Article
Environmental Sciences
James E. Lamping, Harold S. J. Zald, Buddhika D. Madurapperuma, Jim Graham
Summary: This study evaluated the ability of low-cost UAS DAP and HPGPS to accurately predict key forest attributes in various forest conditions in California, USA. The results showed that UAS DAP models were comparable to lidar models, and when combined with low-cost HPGPS, could accurately predict key forest attributes across a range of forest types.
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
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
Remote Sensing
Daniel Kuekenbrink, Mauro Marty, Ruedi Boesch, Christian Ginzler
Summary: This study evaluates the performance of different close-range remote sensing devices for tree detection and diameter at breast height (DBH) extraction in forests. The results show that terrestrial laser scanning systems (TLS) have the highest tree detection rate, while drone-based laser scanning systems (UAVLS) have the lowest tree detection rate. The novel GoPro approach performs moderately well in tree detection and is comparable to LiDAR devices in DBH extraction.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Chemistry, Multidisciplinary
Chenyun Li, Yanfeng Zheng, Xinjie Zhang, Fayun Wu, Linyuan Li, Jingyi Jiang
Summary: This study compared the ability of digital aerial photogrammetry (DAP) and airborne laser scanning (ALS) in predicting canopy structural variables in tropical forests. The results showed that DAP-based methods tended to overestimate canopy cover and leaf area index (LAI) compared to ALS-based methods. ALS-based estimates were also found to be more consistent with satellite retrievals than DAP-based estimates.
APPLIED SCIENCES-BASEL
(2022)
Article
Environmental Sciences
Grigorijs Goldbergs
Summary: This study compared the performance of large-format airborne and satellite imagery, as well as airborne laser scanning (ALS) data, in estimating growing stock in Latvian hemiboreal forests. The results showed that ALS and image-based height metrics had comparable effectiveness in predicting growing stock in dense closed-canopy forests. The research suggests that regularly collected airborne imagery and image-based CHMs can be an efficient solution for forest growing stock calculations and updates. VHRSI can also be a fast and affordable solution for monitoring forest growing stock changes in vast and dense forestland.
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
Environmental Sciences
Todd A. Schroeder, Shingo Obata, Monica Papes, Benjamin Branoff
Summary: The Forest Inventory and Analysis (FIA) program of the U.S. Forest Service aims to estimate various forest attributes using a design-based network of sampling plots. This study explores the use of digital aerial photogrammetric (DAP) point clouds developed from stereo imagery to improve these estimates in southeastern mixed hardwood forests. The results show that using the DAP point clouds improved the precision of forest volume estimates compared to using tree canopy cover data.
Article
Environmental Sciences
Xiaoyao Fu, Zhengnan Zhang, Lin Cao, Nicholas C. Coops, Tristan R. H. Goodbody, Hao Liu, Xin Shen, Xiangqian Wu
Summary: Assessing changes in forest structure over time is crucial for monitoring forest resources, supporting sustainable forest management practices, and providing key insights into changes in the terrestrial carbon cycle. Recent research interest in unmanned aerial vehicle (UAV)-based digital aerial photogrammetry (DAP) technology has led to high-spatial resolution data that enhances forest inventories and monitoring forest dynamics. Direct methods showed improved accuracy in estimating structural parameter changes compared to indirect methods, with metrics like height percentiles and canopy return density being sensitive to changes in structural parameters.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Remote Sensing
Martin Mokros, Tomas Mikita, Arunima Singh, Julian Tomastik, Juliana Chuda, Piotr Wezyk, Karel Kuzelka, Peter Surovy, Martin Klimanek, Karolina Zieba-Kulawik, Rogerio Bobrowski, Xinlian Liang
Summary: The development of devices capable of generating 3D point clouds of the forest has flourished in recent years. Low-cost technologies such as MultiCam, iPad Pro, GeoSlam Horizon, and FARO Focus s70 were compared for tree detection and diameter at breast height estimation. Results showed that TLS provided the most accurate data, while iPad Pro achieved results closest to TLS when DBH > 7 cm.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Computer Science, Information Systems
Ozgun Akcay, Ahmet Cumhur Kinaci, Emin Ozgur Avsar, Umut Aydar
Summary: In geospatial applications, automatic detection and classification of earth objects are crucial. This study proposes a new dual-stream architecture based on DeepLabV3+ to improve semantic segmentation accuracy. The use of data augmentation and Tversky loss function further enhances overall accuracy. The results demonstrate the potential of enhancing traditional semantic segmentation networks and the contribution of geospatial data as the second stream.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2022)
Article
Forestry
Joshua Carpenter, Daniel Rentauskas, Nikhil Makkar, Jinha Jung, Songlin Fei
Summary: Field-based forest inventory plots are crucial for forest studies, providing valuable information about forests. They are now used for validation and training data in forest feature extraction and machine learning algorithms. However, the uncertainty in plot location measurements has undermined their usefulness. A method for accurately measuring plot center coordinates using low-cost targets and orthoimagery has improved the accuracy by an order of magnitude.
