Article
Forestry
Alex Appiah Mensah, Jonas Jonen, Kenneth Nystrom, Jorgen Wallerman, Mats Nilsson
Summary: Recent advancements in remote sensing of forests have shown that bi-temporal ALS data and auxiliary information can accurately predict and map site productivity of Norway spruce and Scots pine populations. Multiple linear regression and non-parametric random forests models were used for prediction, and the predictors included height metrics, altitude, distance to coast, and soil moisture. These models and maps are valuable for operational forest management planning in Sweden.
FOREST ECOLOGY AND MANAGEMENT
(2023)
Article
Forestry
Xavier Gallagher-Duval, Olivier R. van Lier, Richard A. Fournier
Summary: This study aimed to determine the optimal approach for estimating stem diameter distributions (SDD) from airborne laser scanning (ALS) data using different metrics. The results showed that the performance of SDD modality classification models was consistent and the best SDD function parameter models were generally fitted with a combination of metrics. It was found that CHM texture metrics can improve the estimate of SDD parameters and differentiating for modality prior to estimating SSD is especially beneficial in stands with bimodal SDD.
Article
Forestry
Lennart Noordermeer, Erik Naesset, Terje Gobakken
Summary: Newly developed positioning systems in cut-to-length harvesters enable georeferencing of individual trees with submeter accuracy, which has emerged as a valuable tool for forest inventory. The study found that larger grid cells result in more accurate timber volume predictions and are less affected by positioning errors.
Article
Remote Sensing
Mikko Kukkonen, Matti Maltamo, Lauri Korhonen, Petteri Packalen
Summary: The study introduces a method called hybrid tree detection (HTD) utilizing unmanned airborne laser scanning (UALS) point clouds for forest inventories. Through testing at various validation plots, it is concluded that the proposed HTD approach improves the accuracy of traditional individual tree crown delineation (ITC) methods.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Forestry
Janne Raty, Marius Hauglin, Rasmus Astrup, Johannes Breidenbach
Summary: Cut-to-length harvesters collect useful information for modeling relationships between forest attributes and airborne laser scanning (ALS) data. However, the operation of harvesters in mature forests may introduce selection biases and result in systematic errors in forest attribute maps. Regression models were fitted using harvester and ALS data to estimate volume, height, stem frequency, above-ground biomass, basal area, and quadratic mean diameter. The performance of the harvester models was evaluated using national forest inventory plots. The use of model-assisted estimators improved efficiency, but systematic errors were observed in both productive and unproductive forests.
CANADIAN JOURNAL OF FOREST RESEARCH
(2023)
Article
Environmental Sciences
Aaron M. Sparks, Mark Corrao, Alistair M. S. Smith
Summary: This study evaluated the accuracy of seven individual tree detection methods in coniferous forest stands, showing that higher ALS pulse density data resulted in higher ITD accuracy. Omission errors were mainly related to stand density, and the use of simple canopy height model methods could reduce omission errors.
Article
Forestry
Jose Riofrio, Joanne C. White, Piotr Tompalski, Nicholas C. Coops, Michael A. Wulder
Summary: By developing age-independent height growth models, using multi-temporal airborne laser scanning (ALS) data, a comprehensive indicator of site quality for complex and irregular stand structures is provided. This approach leverages the accurate, spatially detailed characterization of canopy heights afforded by ALS data and is independent of stand age, increasing the possible geographic extent of height growth estimates.
FOREST ECOLOGY AND MANAGEMENT
(2023)
Article
Engineering, Aerospace
Ulas Yunus Ozkan, Tufan Demirel, Ibrahim Ozdemir, Serhun Saglam, Ahmet Mert
Summary: This study examined the capability of combined LiDAR/WorldView-3 data in estimating plot-level stand attributes in a complex forest in northwest Turkey. Prediction models were developed at different levels, with multiple linear regression (MLR) and random forest (RF) modeling approaches tested. The results showed higher prediction accuracy for height at tree species level and forest types level, with homogeneous coniferous stands providing higher estimation accuracy. The combination of aerial laser scanning and high resolution satellite data has potential for predicting stand attributes in complex forest ecosystems.
ADVANCES IN SPACE RESEARCH
(2022)
Review
Forestry
Janne Toivonen, Annika Kangas, Matti Maltamo, Mikko Kukkonen, Petteri Packalen
Summary: The role of forests in biodiversity assessment and planning is substantial, as they support approximately 80% of terrestrial biodiversity. However, there is a lack of research in geographical areas and forest types other than temperate and boreal forests.
FOREST ECOLOGY AND MANAGEMENT
(2023)
Article
Ecology
Marc Fuhr, Etienne Lalechere, Jean-Matthieu Monnet, Laurent Berges
Summary: Building a network of interconnected overmature forests is crucial for biodiversity conservation. LiDAR technology can accurately assess forest structural parameters and identify overmature forest patches over large areas. In this study, an index combining forest structural maturity attributes was developed to characterize the maturity of field plots. LiDAR metrics, along with elevation, slope, and echo intensity distribution, were important for predicting forest maturity. The model showed a high correlation between observed and predicted maturity values, indicating accurate ranking of field plots.
