Article
Environmental Sciences
Ralph Dubayah, John Armston, Sean P. Healey, Jamis M. Bruening, Paul L. Patterson, James R. Kellner, Laura Duncanson, Svetlana Saarela, Goran Stahl, Zhiqiang Yang, Hao Tang, J. Bryan Blair, Lola Fatoyinbo, Scott Goetz, Steven Hancock, Matthew Hansen, Michelle Hofton, George Hurtt, Scott Luthcke
Summary: This paper presents the estimation of biomass distribution at global and national levels based on the GEDI investigation, along with the standard error of the estimates. These estimates serve as a baseline for monitoring and assessing the impacts of land use changes on atmospheric CO2 concentrations.
ENVIRONMENTAL RESEARCH LETTERS
(2022)
Article
Environmental Sciences
Yuncheng Deng, Jiya Pan, Jinliang Wang, Qianwei Liu, Jianpeng Zhang
Summary: This study introduces a rapid method for biomass estimation in alpine and canyon areas using space-borne LiDAR data and optical remote-sensing images. By establishing extrapolation and growth models, the aboveground biomass and carbon storage in Shangri-La City were successfully estimated and verified.
Article
Environmental Sciences
Yue Jiao, Dacheng Wang, Xiaojing Yao, Shudong Wang, Tianhe Chi, Yu Meng
Summary: In this study, a measurement method using optical satellite imagery and space LiDAR data fusion was proposed to assess forest emissions reduction. The method demonstrated efficiency and accuracy in predicting forest carbon stock at a large scale. Building a quality control framework is crucial to improve carbon stock estimation and meet carbon standards.
Article
Environmental Sciences
Hao Tang, Lei Ma, Andrew Lister, Jarlath O'Neill-Dunne, Jiaming Lu, Rachel L. Lamb, Ralph Dubayah, George Hurtt
Summary: Large-scale airborne lidar data collections can be used to generate high-resolution forest aboveground biomass maps at the state level and beyond, showing potential for forest carbon planning. This study refines a multi-state level forest carbon monitoring framework to address spatial inconsistencies caused by data quality variability. The use of a linear model maintains good prediction accuracy of aboveground biomass density and mitigates problems related to data quality variability, leading to the generation of a consistent 30 m pixel forest aboveground carbon map covering 11 states in the USA.
ENVIRONMENTAL RESEARCH LETTERS
(2021)
Article
Environmental Sciences
Gaia Vaglio Laurin, Nicola Puletti, Clara Tattoni, Carlotta Ferrara, Francesco Pirotti
Summary: Windstorms are a major disturbance factor for European forests, with the 2018 Vaia storm causing significant ecological and financial losses in Italy. Estimating timber loss using satellite remote sensing faces challenges, highlighting the urgent need for a unified national or regional strategy. Remote sensing-based surveys targeting forests are crucial, especially for European countries lacking reliable forest stocks data.
Article
Environmental Sciences
Shaun R. Levick, Tim Whiteside, David A. Loewensteiner, Mitchel Rudge, Renee Bartolo
Summary: Savanna ecosystems are difficult to map and monitor due to the dynamic nature of their vegetation. UAV-based remote sensing shows potential for providing high-resolution measurements over large areas at regular intervals. However, the suitability of UAV-based LiDAR for mapping and monitoring savanna vegetation structure is not well established.
Article
Engineering, Aerospace
Mohamed Musthafa, Gulab Singh
Summary: This study utilizes GEDI LiDAR data and field-measured biomass to develop a method for estimating forest aboveground biomass (AGB) and develops an AGB model. The results demonstrate the potential of using GEDI LiDAR data to improve the accuracy of forest AGB modeling.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Forestry
Mei Sun, Lei Cui, Jongmin Park, Mariano Garcia, Yuyu Zhou, Carlos Alberto Silva, Long He, Hu Zhang, Kaiguang Zhao
Summary: This study evaluates the accuracy of the GEDI instrument in measuring terrain, forest vertical structures, and aboveground biomass (AGB) using independent airborne lidar data as references. Results show that the GEDI-derived ground elevations are strongly correlated with those from other lidar systems, but with some nonnegligible errors. The correction of geolocation errors significantly improves the correlation between GEDI and lidar measurements and demonstrates the usefulness of GEDI metrics for predicting AGB.
Article
Environmental Sciences
Jorgen S. Saebo, Jacob B. Socolar, Edicson P. Sanchez, Paul Woodcock, Christopher G. Bousfield, Claudia A. M. Uribe, David P. Edwards, Torbjorn Haugaasen
Summary: The rapid development of remote sensing and LiDAR technology has improved estimates of tree architecture and biomass extrapolation. However, current biomass maps show discrepancies and do not match independent ground data. This study investigates the impact of wood specific gravity (WSG) on above-ground biomass (AGB) distribution across different scales. The results suggest that accounting for spatial variation in WSG can significantly improve biomass estimates, calling for further research on the spatial distribution of WSG and potential environmental predictors for accurate large-scale mapping of biomass.
