unknown

The article discusses the state of forest management of private forests and the need to awaken the forest owners for sustainable use of forests. According to the latest national forest inventory in Finland, only 15% of forests had good productivity, 50% had satisfactory and 35% low productivity. This is partly due to the forest owners’ insufficient knowledge on efficient forest management and the actual value of their forest. If the forest owners should have information of the wood resources of their forests in the level production, value and what kind of wood assortments it can in the future produce, there would be a pressure for more efficient forest management. The writer suggests that inventory of private forest holdings larger than, for instance, 25 hectares should be conducted in each municipality. The inventory could be performed in 10% of the forests each year. In this way the forests would be surveyed every 10 years. The information could also be used in taxation of forests. The best method to conduct the survey would be a line survey combined with circular sample plots.

The PDF includes a summary in German.

• Appelroth, E-mail: ea@mm.unknown

A method was developed for relative radiometric calibration of single multitemporal Landsat TM image, several multitemporal images covering each other, and several multitemporal images covering different geographical locations. The radiometrically calibrated different images were used for detecting rapid changes on forest stands. The nonparametric Kernel method was applied for change detection. The accuracy of the change detection was estimated by inspecting the image analysis results in field.

The change classification was applied for controlling the quality of the continuously updated forest stand information. The aim was to ensure that all the manmade changes and any forest damages were correctly updated including the attribute and stand delineation information. The image analysis results were compared with the registered treatments and the stand information base. The stands with discrepancies between these two information sources were recommended to be field inspected.

• Varjo, E-mail: jv@mm.unknown

The field of work of the 7th National Forest Inventory was carried out during 1977–84. This report consists of the analysis of the forest resources, long-term development of forests and the results by ownership categories in Finland.

The area of forestry land, 26.4 million ha, has decreased slightly because of the increase of build-up areas and communication routes. Forest land, which is suitable to growing wood profitably, amounted 20.1 million ha. It has increased, although not as fast as earlier, due to drainage and fertilization of scrub and waste land swamps and the afforestation of agricultural land.

The growing stock volume was 1,660 million m³ and the estimated gross annual increment 68.4 million m³. A large quantity of young, rapidly growing stands, and fellings markedly below the increment, are the principal factors increasing the growing stock. The volume of Scots pine (Pinus sylvestris L.) has increased most but the greatest proportional increase has been in the volume of broadleaved trees.

The silvicultural quality of stands has improved and the increase in saw log tree volume has resulted in an increase in the total growing stock volume. The proportional volume of saw logs, however, has decreased. Both aging mature stands and postponed thinnings increase the risk of losses due to mortality and decay. Too dense stands retard the diameter growth of trees. The proportion of unsuccessful artificial regeneration has increased.

The area of private forests has slightly decreased, while companies and collective bodies have increased their ownership. Non-farmer private ownership already accounts for one half of the area of private forests. The silvicultural quality of company forests is best and the increase of the growing stock and its increment is proportionally greatest in these forests.

The PDF includes a summary in English.

• Kuusela, E-mail: kk@mm.unknown
• Salminen, E-mail: ss@mm.unknown

Emphasis was laid on the finding of regression equations to indicate the dependence of standard error upon various variables in systematic sampling. As a result, the size of sample for a given precision could be computed, under varying alternatives of sample plot size and type. Another task was that of examining inventory costs by means of time studies. On combination of the results in regard to the sample size and survey time, the relative efficiency of different alternatives could be discussed, with a view to the precision of the total volume of growing stock.

The PDF includes a summary in Finnish.

• Nyyssönen, E-mail: an@mm.unknown
• Roiko-Jokela, E-mail: pr@mm.unknown
• Kilkki, E-mail: pk@mm.unknown

• Peltoniemi, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: mikko.peltoniemi@metla.fi
• Thürig, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland; European Forest Institute, Joensuu, Finland E-mail: et@nn.ch
• Ogle, Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, USA E-mail: so@nn.us
• Palosuo, European Forest Institute, Joensuu, Finland E-mail: tp@nn.fi
• Schrumpf, Max-Planck-Institute for Biogeochemistry, Jena, Germany E-mail: ms@nn.de
• Wutzler, Max-Planck-Institute for Biogeochemistry, Jena, Germany E-mail: tw@nn.de
• Butterbach-Bahl, Institute for Meteorology and Climate Research, Forschungszentrum Karlsruhe GmbH, Garmisch-Partenkirchen, Germany E-mail: kbb@nn.de
• Chertov, St. Petersburg State University, St. Petersburg-Peterhof, Russia E-mail: oc@nn.ru
• Komarov, Institute of Physicochemical and Biological Problems in Soil Science of Russian Academy of Sciences, Pushchino, Russia E-mail: ak@nn.ru
• Mikhailov, Institute of Physicochemical and Biological Problems in Soil Science of Russian Academy of Sciences, Pushchino, Russia E-mail: am@nn.ru
• Gärdenäs, Dept. of Soil Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden E-mail: ag@nn.se
• Perry, USDA Forest Service, Northern Research Station, St. Paul, MN USA E-mail: cp@nn.us
• Liski, Finnish Environment Institute, Helsinki, Finland E-mail: jl@nn.fi
• Smith, School of Biological Sciences, University of Aberdeen, Aberdeen, UK E-mail: ps@nn.uk
• Mäkipää, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: raisa.makipaa@metla.fi

In 2019–2023 the 13th Finnish National Forest Inventory (NFI) was implemented by measuring a total of 62 266 sample plots across the country. The methodology of the sampling and measurements was similar as in the previous inventory, but the proportion and number of remeasured permanent plots was increased to improve the monitoring of annual increment and other changes in the forests. Only 6.2 M ha (14%) of Finland’s total land area (30.4 M ha) is other land than forestry land. Productive and poorly productive forests cover 22.9 M ha (75%) of the total land area.  The forest area has remained stable in recent decades but the forest area available for wood supply (FAWS) has decreased due to increased forest protection – 23% of the forestry land and 10% of the productive forest are not available for wood supply. Compared to the previous inventory, forest resources have continued to increase but the average annual increment has declined from 107.8 M m³ to 103.0 M m³. The quality of forests from the timber production point of view has remained relatively good or improved slightly. The area of observed forest damage on FAWS is 8.4 M ha (46% of FAWS area), half of these minor damages with no impact on stand quality. Although the area of forest damage has not increased, the amount of mortality has continued to increase, and is now 8.8 M m³ year^–1. The amount of dead wood has continued to increase in South Finland, while in North Finland the declining trend has turned into a slight increase. Since the 1920s, the area of forestry land has remained stable, but the area of productive forest has increased due to the drainage of poorly productive or treeless peatlands. The total volume of growing stock has increased by 84% and annual increment has more than doubled.

