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In remote sensing-based forest inventories 3D point cloud data, such as acquired from airborne laser scanning, are well suited for estimating the volume of growing stock and stand height, but tree species recognition often requires additional optical imagery. A combination of 3D data and optical imagery can be acquired based on aerial imaging only, by using stereo photogrammetric 3D canopy modeling. The use of aerial imagery is well suited for large-area forest inventories, due to low costs, good area coverage and temporally rapid cycle of data acquisition. Stereo-photogrammetric canopy modeling can also be applied to previously acquired imagery, such as for aerial ortho-mosaic production, assuming that the imagery has sufficient stereo overlap. In this study we compared two stereo-photogrammetric canopy models combined with contemporary satellite imagery in forest inventory. One canopy model was based on standard archived imagery acquired primarily for ortho-mosaic production, and another was based on aerial imagery whose acquisition parameters were better oriented for stereo-photogrammetric canopy modeling, including higher imaging resolution and greater stereo-coverage. Aerial and satellite data were tested in the estimation of growing stock volume, volumes of main tree species, basal area and diameter and height. Despite the better quality of the latter canopy model, the difference of the accuracy of the forest estimates based on the two different data sets was relatively small for most variables (differences in RMSEs were 0â20%, depending on variable). However, the estimates based on stereo-photogrammetrically oriented aerial data retained better the original variation of the forest variables present in the study area.

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 30â076 images (1972â2017) 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,

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 km. 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.2

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 -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.k

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. -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.K

In Finland, there are currently two, parallel sample-plot-based forest inventory systems, which differ in their methodologies, sampling designs, and objectives. One is the National Forest Inventory (NFI), aimed at unbiased inventory results at national and regional level. The other is the Forest Centreâs management-oriented forest inventory based on interpretation of airborne laser scanning and aerial images, with the aim of locally accurate stand-level forest estimates. The National Forest Inventory utilises relascope sample plots with systematic cluster sampling. This inventory method is optimised for accuracy of regional volume estimates. In contrast, the management-oriented forest inventory utilises circular sample plots with an allocation system covering certain pre-defined forest classes in the inventory area. This method is optimised to produce reference data for interpretation of the remote-sensing materials in use. In this study, we tested the feasibility of the National Forest Inventory sample plots in provision of additional reference data for the management-oriented inventory. Various combinations of NFI plots and management inventory plots were tested in the interpretation of the laser and aerial-image data. Adding NFI plots in the reference data generally improved the accuracy of the volume estimates by tree species but not the estimates of total volume or stand mean height and diameter. The difference between the plot types in the NFI and management inventories causes difficulties in combination of the two datasets.

Information on forest biomass is required for several purposes, including estimation of forest bioenergy resources and forest carbon stocks. Airborne laser scanning is today considered the most accurate remote sensing method for forest inventory. The three-dimensional nature of laser scanning data enables estimation of the volumes of the tree canopies. The dimensions of the tree canopies show high correlation with the amount of forest biomass. Optical aerial photographs are often used to complement laser data, for improved distinction between tree species. The paper reports on a study testing the estimation of forest biomass variables in two study areas in Southern Finland. The biomass variables were derived on the basis of tree-level field measurements, with biomass models used for pine, spruce, and birch. The sample-plot-level biomass components were derived on the basis of tree-level data and used as reference data for airborne-laser- and aerialâphotograph-based estimation. Results were slightly better for total biomass (RMSE 22.5% and 23.6% for the two study areas) than total volume (RMSE: 23.4% and 26.1%). Species-specific estimation errors were large in general but varied between the study areas, because of differences in their forest structures.

Map form information on forest biomass is required for estimating bioenergy potentials and monitoring carbon stocks. In Finland, the growing stock of forests is monitored using multi-source forest inventory, where variables are estimated in the form of thematic maps and area statistics by combining information of field measurements, satellite images and other digital map data. In this study, we used the multi-source forest inventory methodology for estimating forest biomass characteristics. The biomass variables were estimated for national forest inventory field plots on the basis of measured tree variables. The plot-level biomass estimates were used as reference data for satellite image interpretation. The estimates produced by satellite image interpretation were tested by cross-validation. The results indicate that the method for producing biomass maps on the basis of biomass models and satellite image interpretation is operationally feasible. Furthermore, the accuracy of the estimates of biomass variables is similar or even higher than that of traditional growing stock volume estimates. The technique presented here can be applied, for example, in estimating biomass resources or in the inventory of greenhouse gases.