JOURNAL OF FORESTRY
(2023)
Article
Forestry
Facundo Pessacg, Francisco Gomez-Fernandez, Matias Nitsche, Nicolas Chamo, Sebastian Torrella, Ruben Ginzburg, Pablo De Cristoforis
Summary: Forestry aerial photogrammetry using Unmanned Aerial Systems (UAS) helps bridge the gap between detailed fieldwork and broad-range satellite imagery-based analysis. However, optical sensors face limitations in collecting and classifying ground points in woodlands. This study proposes a novel method to generate accurate Digital Terrain Models (DTMs) in forested environments and develops a realistic simulator for controlled experimentation.
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
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
Environmental Sciences
Laura Duncanson, James R. Kellner, John Armston, Ralph Dubayah, David M. Minor, Steven Hancock, Sean P. Healey, Paul L. Patterson, Svetlana Saarela, Suzanne Marselis, Carlos E. Silva, Jamis Bruening, Scott J. Goetz, Hao Tang, Michelle Hofton, Bryan Blair, Scott Luthcke, Lola Fatoyinbo, Katharine Abernethy, Alfonso Alonso, Hans-Erik Andersen, Paul Aplin, Timothy R. Baker, Nicolas Barbier, Jean Francois Bastin, Peter Biber, Pascal Boeckx, Jan Bogaert, Luigi Boschetti, Peter Brehm Boucher, Doreen S. Boyd, David F. R. P. Burslem, Sofia Calvo-Rodriguez, Jerome Chave, Robin L. Chazdon, David B. Clark, Deborah A. Clark, Warren B. Cohen, David A. Coomes, Piermaria Corona, K. C. Cushman, Mark E. J. Cutler, James W. Dalling, Michele Dalponte, Jonathan Dash, Sergio de-Miguel, Songqiu Deng, Peter Woods Ellis, Barend Erasmus, Patrick A. Fekety, Alfredo Fernandez-Landa, Antonio Ferraz, Rico Fischer, Adrian G. Fisher, Antonio Garcia-Abril, Terje Gobakken, Jorg M. Hacker, Marco Heurich, Ross A. Hill, Chris Hopkinson, Huabing Huang, Stephen P. Hubbell, Andrew T. Hudak, Andreas Huth, Benedikt Imbach, Kathryn J. Jeffery, Masato Katoh, Elizabeth Kearsley, David Kenfack, Natascha Kljun, Nikolai Knapp, Kamil Kral, Martin Krucek, Nicolas Labriere, Simon L. Lewis, Marcos Longo, Richard M. Lucas, Russell Main, Jose A. Manzanera, Rodolfo Vasquez Martinez, Renaud Mathieu, Herve Memiaghe, Victoria Meyer, Abel Monteagudo Mendoza, Alessandra Monerris, Paul Montesano, Felix Morsdorf, Erik Naesset, Laven Naidoo, Reuben Nilus, Michael O'Brien, David A. Orwig, Konstantinos Papathanassiou, Geoffrey Parker, Christopher Philipson, Oliver L. Phillips, Jan Pisek, John R. Poulsen, Hans Pretzsch, Christoph Rudiger, Sassan Saatchi, Arturo Sanchez-Azofeifa, Nuria Sanchez-Lopez, Robert Scholes, Carlos A. Silva, Marc Simard, Andrew Skidmore, Krzysztof Sterenczak, Mihai Tanase, Chiara Torresan, Ruben Valbuena, Hans Verbeeck, Tomas Vrska, Konrad Wessels, Joanne C. White, Lee J. T. White, Eliakimu Zahabu, Carlo Zgraggen
Summary: This paper presents the development of models used by NASA's Global Ecosystem Dynamics Investigation (GEDI) to estimate forest aboveground biomass density (AGBD). The models were developed using globally distributed field and airborne lidar data, with simulated relative height metrics as predictor variables. The study found that stratification by geographic domain and the use of square root transformation improved model performance.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Eirik Naesset Ramtvedt, Terje Gobakken, Erik Naesset
Summary: This study measured the albedo of single-tree Norway spruce using an unmanned aerial vehicle (UAV) and found that tree structure and snow-masking effect have smaller correlations with albedo. It is recommended to use UAVs with high-precision navigation systems and field-stop devices for further development of fine-spatial UAV-measured albedo.
Article
Environmental Sciences
Michele Dalponte, Alvar J. I. Kallio, Hans Ole orka, Erik Naesset, Terje Gobakken
Summary: This study used airborne hyperspectral data to detect wood decay in Norway spruce forests. The results showed that wood decay could be detected, although the accuracy varied depending on the dataset.
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
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
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)
