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Terryn L, Calders K, Meunier F, Bauters M, Boeckx P, Brede B, Burt A, Chave J, da Costa ACL, D'hont B, Disney M, Jucker T, Lau A, Laurance SGW, Maeda EE, Meir P, Krishna Moorthy SM, Nunes MH, Shenkin A, Sibret T, Verhelst TE, Wilkes P, Verbeeck H. New tree height allometries derived from terrestrial laser scanning reveal substantial discrepancies with forest inventory methods in tropical rainforests. GLOBAL CHANGE BIOLOGY 2024; 30:e17473. [PMID: 39155688 DOI: 10.1111/gcb.17473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 07/31/2024] [Accepted: 08/01/2024] [Indexed: 08/20/2024]
Abstract
Tree allometric models, essential for monitoring and predicting terrestrial carbon stocks, are traditionally built on global databases with forest inventory measurements of stem diameter (D) and tree height (H). However, these databases often combine H measurements obtained through various measurement methods, each with distinct error patterns, affecting the resulting H:D allometries. In recent decades, terrestrial laser scanning (TLS) has emerged as a widely accepted method for accurate, non-destructive tree structural measurements. This study used TLS data to evaluate the prediction accuracy of forest inventory-based H:D allometries and to develop more accurate pantropical allometries. We considered 19 tropical rainforest plots across four continents. Eleven plots had forest inventory and RIEGL VZ-400(i) TLS-based D and H data, allowing accuracy assessment of local forest inventory-based H:D allometries. Additionally, TLS-based data from 1951 trees from all 19 plots were used to create new pantropical H:D allometries for tropical rainforests. Our findings reveal that in most plots, forest inventory-based H:D allometries underestimated H compared with TLS-based allometries. For 30-metre-tall trees, these underestimations varied from -1.6 m (-5.3%) to -7.5 m (-25.4%). In the Malaysian plot with trees reaching up to 77 m in height, the underestimation was as much as -31.7 m (-41.3%). We propose a TLS-based pantropical H:D allometry, incorporating maximum climatological water deficit for site effects, with a mean uncertainty of 19.1% and a mean bias of -4.8%. While the mean uncertainty is roughly 2.3% greater than that of the Chave2014 model, this model demonstrates more consistent uncertainties across tree size and delivers less biased estimates of H (with a reduction of 8.23%). In summary, recognizing the errors in H measurements from forest inventory methods is vital, as they can propagate into the allometries they inform. This study underscores the potential of TLS for accurate H and D measurements in tropical rainforests, essential for refining tree allometries.
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Affiliation(s)
- Louise Terryn
- Q-ForestLab, Department of Environment, Ghent University, Ghent, Belgium
| | - Kim Calders
- Q-ForestLab, Department of Environment, Ghent University, Ghent, Belgium
| | - Félicien Meunier
- Q-ForestLab, Department of Environment, Ghent University, Ghent, Belgium
| | - Marijn Bauters
- Q-ForestLab, Department of Environment, Ghent University, Ghent, Belgium
- ISOFYS - Isotope Bioscience Laboratory, Department of Green Chemistry and Technology, Ghent University, Ghent, Belgium
| | - Pascal Boeckx
- ISOFYS - Isotope Bioscience Laboratory, Department of Green Chemistry and Technology, Ghent University, Ghent, Belgium
| | - Benjamin Brede
- GFZ German Research Centre for Geosciences, Potsdam, Germany
| | | | - Jerome Chave
- Laboratoire Evolution and Biological Diversity (EDB), CNRS/IRD/UPS, Toulouse, France
| | - Antonio Carlos Lola da Costa
- Geociencias, Federal University of Para, Belem, State of Para, Brazil
- Museu Paraense Emilio Goeldi, Belem, State of Para, Brazil
| | - Barbara D'hont
- Q-ForestLab, Department of Environment, Ghent University, Ghent, Belgium
| | - Mathias Disney
- UCL Department of Geography, London, UK
- NERC National Centre for Earth Observation (NCEO-UCL), Swindon, UK
| | - Tommaso Jucker
- School of Biological Sciences, University of Bristol, Bristol, UK
| | - Alvaro Lau
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Wageningen, Gelderland, the Netherlands
| | - Susan G W Laurance
- Centre for Tropical Environmental and Sustainability Science and College of Science and Engineering, James Cook University, Cairns, Australia
| | - Eduardo Eiji Maeda
- Finnish Meteorological Institute, FMI, Helsinki, Finland
- Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland
| | - Patrick Meir
- School of Geosciences, University of Edinburgh, Edinburgh, UK
| | - Sruthi M Krishna Moorthy
- Q-ForestLab, Department of Environment, Ghent University, Ghent, Belgium
- Department of Biology, University of Oxford, Oxford, UK
| | - Matheus Henrique Nunes
- Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland
- Department of Geographical Sciences, University of Maryland, College Park, Maryland, USA
| | - Alexander Shenkin
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University Flagstaff, Flagstaff, Arizona, USA
| | - Thomas Sibret
- Q-ForestLab, Department of Environment, Ghent University, Ghent, Belgium
- ISOFYS - Isotope Bioscience Laboratory, Department of Green Chemistry and Technology, Ghent University, Ghent, Belgium
| | - Tom E Verhelst
- Q-ForestLab, Department of Environment, Ghent University, Ghent, Belgium
| | - Phil Wilkes
- Department of Geography, University College London, London, UK
- NERC National Centre for Earth Observation, Leicester, UK
| | - Hans Verbeeck
- Q-ForestLab, Department of Environment, Ghent University, Ghent, Belgium
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Aguilar FJ, Rodríguez FA, Aguilar MA, Nemmaoui A, Álvarez-Taboada F. Forestry Applications of Space-Borne LiDAR Sensors: A Worldwide Bibliometric Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:1106. [PMID: 38400264 PMCID: PMC10893192 DOI: 10.3390/s24041106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 01/26/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024]
Abstract
The 21st century has seen the launch of new space-borne sensors based on LiDAR (light detection and ranging) technology developed in the second half of the 20th century. Nowadays, these sensors offer novel opportunities for mapping terrain and canopy heights and estimating aboveground biomass (AGB) across local to regional scales. This study aims to analyze the scientific impact of these sensors on large-scale forest mapping to retrieve 3D canopy information, monitor forest degradation, estimate AGB, and model key ecosystem variables such as primary productivity and biodiversity. A worldwide bibliometric analysis of this topic was carried out based on up to 412 publications indexed in the Scopus database during the period 2004-2022. The results showed that the number of published documents increased exponentially in the last five years, coinciding with the commissioning of two new LiDAR space missions: Ice, Cloud, and Land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). These missions have been providing data since 2018 and 2019, respectively. The journal that demonstrated the highest productivity in this field was "Remote Sensing" and among the leading contributors, the top five countries in terms of publications were the USA, China, the UK, France, and Germany. The upward trajectory in the number of publications categorizes this subject as a highly trending research topic, particularly in the context of improving forest resource management and participating in global climate treaty frameworks that require monitoring and reporting on forest carbon stocks. In this context, the integration of space-borne data, including imagery, SAR, and LiDAR, is anticipated to steer the trajectory of this research in the upcoming years.