• Korhonen, Natural Resources Institute Finland (Luke), P.O.Box 68, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-6198-853X E-mail: kari.t.korhonen@luke.fi
• Räty, Natural Resources Institute Finland (Luke), P.O.Box 2, FI-00790, Helsinki, Finland https://orcid.org/0000-0001-9898-8712 E-mail: minna.raty@luke.fi
• Haakana, Natural Resources Institute Finland (Luke), P.O.Box 2, FI-00790, Helsinki, Finland E-mail: helena.haakana@luke.fi
• Heikkinen, Natural Resources Institute Finland (Luke), P.O.Box 2, FI-00790, Helsinki, Finland https://orcid.org/0000-0003-3527-774X E-mail: juha.heikkinen@luke.fi
• Hotanen, Natural Resources Institute Finland (Luke), P.O.Box 68, FI-80100 Joensuu, Finland E-mail: juha-pekka.hotanen@luke.fi
• Kuronen, Natural Resources Institute Finland (Luke), P.O.Box 2, FI-00790, Helsinki, Finland https://orcid.org/0000-0002-8089-7895 E-mail: mikko.kuronen@luke.fi
• Pitkänen, Natural Resources Institute Finland (Luke), P.O.Box 68, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-7583-6297 E-mail: juho.pitkanen@luke.fi

• Noordermeer, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0002-8840-0345 E-mail: lennart.noordermeer@nmbu.no
• Ørka, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0002-7492-8608 E-mail: hans-ole.orka@nmbu.no
• Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: terje.gobakken@nmbu.no

• Stenman, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland https://orcid.org/0000-0003-1176-7840 E-mail: virpi.stenman@helsinki.fi
• Kangas, Natural Resources Institute Finland (Luke), Bio­economy and Environment, P.O. Box 68, FI-80101 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
• Holopainen, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: markus.holopainen@helsinki.fi

Models that predict forest development are essential for sustainable forest management. Constructing growth models via regression analysis or fitting a family of sigmoid equations to construct compatible growth and yield models are two ways these models can be developed. In this study, four species-specific models were developed and compared. A compatible growth and yield stand basal area model and a five-year stand basal area growth model were developed for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). The models were developed using data from permanent inventory plots from the Swedish national forest inventory and long-term experiments. The species-specific models were compared, using independent data from long-term experiments, with a stand basal area growth model currently used in the Swedish forest planning system Heureka (Elfving model). All new models had a good, relatively unbiased fit. There were no apparent differences between the models in their ability to predict basal area development, except for the slightly worse predictions for the Norway spruce growth model. The lack of difference in the model comparison showed that despite the simplicity of the compatible growth and yield models, these models could be recommended, especially when data availability is limited. Also, despite using more and newer data for model development in this study, the currently used Elfving model was equally good at predicting basal area. The lack of model difference indicate that future studies should instead focus on model development for heterogeneous forests which are common but lack in growth and yield modelling research.

• Goude, Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, SE-230 53 Alnarp, Sweden https://orcid.org/0000-0002-2179-292X E-mail: martin.goude@slu.se
• Nilsson, Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, SE-230 53 Alnarp, Sweden E-mail: urban.nilsson@slu.se
• Mason, School of Forestry, University of Canterbury, Private Bag 4800, Christchurch, New Zealand E-mail: euan.mason@canterbury.ac.nz
• Vico, Department of Crop Production Ecology, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden E-mail: giulia.vico@slu.se

Forest management inventories assisted by airborne laser scanner data rely on predictive models traditionally constructed and applied based on data from the same area of interest. However, forest attributes can also be predicted using models constructed with data external to where the model is applied, both temporal and geographically. When external models are used, many factors influence the predictions’ accuracy and may cause systematic errors. In this study, volume, stem number, and dominant height were estimated using external model predictions calibrated using a reduced number of up-to-date local field plots or using predictions from reparametrized models. We assessed and compared the performance of three different calibration approaches for both temporally and spatially external models. Each of the three approaches was applied with different numbers of calibration plots in a simulation, and the accuracy was assessed using independent validation data. The primary findings were that local calibration reduced the relative mean difference in 89% of the cases, and the relative root mean squared error in 56% of the cases. Differences between application of temporally or spatially external models were minor, and when the number of local plots was small, calibration approaches based on the observed prediction errors on the up-to-date local field plots were better than using the reparametrized models. The results showed that the estimates resulting from calibrating external models with 20 plots were at the same level of accuracy as those resulting from a new inventory.