Satellite sensor data have traditionally been used in multi-source forest inventory for estimating forest characteristics. Their advantages generally are large geographic coverage and large spectral range. Another remote sensing data source for forest inventories offering a large geographic coverage is high altitude aerial photography. In high altitude aerial photographs the spectral range is very narrow but the spatial resolution is high. This allows the extraction of texture features for forest inventory purposes. In this study we utilized a Landsat 7 ETM satellite image, a photo mosaic composed of high altitude panchromatic aerial photographs, and a combination of the aforementioned in estimating forest attributes for an area covering approximately 281 000 ha in Forestry Centre Häme-Uusimaa in Southern Finland. Sample plots of 9th National Forest Inventory (NFI9) were used as field data. In the estimation, 6 Landsat 7 ETM image channels were used. For aerial photographs, 4 image channels were composed from the spectral averages and texture features. In combining both data sources, 6 Landsat channels and 3 aerial image texture channels were selected for the analysis. The accuracy of forest estimates based on the Landsat image was better than that of estimates based on high altitude aerial photographs. On the other hand, using the combination of Landsat ETM spectral features and textural features on high altitude aerial photographs improved the estimation accuracy of most forest attributes.

A two-phase sampling design has been applied to forest inventory. First, a large number of first phase sample plots were defined with a square grid in a geographic coordinate system for two study areas of about 1800 and 4500 ha. The first phase sample plots were supplied by auxiliary data of Landsat TM and IRS-1C with principal component transformation for stratification and drawing the second phase sample (field sample). Proportional allocation was used to draw the second phase sample. The number of field sample plots in the two study areas was 300 and 380. The local estimates of five continuous forest stand variables, mean diameter, mean height, age, basal area, and stem volume, were calculated for each of the first phase sample plots. This was done separately by using one auxiliary data source at a time together with the field sample information. However, if the first phase sample plot for which the stand variables were to be estimated was also a field sample plot, the information of that field sample plot was eliminated according to the cross validation principle. This was because it was then possible to calculate mean square errors of estimates related to a specific auxiliary data source. The procedure produced as many estimates for each first phase sample plot and forest stand variable as was the number of auxiliary data sources, i.e. seven estimates: These were based on Landsat TM, IRS-1C, digitized aerial photos, ocular stereoscopic interpretation from aerial photographs, data from old forest inventory made by compartments, Landsat TM95âTM89 difference image and IRS96âTM95 difference image. The final estimates were calculated as weighted averages where the weights were inversely proportional to mean square errors. The alternative estimates were calculated by applying simple rules based on knowledge and the outliers were defined. The study shows that this kind of system for finding outliers for elimination and a weighting procedure improves the accuracy of stand variable estimation.

Five auxiliary data sources (Landsat TM, IRS-IC, digitized aerial photographs, visual photo-interpretation and old forest compartment information) applying three study areas and three estimators, two-phase sampling with stratification, the k nearest neighbors and regression estimator, were examined. Auxiliary data were given for a high number of sample plots, which are here called first phase sample plots. The plots were distributed using a systematic grid over the study areas. Some of the plots were then measured in the field for the necessary ground truth. Each auxiliary data source in combination with field sample information was applied to produce a specific estimator for five forest stand characteristics: mean diameter, mean height, age, basal area, and volume of the growing stock. When five auxiliary data sources were used, each stand characteristic and each first phase sample plot were supplied with five alternative estimates with three alternative estimators. Mean square errors were then calculated for each alternative estimator using the cross validation method. The final estimates were produced by weighting alternative estimates inversely according to the mean square errors related to the corresponding estimator. The result was better than the final estimate of any of the single estimators. The improvement over the best single estimate, as measured in mean square error, was 16.9% on average for all five forest stand characteristics. The improvement was fairly equal for all five forest stand characteristics. Only minor differences among the accuracies of the three alternative estimators were recorded.