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Affiliation(s)
- Fernando J. Aguilar
- Department of Engineering, CIAIMBITAL Research Center, University of Almería, Carretera de Sacramento s/n, 04120 Almería, Spain; (M.A.A.); (A.N.)
| | - Francisco A. Rodríguez
- Ministry of Agriculture, Fisheries, Water and Rural Development, Junta de Andalucía, Calle Tabladilla s/n, 41013 Sevilla, Spain;
| | - Manuel A. Aguilar
- Department of Engineering, CIAIMBITAL Research Center, University of Almería, Carretera de Sacramento s/n, 04120 Almería, Spain; (M.A.A.); (A.N.)
| | - Abderrahim Nemmaoui
- Department of Engineering, CIAIMBITAL Research Center, University of Almería, Carretera de Sacramento s/n, 04120 Almería, Spain; (M.A.A.); (A.N.)
| | - Flor Álvarez-Taboada
- Department of Mining Technology, Topography and Structures, University of León, 24404 Ponferrada, Spain;
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3
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Rog I, Hilman B, Fox H, Yalin D, Qubaja R, Klein T. Increased belowground tree carbon allocation in a mature mixed forest in a dry versus a wet year. GLOBAL CHANGE BIOLOGY 2024; 30:e17172. [PMID: 38343030 DOI: 10.1111/gcb.17172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 01/07/2024] [Accepted: 01/08/2024] [Indexed: 02/15/2024]
Abstract
Tree species differ in their carbon (C) allocation strategies during environmental change. Disentangling species-specific strategies and contribution to the C balance of mixed forests requires observations at the individual tree level. We measured a complete set of C pools and fluxes at the tree level in five tree species, conifers and broadleaves, co-existing in a mature evergreen mixed Mediterranean forest. Our study period included a drought year followed by an above-average wet year, offering an opportunity to test the effect of water availability on tree C allocation. We found that in comparison to the wet year, C uptake was lower in the dry year, C use was the same, and allocation to belowground sinks was higher. Among the five major C sinks, respiration was the largest (ca. 60%), while root exudation (ca. 10%) and reproduction (ca. 2%) were those that increased the most in the dry year. Most trees relied on stored starch for maintaining a stable soluble sugars balance, but no significant differences were detected in aboveground storage between dry and wet years. The detailed tree-level analysis of nonstructural carbohydrates and δ13 C dynamics suggest interspecific differences in C allocation among fluxes and tissues, specifically in response to the varying water availability. Overall, our findings shed light on mixed forest physiological responses to drought, an increasing phenomenon under the ongoing climate change.
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Affiliation(s)
- Ido Rog
- Department of Plant & Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Boaz Hilman
- Department of Biogeochemical Processes, Max-Planck Institute for Biogeochemistry, Jena, Germany
- The Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Hagar Fox
- Department of Plant & Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - David Yalin
- Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Rafat Qubaja
- Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Tamir Klein
- Department of Plant & Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
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Cheng Y, Oehmcke S, Brandt M, Rosenthal L, Das A, Vrieling A, Saatchi S, Wagner F, Mugabowindekwe M, Verbruggen W, Beier C, Horion S. Scattered tree death contributes to substantial forest loss in California. Nat Commun 2024; 15:641. [PMID: 38245523 PMCID: PMC10799937 DOI: 10.1038/s41467-024-44991-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/11/2024] [Indexed: 01/22/2024] Open
Abstract
In recent years, large-scale tree mortality events linked to global change have occurred around the world. Current forest monitoring methods are crucial for identifying mortality hotspots, but systematic assessments of isolated or scattered dead trees over large areas are needed to reduce uncertainty on the actual extent of tree mortality. Here, we mapped individual dead trees in California using sub-meter resolution aerial photographs from 2020 and deep learning-based dead tree detection. We identified 91.4 million dead trees over 27.8 million hectares of vegetated areas (16.7-24.7% underestimation bias when compared to field data). Among these, a total of 19.5 million dead trees appeared isolated, and 60% of all dead trees occurred in small groups ( ≤ 3 dead trees within a 30 × 30 m grid), which is largely undetected by other state-level monitoring methods. The widespread mortality of individual trees impacts the carbon budget and sequestration capacity of California forests and can be considered a threat to forest health and a fuel source for future wildfires.
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Affiliation(s)
- Yan Cheng
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark.
| | - Stefan Oehmcke
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Martin Brandt
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Lisa Rosenthal
- US Geological Survey, Western Ecological Research Center, Three Rivers, Sequoia and Kings Canyon Field Station, Three Rivers, CA, USA
| | - Adrian Das
- US Geological Survey, Western Ecological Research Center, Three Rivers, Sequoia and Kings Canyon Field Station, Three Rivers, CA, USA
| | - Anton Vrieling
- Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
| | - Sassan Saatchi
- University of California, Los Angeles, CA, USA
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Fabien Wagner
- University of California, Los Angeles, CA, USA
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Maurice Mugabowindekwe
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Wim Verbruggen
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Claus Beier
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Stéphanie Horion
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark.
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5
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Graves SJ, Marconi S, Stewart D, Harmon I, Weinstein B, Kanazawa Y, Scholl VM, Joseph MB, McGlinchy J, Browne L, Sullivan MK, Estrada-Villegas S, Wang DZ, Singh A, Bohlman S, Zare A, White EP. Data science competition for cross-site individual tree species identification from airborne remote sensing data. PeerJ 2023; 11:e16578. [PMID: 38144190 PMCID: PMC10749090 DOI: 10.7717/peerj.16578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 11/13/2023] [Indexed: 12/26/2023] Open
Abstract
Data on individual tree crowns from remote sensing have the potential to advance forest ecology by providing information about forest composition and structure with a continuous spatial coverage over large spatial extents. Classifying individual trees to their taxonomic species over large regions from remote sensing data is challenging. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for the task of classification of individual crowns to species identity. The competition included data from three sites to assess each methods' ability to generalize patterns across two sites simultaneously and apply methods to an untrained site. Three different metrics were used to assess and compare model performance. Six teams participated, representing four countries and nine individuals. The highest performing method from a previous competition in 2017 was applied and used as a baseline to understand advancements and changes in successful methods. The best species classification method was based on a two-stage fully connected neural network that significantly outperformed the baseline random forest and gradient boosting ensemble methods. All methods generalized well by showing relatively strong performance on the trained sites (accuracy = 0.46-0.55, macro F1 = 0.09-0.32, cross entropy loss = 2.4-9.2), but generally failed to transfer effectively to the untrained site (accuracy = 0.07-0.32, macro F1 = 0.02-0.18, cross entropy loss = 2.8-16.3). Classification performance was influenced by the number of samples with species labels available for training, with most methods predicting common species at the training sites well (maximum F1 score of 0.86) relative to the uncommon species where none were predicted. Classification errors were most common between species in the same genus and different species that occur in the same habitat. Most methods performed better than the baseline in detecting if a species was not in the training data by predicting an untrained mixed-species class, especially in the untrained site. This work has highlighted that data science competitions can encourage advancement of methods, particularly by bringing in new people from outside the focal discipline, and by providing an open dataset and evaluation criteria from which participants can learn.