ENVIRONMENTAL RESEARCH LETTERS
(2022)
Article
Plant Sciences
Tanja K. K. Petersen, Anders L. L. Kolstad, Jari Kouki, Shawn J. J. Leroux, Lynette R. R. Potvin, Jean-Pierre Tremblay, Martha Wallgren, Fredrik Widemo, Joris P. G. M. Cromsigt, Coline Courtois, Gunnar Austrheim, John Gosse, Michael den Herder, Luise Hermanutz, James D. M. Speed
Summary: This study analyzed the impact of moose on forest canopies across the boreal biome through distributed exclosure experiments. The results showed a uniform response of forest canopies to moose across regions, regardless of environmental gradients. Moose led to a decrease in canopy height, complexity, and above-ground biomass.
JOURNAL OF ECOLOGY
(2023)
Article
Environmental Sciences
Jamis M. Bruening, Rico Fischer, Friedrich J. Bohn, John Armston, Amanda H. Armstrong, Nikolai Knapp, Hao Tang, Andreas Huth, Ralph Dubayah
Summary: Accurately estimating aboveground biomass density (AGBD) is crucial for various scientific applications, and the Global Ecosystem Dynamics Investigation (GEDI) uses waveform lidar to map AGBD globally. This study suggests that lidar waveforms may not be unique to AGBD, potentially contributing to large uncertainties in AGBD predictions. Different forest stands exhibit varying levels of uniqueness in their waveforms, impacting the prediction uncertainty, especially in more structurally complex forests. This presents challenges in accurately predicting AGBD using lidar waveforms and highlights the need for further research on the relationships between lidar remote sensing measurements, forest structure, and AGBD.
ENVIRONMENTAL RESEARCH LETTERS
(2021)
Article
Computer Science, Interdisciplinary Applications
Leyre Torre-Tojal, Aitor Bastarrika, Ana Boyano, Jose Manuel Lopez-Guede, Manuel Grana
Summary: This article utilizes random forest models to estimate the biomass of Pinus radiata species in a region of the Basque Autonomous Community. By tuning the hyperparameters and conducting cross-validation, two models with high R-2 values were obtained. These models were then applied to the municipality of Orozko, predicting a biomass that is 16-18% higher than the predictions made by the Basque Government.
JOURNAL OF COMPUTATIONAL SCIENCE
(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
K. C. Cushman, John Armston, Ralph Dubayah, Laura Duncanson, Steven Hancock, David Janik, Kamil Kral, Martin Krucek, David M. Minor, Hao Tang, James R. Kellner
Summary: In this study, the sensitivity of Global Ecosystem Dynamics Investigation (GEDI) data and aboveground biomass density (AGBD) predictions to leaf phenology was tested. The results suggest that, with consideration of model choice, GEDI data without considering leaf status can be used for AGBD prediction, which increases data availability and reduces sampling error in some forests.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Forestry
Guillermo Palacios-Rodriguez, Luis Quinto, Miguel A. Lara-Gomez, Javier Perez-Romero, Jose Manuel Recio, Marta Alvarez-Romero, Antonio M. Cachinero-Vivar, Salvador Hernandez-Navarro, Rafael M. Navarro-Cerrillo
Summary: This study estimated the carbon stocks in carob plantations in southern Spain using aerial laser scanning data. The results showed that the average carbon accumulation rate during the plantation period was 1.94 kg per tree. These findings are important for understanding the effectiveness of planting trees to mitigate climate change and for the management and conservation of forested areas.
Article
Forestry
Ronald E. McRoberts, Erik Naesset, Terje Gobakken, Gherardo Chirici, Sonia Condes, Zhengyang Hou, Svetlana Saarela, Qi Chen, Goran Stahl, Brian F. Walters
CANADIAN JOURNAL OF FOREST RESEARCH
(2018)
Article
Environmental Sciences
Stefano Puliti, Svetlana Saarela, Terje Gobakken, Goran Stahl, Erik Naesset
REMOTE SENSING OF ENVIRONMENT
(2018)
Article
Environmental Sciences
Wenlu Qi, Svetlana Saarela, John Armston, Goran Stahl, Ralph Dubayah
REMOTE SENSING OF ENVIRONMENT
(2019)
Article
Forestry
Alex Appiah Mensah, Hans Petersson, Svetlana Saarela, Martin Goude, Emma Holmstrom
FOREST ECOLOGY AND MANAGEMENT
(2020)
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
Ralph Dubayah, John Armston, Sean P. Healey, Jamis M. Bruening, Paul L. Patterson, James R. Kellner, Laura Duncanson, Svetlana Saarela, Goran Stahl, Zhiqiang Yang, Hao Tang, J. Bryan Blair, Lola Fatoyinbo, Scott Goetz, Steven Hancock, Matthew Hansen, Michelle Hofton, George Hurtt, Scott Luthcke
Summary: This paper presents the estimation of biomass distribution at global and national levels based on the GEDI investigation, along with the standard error of the estimates. These estimates serve as a baseline for monitoring and assessing the impacts of land use changes on atmospheric CO2 concentrations.