There is considerable interest in identifying and locating natural forests as accurately as possible, because they are deemed essential in preventing biodiversity loss. In the boreal region, natural forests contain a substantial amount of dead wood and exhibit considerable variation in tree age, size, and species composition. However, it is difficult to define natural forests in a quantitative manner. This is an issue, for example, in the Finnish national forest inventory. If naturalness could be related to the metrics derived from tree measurements, it would be easier to locate natural forests based on the inventory data. In this study, we investigated the value of metrics computed from tree locations and tree sizes for the characterization of a key aspect of naturalness, namely, structural naturalness as defined in the Finnish national forest inventory. We used L-moments, Gini coefficient, Lorenz asymmetry, and interquartile range to quantify the variations in tree size at the plot level. We summarized the spatial pattern of trees with a spatial aggregation index. We compared the structural metrics, species proportions, and stand age using the classes of structural naturalness described in the Finnish national forest inventory, which have been determined in the field without strict numerical rules. These categories are 'natural', 'near-natural', and 'non-natural'. We found that the forests evaluated as structurally natural had larger variations in tree size and species composition and showed a more clustered spatial pattern of trees on average, although the variation in the structural metrics was considerable in all three classes. In addition, we used the structural metrics to predict naturalness by employing a random forest algorithm. Based on the structural metrics, it was possible to obtain high precision in the classification only if we simultaneously accepted low recall, and vice versa; the link between the inspected metrics and naturalness evaluated in the field was weak. The stand age separated the three classes more clearly and it also improved the classification. [ABSTRACT FROM AUTHOR]

In forest management planning, errors in predicted stand attributes might lead to suboptimal decisions that result in decreased net present value (NPV). Forest inventory data will have higher value if the amount of suboptimal decisions can be decreased. Therefore, the value of information can be measured through the decrease in inoptimality losses, which are the NPV differences between the optimal and suboptimal decisions. In this study, four alternative sample plot selection strategies with different numbers of sample plots were compared in terms of expected mean inoptimality losses. Stand-level mean inoptimality losses varied between 41.1 x [ha.sup.-1] [euro] and 80.7 x [ha.sup.-1] [euro], depending on the sample plot selection strategy and the number of sample plots used as training data in the k-nearest neighbors imputation method. Mean inoptimality losses decreased substantially when the number of sample plots increased from 25 to 100, and the decreasing trend continued until 500 sample plots. Total inoptimality losses can decrease by approximately 1 million [euro] in an inventory area of 100 000 ha when the number of sample plots is increased from 100 to 500. The measurement of more sample plots can be justified as long as the field measurement costs do not exceed the decrease in inoptimality losses. Key words: forest inventory, forest management planning, inoptimality loss, value of information, cost-plus-loss analysis. Lors de la planification de l'amenagement forestier, des erreurs dans les predictions des attributs de peuplement pourraient conduire a des decisions sous-optimales, entrainant une diminution de la valeur actuelle nette (VAN). Les donnees d'inventaire forestier auront une plus grande valeur si elles servent a diminuer le nombre de decisions sous-optimales. Par consequent, la valeur de l'information peut etre mesuree par la diminution des pertes dues a la sous-optimalite, qui correspondent a la difference de VAN entre les decisions optimales et sous-optimales. Dans cette etude, quatre strategies differentes de selection des placettes d'echantillonnage, avec differents nombres de placettes d'echantillonnage, ont ete comparees en termes de moyennes attendues des pertes dues a la sous-optimalite. Les pertes moyennes dues a la sous-optimalite a l'echelle du peuplement ont varie entre 41,1 [euro] et 80,7 [euro] par hectare, en fonction de la strategie de selection des placettes d'echantillonnage et du nombre de celles-ci utilisees comme donnees d'apprentissage dans la methode d'imputation des k-plus proches voisins. Les pertes moyennes dues a la sous-optimalite ont sensiblement diminue lorsque le nombre de placettes d'echantillonnage est passe de 25 a 100, et la tendance a la baisse s'est poursuivie jusqu'a 500 placettes d'echantillonnage. Les pertes totales dues a la sous-optimalite peuvent diminuer d'environ 1 million [euro] dans une zone d'inventaire de 100 000 ha lorsque le nombre de placettes d'echantillonnage augmente de 100 a 500. La mesure d'un plus grand nombre de placettes d'echantillonnage peut etre justifiee a condition que les couts de mesure sur le terrain ne depassent pas la diminution des pertes par sous-optimalite. [Traduit par la Redaction] Mots-cles: inventaire forestier, planification de l'amenagement forestier, perte due a la sous-optimalite, valeur de l'information, analyse combinee de cout plus perte., 1. Introduction Forest inventories based on airborne laser scanning (ALS) usually consist of data acquisition, model-building, and accuracy assessment with training data, the prediction of stand attributes in a wall-to-wall [...]