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Affiliation(s)
- Sarah J. Graves
- Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, Wisconsin, United States
| | - Sergio Marconi
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States
| | - Dylan Stewart
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, United States
| | - Ira Harmon
- Department of Computer and Information Sciences and Engineering, University of Florida, Gainesville, Florida, United States
| | - Ben Weinstein
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States
| | - Yuzi Kanazawa
- Artificial Intelligence Laboratory, Fujitsu Laboratories Ltd., Kawasaki, Kanagawa, Japan
| | - Victoria M. Scholl
- Earth Lab, Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado at Boulder, Boulder, Colorado, United States
- Department of Geography, University of Colorado at Boulder, Boulder, Colorado, United States
| | - Maxwell B. Joseph
- Earth Lab, Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado at Boulder, Boulder, Colorado, United States
| | - Joseph McGlinchy
- Earth Lab, Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado at Boulder, Boulder, Colorado, United States
| | - Luke Browne
- Yale School of the Environment, Yale University, New Haven, Connecticut, United States
| | - Megan K. Sullivan
- Yale School of the Environment, Yale University, New Haven, Connecticut, United States
| | | | - Daisy Zhe Wang
- Department of Computer and Information Sciences and Engineering, University of Florida, Gainesville, Florida, United States
| | - Aditya Singh
- Department of Agricultural & Biological Engineering, University of Florida, Gainesville, Florida, United States
| | - Stephanie Bohlman
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, United States
| | - Alina Zare
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, United States
- Informatics Institute, University of Florida, Gainesville, Florida, United States
- Biodiversity Institute, University of Florida, Gainesville, Florida, United States
| | - Ethan P. White
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States
- Informatics Institute, University of Florida, Gainesville, Florida, United States
- Biodiversity Institute, University of Florida, Gainesville, Florida, United States
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Karthigesu J, Owari T, Tsuyuki S, Hiroshima T. UAV Photogrammetry for Estimating Stand Parameters of an Old Japanese Larch Plantation Using Different Filtering Methods at Two Flight Altitudes. SENSORS (BASEL, SWITZERLAND) 2023; 23:9907. [PMID: 38139752 PMCID: PMC10747785 DOI: 10.3390/s23249907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/08/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023]
Abstract
Old plantations are iconic sites, and estimating stand parameters is crucial for valuation and management. This study aimed to estimate stand parameters of a 115-year-old Japanese larch (Larix kaempferi (Lamb.) Carrière) plantation at the University of Tokyo Hokkaido Forest (UTHF) in central Hokkaido, northern Japan, using unmanned aerial vehicle (UAV) photogrammetry. High-resolution RGB imagery was collected using a DJI Matrice 300 real-time kinematic (RTK) at altitudes of 80 and 120 m. Structure from motion (SfM) technology was applied to generate 3D point clouds and orthomosaics. We used different filtering methods, search radii, and window sizes for individual tree detection (ITD), and tree height (TH) and crown area (CA) were estimated from a canopy height model (CHM). Additionally, a freely available shiny R package (SRP) and manually digitalized CA were used. A multiple linear regression (MLR) model was used to estimate the diameter at breast height (DBH), stem volume (V), and carbon stock (CST). Higher accuracy was obtained for ITD (F-score: 0.8-0.87) and TH (R2: 0.76-0.77; RMSE: 1.45-1.55 m) than for other stand parameters. Overall, the flying altitude of the UAV and selected filtering methods influenced the success of stand parameter estimation in old-aged plantations, with the UAV at 80 m generating more accurate results for ITD, CA, and DBH, while the UAV at 120 m produced higher accuracy for TH, V, and CST with Gaussian and mean filtering.
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Affiliation(s)
- Jeyavanan Karthigesu
- Department of Global Agricultural Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan; (J.K.); (S.T.); (T.H.)
- Department of Agronomy, Faculty of Agriculture, University of Jaffna, Jaffna 40000, Sri Lanka
| | - Toshiaki Owari
- The University of Tokyo Hokkaido Forest, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Furano 079-1563, Hokkaido, Japan
| | - Satoshi Tsuyuki
- Department of Global Agricultural Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan; (J.K.); (S.T.); (T.H.)
| | - Takuya Hiroshima
- Department of Global Agricultural Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan; (J.K.); (S.T.); (T.H.)
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7
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Henniger H, Huth A, Bohn FJ. A new approach to derive productivity of tropical forests using radar remote sensing measurements. ROYAL SOCIETY OPEN SCIENCE 2023; 10:231186. [PMID: 38026043 PMCID: PMC10663792 DOI: 10.1098/rsos.231186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023]
Abstract
Deriving gross & net primary productivity (GPP & NPP) and carbon turnover time of forests from remote sensing remains challenging. This study presents a novel approach to estimate forest productivity by combining radar remote sensing measurements, machine learning and an individual-based forest model. In this study, we analyse the role of different spatial resolutions on predictions in the context of the Radar BIOMASS mission (by ESA). In our analysis, we use the forest gap model FORMIND in combination with a boosted regression tree (BRT) to explore how spatial biomass distributions can be used to predict GPP, NPP and carbon turnover time (τ) at different resolutions. We simulate different spatial biomass resolutions (4 ha, 1 ha and 0.04 ha) in combination with different vertical resolutions (20, 10 and 2 m). Additionally, we analysed the robustness of this approach and applied it to disturbed and mature forests. Disturbed forests have a strong influence on the predictions which leads to high correlations (R2 > 0.8) at the spatial scale of 4 ha and 1 ha. Increased vertical resolution leads generally to better predictions for productivity (GPP & NPP). Increasing spatial resolution leads to better predictions for mature forests and lower correlations for disturbed forests. Our results emphasize the value of the forthcoming BIOMASS satellite mission and highlight the potential of deriving estimates for forest productivity from information on forest structure. If applied to more and larger areas, the approach might ultimately contribute to a better understanding of forest ecosystems.
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Affiliation(s)
- Hans Henniger
- Department of Ecological Modeling, Helmholtz Centre of Environmental Research (UFZ), Permoserstraße 15, Leipzig 04318, Germany
- Institute for Environmental Systems Research, University of Osnabrück, Barbara Straße 12, Osnabrück 49074, Germany
| | - Andreas Huth
- Department of Ecological Modeling, Helmholtz Centre of Environmental Research (UFZ), Permoserstraße 15, Leipzig 04318, Germany
- Institute for Environmental Systems Research, University of Osnabrück, Barbara Straße 12, Osnabrück 49074, Germany
- iDiv German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig, Puschstraße 4, Leipzig 04103, Germany
| | - Friedrich J. Bohn
- Department of Computational Hydrosystems, Helmholtz Centre of Environmental Research (UFZ), Permoserstraße 15, Leipzig 04318, Germany
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8
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Lang N, Jetz W, Schindler K, Wegner JD. A high-resolution canopy height model of the Earth. Nat Ecol Evol 2023; 7:1778-1789. [PMID: 37770546 PMCID: PMC10627820 DOI: 10.1038/s41559-023-02206-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/24/2023] [Indexed: 09/30/2023]
Abstract
The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information is required to manage terrestrial ecosystems, mitigate climate change and prevent biodiversity loss. Here we present a comprehensive global canopy height map at 10 m ground sampling distance for the year 2020. We have developed a probabilistic deep learning model that fuses sparse height data from the Global Ecosystem Dynamics Investigation (GEDI) space-borne LiDAR mission with dense optical satellite images from Sentinel-2. This model retrieves canopy-top height from Sentinel-2 images anywhere on Earth and quantifies the uncertainty in these estimates. Our approach improves the retrieval of tall canopies with typically high carbon stocks. According to our map, only 5% of the global landmass is covered by trees taller than 30 m. Further, we find that only 34% of these tall canopies are located within protected areas. Thus, the approach can serve ongoing efforts in forest conservation and has the potential to foster advances in climate, carbon and biodiversity modelling.
