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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,

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

Nearly 30% of Finlandâs land area is covered by peatlands. In Northern parts of the country there is a significant amount of low-productive drained peatlands (LPDPs) where the average annual stem volume growth is less than 1 m ha. The re-use of LPDPs has been considered thoroughly since Finnish forest legislation was updated and the forest regeneration prerequisite was removed from LPDPs in January 2014. Currently, forestry is one of the re-use alternatives, thus detailed forest resource information is required for allocating activities. However, current forest inventory practices have not been evaluated for sparse growing stocks (e.g., LPDPs). The purpose of our study was to evaluate the suitability of airborne laser scanning (ALS) for mapping forest inventory attributes in LPDPs. We used ALS data with a density of 0.8 pulses per m, 558 field-measured reference plots (500 from productive forests and 58 from LPDPs) and nearest neighbour (-NN) estimation. Our main aim was to study the sensitivity of predictions to the number of LPDP reference plots used in the -NN estimation. When the reference data consisted of 500 plots from productive forest stands, the root mean square errors (RMSEs) for the prediction accuracy of Loreyâs height, basal area and stem volume were 1.4 m, 2.7 m ha and 13.7 m ha in LPDPs, respectively. When 30 additional reference plots were allocated to LPDPs, the respective RMSEs were 1.1 m, 1.7 m ha and 10.0Â m ha. Additional reference plot allocation did not affect the predictions in productive forest stands.3â12kkk2â13â12â13â1

Terrestrial laser scanning (TLS) has been adopted as a feasible technique to characterize tree stems while the characterization of trees' branching architecture has remained less explored. In general, branching architecture refers to the spatial arrangement of branches and their characteristics that are important when exploring the eco-physiological functioning of trees or assessing tree biomass and wood quality. Our aim was to develop a point cloud processing method for identifying and segmenting individual branches from TLS point clouds. We applied a Cartesian-to-cylinder coordinate transformation and a simple morphological filtering for stem surface reconstruction and stem-branch separation. Then branch origins were identified as their intersections with the stem surface, and individual branches were segmented based on their connectivity with the branch origins. The method, implemented in MATLAB and openly available, was validated on a 0.4-ha mature and managed southern boreal forest stand. The branch identification performance was assessed based on visual interpretation of 364 randomly sampled stem sections from 100 Scots pine (Pinus sylvestris (L.)) trees that were inspected for branch identification accuracy. The results showed that the branches could only be identified up to the height where the stem could be reconstructed. For 90% of the trees, this threshold ranged between 59.3% and 81.2% relative tree heights. Branches located below this threshold were identified with a recall of 75%, a precision of 92%, and an F1-score of 0.82. Based on our study, it appears that in a managed Scots pine stand, most of the branches can be identified with the developed method for the most valuable stem part eligible for logwood. The findings obtained in this study promote the feasibility of using TLS in applications requiring detailed characterization of trees. The developed method can be further used in quantifying the characteristics of individual branches, which could be useful for biomass and wood quality assessment, for example. [ABSTRACT FROM AUTHOR]

Tree species information is important for forest management, especially in seedling stands. To mitigate the spectral admixture of understory reflectance with small and lesser foliaged seedling canopies, we proposed an image pre-processing step based on the canopy threshold (Cth) applied on drone-based multispectral images prior to feeding classifiers. This study focused on (1) improving the classification of seedlings by applying the introduced technique; (2) comparing the classification accuracies of the convolutional neural network (CNN) and random forest (RF) methods; and (3) improving classification accuracy by fusing vegetation indices to multispectral data. A classification of 5417 field-located seedlings from 75 sample plots showed that applying the Cth technique improved the overall accuracy (OA) of species classification from 75.7% to 78.5% on the Cth-affected subset of the test dataset in CNN method (1). The OA was more accurate in CNN (79.9%) compared to RF (68.3%) (2). Moreover, fusing vegetation indices with multispectral data improved the OA from 75.1% to 79.3% in CNN (3). Further analysis revealed that shorter seedlings and tensors with a higher proportion of Cth-affected pixels have negative impacts on the OA in seedling forests. Based on the obtained results, the proposed method could be used to improve species classification of single-tree detected seedlings in operational forest inventory. [ABSTRACT FROM AUTHOR]