In this study, we investigate the use of model-based inference in forest surveys in which auxiliary data are available as a probability sample. We evaluate the effects of model form and sample size on estimators of growing stock volume, based on different types of remotely sensed auxiliary data. The study was performed through Monte Carlo sampling simulation using a two-phase sampling design within a simulated study area resembling the conditions in mid-western Finland. We show that the choice of model has a minor to moderate effect on the precision of model-based estimators. Similarly, the choice of estimator of the variance-covariance matrix of model parameter estimates, which is at the core of uncertainty assessment in model- based inference, was also found to have a minor to moderate effect on the precision of model-based estimators. Regarding sample sizes, the model error contribution to the total variance remains the same regardless of the sample size of the first phase (i.e., the size of the sample of auxiliary data); to reduce the model-error contribution, there is a need to increase the sample size of the second phase (i.e., the size of the sample of field plots for developing regression models). As a baseline for comparisons, model-assisted estimators were applied and found to be about equally precise as the model-based estimators, in accordance with the theory for the case when models are estimated from the sample data. Key words: airborne LiDAR, heteroscedasticity, Landsat, model-based estimator, sampling simulation. Dans cette etude, nous examinons l'utilisation de l'inference fondee sur un modele dans le cas des inventaires forestiers pour lesquels des donnees auxiliaires sont disponibles comme echantillon aleatoire. Nous evaluons les effets de la forme du modele et de la taille de l'echantillon sur les estimateurs du volume sur pied a partir de differents types de donnees auxiliaires de teledetection. L'etude a ete realisee a l'aide d'une simulation d'echantillonnage de Monte Carlo en utilisant un plan d'echantillonnage a deux phases dans une aire d'etude simulee avec des conditions semblables a celles du centre-ouest de la Finlande. Nous montrons que le choix du modele a un effet variant de mineur a modere sur la precision des estimateurs provenant des modeles. De meme, le choix de l'estimateur de la matrice des variances-covariances des estimations des parametres des modeles, qui est au centre de l'estimation de l'incertitude de l'inference fondee sur un modele, a aussi eu un effet variant de mineur a modere sur la precision. En ce qui concerne la taille de l'echantillon, la contribution de l'erreur du modele a la variance totale est demeuree la meme peu importe la taille de l'echantillon de la premiere phase (c.-a-d. la taille de l'echantillon des donnees auxiliaires). Pour reduire la contribution de l'erreur du modele, il faut augmenter la taille de l'echantillon de la deuxieme phase c.-a-d. la taille de l'echantillon des placettes de terrain visant a mettre au point des modeles de regression). A titre de base de reference pour des comparaisons, des estimateurs assistes par des modeles ont ete appliques et ont produit une precision a peu pres egale a celle des estimateurs fondes sur les modeles, ce qui est conforme a la theorie dans le cas des modeles estimes a partir de donnees echantillonnees. [Traduit par la Redaction] Mots-cles: lidar aeroporte, heterocedasticite, Landsat, estimateur fonde sur un modele, simulation d'echantillonnage., 1. Introduction The global interest in forests is increasing due to the multiple services provided by this ecosystem (e.g., Mery et al. 2005). As a consequence, several international agreements such [...]

In large-area forest inventories, a trade-off between the amount of data to be sampled and the corresponding collection costs is necessary. It is not always possible to have a very large data sample when dealing with sampling-based inventories. It is therefore important to optimize the sampling design with the limited resources. Whereas this sort of inventories are subject to these constraints, the availability of remote sensing (RS) data correlated with the forest inventory variables is usually much higher. For this reason, the RS and sampled field measurement data are often used in combination for improving the forest inventory estimation. In this study, we propose a model-based data sampling method founded on Bayesian optimization and machine learning algorithms which utilizes RS data to guide forest inventory sample selection. We evaluate our method in empirical experiments using real-world volume of growing stock data from the Aland region in Finland. The proposed method is compared against two baseline methods: simple random sampling and the local pivotal method. When a suitable model link is selected, the empirical experiments show on best case on average up to 22% and 79% improvement in population mean and variance estimation respectively over baselines. However, the results also illustrate the importance of model selection which has a clear effect on the results. The novelty of the study is in the application of Bayesian optimization in national forest inventory survey sampling. [ABSTRACT FROM AUTHOR]