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Affiliation(s)
- Nico Lang
- EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland.
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
| | - Walter Jetz
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
| | - Konrad Schindler
- EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland
| | - Jan Dirk Wegner
- EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland.
- Institute for Computational Science, University of Zurich, Zürich, Switzerland.
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9
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Lin Y, Filin S, Billen R, Mizoue N. Co-developing an international TLS network for the 3D ecological understanding of global trees: System architecture, remote sensing models, and functional prospects. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2023; 16:100257. [PMID: 36941885 PMCID: PMC10024182 DOI: 10.1016/j.ese.2023.100257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Trees are spread worldwide, as the watchmen that experience the intricate ecological effects caused by various environmental factors. In order to better understand such effects, it is preferential to achieve finely and fully mapped global trees and their environments. For this task, aerial and satellite-based remote sensing (RS) methods have been developed. However, a critical branch regarding the apparent forms of trees has significantly fallen behind due to the technical deficiency found within their global-scale surveying methods. Now, terrestrial laser scanning (TLS), a state-of-the-art RS technology, is useful for the in situ three-dimensional (3D) mapping of trees and their environments. Thus, we proposed co-developing an international TLS network as a macroscale ecotechnology to increase the 3D ecological understanding of global trees. First, we generated the system architecture and tested the available RS models to deepen its ground stakes. Then, we verified the ecotechnology regarding the identification of its theoretical feasibility, a review of its technical preparations, and a case testification based on a prototype we designed. Next, we conducted its functional prospects by previewing its scientific and technical potentials and its functional extensibility. Finally, we summarized its technical and scientific challenges, which can be used as the cutting points to promote the improvement of this technology in future studies. Overall, with the implication of establishing a novel cornerstone-sense ecotechnology, the co-development of an international TLS network can revolutionize the 3D ecological understanding of global trees and create new fields of research from 3D global tree structural ecology to 3D macroecology.
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Affiliation(s)
- Yi Lin
- School of Earth and Space Sciences, Peking University, Beijing, 100871, China
| | - Sagi Filin
- Technion – Israel Institute of Technology, Haifa IL, 32000, Israel
| | - Roland Billen
- Department of Geography, University of Liège, Liège, 4000, Belgium
| | - Nobuya Mizoue
- Faculty of Agriculture, Kyushu University, Fukuoka, 819-0395, Japan
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10
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Liu S, Brandt M, Nord-Larsen T, Chave J, Reiner F, Lang N, Tong X, Ciais P, Igel C, Pascual A, Guerra-Hernandez J, Li S, Mugabowindekwe M, Saatchi S, Yue Y, Chen Z, Fensholt R. The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe. SCIENCE ADVANCES 2023; 9:eadh4097. [PMID: 37713489 PMCID: PMC10881069 DOI: 10.1126/sciadv.adh4097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 08/15/2023] [Indexed: 09/17/2023]
Abstract
Trees are an integral part in European landscapes, but only forest resources are systematically assessed by national inventories. The contribution of urban and agricultural trees to national-level carbon stocks remains largely unknown. Here we produced canopy cover, height and above-ground biomass maps from 3-meter resolution nanosatellite imagery across Europe. Our biomass estimates have a systematic bias of 7.6% (overestimation; R = 0.98) compared to national inventories of 30 countries, and our dataset is sufficiently highly resolved spatially to support the inclusion of tree biomass outside forests, which we quantify to 0.8 petagrams. Although this represents only 2% of the total tree biomass, large variations between countries are found (10% for UK) and trees in urban areas contribute substantially to national carbon stocks (8% for the Netherlands). The agreement with national inventory data, the scalability, and spatial details across landscapes, including trees outside forests, make our approach attractive for operational implementation to support national carbon stock inventory schemes.
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Affiliation(s)
- Siyu Liu
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Martin Brandt
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Nord-Larsen
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Jerome Chave
- Laboratoire Evolution et Diversité Biologique, CNRS, UPS, IRD, Université Paul Sabatier, Toulouse, France
| | - Florian Reiner
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Nico Lang
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Xiaoye Tong
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l’Environnement, CEA/CNRS/UVSQ/Université Paris Saclay, Gif-sur-Yvette, France
| | - Christian Igel
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Adrian Pascual
- Department of Geographical Sciences, University of Maryland, College Park, MD, USA
| | - Juan Guerra-Hernandez
- Forest Research Center, School of Agriculture, University of Lisbon, Lisbon, Portugal
| | - Sizhuo Li
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Maurice Mugabowindekwe
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Sassan Saatchi
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Yuemin Yue
- Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
| | - Zhengchao Chen
- Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Rasmus Fensholt
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
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11
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Wang D, Zhang Z, Zhang D, Huang X. Biomass allometric models for Larix rupprechtii based on Kosak's taper curve equations and nonlinear seemingly unrelated regression. FRONTIERS IN PLANT SCIENCE 2023; 13:1056837. [PMID: 36699831 PMCID: PMC9868817 DOI: 10.3389/fpls.2022.1056837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 12/19/2022] [Indexed: 06/17/2023]
Abstract
The diameter at breast height (DBH) is the most important independent variable in biomass allometry models based on metabolic scaling theory (MST) or geometric theory. However, the fixed position DBH can be misleading in its use of universal scaling laws and lead to some deviation for the biomass model. Therefore, it is still an urgent scientific problem to build a high-precision biomass model system. A dataset of 114 trees was destructively sampled to obtain dry biomass components, including stems, branches, and foliage, and taper measurements to explore the applicability of biomass components to allometric scaling laws and develop a new system of additive models with the diameter in relative height (DRH) for each component of a Larch (Larix principis-rupprechtii Mayr) plantation in northern China. The variable exponential taper equations were modelled using nonlinear regression. In addition, applying nonlinear regression and nonlinear seemingly unrelated regression (NSUR) enabled the development of biomass allometric models and the system of additive models with DRH for each component. The results showed that the Kozak's (II) 2004 variable exponential taper equation could accurately describe the stem shape and diameter in any height of stem. When the diameters in relative height were D0.2, D0.5, and D0.5 for branches, stems, and foliage, respectively, the allometric exponent of the stems and branches was the closest to the scaling relations predicted by the MST, and the allometric exponent of foliage was the most closely related to the scaling relations predicted by geometry theory. Compared with the nonlinear regression, the parameters of biomass components estimated by NSUR were lower, and it was close to the theoretical value and the most precise at forecasting. In the study of biomass process modelling, utilizing the DRH by a variable exponential taper equation can confirm the general biological significance more than the DBH of a fixed position.
