Multi-source National Forest Inventory Research Articles

Using Multi-Source National Forest Inventory Data for the Prediction of Tree Lists of Individual Stands for Long-Term Simulation

Forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, multispectral satellite images and numerical map data. We evaluated k-nearest neighbors (k-NN) method-based predictions of forest variables for pixels in predicting tree lists of individual stands, including tree diameters at breast height and tree heights and then calculated stem volumes and tree species proportions. We compared alternative parameters (k-NN) using k of either 1 or 5 according to preliminary plot-level study and applying either measured trees (1-NN_trees) or mean stand characteristics (k-NN_stand). In the 1-NN_trees method, a tree list was generated based on the measured trees of the NFI plots, whereas in the 1-NN_stand and 5-NN_stand methods, a Weibull-based diameter distribution was recovered from the stand characteristics of the same inventory plots. In both methods, tree lists were predicted for each 16 m × 16 m pixel included in the stand compartment. Both methods performed well and resulted in 8–14% differences in the total volume compared with the field inventory of the 27 stands used for the evaluation. Moreover, the main tree species was correctly predicted for 74% of cases. The RMSE in total volume ranged from 25% (5-NN_stand) to 31% (1-NN_stand), while the smallest RMSEs in volume by tree species were 61% for broadleaves and 65% for pine and spruce using the 5-NN_stand. When comparing input data for a long-term growth simulation, the choice of the method was less influential as the effect of the error in the initial stand characteristics decreased over time during the simulation period. After 30-year simulation of the inventoried stands, the respective RMSEs were 9.4% for total volume and 39%, 50% and 59% for tree species, respectively. The satellite-based data with NFI plots were useful for predicting tree lists for pixels of a stand. However, the accuracy for operational forest management was still questionable. For a larger area’s strategic information, the accuracy is considered adequate.

Using Multi-Source National Forest Inventory Data for the Prediction of Tree Lists of Individual Stands for Long-Term Simulation

Forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, multispectral satellite images and numerical map data. We evaluated k-nearest neighbors (k-NN) method-based predictions of forest variables for pixels in predicting tree lists of individual stands, including tree diameters at breast height and tree heights and then calculated stem volumes and tree species proportions. We compared alternative parameters (k-NN) using k of either 1 or 5 according to preliminary plot-level study and applying either measured trees (1-NN_trees) or mean stand characteristics (k-NN_stand). In the 1-NN_trees method, a tree list was generated based on the measured trees of the NFI plots, whereas in the 1-NN_stand and 5-NN_stand methods, a Weibull-based diameter distribution was recovered from the stand characteristics of the same inventory plots. In both methods, tree lists were predicted for each 16 m × 16 m pixel included in the stand compartment. Both methods performed well and resulted in 8–14% differences in the total volume compared with the field inventory of the 27 stands used for the evaluation. Moreover, the main tree species was correctly predicted for 74% of cases. The RMSE in total volume ranged from 25% (5-NN_stand) to 31% (1-NN_stand), while the smallest RMSEs in volume by tree species were 61% for broadleaves and 65% for pine and spruce using the 5-NN_stand. When comparing input data for a long-term growth simulation, the choice of the method was less influential as the effect of the error in the initial stand characteristics decreased over time during the simulation period. After 30-year simulation of the inventoried stands, the respective RMSEs were 9.4% for total volume and 39%, 50% and 59% for tree species, respectively. The satellite-based data with NFI plots were useful for predicting tree lists for pixels of a stand. However, the accuracy for operational forest management was still questionable. For a larger area’s strategic information, the accuracy is considered adequate.

Spatial patterns of biomass change across Finland in 2009–2015

Forest characteristics vary largely at the regional level and in smaller geographic areas in Finland. The amount of greenhouse gas emissions is related to changes in biomass and the soil type (e.g. upland soils vs. peatlands). In this paper, estimating and explaining spatial patterns of tree biomass change across Finland was the main interest. We analysed biomass changes on different soil and site types between the years 2009 and 2015 using the Finnish multi-source national forest inventory (MS-NFI) raster layers. MS-NFI method is based on combining information from satellite imagery, digital maps and national forest inventory (NFI) field data. Automatic segmentation was used to create silvicultural management and treatment units. An average biomass estimate of the segmented MS-NFI (MS–NFI–seg) map was 73.9 tons ha−1 compared to the national forest inventory estimate of 76.5 tons ha−1 in 2015. Forest soil type had a similar effect on average biomass in MS–NFI–seg and NFI data. Despite good regional and country-level results, segmentation narrowed the biomass distributions. Hence, biomass changes on segments can be considered only approximate values; also, those small differences in average biomass may accumulate when map layers from more than one time point are compared. A kappa of 0.44 was achieved for precision when comparing undisturbed and disturbed forest stands in the segmented Global Forest Change data (GFC-seg) and MS–NFI–seg map. Compared to NFI, 69% and 62% of disturbed areas were detected by GFC-seg and MS–NFI–seg, respectively. Spatially accurate map data of biomass changes on forest land improve the ability to suggest optimal management alternatives for any patch of land, e.g. in terms of climate change mitigation.