• de Lera Garrido, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: ana.de.lera@nmbu.no
• Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: terje.gobakken@nmbu.no
• Ørka, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: hans-ole.orka@nmbu.no
• Næsset, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no
• Bollandsås, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: ole.martin.bollandsas@nmbu.no

Newly developed positioning systems in cut-to-length harvesters enable georeferencing of individual trees with submeter accuracy. Together with detailed tree measurements recorded during processing of the tree, georeferenced harvester data are emerging as a valuable tool for forest inventory. Previous studies have shown that harvester data can be linked to airborne laser scanner (ALS) data to estimate a range of forest attributes. However, there is little empirical evidence of the benefits of improved positioning accuracy of harvester data. The two objectives of this study were to (1) assess the accuracy of timber volume estimation using harvester data and ALS data acquired with different scanners over multiple years and (2) assess how harvester positioning errors affect merchantable timber volume predicted and estimated from ALS data. We used harvester data from 33 commercial logging operations, comprising 93 731 harvested stems georeferenced with sub-meter accuracy, as plot-level training data in an enhanced area-based inventory approach. By randomly altering the tree positions in Monte Carlo simulations, we assessed how prediction and estimation errors were influenced by different combinations of simulated positioning errors and grid cell sizes. We simulated positioning errors of 1, 2, …, 15 m and used grid cells of 100, 200, 300 and 400 m². Values of root mean square errors obtained for cell-level predictions of timber volume differed significantly for the different grid cell sizes. The use of larger grid cells resulted in a greater accuracy of timber volume predictions, which were also less affected by positioning errors. Accuracies of timber volume estimates at logging operation level decreased significantly with increasing levels of positioning error. The results highlight the benefit of accurate positioning of harvester data in forest inventory applications. Further, the results indicate that when estimating timber volume from ALS data and inaccurately positioned harvester data, larger grid cells are beneficial.

• Noordermeer, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway E-mail: lennart.noordermeer@nmbu.no
• Næsset, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no
• Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway E-mail: terje.gobakken@nmbu.no

We describe the methodology applied in the 12th national forest inventory of Finland (NFI12) and describe the state of Finland’s forests as well as the development of some key parameters since 1920s. According to the NFI12, the area of forestry land (consisting of productive and poorly productive forest, unproductive land, and other forestry land) is 26.2 M ha. The area of forestry land has decreased from 1920s to 1960s due to expansion of agriculture and built-up land. 20% of the forestry land is not available for wood supply and 13% is only partly available for wood supply. The area of peatlands is 8.8 M ha, which is one third of the forestry land. 53% of the current area of peatlands is drained. The volume of growing stock, 2500 M m³, is 1.7 times the volume estimated in NFI1 in the 1920s for the current territory of Finland. The estimated annual volume increment is 107.8 M m³. The increment estimate has doubled since the estimate of NFI2 implemented in late 1930s. The annual mortality is estimated to 7 M m³, which is 0.5 M m³ more than according to the previous inventory. Serious or complete damage was observed on 2% of the productive forest available for wood supply. The amount of dead wood is on average 5.8 m³ ha^–1 in productive forests. Since the NFI9 (1996–2003) the amount of dead wood has increased in South Finland and decreased in North Finland both in protected forests and forests available for wood supply (FAWS). The area of natural or almost natural forests on productive forest is 380 000 ha, out of this, 42 000 ha are in FAWS and 340 000 ha in protected forests.

• Korhonen, Natural Resources Institute Finland (Luke), P.O. Box 68, FI-80100 Joensuu, Finland E-mail: kari.t.korhonen@luke.fi
• Ahola, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: arto.ahola@luke.fi
• Heikkinen, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: juha.heikkinen@luke.fi
• Henttonen, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: helena.henttonen@luke.fi
• Hotanen, Natural Resources Institute Finland (Luke), P.O. Box 68, FI-80100 Joensuu, Finland E-mail: juha-pekka.hotanen@luke.fi
• Ihalainen, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: anttivj.ihalainen@elisanet.fi
• Melin, Natural Resources Institute Finland (Luke), P.O. Box 68, FI-80100 Joensuu, Finland E-mail: markus.melin@luke.fi
• Pitkänen, Natural Resources Institute Finland (Luke), P.O. Box 68, FI-80100 Joensuu, Finland E-mail: juho.pitkanen@luke.fi
• Räty, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: minna.raty@luke.fi
• Sirviö, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: maria.sirvio@uudenmaanliitto.fi
• Strandström, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: mikael.strandstrom@luke.fi

Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m² collected over 350 km² of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.

• Ørka, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0002-7492-8608 E-mail: hans-ole.orka@nmbu.no
• Hansen, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway; Norwegian Forest Extension Institute, Honnevegen 60, NO-2836 Biri, Norway https://orcid.org/0000-0001-5174-4497 E-mail: eh@skogkurs.no
• Dalponte, Department of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, Via E. Mach 1, 38010 San Michele all’Adige, TN, Italy https://orcid.org/0000-0001-9850-8985 E-mail: michele.dalponte@fmach.it
• Gobakken, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0001-5534-049X E-mail: terje.gobakken@nmbu.no
• Næsset, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no

Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by k nearest neighbor (k-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available.

• Kukkonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mikko.kukkonen@uef.fi
• Kotivuori, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: eetu.kotivuori@uef.fi
• Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
• Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
• Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi

Since the 1990’s, forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, optical satellite images and numerical map data using a non-parametric k-nearest neighbour method. In Finland, thematic maps of forest variables have been produced by the means of multi-source NFI (MS-NFI) for eight to ten times depending on the geographical area, but the resulting time series have not been systematically utilized. The objective of this study was to explore the possibilities of the time series for monitoring the key ecosystem condition indicators for forests. To this end, a contextual Mann-Kendall (CMK) test was applied to detect trends in time-series of two decades of thematic maps. The usefulness of the observed trends may depend both on the scale of the phenomena themselves and the uncertainties involved in the maps. Thus, several spatial scales were tested: the MS-NFI maps at 16 × 16 m² pixel size and units of 240 × 240 m², 1200 × 1200 m² and 12  000 × 12  000 m² aggregated from the MS-NFI map data. The CMK test detected areas of significant increasing trends of mean volume on both study sites and at various unit sizes except for the original thematic map pixel size. For other variables such as the mean volume of tree species groups, the proportion of broadleaved tree species and the stand age, significant trends were mostly found only for the largest unit size, 12  000 × 12  000 m². The multiple testing corrections decreased the amount of significant p-values from the CMK test strongly. The study showed that significant trends can be detected enabling indicators of ecosystem services to be monitored from a time-series of satellite image-based thematic forest maps.