REMOTE SENSING IN ECOLOGY AND CONSERVATION
(2022)
Article
Forestry
Nils Lindgren, Andre Wastlund, Inka Bohlin, Kenneth Nystrom, Mats Nilsson, Hakan Olsson
Summary: This study compared the potential of using contemporary Digital Photogrammetry (DP) data and older Airborne Laser Scanning (ALS) data for predicting forest growing stock volume, showing that combining both types of data improved predictions compared to using only one type of data.
SCANDINAVIAN JOURNAL OF FOREST RESEARCH
(2021)
Article
Environmental Sciences
Henrik J. Persson, Kenneth Olofsson, Johan Holmgren
Summary: This study compared a two-phase laser-scanning method with traditional field inventory for forest stands in Sweden, showing that laser-scanning provided significantly higher accuracy and efficiency in measuring tree height, classifying tree species, and estimating forest variables. The results demonstrated the potential for laser-scanning to replace manual field inventories in the future.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Ecology
Adrian Pascual, Juan Guerra-Hernandez
Summary: Comparing ALS time-series is crucial for landscape conservation planning, especially in monitoring forest ecosystems. Modellers need to consider phenology when comparing ALS-based maps of ground elevation or canopy height between different years. This study demonstrates the comparison of two ALS surveys conducted in a National Park in Northwest Spain, using the same algorithms for data interpretation and generating digital terrain models and canopy height models. A hybrid approach was implemented to correct discrepancies caused by differences in phenology or scaling, reducing uncertainty and providing a solid evaluation of emerging problems in multi-temporal ALS surveys.
ECOLOGICAL INFORMATICS
(2023)
Article
Forestry
Johannes Breidenbach, Janis Ivanovs, Annika Kangas, Thomas Nord-Larsen, Mats Nilsson, Rasmus Astrup
Summary: This study assembled design-based estimators using National Forest Inventory data to provide estimates relevant for greenhouse gas inventories. By leveraging remote sensing auxiliary data and field data, they improved estimators for living-biomass carbon stock loss. The combination of these data sources resulted in considerable efficiency gains for national and subnational estimates.
CANADIAN JOURNAL OF FOREST RESEARCH
(2021)
Article
Environmental Sciences
Frederico Tupinamba-Simoes, Adrian Pascual, Juan Guerra-Hernandez, Cristobal Ordonez, Tiago de Conto, Felipe Bravo
Summary: The use of mobile laser scanning allows for high-resolution, 3D description of forest ecosystems. This study tested a mobile Handheld Laser Scanning (HLS) system in a structurally complex Mediterranean forest in Spain to estimate tree attributes. The HLS approach achieved a high tree detection rate and accurate estimation of diameter at breast height (DBH) and tree height. The study demonstrates the feasibility and efficiency of HLS in mapping trees in mixed forests, with potential application in forest monitoring programs.
Article
Multidisciplinary Sciences
Claire Celine Devos, Mikael Ohlson, Erik Naesset, Ole Martin Bollandsas
Summary: This study sampled soil organic carbon (SOC) stocks in forest-tundra ecotones in northern Norway to examine the differences between forest and tundra soils and the factors influencing SOC stock sizes. The results showed that forest soils had higher SOC stocks than tundra soils, but SOC storage varied greatly within and between study sites. The study also found that an upward forest expansion did not necessarily lead to changes in SOC storage at all sites.
SCIENTIFIC REPORTS
(2022)
Article
Environmental Sciences
Benjamin Allen, Michele Dalponte, Hans Ole Orka, Erik Naesset, Stefano Puliti, Rasmus Astrup, Terje Gobakken
Summary: This study used hyperspectral imagery collected from unmanned aerial vehicles to detect wood decay in Norway spruce. The results showed that UAV-based sensors offer flexibility and potential cost advantages. The classification accuracy was higher when using the 490-band hyperspectral imagery compared to the 29-band imagery.