NutSpaFHy—A Distributed Nutrient Balance Model to Predict Nutrient Export from Managed Boreal Headwater Catchments

Responsible forest management requires accounting for adverse environmental effects, such as increased nutrient export to water courses. We constructed a spatially-distributed nutrient balance model NutSpaFHy that extends the hydrological model SpaFHy by introducing a grid-based nutrient balance sub-model and a conceptual solute transport routine to approximate total nitrogen (N) and phosphorus (P) export to streams. NutSpaFHy uses openly-available Multi-Source National Forest Inventory data, soil maps, topographic databases, location of water bodies, and meteorological variables as input, and computes nutrient processes in monthly time-steps. NutSpaFHy contains two calibrated parameters both for N and P, which were optimized against measured N and P concentrations in runoff from twelve forested catchments distributed across Finland. NutSpaFHy was independently tested against six catchments. The model produced realistic nutrient exports. For one catchment, we simulated 25 scenarios, where clear-cuts were located differently with respect to distance to water body, location on mineral or peat soil, and on sites with different fertility. Results indicate that NutSpaFHy can be used to identify current and future nutrient export hot spots, allowing comparison of logging scenarios with variable harvesting area, location and harvest techniques, and to identify acceptable scenarios that preserve the wood supply whilst maintaining acceptable level of nutrient export.

Representation of tree cover in global land cover products: Finland as a case study area

Forest extent mapping is required for climate modeling and monitoring changes in ecosystem state. Different global land cover (LC) products employ simple tree cover (referred also as “forest cover” or even “vegetation cover”) definitions to differentiate forests from non-forests. Since 1990, a large number of forest extent maps have become available. Although many studies have compared forest extent data, they often use old data (i.e., around the year 2000). In this study, we assessed tree cover representations of three different annual, global LC products (MODIS VCF (MOD44B, Collection 6 (C6)), MCD12Q1 (C6), and CCI LC (v.2.1.1)) using the Finnish Multi-Source National Forest Inventory (MS-NFI) data for the year 2017. In addition, we present an intercomparison approach for analyzing spatial representations of coniferous and deciduous species. Intercomparison of different LC products is often overlooked due to challenges involved in non-standard and overlapping LC class definitions. Global LC products are used for monitoring changes in land use and land cover and modeling of surface fluxes. Given that LC is a major driver of global change through modifiers such as land surface albedo, more attention should be paid to spatial mapping of coniferous and deciduous species. Our results show that tree cover was either overestimated or underestimated depending on the LC product, and classification accuracy varied between 42 and 75%. Intercomparison of the LC products showed large differences in conifer and deciduous species spatial distributions. Spatial mapping of coniferous and deciduous tree covers was the best represented by the CCI LC product as compared with the reference MS-NFI data.

Trade-offs between wood production and forest grouse habitats in two regions with distinctive landscapes

BackgroundForest management affects the viability of forest grouse populations, causing alterations to habitat quality. At the regional level, common targets for wood harvesting and safeguarding of specific habitats are negotiated between various stakeholders. Analysing potential trade-offs between forest grouse habitats and wood production in the region could support resource-smart decision making.MethodsIn this paper, we compiled trade-off curves represented as production possibility frontiers demonstrating the relationship between forest grouse habitat area and wood removal, using a Finnish forestry dynamics model and the Finnish Multi-Source National Forest Inventory. For the modelling of forest grouse habitats, a landscape-level occurrence model based on nationwide wildlife triangle census data was used. Five alternative forest scenarios in terms of wood removal were compiled for two study areas in Finland representing two different landscape structures.Results and conclusionsResults showed that impacts on forest grouse habitats were case-specific. In the southern study area, increasing the roundwood harvesting rate affected grouse habitats more strongly as forests were already fragmented for other land uses and became more spruce-dominated over time. If the maximum sustainable removal rate was implemented, predicted grouse habitat area was 55% less than in a no-removal scenario. In the eastern study area, a more heavily forested region, the decrease was far lower at 22%. Scenario results were also compared to levels of recorded (business as usual) wood removal and that envisaged by valid regional forestry programmes, and their sustainability in terms of grouse habitat area was discussed. The production possibility frontiers calculated in this study support the evaluation of the loss of suitable habitat caused by different wood harvesting rates, or vice versa, the economic cost of increasing habitat areas.