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Affiliation(s)
- Dongzhi Wang
- College of Forestry, Hebei Agricultural University, Baoding, China
| | - Zhidong Zhang
- College of Forestry, Hebei Agricultural University, Baoding, China
| | - Dongyan Zhang
- College of Economics and Management, Hebei Agricultural University, Baoding, China
| | - Xuanrui Huang
- College of Forestry, Hebei Agricultural University, Baoding, China
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12
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Mugabowindekwe M, Brandt M, Chave J, Reiner F, Skole DL, Kariryaa A, Igel C, Hiernaux P, Ciais P, Mertz O, Tong X, Li S, Rwanyiziri G, Dushimiyimana T, Ndoli A, Uwizeyimana V, Lillesø JPB, Gieseke F, Tucker CJ, Saatchi S, Fensholt R. Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda. NATURE CLIMATE CHANGE 2022; 13:91-97. [PMID: 36684409 PMCID: PMC9845119 DOI: 10.1038/s41558-022-01544-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 10/31/2022] [Indexed: 06/17/2023]
Abstract
Trees sustain livelihoods and mitigate climate change but a predominance of trees outside forests and limited resources make it difficult for many tropical countries to conduct automated nation-wide inventories. Here, we propose an approach to map the carbon stock of each individual overstory tree at the national scale of Rwanda using aerial imagery from 2008 and deep learning. We show that 72% of the mapped trees are located in farmlands and savannas and 17% in plantations, accounting for 48.6% of the national aboveground carbon stocks. Natural forests cover 11% of the total tree count and 51.4% of the national carbon stocks, with an overall carbon stock uncertainty of 16.9%. The mapping of all trees allows partitioning to any landscapes classification and is urgently needed for effective planning and monitoring of restoration activities as well as for optimization of carbon sequestration, biodiversity and economic benefits of trees.
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Affiliation(s)
- Maurice Mugabowindekwe
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
- Centre for Geographic Information Systems and Remote Sensing, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Martin Brandt
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Jérôme Chave
- Laboratoire Evolution et Diversité Biologique, CNRS, UPS, IRD, Université Paul Sabatier, Toulouse, France
| | - Florian Reiner
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - David L. Skole
- Global Observatory for Ecosystem Services, Department of Forestry, Michigan State University, East Lansing, MI USA
| | - Ankit Kariryaa
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Christian Igel
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | | | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l’Environnement, CEA/CNRS/UVSQ/Université Paris Saclay, Gif-sur-Yvette, France
| | - Ole Mertz
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Xiaoye Tong
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Sizhuo Li
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
- Université Paris Saclay, Gif-sur-Yvette, France
| | - Gaspard Rwanyiziri
- Centre for Geographic Information Systems and Remote Sensing, College of Science and Technology, University of Rwanda, Kigali, Rwanda
- Department of Geography and Urban Planning, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Thaulin Dushimiyimana
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Alain Ndoli
- International Union for Conservation of Nature—Eastern and Southern Africa Region, Kigali, Rwanda
| | - Valens Uwizeyimana
- General Directorate of Land, Water, and Forestry, Ministry of Environment, Kigali, Rwanda
- Division of Forest, Nature and Landscape, Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
| | | | - Fabian Gieseke
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
- Department of Information Systems, University of Münster, Münster, Germany
| | - Compton J. Tucker
- Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD USA
| | - Sassan Saatchi
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA USA
| | - Rasmus Fensholt
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
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13
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Besson M, Alison J, Bjerge K, Gorochowski TE, Høye TT, Jucker T, Mann HMR, Clements CF. Towards the fully automated monitoring of ecological communities. Ecol Lett 2022; 25:2753-2775. [PMID: 36264848 PMCID: PMC9828790 DOI: 10.1111/ele.14123] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/09/2022] [Accepted: 09/06/2022] [Indexed: 01/12/2023]
Abstract
High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.
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Affiliation(s)
- Marc Besson
- School of Biological SciencesUniversity of BristolBristolUK,Sorbonne Université CNRS UMR Biologie des Organismes Marins, BIOMBanyuls‐sur‐MerFrance
| | - Jamie Alison
- Department of EcoscienceAarhus UniversityAarhusDenmark,UK Centre for Ecology & HydrologyBangorUK
| | - Kim Bjerge
- Department of Electrical and Computer EngineeringAarhus UniversityAarhusDenmark
| | - Thomas E. Gorochowski
- School of Biological SciencesUniversity of BristolBristolUK,BrisEngBio, School of ChemistryUniversity of BristolCantock's CloseBristolBS8 1TSUK
| | - Toke T. Høye
- Department of EcoscienceAarhus UniversityAarhusDenmark,Arctic Research CentreAarhus UniversityAarhusDenmark
| | - Tommaso Jucker
- School of Biological SciencesUniversity of BristolBristolUK
| | - Hjalte M. R. Mann
- Department of EcoscienceAarhus UniversityAarhusDenmark,Arctic Research CentreAarhus UniversityAarhusDenmark
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14
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Stovall AEL, Vorster A, Anderson R, Evangelista P. Developing nondestructive species‐specific tree allometry with terrestrial laser scanning. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.14027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Atticus E. L. Stovall
- NASA Goddard Space Flight Center Greenbelt Maryland USA
- Department of Geographical Sciences University of Maryland College Park Maryland USA
| | - Anthony Vorster
- Natural Resource Ecology Laboratory Colorado State University Fort Collins Colorado USA
| | - Ryan Anderson
- Natural Resource Ecology Laboratory Colorado State University Fort Collins Colorado USA
| | - Paul Evangelista
- Natural Resource Ecology Laboratory Colorado State University Fort Collins Colorado USA
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15
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Ulm F, Estorninho M, de Jesus JG, de Sousa Prado MG, Cruz C, Máguas C. From a Lose-Lose to a Win-Win Situation: User-Friendly Biomass Models for Acacia longifolia to Aid Research, Management and Valorisation. PLANTS (BASEL, SWITZERLAND) 2022; 11:2865. [PMID: 36365319 PMCID: PMC9658486 DOI: 10.3390/plants11212865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/22/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Woody invasive species pose a big threat to ecosystems worldwide. Among them, Acacia longifolia is especially aggressive, fundamentally changing ecosystem structure through massive biomass input. This biomass is rarely harvested for usage; thus, these plants constitute a nuisance for stakeholders who invest time and money for control without monetary return. Simultaneously, there is an increased effort to valorise its biomass, e.g., for compost, growth substrate or as biofuel. However, to incentivise A. longifolia harvest and usage, stakeholders need to be able to estimate what can be obtained from management actions. Thus, the total biomass and its quality (C/N ratio) need to be predicted to perform cost-benefit analyses for usage and determine the level of invasion that has already occurred. Here, we report allometric biomass models for major biomass pools, as well as give an overview of biomass quality. Subsequently, we derive a simplified volume-based model (BM ~ 6.297 + 0.982 × Vol; BM = total dry biomass and Vol = plant volume), which can be applied to remote sensing data or with in situ manual measurements. This toolkit will help local stakeholders, forest managers or municipalities to predict the impact and valorisation potential of this invasive species and could ultimately encourage its management.