Forest harvesting operations with heavy machinery can lead to significant soil rutting. Risks of rutting depend on the soil bearing capacity which has considerable spatial and temporal variability. Trafficability prediction is required in the selection of suitable operation sites for a given time window and conditions, and for on-site route optimization during the operation. Integrative tools are necessary to plan and carry out forest operations with minimal negative ecological and economic impacts. This study demonstrates a trafficability prediction framework that utilizes a spatial hydrological model and a wide range of spatial data. Trafficability was approached by producing a rut depth prediction map at a 16 × 16 m grid resolution, based on the outputs of a general linear mixed model developed using field data from Southern Finland, modelled daily soil moisture, spatial forest inventory and topography data, along with field measured rolling resistance and information on the mass transported through the grid cells. Dynamic rut depth prediction maps were produced by accounting for changing weather conditions through hydrological modelling. We also demonstrated a generalization of the rolling resistance coefficient, measured with harvester CAN-bus channel data. Future steps towards a nationwide prediction framework based on continuous data flow, process-based modelling and machine learning are discussed. [ABSTRACT FROM AUTHOR]

Animals use and select habitat at multiple hierarchical levels and at different spatial scales within each level. Still, there is little knowledge on the scale effects at different spatial levels of species occupancy patterns. The objective of this study was to examine nonlinear effects and optimal-scale landscape characteristics that affect occupancy of the Siberian flying squirrel, Pteromys volans, in South- and Mid-Finland. We used presence-absence data ( n = 10,032 plots of 9 ha) and novel approach to separate the effects on site-, landscape-, and regional-level occupancy patterns. Our main results were: landscape variables predicted the placement of population patches at least twice as well as they predicted the occupancy of particular sites; the clear optimal value of preferred habitat cover for species landscape-level abundance is a surprisingly low value (10% within a 4 km buffer); landscape metrics exert different effects on species occupancy and abundance in high versus low population density regions of our study area. We conclude that knowledge of regional variation in landscape utilization will be essential for successful conservation of the species. The results also support the view that large-scale landscape variables have high predictive power in explaining species abundance. Our study demonstrates the complex response of species occurrence at different levels of population configuration on landscape structure. The study also highlights the need for data in large spatial scale to increase the precision of biodiversity mapping and prediction of future trends. [ABSTRACT FROM AUTHOR]

Small unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees representing various tree species and developmental stages were collected in June 2014 using a UAV remote sensing system equipped with a frame format hyperspectral camera and an RGB camera in highly variable weather conditions. Dense point clouds were measured photogrammetrically by automatic image matching using high resolution RGB images with a 5 cm point interval. Spectral features were obtained from the hyperspectral image blocks, the large radiometric variation of which was compensated for by using a novel approach based on radiometric block adjustment with the support of in-flight irradiance observations. Spectral and 3D point cloud features were used in the classification experiment with various classifiers. The best results were obtained with Random Forest and Multilayer Perceptron (MLP) which both gave 95% overall accuracies and an F-score of 0.93. Accuracy of individual tree identification from the photogrammetric point clouds varied between 40% and 95%, depending on the characteristics of the area. Challenges in reference measurements might also have reduced these numbers. Results were promising, indicating that hyperspectral 3D remote sensing was operational from a UAV platform even in very difficult conditions. These novel methods are expected to provide a powerful tool for automating various environmental close-range remote sensing tasks in the very near future. [ABSTRACT FROM AUTHOR]

The boreal region is facing intensifying resource extraction pressure, but the lack of comprehensive biodiversity data makes operative forest conservation planning difficult. Many countries have implemented forest inventory schemes and are making extensive and up-to-date forest databases increasingly available. Some of the more detailed inventory databases, however, remain proprietary and unavailable for conservation planning. Here, we investigate how well different open and proprietary forest inventory data sets suit the purpose of conservation prioritization in Finland. We also explore how much priorities are affected by using the less accurate but open data. First, we construct a set of indices for forest conservation value based on quantitative information commonly found in forest inventories. These include the maturity of the trees, tree species composition, and site fertility. Secondly, using these data and accounting for connectivity between forest types, we investigate the patterns in conservation priority. For prioritization, we use Zonation, a method and software for spatial conservation prioritization. We then validate the prioritizations by comparing them to known areas of high conservation value. We show that the overall priority patterns are relatively consistent across different data sources and analysis options. However, the coarse data cannot be used to accurately identify the high-priority areas as it misses much of the fine-scale variation in forest structures. We conclude that, while inventory data collected for forestry purposes may be useful for forest conservation purposes, it needs to be detailed enough to be able to account for more fine-scaled features of high conservation value. These results underline the importance of making detailed inventory data publicly available. Finally, we discuss how the prioritization methodology we used could be integrated into operative forest management, especially in countries in the boreal zone. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a key variable for many ecological models, but it is typically not available from basic forest inventories. In this study, we (1) construct a high-resolution LAI map using k nearest-neighbor (k-NN) imputation based on National Forest Inventory data and Landsat 5 TM images (Landsat-NFI LAI), and (2) examine a moderate-resolution LAI map produced based on reduced simple ratio derived from MODIS refectances (MODIS-RSR LAI). The maps cover all the forested areas in Finland. Country-level averages of Land- sat-NFI and MODIS-RSR LAI were at same level, but several geographical and land-use related differences between them were detected. Difference was the largest in the lake district of Finland and in northern Finland, and it increased with decreasing share of forests and increasing share of deciduous trees. As MODIS-RSR LAI does not take into account the sub- pixel variation in land use, Landsat-NFI LAI was found to produce more reliable estimates. [ABSTRACT FROM AUTHOR]