Reducing error in small-area estimates of multi-source forest inventory by multi-temporal data fusion

Abstract Since the 1990s, forest resource maps and forest variable estimates for small areas have been produced by combining national forest inventory (NFI) field plot data, optical satellite images and numerical map data. A non-parametric $k$-NN method has frequently been employed. In Finland, such multi-source NFI (MS-NFI) forest variable estimates for municipalities have been produced eight times. A relatively large variation has been observed between subsequent estimates. In this study, a large-scale evaluation of small-area estimates from an MS-NFI conducted in 2013 was carried out in comparison with pure NFI field data-based estimates and error estimates. The proportion of municipalities with significant differences was larger than expected, e.g. over 10% for the mean volume, which indicates systematic error in the small-area estimates. A multi-temporal data fusion combining MS-NFI estimators from three time points—2011, 2013 and 2015—was tested as a means to improve single time point MS-NFI estimates of the mean volumes of growing stock and of tree species groups. A generalized least squares (GLS) technique and unweighted averaging were tested. The improvement was small but consistent when validated against the NFI field data-based estimates for the municipalities. The unweighted averaging worked nearly as well as a GLS estimator.

Assessing local trends in indicators of ecosystem services with a time series of forest resource maps

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 -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 m pixel size and units of 240 × 240 m, 1200 × 1200 m and 12 000 × 12 000 m 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 m. The multiple testing corrections decreased the amount of significant -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.k22222p

Forest inventories for small areas using drone imagery without in-situ field measurements

Drone applications are becoming increasingly common in the arena of forest management and forest inventories. In particular, the use of photogrammetrically derived drone-based image point clouds (DIPC) in individual tree detection has become popular. Use of an area-based approach (ABA) in small areas has also been considered. However, in-situ field measurements of sample plots substantially increase the cost of small area forest inventories. Therefore, we examined whether small-scale forest management inventories could be carried out without local field measurements. We used nationwide and regional ABA models for stem volumes fitted with airborne laser scanning (ALS) data to predict stem volumes using corresponding metrics calculated from DIPC data. The stem volumes were predicted at the cell level (15 × 15 m) and aggregated to test plots (30 × 30 m). Height metrics for the dominant tree layer from the DIPC data showed strong correlations with similar metrics computed from the ALS data. The ALS-based models applied with DIPC metrics performed well, especially if the ABA model was fitted in the same geographical area (regional model) and the inventory units were disaggregated to coniferous and deciduous dominated stands using auxiliary information from Multi-source National Forest Inventory data (root mean square error at 30 × 30 m level was 13.1%). The corresponding root mean square error associated with the nationwide ABA model was 20.0% with an overestimation (mean difference 9.6%).

Using ALS raster data in forest planning

Raster type of forest inventory data with site and growing stock variables interpreted for small square-shaped grid cells are increasingly available for forest planning. In Finland, there are two sources of this type of lattice data: the multisource national forest inventory and the inventory that is based on airborne laser scanning (ALS). In both cases, stand variables are interpreted for 16 m × 16 m cells. Both data sources cover all private forests of Finland and are freely available for forest planning. This study analyzed different ways to use the ALS raster data in forest planning. The analyses were conducted for a grid of 375 × 375 cells (140,625 cells, of which 97,893 were productive forest). The basic alternatives were to use the cells as calculation units throughout the planning process, or aggregate the cells into segments before planning calculations. The use of cells made it necessary to use spatial optimization to aggregate cuttings and other treatments into blocks that were large enough for the practical implementation of the plan. In addition, allowing premature cuttings in a part of the cells was a prerequisite for compact treatment areas. The use of segments led to 5–9% higher growth predictions than calculations based on cells. In addition, the areas of the most common fertility classes were overestimated and the areas of rare site classes were underestimated when segments were used. The shape of the treatment blocks was more irregular in cell-based planning. Using cells as calculation units instead of segments led to 20 times longer computing time of the whole planning process than the use of segments when the number of grid cells was approximately 100,000.

