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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,

Tree species identification constitutes a bottleneck in remote sensing-based forest inventory. In passive images the differentiating features overlap and bidirectional reflectance hampers analysis. Airborne LiDAR provides radiometric and geometric information. We examined the single-trees-level response of two LiDAR sensors in over 13 000 forest trees in southern Finland. We focused on the commercially important species. Our aims were to 1) explore the relevant LiDAR features and study their dependencies on stand and tree variables, 2) examine two sensors and their fusion, 3) quantify the gain from intensity normalizations, 4) examine the importance of the size of the training set, and 5) determine the effects of stand age and site fertility. A set of 570 semiurban broad-leaved trees and exotic conifers was analyzed to 6) examine the LiDAR signal in the economically less important species. An accuracy of 88 90% was achieved in the classification of Scots pine, Norway spruce, and birch, using intensity variables. Spruce and birch showed the highest levels of confusion. Downsizing the training set from 30% to 2.5% of all trees had only a marginal effect on the performance of classifiers. The intensity features were dependent on the absolute and relative sizes of trees, especially for birch. The results suggest that leaf size, orientation, and foliage density affect the intensity, which is thus not affected by reflectance only. Some of the ecologically important species in Finland may be separable, since they gave rise to high intensity values. Comparison of the sensors implies that performance of the intensity data for species classification varies between sensors for reasons that remained uncertain. Both range and gain receiver normalization improved species classification. Weighting of the intensity values improved the fusion of two LiDAR datasets.

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]

Automating forest machines to optimize the forest value chain requires the ability to map the surroundings of the machine and to conduct accurate measurements of nearby trees. In the near-to-medium term, integrating a forest harvester with a mobile laser scanner system may have multiple applications, including real-time assistance of the harvester operator using laser-scanner-derived tree measurements and the collection of vast amounts of training data for large-scale airborne laser scanning-based surveys at the individual tree level. In this work, we present a comprehensive processing flow for a mobile laser scanning (MLS) system mounted on a forest harvester starting from the localization of the harvester under the forest canopy followed by accurate and automatic estimation of tree attributes, such as diameter at breast height (DBH) and stem curve. To evaluate our processing flow, we recorded and processed MLS data from a commercial thinning operation on three test strips with a total driven length ranging from 270 to 447 m in a managed Finnish spruce forest stand containing a total of 658 reference trees within a distance of 15 m from the harvester trajectory. Localization reference was obtained by a robotic total station, while reference tree attributes were derived using a high-quality handheld laser scanning system. As some applications of harvester-based MLS require real-time capabilities while others do not, we investigated the positioning accuracy both for real-time localization of the harvester and after the optimization of the full trajectory. In the real-time positioning mode, the absolute localization error was on average 2.44 m, while the corresponding error after the full optimization was 0.21 m. Applying our automatic stem diameter estimation algorithm for the constructed point clouds, we measured DBH and stem curve with a root-mean-square error (RMSE) of 3.2 cm and 3.6 cm, respectively, while detecting approximately 90% of the reference trees with DBH > 20 cm that were located within 15 m from the harvester trajectory. To achieve these results, we demonstrated a distance-adjusted bias correction method mitigating diameter estimation errors caused by the high beam divergence of the laser scanner used. [ABSTRACT FROM AUTHOR]

This study examines the precision of spatial data required for archaeological sites in Finland from a forest management perspective, identifies archaeological sites at risk due to forestry activities, and investigates potential collaboration between archaeology and forestry to enhance the preservation and understanding of these sites. Due to outdated methods and the lack of systematic field surveys, challenges in achieving accurate spatial data for archaeological sites are highlighted through a literature review, case studies, and fieldwork. Our findings indicate that a significant portion of sites lack boundary delineation, exposing them to the risk of destruction. Moreover, the study reveals limited collaboration between the archaeology and forestry sectors, despite mutual interests in site preservation. We advocate for improved spatial data accuracy using modern technological methods like precision GPS and enhanced cooperation between archaeologists and forestry professionals. This approach aims to protect Finland's archaeological heritage while promoting responsible forestry practices. Notably, fieldwork enabled the precise determination of the location of Finland's first separate plague cemetery, underscoring the value of direct field verification in enriching historical and archaeological understanding. [ABSTRACT FROM AUTHOR]

Hyperspectral LiDAR (HSL) has been utilized as an efficacious technique in object classification and recognition based on its unique capability to obtain ranges and spectra synchronously. Different kinds of HSL prototypes with varied structures have been promoted and measured its performance. However, almost all of these HSL prototypes employ complex and large spectroscopic devices, such as an Acousto-Optic Tunable Filter and Liquid-Crystal Tunable Filter, which makes this HSL system bulky and expensive, and then hinders its extensive application in many fields. In this paper, a smart and smaller spectroscopic component, an intergraded optical filter (IOF), is promoted to miniaturize these HSL systems. The system calibration, range precision, and spectral profile experiments were carried out to test the HSL prototype. Although the IOF employed here only covered a wavelength range of 699–758 nm with a six-channel passband and showed a transmittance of less than 50%, the HSL prototype showed excellent performance in ranging and spectral profile collecting. The spectral profiles collected are well in accordance with those acquired based on the AOTF. The spectral profiles of the fruits, vegetables, plants, and ore samples collected by the HSL based on an IOF can effectively reveal the status of the plants, the component materials, and ore species. Finally, we also showed the integrated design of the HSL based on a three-dimensional IOF and combined with a detector. The performance and designs of this HSL system based on an IOF show great potential for miniaturizing in some specific applications. [ABSTRACT FROM AUTHOR]