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Forest management planning in Finland is currently adopting a new-generation forest inventory method, which is based on interpretation of airborne laser scanning data and digital aerial images. The inventory method is based on a systematic grid, where the grid elements serve as inventory units, for which the laser and aerial image data are extracted and the forest variables estimated. As an alternative or a complement to the grid elements, image segments can be used as inventory units. The image segments are particularly useful as the basis for generation of the silvicultural treatment and cutting units since their boundaries should follow the actual stand borders, whereas when using grid elements it is typical that some of them cover parts of several forest stands. The proportion of the so-called mixed cells depends on the size of the grid elements and the average size and shape of the stands. In this study, we carried out automatic segmentation of two study areas on the basis of laser and aerial image data with a view to delineating micro-stands that are homogeneous in relation to their forest attributes. Further, we extracted laser and aerial image features for both systematic grid elements and segments. For both units, the feature set used for estimating the forest attributes was selected by means of a genetic algorithm. Of the features selected, the majority (61-79%) were based on the airborne laser scanning data. Despite the theoretical advantages of the image segments, the laser and aerial features extracted from grid elements seem to work better than features extracted from image segments in estimation of forest attributes. We conclude that estimation should be carried out at grid level with an area-specific combination of features and estimates for image segments to be derived on the basis of the grid-level estimates. [ABSTRACT FROM AUTHOR]

Abstract: The growing stock more than doubled from 1.6 to 3.4millionm3 between 1912 and 2005 in forests on an area of 387km2 in southern Finland. The stock expansion continued for 93 years noting interim results, which were available for 1959, 1982, 1994 and 1999. Forested area in the region hardly changed. Carbon sequestration was mainly a result of a long-term recovery from forest degradation, a legacy of land use in the 18th and 19th centuries. Tree demography responded to management change especially of mature stands: Average tree size and stocking density of stands increased. On average the expanding biomass stock sequestered 18tons C annually per km2 (18g C per m2). In comparison, the emissions of fossil carbon in the region were estimated at 12tons C per km2 (12g C per m2) on average. However, fossil CO2 emissions exceeded biomass sequestration in recent decades. The powerful and persistent expansion of the carbon stock was an unintended co-benefit of forestry, which was motivated by the intention to improve timber yield. On the more negative side the change in management introduced clear-cuts, and a loss of diverse elements of the pre-industrial biota. [Copyright &y& Elsevier]

Both satellite images and aerial photographs are now used operationally in Finland's forestry for different tasks; satellite images are used for national forest inventory purposes and aerial images for forest management planning. Due to the double coverage, it could be advantageous to utilize the strengths of both image types. The aim of this study was to evaluate the potential of the combination of Landsat ETM+ spectral and aerial photograph spectral and textural features for forest variable estimation. The studied stand variables were mean height, basal area per hectare, and the volume of the growing stock. Several approaches were tested when combining the image data sources: feature selection, feature weighting, satellite image-based stratification, and combination of individual estimates by weighting. The highest accuracies were obtained when both data sources were used. There were several good ways to combine the data sources. Feature selection with genetic algorithm and subsequent feature weighting gave the lowest mean volume RMSE (63.7 m³/ha, 65.3 percent of the mean). [ABSTRACT FROM AUTHOR]