ENVIRONMENTAL RESEARCH LETTERS
(2022)
Article
Environmental Sciences
P. Varvia, L. Korhonen, A. Bruguiere, J. Toivonen, P. Packalen, M. Maltamo, S. Saarela, S. C. Popescu
Summary: This study explored the effects of snow presence/absence and depth, solar noise, and strong/weak beam differences on the ICESat2 data for forest aboveground biomass (AGB) estimation. The results showed that using strong beam night data from snowless conditions yielded the smallest root mean square error (RMSE) of 26.9% for AGB estimation. If more data are needed, it is recommended to use only strong beam data and construct separate models for different data subsets.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Svetlana Saarela, Soren Hol, Sean P. Healey, Paul L. Patterson, Zhiqiang Yang, Hans-Erik Andersen, Ralph O. Dubayah, Wenlu Qi, Laura I. Duncanson, John D. Armston, Terje Gobakken, Erik Naesset
Summary: NASA's GEDI mission provides data for estimating aboveground forest biomass (AGB) in temperate and pan-tropical regions. This study compares the performance of two prediction frameworks, hybrid inference and hierarchical model-based inference, in the context of the GEDI mission. The results show that hybrid inference generally has smaller root mean square error (RMSE), making it suitable for most GEDI applications. However, in cases where GEDI data are sparse, hierarchical model-based inference is preferred.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Nils Lindgren, Kenneth Nystrom, Svetlana Saarela, Hakan Olsson, Goran Stahl
Summary: Data assimilation (DA) is often used to improve predictions by merging observation data. Although RS data can be used for forest inventory, the application of DA in this field has been limited. The problem lies in the strong correlation of errors in subsequent predictions, which affects the efficiency of DA. This study investigates whether classical calibration of RS-based predictions can eliminate bias and enhance DA results. Simulation results suggest that classical calibration eliminates bias and improves DA outcomes.
Article
Environmental Sciences
Ronald E. McRoberts, Erik Naesset, Zhengyang Hou, Göran Stahl, Svetlana Saarela, Jessica Esteban, Davide Travaglini, Jahangir Mohammadi, Gherardo Chirici
Summary: When probability samples are not available, the model-based framework and resampling methods like the bootstrap become essential options for constructing prediction intervals and obtaining standard errors. This study aims to develop a procedure for terminating resampling that ensures the stability of standard error estimation using bootstrap with one million replications. The primary contribution of this study is the development and demonstration of criteria for terminating resampling to stabilize the bootstrap estimate of the standard error.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Remote Sensing
Fangting Chen, Zhengyang Hou, Svetlana Saarela, Ronald E. McRoberts, Goran Stahl, Annika Kangas, Petteri Packalen, Bo Li, Qing Xu
Summary: Remote sensing has improved forest inventory by using model-based inference to estimate parameters of interest from fine to coarse spatial resolution, facilitating decision making for natural resource management.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Remote Sensing
Lennart Noordermeer, Jaime Candelas Bielza, Svetlana Saarela, Terje Gobakken, Ole Martin Bollandsas, Erik Naesset
Summary: The objective of this study is to monitor tree occupancy and height in the alpine treeline ecotone using a series of ALS data. Longitudinal surveys were conducted in 25 sites along the Scandinavian Mountain Range during 2008, 2012, and 2018. Comparisons between ALS-based estimates and field-based estimates were made, and uncertainty estimates were provided. The results showed no significant changes in tree occupancy and height over time.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Multidisciplinary Sciences
Svetlana Saarela, Petri Varvia, Lauri Korhonen, Zhiqiang Yang, Paul L. Patterson, Terje Gobakken, Erik Naesset, Sean P. Healey, Goran Stahl
Summary: This article introduces how to derive predictors and their variances using hierarchically nested models, and extends the analysis to cases involving three modeling steps. The study utilizes a combination of multiple data sources to obtain reliable information about Earth's ecosystems, and addresses challenges in data integration and uncertainty assessment.
Article
Forestry
Svetlana Saarela, Andre Wastlund, Emma Holmstrom, Alex Appiah Mensah, Soren Holm, Mats Nilsson, Jonas Fridman, Goran Stahl