Erkki Tomppo

Estimating carbon accumulation rates of undrained mires in Finland–application to boreal and subarctic regions:

Equations based on empirical relationships of peat physical properties were used to estimate the average long-term apparent rate of carbon accumulation (LORCA) in Finnish mire vegetation regions. The results were generalized to the boreal and subarctic regions. Analyses of 1302 dated peat cores were used to infer carbon accumulation for each mire vegetation region of Finland. The area-weighted LORCA for Finnish undrained mire areas was 18.5 g m 2 yr 1 and the total carbon sink 0.79 Tg yr 1 (1 Tg = 1012g). The total carbon pool of Finnish undrained mires was estimated as 2257 Tg. The aapa-mire region included 80% of the total net accumulation rate of carbon and 85% of the total carbon reservoirs of Finnish undrained mires. LORCA was signi” cantly higher in the raised-bog region, 26.1 g m 2 yr 1, compared with the aapa-mire region, 17.3 g m 2 yr 1, and bogs generally had a higher LORCA 20.8 g m 2 yr 1, than fens 16.9 g m 2 yr 1. The total C sink for boreal and subarctic mires was estimated at 66 Tg yr 1 which is about 31% lower than the previous estimates. The total C pool of all boreal and subarctic mires was estimated at 270–370 Pg (1 Pg = 1015g).

Selecting estimation parameters for the Finnish multisource National Forest Inventory

Abstract The paper examines the selection of parameters for the nonparametric k-NN estimation method that is used in the Finnish multisource National Forest Inventory (MS-NFI). The MS-NFI utilises NFI field plot data, optical area satellite images and digital maps and produces forest variable estimates from the single pixel level up to the national level. The most important parameters to be selected are: the distance metric, the number of the nearest neighbours, k, parameters related to the digital elevation model, stratification of the image data, as well as the width of the moving geographical horizontal and vertical reference areas (HRAs and VRAs). The root mean square errors (RMSEs) and significance of biases at pixel level were evaluated in order to find optimal parameters. A leave-one-out cross-validation method was applied. The emphasis is placed on the search for moving geographical HRAs and VRAs, as well as in the stratification of the field plots and the satellite images on the basis of auxiliary data. Stratification reduces the bias of the estimates significantly within each strata. With the current sampling intensity of the Finnish national forest inventory, a geographical HRA with a radius of 40–50 km was found optimal for the total volume estimates and for volumes by tree species in the mineral land map stratum. On the average, there was a sufficient number of field plots to cover the variation of forest variables within the image area to be analysed. The inclusion of field plot data beyond this area introduced bias to the estimates. For the peatland strata, a wider reference area, 60–90 km, was needed. A VRA, together with topographic correction of the digital values of images, reduced the standard error of the volume estimates in Northern Finland.

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

Abstract Tremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic areas: statistical classification and prediction techniques used to construct maps and related spatial products, accuracy assessment methods, map-based statistical inference, and two emerging topics, change detection and use of lidar data. Multiple general conclusions were drawn from the review: (1) remotely sensed data greatly contribute to the construction of forest attribute maps and related spatial products and to the reduction of inventory costs; (2) parametric prediction techniques, accuracy assessment methods and probability-based (design-based) inferential methods are generally familiar and mature, although inference is surprisingly seldom addressed; (3) non-parametric prediction techniques and model-based inferential methods lack maturity and merit additional research; (4) change detection methods, with their great potential for adding a spatial component to change estimates, will mature rapidly; and (5) lidar applications, although currently immature, add an entirely new dimension to remote sensing research and will also mature rapidly. Crucial forest sustainability and climate change applications will continue to push all aspects of remote sensing to the forefront of forest research and operations.

Advances and emerging issues in national forest inventories

Abstract National forest inventories (NFIs) have a long history, although their current major features date only to the early years of the twentieth century. Recent issues such as concern over the effects of acid deposition, biodiversity, forest sustainability, increased demand for forest data, international reporting requirements and climate change have led to the expansion of NFIs to include more variables, greater diversity in sampling protocols and a generally more holistic approach. This review focuses on six selected topics: (1) a brief historical review; (2) a summary of common structural features of NFIs; (3) a brief review of international reporting requirements using NFI data with an emphasis on approaches to harmonized estimation; (4) an overview of inventory estimation methods that can be enhanced with remotely sensed data; (5) an overview of nearest neighbors prediction and estimation techniques; and (6) a brief overview of several emerging issues including carbon inventories in developing countries and use of lidar data. Although general inventory principles will remain unchanged, sampling designs, plot configurations and measurement protocols will require modification before they can be applied in countries with tropical forests. Technological advances, particularly in the use of remotely sensed data, including lidar data, have led to greater inventory efficiencies, better maps and accurate estimation for small areas.

Adapting Finnish Multi-Source Forest Inventory Techniques to the New Zealand Preharvest Inventory

To measure many small areas in a forest, each to the required detail and precision, and sufficiently frequently, is too expensive using ground-based inventories alone. The Finnish satellite image-aided National Forest Inventory (NFI) method was modified and tested with a view to developing a new preharvesting forest inventory method for assessing the volumes of potential timber assortments (log products) in New Zealand radiata pine (Pinus radiata D. Don) plantations. Data from 188 ground plots were supplied by a forest company for a 1000 ha block of radiata pine. This material was combined with known stand boundaries from maps and data from a Landsat TM image in order to predict for each pixel and each stand the pruned and unpruned sawlog, pulp and total standing volumes. A k-nearest neighbour algorithm was applied for the estimation. Some stand characteristics, obtained from stand histories, were used as co-variates, in addition to spectral features. Cross-validation tests indicated that estimates of plo...