Calibration of nationwide airborne laser scanning based stem volume models

In-situ field measurements of sample plots are a major cost component in airborne laser scanning (ALS) based forest inventories. Field measurements on new inventory areas can be reduced by utilizing existing stand attribute models from former inventory areas. We constructed a nationwide model for stem volume, and examined seven different calibration scenarios using 22 inventory areas distributed evenly throughout Finland. These scenarios can be divided into three main categories: 1) calibration with additional predictor variables, 2) calibration with 200 geographically nearest sample plots, and 3) calibration with MS-NFI (Multi-source National Forest Inventory of Finland) volume at the target inventory area. Calibration with degree days, precipitation, and proportion of birch resulted in the greatest decrease in error rate of stem volume predictions. The mean of the root mean square errors (RMSE) among the 22 inventory areas decreased from 28.6% to 25.9%, and the standard deviation of RMSEs from 4.3% to 3.9% using three additional predictor variables. Correspondingly, the mean and standard deviation of absolute values of mean differences (|MD|) decreased from 8.3% to 5.6% and from 5.6% to 4.4%, respectively. All calibration scenarios decreased the error rate, especially the high |MDs| observed in the northern part of Finland. Calibration with sample plots from geographically nearest inventory areas was useful when there were sample plots that offered a good representation of the target area. MS-NFI based calibration was also a reasonable option if loggings and other inconsistencies between datasets could be detected and accounted for.

An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data

Abstract. Forest management affects the distribution of tree species and the age class of a forest, shaping its overall structure and functioning and in turn the surface–atmosphere exchanges of mass, energy, and momentum. In order to attribute climate effects to anthropogenic activities like forest management, good accounts of forest structure are necessary. Here, using Fennoscandia as a case study, we make use of Fennoscandic National Forest Inventory (NFI) data to systematically classify forest cover into groups of similar aboveground forest structure. An enhanced forest classification scheme and related lookup table (LUT) of key forest structural attributes (i.e., maximum growing season leaf area index (LAImax), basal-area-weighted mean tree height, tree crown length, and total stem volume) was developed, and the classification was applied for multisource NFI (MS-NFI) maps from Norway, Sweden, and Finland. To provide a complete surface representation, our product was integrated with the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) map of present day land cover (v.2.0.7). Comparison of the ESA LC and our enhanced LC products (https://doi.org/10.21350/7zZEy5w3) showed that forest extent notably (κ = 0.55, accuracy 0.64) differed between the two products. To demonstrate the potential of our enhanced LC product to improve the description of the maximum growing season LAI (LAImax) of managed forests in Fennoscandia, we compared our LAImax map with reference LAImax maps created using the ESA LC product (and related cross-walking table) and PFT-dependent LAImax values used in three leading land models. Comparison of the LAImax maps showed that our product provides a spatially more realistic description of LAImax in managed Fennoscandian forests compared to reference maps. This study presents an approach to account for the transient nature of forest structural attributes due to human intervention in different land models.

Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging

Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging

Comparing regional forest policy scenarios in terms of predicted suitable habitats for the Siberian flying squirrel (Pteromys volans)

ABSTRACTForecasting the potential impacts of forest policies on species of special conservation value is a prerequisite for safeguarding forest biodiversity. In this study, regional forest policy scenarios were compared in terms of predicted habitats suitable for the Siberian flying squirrel (Pteromys volans). To derive both patch- and landscape-scale models to predict species presence in a forest stand, species occurrence data from a systematic field survey covering the whole distribution area of the flying squirrel in Finland and Multi-Source National Forest Inventory data were combined. Then, the Finnish forestry model MELA and the derived occupancy models were applied to predict the quantity of suitable habitats for flying squirrels in three different 50-year policy scenarios. The results confirm that increasing the utilization of felling potential from the level of business-as-usual to the level stated as policy targets in regional forest programs decreases the amount of suitable habitat in the future. However, regional forest programs had a less drastic impact on habitats than maximum sustainable removal, except in two regions. It should be noted that the occupancy models seemed to fail on sites that experts deem to be most suitable for the species. Obviously, there are other factors than forest management affecting presence.