• Katila, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland; https://orcid.org/0000-0001-6946-5736 E-mail: matti.katila@luke.fi
• Rajala, Natural Resources Institute Finland (Luke), Natural resources, Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: tuomas.rajala@luke.fi
• Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi

The aim of this study was to estimate economic losses, which are caused by forest inventory errors of tree species proportions and site types. Our study data consisted of ground truth data and four sets of erroneous tree species proportions. They reflect the accuracy of tree species proportions in four remote sensing data sets, namely 1) airborne laser scanning (ALS) with 2D aerial image, 2) 2D aerial image, 3) 3D and 2D aerial image data together and 4) satellite data. Furthermore, our study data consisted of one simulated site type data set. We used the erroneous tree species proportions to optimise the timing of forest harvests and compared that to the true optimum obtained with ground truth data. According to the results, the mean losses of Net Present Value (NPV) because of erroneous tree species proportions at an interest rate of 3% varied from 124.4 € ha^–1 to 167.7 € ha^–1. The smallest losses were observed using tree species proportions predicted using ALS data and largest using satellite data. In those stands, respectively, in which tree species proportion errors actually caused economic losses, they were 468 € ha^–1 on average with tree species proportions based on ALS data. In turn, site type errors caused only small losses. Based on this study, accurate tree species identification seems to be very important with respect to operational forest inventory.

• Haara, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: arto.haara@luke.fi
• Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80101 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
• Tuominen, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 2, FI-00791 Helsinki, Finland https://orcid.org/0000-0001-5429-3433 E-mail: sakari.tuominen@luke.fi

This study examines the alternatives to include crown base height (CBH) predictions in operational forest inventories based on airborne laser scanning (ALS) data. We studied 265 field sample plots in a strongly pine-dominated area in northeastern Finland. The CBH prediction alternatives used area-based metrics of sparse ALS data to produce this attribute by means of: 1) Tree-level imputation based on the k-nearest neighbor (k-nn) method and full field-measured tree lists including CBH observations as reference data; 2) Tree-level mixed-effects model (LME) prediction based on tree diameter (DBH) and height and ALS metrics as predictors of the models; 3) Plot-level prediction based on analyzing the computational geometry and topology of the ALS point clouds; and 4) Plot-level regression analysis using average CBH observations of the plots for model fitting. The results showed that all of the methods predicted CBH with an accuracy of 1–1.5 m. The plot-level regression model was the most accurate alternative, although alternatives producing tree-level information may be more interesting for inventories aiming at forest management planning. For this purpose, k-nn approach is promising and it only requires that field measurements of CBH is added to the tree lists used as reference data. Alternatively, the LME-approach produced good results especially in the case of dominant trees.

• Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
• Karjalainen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: tomimkarjalainen@gmail.com
• Repola, Natural Resources Institute of Finland (Luke), Natural resources, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail: jaakko.repola@luke.fi
• Vauhkonen, Natural Resources Institute of Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, 80100 Joensuu, Finland E-mail: jari.vauhkonen@luke.fi

Airborne laser scanning (ALS) has been the main method for acquiring data for forest management planning in Finland and Norway in the last decade. Recently, digital aerial photogrammetry (DAP) has provided an interesting alternative, as the accuracy of stand-based estimates has been quite close to that of ALS while the costs are markedly smaller. Thus, it is important to know if the better accuracy of ALS is worth the higher costs for forest owners. In many recent studies, the value of forest inventory information in the harvest scheduling has been examined, for instance through cost-plus-loss analysis. Cost-plus-loss means that the quality of the data is accounted for in monetary terms through calculating the losses due to errors in the data in the forest management planning context. These costs are added to the inventory costs. In the current study, we compared the losses of ALS and DAP at plot level. According to the results, the data produced using DAP are as good as data produced using ALS from a decision making point of view, even though ALS is slightly more accurate. ALS is better than DAP only if the data will be used for more than 15 years before acquiring new data, and even then the difference is quite small. Thus, the increased errors in DAP do not significantly affect the results from a decision making point of view, and ALS and DAP data can be equally well recommended to the forest owners for management planning. The decision of which data to acquire, can thus be made based on the availability of the data on first hand and the costs of acquiring it on the second hand.

• Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80170 Joensuu, Finland E-mail: annika.kangas@luke.fi
• Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: terje.gobakken@nmbu.no
• Puliti, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: stefano.puliti@nibio.no
• Hauglin, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: marius.hauglin@nmbu.no
• Naesset, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no

Remote sensing using unmanned aerial vehicle (UAV) -borne sensors is currently a highly interesting approach for the estimation of forest characteristics. 3D remote sensing data from airborne laser scanning or digital stereo photogrammetry enable highly accurate estimation of forest variables related to the volume of growing stock and dimension of the trees, whereas recognition of tree species dominance and proportion of different tree species has been a major complication in remote sensing-based estimation of stand variables. In this study the use of UAV-borne hyperspectral imagery was examined in combination with a high-resolution photogrammetric canopy height model in estimating forest variables of 298 sample plots. Data were captured from eleven separate test sites under weather conditions varying from sunny to cloudy and partially cloudy. Both calibrated hyperspectral reflectance images and uncalibrated imagery were tested in combination with a canopy height model based on RGB camera imagery using the k-nearest neighbour estimation method. The results indicate that this data combination allows accurate estimation of stand volume, mean height and diameter: the best relative RMSE values for those variables were 22.7%, 7.4% and 14.7%, respectively. In estimating volume and dimension-related variables, the use of a calibrated image mosaic did not bring significant improvement in the results. In estimating the volumes of individual tree species, the use of calibrated hyperspectral imagery generally brought marked improvement in the estimation accuracy; the best relative RMSE values for the volumes for pine, spruce, larch and broadleaved trees were 34.5%, 57.2%, 45.7% and 42.0%, respectively.

