In this study, a novel approach to map forest attributes has been investigated for boreal forests in Sweden. The methodology relies on machine learning, utilizing a combination of remote sensing data and field data for both training and evaluating the proposed models. To ensure the accuracy in estimating forest attributes at any given time, the approach incorporates a broad range of available remote sensing data including airborne laser scanning (ALS) data, weekly satellite data from Sentinel-1 and Sentinel-2, and global forest map data. However, in this study focus has been on utilizing ALS data. The field data utilized in the study are derived from the Swedish National Forest Inventory and encompass measurements of key forest variables such as above-ground biomass, stem volume, basal area-weighted mean tree height, basal area-weighted mean diameter at breast height, and basal area. The potential of exporting knowledge gained from mapping Sweden to other forested landscapes such as in Latvia, using model updating with limited reference data from the new targeted area will be the next step to investigate. Here, data from Sweden were used to take the first steps towards developing a mapping methodology. The results demonstrate a promising potential of the proposed approach that will showcase new possibilities to share knowledge of updated forest mapping using the increasing flow of high-precision remote sensing data.

This paper presents the first results of the ongoing development of new forest mapping methods for the Swedish national forest mapping case using Airborne Laser Scanning (ALS) data, utilizing the recent findings in machine learning (ML) and Artificial Intelligence (AI) techniques. We used Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) as ML models. In addition, Neural networks (NN) based approaches were utilized in this study. ALS derived features were used to estimate the stem volume (V), above-ground biomass (AGB), basal area (B), tree height (H), stem diameter (D), and forest stand age (A). XGBoost ML algorithm outperformed RF 1 % to 3 % in the R² metric. NN model performed similar to ML model, however it is superior in the estimation of V, AGB, and B parameters.

This paper presents the project ForestMap – a project aiming to develop and distribute new methods, which provide the benefits of accurate forest maps to a global audience. Using the recent developments in remote sensing, machine learning, and Artificial Intelligence (AI) the goal is to export the Scandinavian success stories to a wide range of stakeholders in the world.

Remote sensing (RS) techniques have been used for mapping forest variables, such as stem volume (important for forest management activities associated with timber production), over large areas which can be updated more frequently than with field inventory (FI) data. In this study, wall-to-wall TanDEM-X synthetic aperture radar images were used as auxiliary RS data for model-based prediction of stand-level volumes for two models, trained using volumes computed from FI (A) and airborne laser scanning estimations (B), respectively. The models were validated with harvester data available for independent stands. It was observed that the performance of model B was slightly better compared to model A based on adjusted R2and root mean squared error values. Therefore, it can be concluded that a completely RS based approach for prediction and mapping of stand volumes would be as promising as a method based on FI data along with being cost- and labour-efficient.

SLU Forest Map are maps of the Swedish forest state, produced by the Swedish University of Agricultural Sciences (SLU) from satellite images using the Swedish National Forest Inventory sample plots as reference data. Until now, four maps have been produced, in raster format (12.5 × 12.5 m2to 25 × 25 m2cell sizes), with estimates of basal area-weighted mean tree height, basal area-weighted mean stem diameter, stand age, total as well as species-specific stem volume, for the years 2000, 2005, 2010, and 2015. These maps provide publicly available data, free of charge, supporting a wide range of applications; scientific research as well as operational uses in forest management planning, biodiversity assessment, and monitoring. This paper presents SLU Forest Map, the data and methods utilised in the production, and a new consistent evaluation of the estimation accuracy for each variable and mapped year.

BACKGROUND: A range of spatially resolved transcriptomic methods has recently emerged as a way to spatially characterize the molecular and cellular diversity of a tissue. As a consequence, an increasing number of computational techniques are developed to facilitate data analysis. There is also a need for versatile user friendly tools that can be used for a de novo exploration of datasets. RESULTS: Here we present MATLAB-based Analysis toolbox for in situ sequencing (ISS) expression maps (Matisse). We demonstrate Matisse by characterizing the 2-dimensional spatial expression of 119 genes profiled in a mouse coronal section, exploring different levels of complexity. Additionally, in a comprehensive analysis, we further analyzed expression maps from a second technology, osmFISH, targeting a similar mouse brain region. CONCLUSION: Matisse proves to be a valuable tool for initial exploration of in situ sequencing datasets. The wide set of tools integrated allows for simple analysis, using the position of individual reads, up to more complex clustering and dimensional reduction approaches, taking cellular content into account. The toolbox can be used to analyze one or several samples at a time, even from different spatial technologies, and it includes different segmentation approaches that can be useful in the analysis of spatially resolved transcriptomic datasets.