Multidecadal analysis of forest growth and albedo in boreal Finland

It is well known that forests serve as carbon sinks. However, the balancing effect of afforestation and increased forest density on global warming due to carbon storage may be lost by low albedo (thus high absorption) of the forests. In the last 30 years, there has been a steady increase in the growing stock of Finnish forests by nearly a quarter while the area of the forests has remained virtually unchanged. Such increase in forest density together with the availability of detailed forest inventories provided by the Multi-Source National Forest Inventory (MS-NFI) in high spatial resolution makes Finland an ideal candidate for exploring the effects of increased forest density on satellite derived estimates of bio-geochemical products e.g. albedo (directional-hemispherical reflectance, DHR), fraction of photosynthetically active radiation absorbed by canopies (fAPAR), leaf area index (LAI) and normalized difference vegetation index (NDVI) in both current and long-term perspective.In this study, we first used MODIS-based vegetation satellite products for Finnish forests to study their seasonal patterns and interrelations. Next, the peak growing season observations are linked to the MS-NFI database to yield the generic relationships between forest density and the satellite-derived vegetation indicators. Finally, long-term GIMMS3g datasets between 1982 and 2011 (2008 for DHR) are analyzed and interpreted using forest inventory data. The vegetation peak growing season NIR DHR and VIS DHR showed weak to moderate negative correlation with fAPAR, whereas there was no correlation between NIR DHR and fAPAR. Next, we show that the spectral albedos in the near-infrared region (NIR DHR) showed weak negative correlation with forest biomass, basal area or canopy cover whereas, as expected, the spectral albedo in the visible region (VIS DHR) correlated negatively with these measures of forest density. Interestingly, the increase in forest density (biomass per ha) of Finnish forests during the last 30 years was not accompanied by trends in the indicators of vegetation ‘greenness’ and photosynthetic productivity (fAPAR, LAI and NDVI) or in forest albedo (DHR). Even though there were small increases in both DHR, fAPAR, LAI and NDVI and from the start to the end of the study period (1982–2011), the pattern and magnitude of change in these variables did not follow the development of forest biomass in the different NFI inventories.

Forest stands volume estimation by using Finnish Multi-Source National Forest Inventory in Stołowe Mountains National Park

Abstract The purpose of the present study was to convey to the reader the method and application of the Finnish Multi-Source National Forest Inventory (MS-NFI) that was devised in the Finnish Forest Research Institute. The study area concerned is Stołowe Mountains National Park, which is located in the south-western Poland, near the border with the Czech Republic. To accomplish the above mentioned aim, the following data have been applied: timber volume derived from field sample plots, satellite image, digital map data and digital elevation model. The Pearson correlation coefficient between independent and dependent variables has been verified. Furthermore, the non-parametric k-nearest neighbours (k-NN) technique and genetic algorithm have been used in order to estimate forest stands biomass at the pixel level. The error estimates have been obtained by leave-one-out cross-validation method. The main computed forest stands features were total and mean timber volume as well as maximum and minimum biomass occurring in the examined area. In the final step, timber volume map of the growing stock has been created.

Predicting vegetation characteristics in a changing environment by means of laser scanning

Accurate and up-to-date information concerning vegetation characteristics is needed for decision-making from individual-tree-level management activities to the strategic planning of forest resources. Outdated information may lead to unbeneficial or even wrong decisions, at least when it comes to the timing of management activities. Airborne laser scanning (ALS) has so far been successfully used for applications involving detailed vegetation mapping because of its capability to simultaneously produce accurate information on vegetation and ground surfaces. The aim of this dissertation was to develop methods for characterizing vegetation and its changes in varying environments. A method called multisource single-tree inventory (MS-STI) was developed in substudy I to update urban tree attributes. In MSSTI stem map was produced with terrestrial laser scanning and by combining the stem map with predictors derived from ALS data it was possible to obtain improved estimates of diameter-at-breast height but also to produce new attributes such as height and crown size. Boat-based mobile laser scanning (MLS) data were employed in substudy II to map riverbank vegetation and identify changes. The overall classification accuracy of 73% was obtained, which is similar to accuracies found in other studies. With multi-temporal MLS data sets changes in vegetation were mapped year to year. In substudy III, open access ALS data were combined with multisource national forest inventory (NFI) data to investigate the drivers associated to wind damage. The special interest was in ALS-based predictors to map areas with wind disturbance and apply logistic regression to produce a continuous probability surface of wind predisposition to identify areas most likely to experience wind damage. The results demonstrated that a combination of ALS and multisource NFI in the modelling approach increased the prediction accuracy from 76% to 81%. The dissertation showed the capability of ALS and MLS for characterizing vegetation and mapping changes in varying environments. The developed applications could increase and expand the utilization of multi-temporal 3D data sets as well as increase data value. The results of this dissertation can be utilized in producing more accurate, diverse, and up-to-date information for decision-making related to natural resources.