• Tuominen, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 2, FI-00791 Helsinki, Finland http://orcid.org/0000-0001-5429-3433 E-mail: sakari.tuominen@luke.fi
• Balazs, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: andras.balazs@luke.fi
• Honkavaara, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430 Masala, Finland E-mail: eija.honkavaara@nls.fi
• Pölönen, University of Jyväskylä, Faculty of Information Technology, P.O. Box 35, FI-40014 Jyväskylä, Finland E-mail: ilkka.polonen@jyu.fi
• Saari, VTT Microelectronics, P.O. Box 1000, FI-02044 VTT, Finland E-mail: heikki.saari@vtt.fi
• Hakala, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430 Masala, Finland E-mail: teemu.hakala@nls.fi
• Viljanen, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430 Masala, Finland E-mail: niko.viljanen@nls.fi

Optical 2D remote sensing techniques such as aerial photographing and satellite imaging have been used in forest inventory for a long time. During the last 15 years, airborne laser scanning (ALS) has been adopted in many countries for the estimation of forest attributes at stand and sub-stand levels. Compared to optical remote sensing data sources, ALS data are particularly well-suited for the estimation of forest attributes related to the physical dimensions of trees due to its 3D information. Similar to ALS, it is possible to derive a 3D forest canopy model based on aerial imagery using digital aerial photogrammetry. In this study, we compared the accuracy and spatial characteristics of 2D satellite and aerial imagery as well as 3D ALS and photogrammetric remote sensing data in the estimation of forest inventory variables using k-NN imputation and 2469 National Forest Inventory (NFI) sample plots in a study area covering approximately 5800 km². Both 2D data were very close to each other in terms of accuracy, as were both the 3D materials. On the other hand, the difference between the 2D and 3D materials was very clear. The 3D data produce a map where the hotspots of volume, for instance, are much clearer than with 2D remote sensing imagery. The spatial correlation in the map produced with 2D data shows a lower short-range correlation, but the correlations approach the same level after 200 meters. The difference may be of importance, for instance, when analyzing the efficiency of different sampling designs and when estimating harvesting potential.

• Tuominen, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: sakari.tuominen@luke.fi
• Pitkänen, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: timo.p.pitkanen@luke.fi
• Balazs, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: andras.balazs@luke.fi
• Kangas, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: Annika.Kangas@luke.fi

Exploring the possibility to produce nation-wide forest attribute maps using stereophotogrammetry of aerial images, the national terrain model and data from the National Forest Inventory (NFI). The study areas are four image acquisition blocks in mid- and south Sweden. Regression models were developed and applied to 12.5 m × 12.5 m raster cells for each block and validation was done with an independent dataset of forest stands. Model performance was compared for eight different forest types separately and the accuracies between forest types clearly differs for both image- and LiDAR methods, but between methods the difference in accuracy is small at plot level. At stand level, the root mean square error in percent of the mean (RMSE%) were ranging: from 7.7% to 10.5% for mean height; from 12.0% to 17.8% for mean diameter; from 21.8% to 22.8% for stem volume; and from 17.7% to 21.1% for basal area. This study clearly shows that aerial images from the national image program together with field sample plots from the NFI can be used for large area forest attribute mapping.

• Bohlin, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden http://orcid.org/0000-0002-3318-5967 E-mail: jonas.bohlin@slu.se
• Bohlin, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden http://orcid.org/0000-0003-1224-6684 E-mail: inka.bohlin@slu.se
• Jonzén, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden E-mail: jonas.jonzen@slu.se
• Nilsson, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden http://orcid.org/0000-0001-7394-6305 E-mail: mats.nilsson@slu.se

The aim of this study was to examine how well stem volume, above-ground biomass and dominant height can be predicted using nationwide airborne laser scanning (ALS) based regression models. The study material consisted of nine practical ALS inventory projects taken from different parts of Finland. We used field sample plots and airborne laser scanning data to create nationwide and regional models for each response variable. The final models had one or two ALS predictors, which were chosen based on the root mean square error (RMSE), and cross-validated. Finally, we tested how much predictions would improve if the nationwide models were calibrated with a small number of regional sample plots. Although forest structures differ among different parts of Finland, the nationwide volume and biomass models performed quite well (leave-inventory-area-out RMSE 22.3% to 33.8%, mean difference [MD] –13.8% to 18.7%) compared with regional models (leave-plot-out RMSE 20.2% to 26.8%). However, the nationwide dominant height model (RMSE 5.4% to 7.7%, MD –2.0% to 2.8%, with the exception of the Tornio region – RMSE 11.4%, MD –9.1%) performed nearly as well as the regional models (RMSE 5.2% to 6.7%). The results show that the nationwide volume and biomass models provided different means than real means at regional level, because forest structure and ALS device have a considerable effect on the predictions. Large MDs appeared especially in northern Finland. Local calibration decreased the MD and RMSE of volume and biomass models. However, the nationwide dominant height model did not benefit much from calibration.

• Kotivuori, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: eetu.kotivuori@uef.fi
• Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
• Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi

Accurate timber assortment information is required before cuttings to optimize wood allocation and logging activities. Timber assortments can be derived from diameter-height distribution that is most often predicted from the stand characteristics provided by forest inventory. The aim of this study was to assess and compare the accuracy of three different pre-harvest inventory methods in predicting the structure of mainly Scots pine-dominated, clear-cut stands. The investigated methods were an area-based approach (ABA) based on airborne laser scanning data, the smartphone-based forest inventory Trestima app and the more conventional pre-harvest inventory method called EMO. The estimates of diameter-height distributions based on each method were compared to accurate tree taper data measured and registered by the harvester’s measurement systems during the final cut. According to our results, grid-level ABA and Trestima were generally the most accurate methods for predicting diameter-height distribution. ABA provides predictions for systematic 16 m × 16 m grids from which stand-wise characteristics are aggregated. In order to enable multimodal stand-wise distributions, distributions must be predicted for each grid cell and then aggregated for the stand level, instead of predicting a distribution from the aggregated stand-level characteristics. Trestima required a sufficient sample for reliable results. EMO provided accurate results for the dominating Scots pine but, it could not capture minor admixtures. ABA seemed rather trustworthy in predicting stand characteristics and diameter distribution of standing trees prior to harvesting. Therefore, if up-to-date ABA information is available, only limited benefits can be obtained from stand-specific inventory using Trestima or EMO in mature pine or spruce-dominated forests.