Improving the accuracy of multisource forest inventory estimates to reducing plot location error — a multicriteria approach

Abstract A procedure is introduced to reassign satellite image information to field plot data of forest inventory by a multicriteria approach. The method can be utilised in satellite-image-aided forest inventories, e.g., as in the Finnish Multi-Source National Forest Inventory (FMS-NFI) since 1990. This inventory method presumes that the field sample plots are geographically accurately located and each individual field plot can be identified with a specific picture element of the applied satellite image. Small area estimation errors are highly sensitive to the location errors of field plots with respect to the satellite image. In spite of current GPS systems, an accurate location is extremely difficult to achieve due to map errors and errors in rectifying a satellite image on a map. Satellite image information is reassigned to the field plots within an n × n image window around the assumed location. A weighted function of the correlation coefficients of the selected image and field variables is used as a scaling function in this multicriteria optimisation approach. The root mean square errors (RMSEs) of estimates, produced with a nonparametric k -nearest-neighbour ( k -nn) estimation method, are applied as a criterion when judging the final goodness of the relocation. The relocation reduces the pixel-level RMSE of total volume per hectare by 36%.

C and N storage in living trees within Finland since 1950s

Living biomass contains 45 to 60% carbon and 0.05 to 3% nitrogen, in dry weight. Like throughout Europe, the amount of living biomass in Finnish forests has increased on average over the last decades, largely because of changes in forest management. The storage of organic C and N in biomass has also increased.Changes in biomass vary between regions. Data were analysed on changes in the last 30–40 years in C and N storage in living trees in Finland, subdivided into 20 regions. Tree biomass increased in 17 regions, and decreased in 3 regions. The storage rate varied between -170 and +480 kg C ha-1 a-1, and between −0.5 and +1.2 kg N ha-1 a-1.Nitrogen accumulation in trees was less than 15% of atmospheric N deposition in all regions. Although the eventual increase of the nitrogen concentration in tree tissues was omitted, it is not possible that living biomass has been the major sink for atmospheric N deposition to forests. A hypothesis is presented that the main sink is litter layer and organic soil. Carbon can also be accumulating in soils essentially faster than hitherto estimated in analyses of carbon budgets of European forests.

The Finnish National Forest Inventory

The National Forest Inventory (NFI) of Finland has produced large-area forest resource infor- mation since the beginning of 1920s (Ilvessalo 1927). When the 10 th inventory (NFI10) started in 2004, the design was changed and the rotation shortened to 5 years. Measurements are done in the entire country each year through measuring one-fifth of the plots. About one-fifth of all plots are measured as perma- nent. Using field data only, it is possible to compute reliable estimates for large areas, the minimum size of the area is typically some hundreds of thousands of hectares. In practical forestry, estimates are also often required for smaller units such as municipalities with typical areas of tens of thousands of hectares. This is possible only if ancillary data are used in addition to sparse field data. The Finnish multisource NFI uses satellite images and digital map data, in addition to field data, and produces estimates for small areas and wall-to-wall maps. Information from the Finnish NFI has traditionally been used in large area forest management planning, such as planning regional and national level cutting, improving silviculture and forest regimes, making decisions concerning forest industry investments, and providing a basis for forest income taxation. The NFI also provides forest resource information for national and international forest statistics and processes such as the United Nations Food and Agriculture Organizations (FAO) Forest Resource Assessment process and the Land Use, Land-Use Change and Forestry reporting of the United Nations Framework Convention on Climate Change.. Sampling designs for the ninth inventory rotation (NFI9)—conducted from 1996 to 2003—and NFI10 are described, as well as the basic principles of estimation methods based on field data only.

Effects of Connectivity and Spatial Resolution of Analyses on Conservation Prioritization across Large Extents

The outcome of analyses that prioritize locations for conservation on the basis of distributions of species, land cover, or other elements is influenced by the spatial resolution of data used in the analyses. We explored the influence of data resolution on prioritization of Finnish forests with Zonation, a software program that ranks the priority of cells in a landscape for conservation. We used data on the distribution of different forest types that were aggregated to nine different resolutions ranging from 0.1 × 0.1 km to 25.6 × 25.6 km. We analyzed data at each resolution with two variants of Zonation that had different criteria for prioritization, with and without accounting for connectivity and with and without adjustment for the effect on the analysis of edges between areas at the project boundary and adjacent areas for which data do not exist. Spatial overlap of the 10% of cells ranked most highly when data were analyzed at different resolutions varied approximately from 15% to 60% and was greatest among analyses with similar resolutions. Inclusion of connectivity or edge adjustment changed the location of areas that were prioritized for conservation. Even though different locations received high priority for conservation in analyses with and without accounting for connectivity, accounting for connectivity did not reduce the representation of different forest types. Inclusion of connectivity influenced most the outcome of fine-resolution analyses because the connectivity extents that we based on dispersal distances of typical forest species were small. When we kept the area set aside for conservation constant, representation of the forest types increased as resolution increased. We do not think it is necessary to avoid use of high-resolution data in spatial conservation prioritization. Our results show that large extent, fine-resolution analyses are computationally feasible, and we suggest they can give more flexibility to implementation of well-connected reserve networks.

The Finnish multisource national forest inventory: small-area estimation and map production

A driving force motivating development of the multisource national forest inventory (MS-NFI) in connection with the Finnish national forest inventory (NFI) was the desire to obtain forest resource information for smaller areas than is possible using field data only without significantly increasing the cost of the inventory. A basic requirement for the method was that it provide applicable information for forestry decision making; e.g., volume estimates - possibly by subclasses such as tree species - timber assortments, and stand-age classes.