Evaluation of bilberry and cowberry yield models by comparing model predictions with field measurements from North Karelia, Finland

Models estimated for non-wood forest products (NWFPs) are used to support the multi-objective use of forest and related decision making in various ways. In Finland, bilberries (Vaccinium myrtillus) and cowberries (Vaccinium vitis-idaea) are among the most important NWFPs, and yield models for them have been estimated and linked into forest simulators. So far, berry yield models have not been evaluated using independent data that were not used in model estimation. In this study, independent data on berry yields, as well as stand and site characteristics used as predictors in yield models, were measured from a total of 230 sample plots in North Karelia, Finland. The predictions of four bilberry and four cowberry models published previously were evaluated on the basis of the models’ prediction bias, precision and applicability in locating the best berry stands for picking. The bias and precision of the models varied highly. Due to bias, the models should be calibrated before applying them in forest decision support systems. The models for bilberry located the best berry stands more reliably than did those for cowberry. Visits to twice as many stands, as guided by the models, were needed to harvest a given amount of bilberries, but, for cowberry, the number of stands to be visited was four-fold in comparison to the situation where the measured yields were known. Besides the forest inventory data measured in the field, we also used the freely available multi-source national forest inventory (MS-NFI) data covering the whole of Finland as input data for the berry models. Both data sets were equally suitable to be used as the input data for the berry models.

Using multi-source data to map and model the predisposition of forests to wind disturbance

Natural disturbances such as wind are known to cause threats to ecosystem services as well as sustainable forest ecosystem management. The objective of this research was to better understand and quantify drivers of predisposition to wind disturbance, and to model and map the probability of wind-induced forest disturbances (PDIS) in order to support forest management planning. To accomplish this, we used open-access airborne light detection and ranging (LiDAR) data as well as multi-source National Forest Inventory (NFI) data to model PDIS in southern Finland. A strong winter storm occurred in the study area in December 2011. High spatial resolution aerial images, acquired after the disturbance event, were used as reference data. Potential drivers associated with PDIS were examined using a multivariate logistic regression model. The model based on LiDAR provided good agreement with detected areas susceptible to wind disturbance (73%); however, when LiDAR was combined with multi-source NFI data, the results were more promising: prediction accuracy increased to 81%. The strongest predictors in the model were mean canopy height, mean elevation, and stem volume of the main tree species (Norway spruce and Scots pine). Our results indicate that open-access LiDAR data can be used to model and map the probability of predisposition to wind disturbance, providing spatially detailed, valuable information for planning and mitigation purposes.

Sparse Density, Leaf-Off Airborne Laser Scanning Data in Aboveground Biomass Component Prediction

The demand for cost-efficient forest aboveground biomass (AGB) prediction methods is growing worldwide. The National Land Survey of Finland (NLS) began collecting airborne laser scanning (ALS) data throughout Finland in 2008 to provide a new high-detailed terrain elevation model. Similar data sets are being collected in an increasing number of countries worldwide. These data sets offer great potential in forest mapping related applications. The objectives of our study were (i) to evaluate the AGB component prediction accuracy at a resolution of 300 m2 using sparse density, leaf-off ALS data (collected by NLS) derived metrics as predictor variables; (ii) to compare prediction accuracies with existing large-scale forest mapping techniques (Multi-source National Forest Inventory, MS-NFI) based on Landsat TM satellite imagery; and (iii) to evaluate the accuracy and effect of canopy height model (CHM) derived metrics on AGB component prediction when ALS data were acquired with multiple sensors and varying scanning parameters. Results showed that ALS point metrics can be used to predict component AGBs with an accuracy of 29.7%–48.3%. AGB prediction accuracy was slightly improved using CHM-derived metrics but CHM metrics had a more clear effect on the estimated bias. Compared to the MS-NFI, the prediction accuracy was considerably higher, which was caused by differences in the remote sensing data utilized.