• Siipilehto, Natural Research Institute Finland (Luke), Management and Production of Renewable Resources, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jouni.siipilehto@luke.fi
• Lindeman,  Natural Research Institute Finland, Green Technology, Kaironiementie 15, 39700 Parkano E-mail: harri.lindeman@luke.fi
• Vastaranta, University of Helsinki, Department of Forest Sciences, P.O. Box 62 (Viikinkaari 11), FI-00014 University of Helsinki E-mail: mikko.vastaranta@helsinki.fi
• Yu, Finnish Geospatial Research Institute (FGI), Department of Remote Sensing and Photogrammetry, National Land Survey of Finland, P.O. Box 15 (Geodeetinrinne 2), FI-02431, Masala, Finland E-mail: xiaowei.yu@maanmittauslaitos.fi
• Uusitalo,  Natural Research Institute Finland, Green Technology, Kaironiementie 15, 39700 Parkano E-mail: jori.uusitalo@luke.fi

In this paper we examine the feasibility of data from unmanned aerial vehicle (UAV)-borne aerial imagery in stand-level forest inventory. As airborne sensor platforms, UAVs offer advantages cost and flexibility over traditional manned aircraft in forest remote sensing applications in small areas, but they lack range and endurance in larger areas. On the other hand, advances in the processing of digital stereo photography make it possible to produce three-dimensional (3D) forest canopy data on the basis of images acquired using simple lightweight digital camera sensors. In this study, an aerial image orthomosaic and 3D photogrammetric canopy height data were derived from the images acquired by a UAV-borne camera sensor. Laser-based digital terrain model was applied for estimating ground elevation. Features extracted from orthoimages and 3D canopy height data were used to estimate forest variables of sample plots. K-nearest neighbor method was used in the estimation, and a genetic algorithm was applied for selecting an appropriate set of features for the estimation task. Among the selected features, 3D canopy features were given the greatest weight in the estimation supplemented by textural image features. Spectral aerial photograph features were given very low weight in the selected feature set. The accuracy of the forest estimates based on a combination of photogrammetric 3D data and orthoimagery from UAV-borne aerial imaging was at a similar level to those based on airborne laser scanning data and aerial imagery acquired using purpose-built aerial camera from the same study area.

• Tuominen, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@luke.fi
• Balazs, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: andras.balazs@luke.fi
• Saari, VTT Technical Research Centre of Finland Ltd, P.O. Box 1000, FI-02044 VTT, Finland E-mail: Heikki.Saari@vtt.fi
• Pölönen, University of Jyväskylä, Department of Mathematical Information Technology, P.O. Box 35, FI-40014 University of Jyväskylä, Finland E-mail: ilkka.polonen@jyu.fi
• Sarkeala, Mosaicmill Oy, Kultarikontie 1, FI-01300 Vantaa, Finland E-mail: janne.sarkeala@mosaicmill.com
• Viitala, Häme University of Applied Sciences (HAMK), P.O. Box 230, FI-13101 Hämeenlinna, Finland E-mail: Risto.Viitala@hamk.fi

• Tuominen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@metla.fi
• Pitkänen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: juho.pitkanen@metla.fi
• Balazs, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: andras.balazs@metla.fi
• Korhonen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: kari.t.korhonen@metla.fi
• Hyvönen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: pekka.hyvonen@metla.fi
• Muinonen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: eero.muinonen@metla.fi

• Gobakken, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, Ås, Norway E-mail: terje.gobakken@umb.no
• Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
• Næsset, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, Ås, Norway E-mail: erik.naesset@umb.no

• Wulff, Swedish University of Agricultural Sciences, Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: soren.wulff@slu.se
• Roberge, Swedish University of Agricultural Sciences, Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: cornelia.roberge@slu.se
• Ringvall, Swedish University of Agricultural Sciences, Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: anna.ringvall@slu.se
• Holm, Swedish University of Agricultural Sciences, Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: soren.holm@slu.se
• Ståhl, Swedish University of Agricultural Sciences, Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: goran.stahl@slu.se

• Magnussen, Canadian Forest Service, Natural Resources Canada, 505 West Burnside Road, Victoria BC V8Z 1M5 Canada E-mail: steen.magnussen@nrcan.gc.ca

• Tuominen, Finnish Forest Research Institute, P.O.Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@metla.fi
• Haapanen, Finnish Forest Research Institute, P.O.Box 18, FI-01301 Vantaa, Finland E-mail: reija.haapanen@gmail.com

• de Mello, Federal University of Sergipe, Brazil E-mail: aadm@nn.br
• Nutto, Federal University of Paraná, Brazil E-mail: lnutto.ufpr@gmail.com
• Weber, Federal University of Paraná E-mail: ksw@nn.br
• Sanquetta, Carlos Eduardo Sanquetta E-mail: ces@nn.br
• Monteiro de Matos, Jorge Luis Monteiro de Matos E-mail: jlmdm@nn.br
• Becker, University of Freiburg, Institute of Forest Utilization and Work Science, Germany E-mail: gb@nn.de

• Wallenius, Metsähallitus, P.O. Box 94, FI-01301 Vantaa, Finland E-mail: tarja.wallenius@metsa.fi
• Laamanen, Metsähallitus, P.O. Box 94, FI-01301 Vantaa, Finland E-mail: rl@nn.fi
• Peuhkurinen, Oy Arbonaut Ltd, Helsinki, Finland E-mail: jp@nn.fi
• Mehtätalo, University of Eastern Finland, School of Forest Sciences, Joensuu, Finland E-mail: lm@nn.fi
• Kangas, University of Helsinki, Department of Forest Sciences, Helsinki, Finland E-mail: ak@nn.fi