Abstract: Most multi-source forest inventory (MSFI) applications have thus far been based on the use of medium resolution satellite imagery, such as Landsat TM. The high plot and stand level estimation errors of these applications have, however, restricted their use in forest management planning. One reason suggested for the high estimation errors has been the coarse spatial resolution of the imagery employed. Therefore, very high spatial resolution (VHR) imagery sources provide interesting data for stand-level inventory applications. However, digital interpretation of VHR imagery, such as aerial photographs, is more complicated than the use of traditional satellite imagery. Pixel-by-pixel analysis is not applicable to VHR imagery because a single pixel is small in relation to the object of interest, i.e. a forest stand, and therefore it does not adequately represent the spectral properties of a stand. Additionally in aerial photographs, the spectral properties of the objects are dependent on their location in the image. Therefore, MSFI applications based on aerial imagery must employ features that are less sensitive to their location in the image and that have been derived using the spatial neighborhood of each pixel, e.g. a square-shaped window of pixels. In this experiment several spectral and textural features were extracted from color-infrared aerial photographs and employed in estimation of forest attributes. The features were extracted from original, normalized difference vegetation index and channel ratio images. The correlations between the extracted image features and forest attributes measured from sample plots were examined. Additionally, the spectral and textural features were used for estimating the forest attributes of sample plots, applying the k nearest neighbor estimation method. The results show that several spectral and textural image features that are moderately or well correlated with the forest attributes. Furthermore, the accuracy of forest attribute estimation can be significantly improved by a careful selection of image features. [Copyright &y& Elsevier]

The aim of this study was to develop a method for segment-based forest inventory and determine whether segment-level inventories can be used in forest management planning. The study area covered 76 ha located in two different aerial photographs in eastern Finland. The study area was segmented into 220 segments with the aid of aerial photographs and the segment-level forest characteristics were assessed in the field using relascope sample plots and a field computer which displayed the aerial photographs, segment borders and surveyor's location on the screen. The segment estimates were calculated as weighted averages of k nearest neighbours (kNN) for the segments and the sample plots. The estimates were tested with a cross-validation technique. The averages and the standard deviations of the spectral values of aerial images extracted for the segments and the sample plots were used in the kNN estimation. The relative root mean square error of the mean volume was 58. 1 t%, (bias 6.4%) at the segment level and 57.9% (bias 0,9%) at the sample plot level. The segment- based approach studied here needs further research and improvement before it can he applied to forest management planning. [ABSTRACT FROM AUTHOR]

Digitized colour infrared images are a potential information source for multi-source forest inventory applications and particularly for estimating the forest characteristics of relatively small areas. The main problem in computer-aided image analysis of aerial photographs is the presence of bidirectional reflectance, which causes the spectral values of the image pixels to depend on their location in the image. Because of this, and the lack of operational methods for radiometric correction, the full potential of aerial photographs has not been utilised. This paper presents a local radiometric correction method that can be used for reducing the effect of bidirectional reflectance. The method employs satellite images that are less affected by the bidirectional reflectance for the local adjustment of the registered pixel values of aerial photographs. Because of the different spatial resolution of the aerial and satellite imagery, the local correction coefficients are computed for units that are larger than a single aerial photo pixel. In this way, the general level of brightness of the correction units can be determined on the basis of the satellite imagery while retaining the finer spatial resolution of the original aerial photo. The main advantage of the suggested method is that it does not require complex mathematical models for simulating the effect of bidirectional reflectance, neither does it require a priori knowledge of the actual forest attributes in the inventory area, but relies only on the image data. [Copyright &y& Elsevier]

Miniaturized hyperspectral imaging techniques have developed rapidly in recent years and have become widely available for different applications. Combining calibrated hyperspectral imagery with inverse physically based reflectance models is an interesting approach for estimating chlorophyll concentrations that are good indicators of vegetation health. The objective of this study was to develop a novel approach for retrieving chlorophyll a and b values from remotely sensed data by inverting the stochastic model of leaf optical properties using a one-dimensional convolutional neural network. The inversion results and retrieved values are validated in two ways: A classical machine learning validation dataset and calculating chlorophyll maps from empirical remotely sensed hyperspectral data and comparing them to TCARI OSAVI , an index that has strong negative correlation with chlorophyll concentration. With the validation dataset, coefficients of determination ( R 2 ) of 0.97 were obtained for chlorophyll a and 0.95 for chlorophyll b. The chlorophyll maps correlate with the TCARI OSAVI map. The correlation coefficient (R) is −0.87 for chlorophyll a and −0.68 for chlorophyll b in selected plots. These results indicate that the approach is highly promising approach for estimating vegetation chlorophyll content. [ABSTRACT FROM AUTHOR]