Article
Environmental Sciences
Ronald E. McRoberts, Erik Naesset, Juha Heikkinen, Qi Chen, Victor Strimbu, Jessica Esteban, Zhengyang Hou, Francesca Giannetti, Jahangir Mohammadi, Gherardo Chirici
Summary: The model-assisted difference and regression estimators are widely used in forest inventory and remotely sensed data to improve the accuracy of inventory parameter estimates. However, there is inconsistency in the definition of associated terminology and the use of notation. This study aims to establish consistent and operationally useful definitions, bridge the notation gap, and evaluate the impact of sample size, model form, and g-weights on the unbiasedness of regression estimators.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Ronald E. McRoberts, Erik Naesset, Sassan Saatchi, Shaun Quegan
Summary: This study demonstrates a statistically rigorous inference method for map-based estimates, which requires model-based inferential methods. The study also shows that bootstrapping is an effective means of estimating the required variances and covariances. The primary results indicate that metadata should be provided by map makers, and bootstrapping can be used to estimate the variances and covariances.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Forestry
Ana de Lera Garrido, Terje Gobakken, Marius Hauglin, Erik Naesset, Ole Martin Bollandsas
Summary: The aim of this study was to analyze the accuracy of forest attribute predictions from the nationwide forest attribute map (SR16). Field observations from 33 forest inventory projects in Norway were used for validation. The overall results showed satisfactory accuracy, but there were large differences in accuracy among different inventory projects, with forest structure being the most influential factor.
SCANDINAVIAN JOURNAL OF FOREST RESEARCH
(2023)
Article
Forestry
Marie-Claude Jutras-Perreault, Erik Naesset, Terje Gobakken, Hans Ole Orka
Summary: This study utilized ALS data and vegetation indices from optical images to predict the presence of standing dead trees in a managed forest in Southern Norway. Area-based regression models were initially tested but proved to be statistically insignificant due to limited ground reference information. A tree-based approach, however, successfully identified standing dead trees based on ALS point cloud data and vegetation indices.
SCANDINAVIAN JOURNAL OF FOREST RESEARCH
(2023)
Article
Environmental Sciences
Marie-Claude Jutras-Perreault, Terje Gobakken, Erik Naesset, Hans Ole orka
Summary: This study proposes a tree-based approach that combines remote sensing data to predict the presence of standing dead trees (SDT) in forests. By comparing different remotely sensed data sources, it was found that NDVI calculated from aerial images accurately predicts the presence of SDT, while NDVI calculated from satellite images is less accurate.
Article
Remote Sensing
Endre Hansen, Julius Wold, Michele Dalponte, Terje Gobakken, Lennart Noordermeer, Hans Ole Orka
Summary: The study applied area-based approaches to predict rot occurrence, rot severity, and rot volume. Random Forest models were built and validated using remotely sensed data and ground reference data. The results showed that rot volume models performed better due to the correlation between timber volume and rot volume.
EUROPEAN JOURNAL OF REMOTE SENSING
(2023)
Article
Environmental Sciences
Ritwika Mukhopadhyay, Erik Naesset, Terje Gobakken, Ida Marielle Mienna, Jaime Candelas Bielza, Gunnar Austrheim, Henrik Jan Persson, Hans Ole orka, Bjorn-Eirik Roald, Ole Martin Bollandsas
Summary: Due to climate change, treelines are shifting to higher altitudes and latitudes. Accurately estimating the biomass of trees and shrubs in alpine areas is crucial for carbon reporting. This study utilized remotely sensed data, such as airborne laser scanning (ALS) and digital aerial photogrammetry (DAP), to estimate aboveground biomass (AGB) in a treeline ecotone in Southern Norway. Despite weak fit of the prediction models, the estimates showed adequate precision with relatively narrow confidence intervals (CIs). The results suggest that ALS and DAP data can be effectively used for AGB estimation in treeline ecotones.
Article
Forestry
Victor F. Strimbu, Tron Eid, Terje Gobakken
Summary: This paper introduces a software tool called GAYA 2.0 for simulating forest development and performing scenario analysis to evaluate carbon sequestration potential, net present value, and climate change impacts under different management regimes. A case study in Norway is presented to demonstrate the tool's potential, showing changes in optimal management strategies and future predictions of forest carbon balance.
Article
Forestry
Ana Aza, A. Maarit I. Kallio, Timo Pukkala, Ari Hietala, Terje Gobakken, Rasmus Astrup
Summary: This study examines the economic benefits of converting rot-infested Norway spruce stands to Scots pine. It proposes a Precision forestry method to determine the optimal tree species at a pixel level in heterogeneous stands. The findings suggest that shifting to Scots pine is more profitable when rot levels are high, and the method increases net present value in almost every stand.
Article
Forestry
A. Q. Nyrud, K. M. A. Heltorp, Anders Roos, Francisco X. Aguilar, Katja Lahtinen, Noora Viholainen, Sami Berghall, Anne Toppinen, B. J. Thorsen, Matleena Kniivila, Antti Haapala, Elias Hurmekoski, T. Hujala, H. F. Hoen
Summary: This study investigated public attitudes towards multi-storey wood buildings in seven European countries and found that the level of knowledge about wood buildings was lower in countries where brick, stone, and concrete were commonly used in construction, such as the United Kingdom, Germany, and Denmark. Finland and Sweden had the most positive attitudes towards wood buildings. The study also identified factors such as fire vulnerability, material solidity, indoor environment, and moisture vulnerability that influenced people's perception of wood buildings as a nice place to live.
SCANDINAVIAN JOURNAL OF FOREST RESEARCH
(2024)