• Villikka, University of Easten Finland, Department of Forest Sciences, Joensuu, Finland E-mail: mv@nn.fi
• Packalén, University of Easten Finland, Department of Forest Sciences, Joensuu, Finland E-mail: petteri.packalen@uef.fi
• Maltamo, University of Easten Finland, Department of Forest Sciences, Joensuu, Finland E-mail: mm@nn.fi

• Wutzler, Max Planck Institute for Biogeochemistry, Hans-Knöll-Strasse 10, 07745 Jena, Germany E-mail: twutz@bgc-jena.mpg.de
• Profft, Max Planck Institute for Biogeochemistry, Hans-Knöll-Stra§e 10, 07745 Jena, Germany E-mail: ip@nn.de
• Mund, Max Planck Institute for Biogeochemistry, Hans-Knöll-Stra§e 10, 07745 Jena, Germany E-mail: mm@nn.de

• Kitahara, Graduate School of Bioresource and Bioenvironmental Science, Kyushu University, 6-10-1 Hakozaki, Higashiku, Fukuoka 812-8581, Japan E-mail: bunsho@ffpri.affrc.go.jp
• Mizoue, Faculty of Agriculture, Kyushu University, Fukuoka, Japan E-mail: nm@nn.jp
• Yoshida, Faculty of Agriculture, Kyushu University, Fukuoka, Japan E-mail: sy@nn.jp

• Coggins, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, B.C., Canada V6T 1Z4 E-mail: scoggins@interchange.ubc.ca
• Coops, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, B.C., Canada V6T 1Z4 E-mail: ncc@nn.ca
• Wulder, Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Rd., Victoria, B.C., Canada V8Z 1M5 E-mail: maw@nn.ca

• Tuominen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@metla.fi
• Eerikäinen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: ke@nn.fi
• Schibalski, University of Potsdam, Karl-Liebknecht-Strasse 24–25, 14476 Potsdam, Germany E-mail: as@nn.de
• Haakana, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: mh@nn.fi
• Lehtonen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: al@nn.fi

• Eskelson, Oregon State University, Department of Forest Engineering, Resources and Management, 204 Peavy Hall, Corvallis, Oregon 97331, USA E-mail: bianca.eskelson@oregonstate.edu
• Barrett, Oregon State University, Department of Forest Engineering, Resources and Management, 204 Peavy Hall, Corvallis, Oregon 97331, USA E-mail: tmb@nn.us
• Temesgen, Oregon State University, Department of Forest Engineering, Resources and Management, 204 Peavy Hall, Corvallis, Oregon 97331, USA E-mail: ht@nn.us

• Nuutinen, European Forest Institute, Torikatu 34, FI-80100 Joensuu, Finland E-mail: tuula.nuutinen@efi.int
• Kilpeläinen, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: ak@nn.fi
• Hirvelä, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: hh@nn.fi
• Härkönen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: kh@nn.fi
• Ikonen, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: vpi@nn.fi
• Lempinen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: rl@nn.fi
• Peltola, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: hp@nn.fi
• Wilhelmsson, Skogforsk, Uppsala Science Park, SE-751 83 Uppsala, Sweden E-mail: lw@nn.fi
• Kellomäki, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: sk@nn.fi

• Holopainen, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: markus.holopainen@helsinki.fi
• Talvitie, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: mt@nn.fi

• Hyvönen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: pekka.hyvonen@metla.fi
• Anttila, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: pa@nn.fi

• Muinonen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: eero.muinonen@metla.fi

• Tuominen, Finnish Forest Research Institute, Unioninkatu 40 A, FI-00170 Helsinki, Finland E-mail: sakari.tuominen@metla.fi
• Haakana, Finnish Forest Research Institute, Unioninkatu 40 A, FI-00170 Helsinki, Finland E-mail: mh@nn.fi

• Kalliovirta, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: jk@nn.fi
• Tokola, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: timo.tokola@helsinki.fi

• Nevalainen, Finnish Forest Research Institute, Joensuu Research Centre, P.O. Box 68, FIN-80101 Joensuu, Finland E-mail: seppo.nevalainen@metla.fi

• Pakkala, Finnish Museum of Natural History, University of Helsinki, P.O. Box 17, FIN-00014 University of Helsinki, Finland E-mail: timo.pakkala@helsinki.fi
• Hanski, Department of Ecology and Systematics, University of Helsinki, P.O. Box 47, FIN-00014 University of Helsinki, Finland E-mail: ih@nn.fi
• Tomppo, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: et@nn.fi

• Tomppo, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: erkki.tomppo@metla.fi
• Korhonen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: ktk@nn.fi
• Heikkinen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: jh@nn.fi
• Yli-Kojola, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: hyk@nn.fi

• Tuominen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: sakari.tuominen@metla.fi
• Poso, Department of Forest Resource Management, P.O. Box 27, FIN-00014 University of Helsinki, Finland E-mail: sp@nn.fi

• Jalkanen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: anneli.jalkanen@metla.fi

• Nabuurs, European Forest Institute (EFI), Torikatu 34, FIN-80100 Joensuu, Finland; Wageningen University and Research Center, ALTERRA, P.O. Box 47, NL 6700 AA Wageningen, The Netherlands E-mail: g.j.nabuurs@alterra.wag-ur.nl
• Schelhaas, European Forest Institute (EFI), Torikatu 34, FIN-80100 Joensuu, Finland; Wageningen University and Research Center, ALTERRA, P.O. Box 47, NL 6700 AA Wageningen, The Netherlands E-mail: mjs@nn.nl
• Pussinen, European Forest Institute (EFI), Torikatu 34, FIN-80100 Joensuu, Finland; Wageningen University and Research Center, ALTERRA, P.O. Box 47, NL 6700 AA Wageningen, The Netherlands E-mail: ap@nn.nl