• We created a forest wind sensitivity map by using statistical models and GIS data. • We compared three modelling methods and tested them with independent data. • More complex methods did not improve results compared to a generalized linear model. • The created maps were able to identify vulnerable forests in independent test data. • The map can be used for better consideration of damage risks in forest management. Windstorms cause major disturbances in European forests. Forest management can play a key role in making forests more persistent to disturbances. However, better information is needed to support decision making that effectively accounts for wind disturbances. Here we show how empirical probability models of wind damage, combined with existing spatial data sets, can be used to provide fine-scale spatial information about disturbance probability over large areas. First, we created stand-level damage probability models using wind damage observations within 5-year time window in national forest inventory data (NFI). Model predictors described forest characteristics, forest management history, 10-year return-rate of maximum wind speed, and soil, site and climate conditions. We tested three different methods for creating the damage probability models – generalized linear models (GLM), generalized additive models (GAM) and boosted regression trees (BRT). Then, damage probability maps were calculated by combining the models with GIS data sets representing the model predictors. Finally, we demonstrated the predictive performance of the damage probability maps with a large, independent test data of over 33,000 NFI plots, which shows that the maps are able to identify vulnerable forests also in new wind damage events, with area under curve value (AUC) > 0.7. Use of the more complex methods (GAM and BRT) was not found to improve the performance of the map compared to GLM, and therefore we prefer using the simpler GLM method that can be more easily interpreted. The map allows identification of vulnerable forest areas in high spatial resolution (16 m × 16 m), making it useful in assessing the vulnerability of individual forest stands when making management decisions. The map is also a powerful tool for communicating disturbance risks to forest owners and managers and it has the potential to steer forest management practices to a more disturbance-aware direction. Our study showed that in spite of the inherent stochasticity of the wind and damage phenomena at all spatial scales, it can be modelled with good accuracy across large spatial scales when existing ground and earth observation data sources are combined smartly. With improving data quality and availability, map-based risk assessments can be extended to other regions and other disturbance types. [ABSTRACT FROM AUTHOR]

Recognition of tree species and geospatial information on tree species composition is essential for forest management. In this study, tree species recognition was examined using hyperspectral imagery from visible to near-infrared (VNIR) and short-wave infrared (SWIR) camera sensors in combination with a 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum with a diverse selection of 26 tree species from 14 genera was used as a test area. Aerial hyperspectral imagery and high spatial resolution photogrammetric color imagery were acquired from the test area using unmanned aerial vehicle (UAV) borne sensors. Hyperspectral imagery was processed to calibrated reflectance mosaics and was tested along with the mosaics based on original image digital number values (DN). Two alternative classifiers, a k nearest neighbor method (k-nn), combined with a genetic algorithm and a random forest method, were tested for predicting the tree species and genus, as well as for selecting an optimal set of remote sensing features for this task. The combination of VNIR, SWIR, and 3D features performed better than any of the data sets individually. Furthermore, the calibrated reflectance values performed better compared to uncorrected DN values. These trends were similar with both tested classifiers. Of the classifiers, the k-nn combined with the genetic algorithm provided consistently better results than the random forest algorithm. The best result was thus achieved using calibrated reflectance features from VNIR and SWIR imagery together with 3D point cloud features; the proportion of correctly-classified trees was 0.823 for tree species and 0.869 for tree genus. [ABSTRACT FROM AUTHOR]

When planning conservation and reintroductions of native species and control of invasive species, it is important to understand the environmental factors affecting species distributions. Habitat suitability largely determines where species can occur, but in addition to habitat and food resources, it is important to also consider human disturbance and climate when mapping possible distributions. We studied the environmental factors affecting the present distribution of the native Eurasian beaver (Castor fiber) and the invasive North American beaver (C. canadensis) in Finland. We compared the role of food resources, aquatic habitat type, anthropogenic disturbance and climate for beaver occurrence with species distribution models. We created maps of suitable areas for the two species and compared how the results would change if these two ecologically similar species were modelled together. We found that aquatic habitat type and climate largely explain the present distributions of the species. In addition, there were differences between the species in their preference for food resources, aspen, birch and grey alder. However, we suspect that the species' reintroduction history, i.e. the differences in the present environment, much explain the differences found between the species. We conclude that when planning possible reintroductions for the Eurasian beaver, factors other than food resources should also be considered. We also suggest that studies on habitat suitability should consider the history of the species and include data from different environments. Thus, more comprehensive information for management planning would be achieved and a better ability to predict the location of optimal areas for the species. [ABSTRACT FROM AUTHOR]