In this study, stem volume measured by the Swedish National Forest Inventory were modelled using the k nearest neighbor (kNN) algorithm, with k=1, 3, or 5 neighbors. As independent variables, the combination of two satellite sensors were used: the active radar sensor TanDEM-X and the passive optical sensor Sentinel-2. The results indicate that stem volume per species can be predicted relatively accurately, mainly due to the inclusion of Sentinel-2 data, while the total stem volume is largely predicted well due to inclusion of the TanDEM-X phase height. The prediction of total stem volume was, however, not significantly improved with the additional spectral information from Sentinel-2 about the tree species. The kNN method is somewhat limited in the highest range of volumes, since no extrapolation is supported. Thus, it is important to have a reference dataset representing the entire range of the population for a successful application. The main advantage of combining the two data sources is the convenient procedure of obtaining both the tree species classification and volumes (divided per species) in a single method. It is concluded, that when sufficient reference data are available, the kNN approach with a combination of radar and optical data provides additional information about the stem volumes (in terms of tree species), but without improving the prediction of the total stem volume accuracy.

In this study, mapping of tree growth was performed using data from the two nation-wide acquisitions of airborne laser scanning in Sweden. Following the successful first national acquisition performed in 2009 - 2015, a new, repeated, scanning is now launched and ongoing. The first scanning provided new, accurate (in accuracy as well as in spatial resolution) data about the forest and quickly found wide-spread use in the forest industry. It outperformed previous methods and provided a new standard of data capture for forest management planning. The addition of a second scanning provide information also about changes, where forest tree growth is of high interest in the industry. This study presents the first results from large-scale assessment of growth for basal area-weighted mean tree height (H) and mean stem volume (V), using the bi-temporal scannings and sample-plot data from the National Forest Inventory. Growth was most accurately assessed by the direct change metrics of the scannings, although the accuracies were moderate. The accuracy of forecasts, i.e. only utilizing the predicted forest state at the first scanning, were similar for H but inferior for V, though.

The development of drone technology has been rapid in the last decade, providing highly competent and economical platforms for applied remote sensing. Mapping forest using drones is not an economical alternative for most operational applications in forestry and environmental monitoring, due to the limited area covered. However, the potential of drone-based remote sensing is expected to be very large in research applications. In this study, a standard, small, four-rotor drone is used as platform for a multispectral camera to accurately collect 3D-data about the forest canopy, data used to produce maps of forest variables. These maps proved to be very valuable input for development of new models for the exchange of green-house gases in boreal and hemi-boreal forests. The task was performed within the ICOS initiative at three sites in Sweden where each has a 150 m high tower monitoring green-house gas fluxes at many levels in the atmosphere.

A first evaluation of ALOS-2 PALSAR-2 data for forest stem volume estimation has been performed at a coniferous dominated test site in southern Sweden. Both the Fine Beam Dual (FBD) polarization and the Quad-polarimetric mode were investigated. Forest plots with stem volume reaching up to a maximum of about 620 m3ha−1(corresponding to 370 tons ha−1) were analyzed by relating backscatter intensity to field data using an exponential model derived from the Water Cloud Model. The estimation accuracy of stem volume at plot level (0.5 ha) was calculated in terms of Root Mean Square Error (RMSE). For the best case investigated an RMSE of 39.8% was obtained using one of the FBD HV-polarized images. The corresponding RMSE for the FBD HH-polarized images was 43.9%. In the Quad-polarimetric mode the lowest RMSE at HV- and HH-polarization was found to be 43.1% and 66.1%, respectively.

BACKGROUND: Procedures for genetic analyses based on oligonucleotide probes are powerful tools that can allow highly parallel investigations of genetic material. Such procedures require the design of large sets of probes using application-specific design constraints. RESULTS: ProbeMaker is a software framework for computer-assisted design and analysis of sets of oligonucleotide probe sequences. The tool assists in the design of probes for sets of target sequences, incorporating sequence motifs for purposes such as amplification, visualization, or identification. An extension system allows the framework to be equipped with application-specific components for evaluation of probe sequences, and provides the possibility to include support for importing sequence data from a variety of file formats. CONCLUSION: ProbeMaker is a suitable tool for many different oligonucleotide design and analysis tasks, including the design of probe sets for various types of parallel genetic analyses, experimental validation of design parameters, and in silico testing of probe sequence evaluation algorithms.