• Poso, Department of Forest Resource Management, P.O. Box 24 (Unioninkatu 40 B), FIN-00014 University of Helsinki, Finland E-mail: simo.poso@helsinki.fi
• Wang, Department of Forest Resource Management, P.O. Box 24 (Unioninkatu 40 B), FIN-00014 University of Helsinki, Finland E-mail: gw@nn.fi
• Tuominen, Department of Forest Resource Management, P.O. Box 24 (Unioninkatu 40 B), FIN-00014 University of Helsinki, Finland E-mail: st@nn.fi

• Mattila, Finnish Forest Research Institute, Joensuu Research Station, P.O. Box 68, FIN-80101 Joensuu, Finland E-mail: eero.mattila@metla.fi

There is growing interest in the use of Landsat data to enable forest monitoring over large areas. Free and open data access combined with high performance computing have enabled new approaches to Landsat data analysis that use the best observation for any given pixel to generate an annual, cloud-free, gap-free, surface reflectance image composite. Finland has a long history of incorporating Landsat data into its National Forest Inventory to produce forest information in the form of thematic maps and small area statistics on a variety of forest attributes. Herein we explore the spatial and temporal characteristics of the Landsat archive in the context of forest monitoring in Finland. The United States Geological Survey Landsat archive holds a total of 30 076 images (1972–2017) for 66 scenes (each 185 km by 185 km in size) representing the terrestrial area of Finland, of which 93.6% were acquired since 1984 with a spatial resolution of 30 m. Approximately 16.3% of the archived images have desired compositing characteristics (acquired within August 1 ±30 days, <70% cloud cover, 30 m spatial resolution). Data from the Landsat archive can augment forest monitoring efforts in Finland, provide new information for science and applications, and enable retrospective, systematic analyses to characterize the development of Finnish forests over the past three decades. The capacity to monitor trends based upon this multi-decadal record with the addition of new measurements is of benefit to multisource inventories and offers nationally comprehensive spatially-explicit datasets for a wide range of stakeholders and applications.

• Saarinen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland; School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland https://orcid.org/0000-0003-2730-8892 E-mail: ninni.saarinen@helsinki.fi
• White, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland; Canadian Forest Service, (Pacific Forestry Center), Natural Resources Canada, 506 West Burnside Road, Victoria, BC, V8Z 1M5, Canada http://orcid.org/0000-0003-4674-0373 E-mail: joanne.white@canada.ca
• Wulder, Canadian Forest Service, (Pacific Forestry Center), Natural Resources Canada, 506 West Burnside Road, Victoria, BC, V8Z 1M5, Canada https://orcid.org/0000-0002-6942-1896 E-mail: mike.wulder@canada.ca
• Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, FI-80100 Joensuu, Finland E-mail: annika.kangas@luke.fi
• Tuominen, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: sakari.tuominen@luke.fi
• Kankare, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ville.kankare@helsinki.fi
• Holopainen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: markus.holopainen@helsinki.fi
• Hyyppä, Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02431 Masala, Finland E-mail: juha.hyyppa@nls.fi
• Vastaranta, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland https://orcid.org/0000-0001-6552-9122 E-mail: mikko.vastaranta@uef.fi

Dieback of the common ash (Fraxinus excelsior L.) has been spreading throughout Europe since the 1990s, causing severe ecological and economical consequences; however, detailed statistics on its dynamics have been published rarely. This paper presents the dynamics of mature ash-dominated stands in Latvia for the period 2005–2015. Data from the national forest inventory and a permanent sampling plot network were summarised. According to the official statistics, the dieback has caused a twofold decrease in area of the ash stands (from 21 891 to 13 011 ha, which respectively comprised ca. 0.8 to ca. 0.4% of the total forest area). The official statistics on standing volume appeared biased, as they did not account for increased mortality. According to the permanent sampling plots, standing volume and stand density have been affected even more, having decreased by 53.1 and 69.9%, respectively, compared to 2005 (the stand density and standing volume of ash in 2015 was 77 individuals ha^–1 and 151 m³ ha^–1, respectively). The mortality of the trees has not been stable. Stand density decreased faster during 2005–2009 compared to 2010–2015, with mortality rates of 9.6 and 8.2% year^–1, respectively. In contrast, the decrease in standing volume in 2005–2009 was slower than in 2010–2015 (mortality rates were 4.7 and 7.7% year^–1, respectively) because trees with smaller dimensions were more susceptible to the dieback. Nevertheless, the observed mortality rates clearly indicate negative prospects for ash stands in Latvia.

• Matisone, Latvian State Forest Research Institute (LSFRI) Silava, Rigas str. 111, Salaspils, Latvia, LV2169 E-mail: ilze.matisone@silava.lv
• Matisons, Latvian State Forest Research Institute (LSFRI) Silava, Rigas str. 111, Salaspils, Latvia, LV2169 E-mail: robism@inbox.lv
• Laiviņš, Latvian State Forest Research Institute (LSFRI) Silava, Rigas str. 111, Salaspils, Latvia, LV2169 E-mail: maris.laivins@silava.lv
• Gaitnieks, Latvian State Forest Research Institute (LSFRI) Silava, Rigas str. 111, Salaspils, Latvia, LV2169 E-mail: talis.gaitnieks@silava.lv

• Villasante, Agroforestry Departament, Universitat de Lleida, Av. Rovira Roure 177, 25198 Lleida, Spain http://orcid.org/0000-0002-7549-7424 E-mail: avillasante@eagrof.udl.cat
• Fernandez, Agroforestry Departament, Universitat de Lleida, Av. Rovira Roure 177, 25198 Lleida, Spain E-mail: cfernandez@eagrof.udl.cat

• Katila, Finnish Forest Research Institute, Unioninkatu 40 A, FI-00170 Helsinki, Finland E-mail: mk@nn.fi

• Päivinen, European Forest Institute, Torikatu 34, FIN-80101 Joensuu, Finland E-mail: risto.paivinen@efi.fi
• Anttila, European Forest Institute, Torikatu 34, FIN-80101 Joensuu, Finland E-mail: pa@nn.fi