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Affiliation(s)
- Florian Ulm
- cE3c–Center for Ecology, Evolution and Environmental Changes & CHANGE–Global Change and Sustainability Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal
| | - Mariana Estorninho
- cE3c–Center for Ecology, Evolution and Environmental Changes & CHANGE–Global Change and Sustainability Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal
| | - Joana Guedes de Jesus
- cE3c–Center for Ecology, Evolution and Environmental Changes & CHANGE–Global Change and Sustainability Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal
| | - Miguel Goden de Sousa Prado
- cE3c–Center for Ecology, Evolution and Environmental Changes & CHANGE–Global Change and Sustainability Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal
- Sousa Prado & Filhos, Agropecuária Lda, 7645-239 Vila Nova de Milfontes, Portugal
| | - Cristina Cruz
- cE3c–Center for Ecology, Evolution and Environmental Changes & CHANGE–Global Change and Sustainability Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal
| | - Cristina Máguas
- cE3c–Center for Ecology, Evolution and Environmental Changes & CHANGE–Global Change and Sustainability Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal
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16
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Dettmann GT, MacFarlane DW, Radtke PJ, Weiskittel AR, Affleck DLR, Poudel KP, Westfall J. Testing a generalized leaf mass estimation method for diverse tree species and climates of the continental United States. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2646. [PMID: 35524985 PMCID: PMC9787613 DOI: 10.1002/eap.2646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
Estimating tree leaf biomass can be challenging in applications where predictions for multiple tree species is required. This is especially evident where there is limited or no data available for some of the species of interest. Here we use an extensive national database of observations (61 species, 3628 trees) and formulate models of varying complexity, ranging from a simple model with diameter at breast height (DBH) as the only predictor to more complex models with up to 8 predictors (DBH, leaf longevity, live crown ratio, wood specific gravity, shade tolerance, mean annual temperature, and mean annual precipitation), to estimate tree leaf biomass for any species across the continental United States. The most complex with all eight predictors was the best and explained 74%-86% of the variation in leaf mass. Consideration was given to the difficulty of measuring all of these predictor variables for model application, but many are easily obtained or already widely collected. Because most of the model variables are independent of species and key species-level variables are available from published values, our results show that leaf biomass can be estimated for new species not included in the data used to fit the model. The latter assertion was evaluated using a novel "leave-one-species-out" cross-validation approach, which showed that our chosen model performs similarly for species used to calibrate the model, as well as those not used to develop it. The models exhibited a strong bias toward overestimation for a relatively small subset of the trees. Despite these limitations, the models presented here can provide leaf biomass estimates for multiple species over large spatial scales and can be applied to new species or species with limited leaf biomass data available.
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Affiliation(s)
- Garret T. Dettmann
- Virginia Tech, Forest Resources and Environmental ConservationBlacksburgVirginiaUSA
| | | | - Philip J. Radtke
- Virginia Tech, Forest Resources and Environmental ConservationBlacksburgVirginiaUSA
| | | | - David L. R. Affleck
- WA Franke College of Forestry and ConservationUniversity of MontanaMissoulaMontanaUSA
| | - Krishna P. Poudel
- Department of ForestryMississippi State UniversityMississippi StateMississippiUSA
| | - James Westfall
- USDA Forest Service, Northern Research StationNewtown SquarePennsylvaniaUSA
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17
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Jucker T, Fischer FJ, Chave J, Coomes DA, Caspersen J, Ali A, Loubota Panzou GJ, Feldpausch TR, Falster D, Usoltsev VA, Adu‐Bredu S, Alves LF, Aminpour M, Angoboy IB, Anten NPR, Antin C, Askari Y, Muñoz R, Ayyappan N, Balvanera P, Banin L, Barbier N, Battles JJ, Beeckman H, Bocko YE, Bond‐Lamberty B, Bongers F, Bowers S, Brade T, van Breugel M, Chantrain A, Chaudhary R, Dai J, Dalponte M, Dimobe K, Domec J, Doucet J, Duursma RA, Enríquez M, van Ewijk KY, Farfán‐Rios W, Fayolle A, Forni E, Forrester DI, Gilani H, Godlee JL, Gourlet‐Fleury S, Haeni M, Hall JS, He J, Hemp A, Hernández‐Stefanoni JL, Higgins SI, Holdaway RJ, Hussain K, Hutley LB, Ichie T, Iida Y, Jiang H, Joshi PR, Kaboli H, Larsary MK, Kenzo T, Kloeppel BD, Kohyama T, Kunwar S, Kuyah S, Kvasnica J, Lin S, Lines ER, Liu H, Lorimer C, Loumeto J, Malhi Y, Marshall PL, Mattsson E, Matula R, Meave JA, Mensah S, Mi X, Momo S, Moncrieff GR, Mora F, Nissanka SP, O'Hara KL, Pearce S, Pelissier R, Peri PL, Ploton P, Poorter L, Pour MJ, Pourbabaei H, Dupuy‐Rada JM, Ribeiro SC, Ryan C, Sanaei A, Sanger J, Schlund M, Sellan G, Shenkin A, Sonké B, Sterck FJ, Svátek M, Takagi K, Trugman AT, Ullah F, Vadeboncoeur MA, Valipour A, Vanderwel MC, Vovides AG, Wang W, Wang L, Wirth C, Woods M, Xiang W, Ximenes FDA, Xu Y, Yamada T, Zavala MA. Tallo: A global tree allometry and crown architecture database. GLOBAL CHANGE BIOLOGY 2022; 28:5254-5268. [PMID: 35703577 PMCID: PMC9542605 DOI: 10.1111/gcb.16302] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/12/2022] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.
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Affiliation(s)
- Tommaso Jucker
- School of Biological SciencesUniversity of BristolBristolUK
| | | | - Jérôme Chave
- Laboratoire Évolution et Diversité Biologique (EDB)UMR 5174 (CNRS/IRD/UPS)Toulouse Cedex 9France
- Université ToulouseToulouse Cedex 9France
| | - David A. Coomes
- Conservation Research InstituteUniversity of CambridgeCambridgeUK
| | - John Caspersen
- Institute of Forestry and ConservationUniversity of TorontoTorontoOntarioCanada
| | - Arshad Ali
- Forest Ecology Research Group, College of Life SciencesHebei UniversityBaodingHebeiChina
| | - Grace Jopaul Loubota Panzou
- Université de Liège, Gembloux Agro‐Bio TechGemblouxBelgium
- Laboratoire de Biodiversité, de Gestion des Ecosystèmes et de l'Environnement (LBGE), Faculté des Sciences et TechniquesUniversité Marien NgouabiBrazzavilleRepublic of Congo
| | - Ted R. Feldpausch
- College of Life and Environmental SciencesUniversity of ExeterExeterUK
| | - Daniel Falster
- Evolution & Ecology Research CentreUniversity of New South Wales SydneySydneyNew South WalesAustralia
| | - Vladimir A. Usoltsev
- Department of ForestryUral State Forest Engineering UniversityYekaterinburgRussia
- Department of Forest DynamicsBotanical Garden of the Ural Branch of Russian Academy of SciencesYekaterinburgRussia
| | - Stephen Adu‐Bredu
- Forestry Research Institute of Ghana, Council for Scientific and Industrial ResearchUniversityKumasiGhana
| | - Luciana F. Alves
- Center for Tropical Research, Institute of the Environment and SustainabilityUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Mohammad Aminpour
- Natural Recourses and Watershed Management Office, West Azerbaijan ProvinceUrmiaIran
| | - Ilondea B. Angoboy
- Institut National pour l'Etude et la Recherche AgronimiquesDemocratic Republic of the Congo
| | - Niels P. R. Anten
- Center for Crop Systems AnalysisWageningen UniversityWageningenThe Netherlands
| | - Cécile Antin
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
| | - Yousef Askari
- Research Division of Natural Resources, Kohgiluyeh and Boyerahmad Agriculture and Natural Resources Research and Education Center, AREEOYasoujIran
| | - Rodrigo Muñoz
- Departamento de Ecología y Recursos Naturales, Facultad de CienciasUniversidad Nacional Autónoma de México, CoyoacánCiudad de MéxicoMexico
- Forest Ecology and Forest Management GroupWageningen UniversityWageningenThe Netherlands
| | | | - Patricia Balvanera
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de MéxicoMoreliaMichoacánMexico
| | | | - Nicolas Barbier
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
| | | | - Hans Beeckman
- Service of Wood BiologyRoyal Museum for Central AfricaTervurenBelgium
| | - Yannick E. Bocko
- Laboratoire de Biodiversité, de Gestion des Ecosystèmes et de l'Environnement (LBGE), Faculté des Sciences et TechniquesUniversité Marien NgouabiBrazzavilleRepublic of Congo
| | - Ben Bond‐Lamberty
- Pacific Northwest National LaboratoryJoint Global Change Research InstituteCollege ParkMarylandUSA
| | - Frans Bongers
- Forest Ecology and Forest Management GroupWageningen UniversityWageningenThe Netherlands
| | - Samuel Bowers
- School of GeoSciencesUniversity of EdinburghEdinburghUK
| | - Thomas Brade
- School of GeoSciencesUniversity of EdinburghEdinburghUK
| | - Michiel van Breugel
- Yale‐NUS CollegeSingapore
- ForestGEOSmithsonian Tropical Research InstituteApartadoPanamaRepublic of Panama
- Department of GeographyNational University of SingaporeSingapore
| | | | - Rajeev Chaudhary
- Division Forest OfficeMinistry of ForestDhangadhiSudurpashchim ProvinceNepal
| | - Jingyu Dai
- College of Urban and Environmental Sciences and MOE Laboratory for Earth Surface ProcessesPeking UniversityBeijingChina
| | - Michele Dalponte
- Research and Innovation Centre, Fondazione Edmund MachSan Michele all'AdigeItaly
| | - Kangbéni Dimobe
- Institut des Sciences de l'Environnement et du Développement Rural (ISEDR)Université de DédougouDédougouBurkina Faso
| | - Jean‐Christophe Domec
- Bordeaux Sciences Agro‐UMR ISPA, INRAEBordeauxFrance
- Nicholas School of the EnvironmentDuke UniversityDurhamNCUSA
| | | | | | - Moisés Enríquez
- Departamento de Ecología y Recursos Naturales, Facultad de CienciasUniversidad Nacional Autónoma de México, CoyoacánCiudad de MéxicoMexico
| | - Karin Y. van Ewijk
- Department of Geography and Planning, Queen's UniversityKingstonOntarioCanada
| | | | | | - Eric Forni
- CIRAD, UPR Forêts et SociétésMontpellierFrance
| | | | - Hammad Gilani
- Institute of Space Technology, Islamabad HighwayIslamabadPakistan
| | | | | | - Matthias Haeni
- Swiss Federal Research Institute WSLBirmensdorfSwitzerland
| | - Jefferson S. Hall
- ForestGEOSmithsonian Tropical Research InstituteApartadoPanamaRepublic of Panama
| | - Jie‐Kun He
- Spatial Ecology Lab, School of Life SciencesSouth China Normal UniversityGuangzhouGuangdongChina
| | - Andreas Hemp
- Department of Plant SystematicsUniversity of BayreuthBayreuthGermany
| | | | | | | | - Kiramat Hussain
- Gilgit‐Baltistan Forest Wildlife and Environment DepartmentGilgitPakistan
| | - Lindsay B. Hutley
- Research Institute for the Environment & LivelihoodsCharles Darwin UniversityCasuarinaNorthern TerritoryAustralia
| | - Tomoaki Ichie
- Faculty of Agriculture and Marine ScienceKochi UniversityNankokuKochiJapan
| | - Yoshiko Iida
- Forestry and Forest Products Research InstituteTsukubaIbarakiJapan
| | - Hai‐sheng Jiang
- Spatial Ecology Lab, School of Life SciencesSouth China Normal UniversityGuangzhouGuangdongChina
| | | | - Hasan Kaboli
- Faculty of Desert Studies Semnan UniversitySemnanIran
| | | | - Tanaka Kenzo
- Japan International Research Center for Agricultural SciencesTsukubaIbarakiJapan
| | - Brian D. Kloeppel
- Department of Geosciences and Natural ResourcesWestern Carolina UniversityCullowheeNorth CarolinaUSA
- Graduate School and ResearchWestern Carolina UnversityCullowheeNorth CarolinaUSA
| | - Takashi Kohyama
- Faculty of Environmental Earth ScienceHokkaido UniversitySapporoJapan
| | - Suwash Kunwar
- Division Forest OfficeMinistry of ForestDhangadhiSudurpashchim ProvinceNepal
- Department of Forest Resources Management, College of ForestryNanjing Forestry UniversityNanjingJiangsuChina
| | - Shem Kuyah
- Jomo Kenyatta University of Agriculture and Technology (JKUAT)NairobiKenya
| | - Jakub Kvasnica
- Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood TechnologyMendel University in BrnoBrnoCzech Republic
| | - Siliang Lin
- Guangdong Provincial Key Laboratory of High Technology for Plant Protection, Plant Protection Research InstituteGuangdong Academy of Agricultural SciencesGuangzhouGuangdongChina
| | - Emily R. Lines
- Department of GeographyUniversity of CambridgeCambridgeUK
| | - Hongyan Liu
- College of Urban and Environmental Sciences and MOE Laboratory for Earth Surface ProcessesPeking UniversityBeijingChina
| | - Craig Lorimer
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Jean‐Joël Loumeto
- Laboratoire de Biodiversité, de Gestion des Ecosystèmes et de l'Environnement (LBGE), Faculté des Sciences et TechniquesUniversité Marien NgouabiBrazzavilleRepublic of Congo
| | - Yadvinder Malhi
- Environmental Change Institute, School of Geography and the EnvironmentUniversity of OxfordOxfordUK
| | - Peter L. Marshall
- Faculty of ForestryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Eskil Mattsson
- IVL Swedish Environmental Research InstituteGöteborgSweden
- Gothenburg Global Biodiversity Centre (GGBC), GothenburgSweden
| | - Radim Matula
- Faculty of Forestry and Wood SciencesCzech University of Life Sciences Prague, Prague 6SuchdolCzech Republic
| | - Jorge A. Meave
- Departamento de Ecología y Recursos Naturales, Facultad de CienciasUniversidad Nacional Autónoma de México, CoyoacánCiudad de MéxicoMexico
| | - Sylvanus Mensah
- Laboratoire de Biomathématiques et d'Estimations Forestières, Faculté des Sciences AgronomiquesUniversité d'Abomey CalaviCotonouBenin
| | - Xiangcheng Mi
- State Key Laboratory of Vegetation and Environmental Change, Institute of BotanyChinese Academy of SciencesBeijingChina
| | - Stéphane Momo
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
- Laboratoire de Botanique systématique et d'Ecologie, Département des Sciences Biologiques, Ecole Normale SupérieureUniversité de Yaoundé IYaoundéCameroon
| | - Glenn R. Moncrieff
- Fynbos Node, South African Environmental Observation NetworkClaremontSouth Africa
- Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical SciencesUniversity of Cape TownRondeboschSouth Africa
| | - Francisco Mora
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de MéxicoMoreliaMichoacánMexico
| | - Sarath P. Nissanka
- Department of Crop Science, Faculty of AgricultureUniversity of PeradeniyaPeradeniyaSri Lanka
| | | | | | - Raphaël Pelissier
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
| | - Pablo L. Peri
- Universidad Nacional de la Patagonia Austral (UNPA) ‐ Instituto Nacional de Tecnología Agropecuaria (INTA) ‐ CONICETRío GallegosSanta CruzArgentina
| | - Pierre Ploton
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
| | - Lourens Poorter
- Forest Ecology and Forest Management GroupWageningen UniversityWageningenThe Netherlands
| | | | - Hassan Pourbabaei
- Department of Forestry, Faculty of Natural ResourcesUniversity of GuilanSomehsaraIran
| | - Juan Manuel Dupuy‐Rada
- Centro de Investigación Científica de Yucatán A.C., Unidad de Recursos NaturalesMéridaYucatánMexico
| | - Sabina C. Ribeiro
- Centro de Ciências Biológicas e da NaturezaUniversidade Federal do Acre, Campus UniversitárioRio BrancoBrazil
| | - Casey Ryan
- School of GeoSciencesUniversity of EdinburghEdinburghUK
| | - Anvar Sanaei
- Systematic Botany and Functional Biodiversity, Institute of BiologyLeipzig UniversityLeipzigGermany
| | | | - Michael Schlund
- Department of Natural Resources, Faculty of Geo‐information Science and Earth Observation (ITC)University of TwenteEnschedeThe Netherlands
| | - Giacomo Sellan
- UMR EcoFoG, CNRSKourouFrench Guiana
- Department of Natural SciencesManchester Metropolitan UniversityManchesterUK
| | - Alexander Shenkin
- Environmental Change Institute, School of Geography and the EnvironmentUniversity of OxfordOxfordUK
| | - Bonaventure Sonké
- Laboratoire de Botanique systématique et d'Ecologie, Département des Sciences Biologiques, Ecole Normale SupérieureUniversité de Yaoundé IYaoundéCameroon
| | - Frank J. Sterck
- Forest Ecology and Forest Management GroupWageningen UniversityWageningenThe Netherlands
| | - Martin Svátek
- Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood TechnologyMendel University in BrnoBrnoCzech Republic
| | - Kentaro Takagi
- Field Science Center for Northern BiosphereHokkaido UniversityHoronobeJapan
| | - Anna T. Trugman
- Department of GeographyUniversity of California Santa BarbaraSanta BarbaraCaliforniaUSA
| | - Farman Ullah
- Forest Ecology Research Group, College of Life SciencesHebei UniversityBaodingHebeiChina
- Department of Forest Resources Management, College of ForestryNanjing Forestry UniversityNanjingJiangsuChina
| | | | - Ahmad Valipour
- Department of Forestry and The Center for Research and Development of Northern Zagros ForestryUniversity of KurdistanErbilIran
| | | | - Alejandra G. Vovides
- School of Geographical and Earth SciencesUniversity of Glasgow, East QuadrangleGlasgowUK
| | - Weiwei Wang
- State Key Laboratory of Vegetation and Environmental Change, Institute of BotanyChinese Academy of SciencesBeijingChina
| | - Li‐Qiu Wang
- Department of Forest Resources Management, College of ForestryNanjing Forestry UniversityNanjingJiangsuChina
| | - Christian Wirth
- Systematic Botany and Functional Biodiversity, Institute of BiologyUniversity of LeipzigLeipzigGermany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
| | - Murray Woods
- Ontario Ministry of Natural ResourcesNorth BayOntarioCanada
| | - Wenhua Xiang
- Faculty of Life Science and TechnologyCentral South University of Forestry and TechnologyChangshaHunanChina
| | | | - Yaozhan Xu
- State Key Laboratory of Aquatic Botany and Watershed EcologyWuhan Botanical Garden, Chinese Academy of SciencesWuhanChina
- Center of Conservation Biology, Core Botanical GardensChinese Academy of SciencesWuhanChina
| | - Toshihiro Yamada
- Graduate School of Integrated Sciences of LifeHiroshima UniversityHiroshimaJapan
| | - Miguel A. Zavala
- Forest Ecology and Restoration Group (FORECO), Departamento de Ciencias de la VidaUniversidad de AlcaláMadridSpain
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18
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Use of Unoccupied Aerial Systems to Characterize Woody Vegetation across Silvopastoral Systems in Ecuador. REMOTE SENSING 2022. [DOI: 10.3390/rs14143386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The trees in pastures are recognized for the benefits they provide to livestock, farmers, and the environment; nevertheless, their study has been restricted to small areas, making it difficult to upscale this information to national levels. For tropical developing countries, it is particularly important to understand the contribution of these systems to national carbon budgets. However, the costs associated with performing field measurements might limit the acquisition of this information. The use of unoccupied aerial systems (UAS) for ecological surveys has proved useful for collecting information at larger scales and with significantly lower costs. This study proposes a methodology that integrates field and UAS surveys to study trees on pasture areas across different terrain conditions. Our overall objective was to test the suitability of UAS surveys to the estimation of aboveground biomass (AGB), relying mainly on open-source software. The tree heights and crown diameters were measured on 0.1-hectare circular plots installed on pasture areas on livestock farms in the Amazon and Coastal regions in Ecuador. An UAS survey was performed on 1-hectare plots containing the circular plots. Field measurements were compared against canopy-height model values and biomass estimates using the two sources of information. Our results demonstrate that UAS surveys can be useful for identifying tree spatial arrangements and provide good estimates of tree height (RMSE values ranged from 0.01 to 3.53 m), crown diameter (RMSE values ranged from 0.04 to 4.47 m), and tree density (density differences ranging from 21.5 to 64.3%), which have a direct impact on biomass estimates. The differences in biomass estimates between the UAS and the field-measured values ranged from 25 to 75%, depending on site characteristics, such as slope and tree coverage. The results suggest that UASs are reliable and feasible tools with which to study tree characteristics on pastures, covering larger areas than field methods only.
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19
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Fan Y, Li G, Wu J. Research on monitoring overground carbon stock of forest vegetation communities based on remote sensing technology. PROCEEDINGS OF THE INDIAN NATIONAL SCIENCE ACADEMY 2022. [DOI: 10.1007/s43538-022-00080-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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20
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Parsing Long-Term Tree Recruitment, Growth, and Mortality to Identify Hurricane Effects on Structural and Compositional Change in a Tropical Forest. FORESTS 2022. [DOI: 10.3390/f13050796] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
After hurricane disturbances in tropical forests, the size structure and species composition are affected by immediate mortality, and subsequent recruitment and individual growth. Often, immediate post-disturbance stand-level data are presented but understanding of the components that affect changes in growth and longer-term responses to forest structure and composition are lacking. To answer questions about how mortality, recruitment, and growth change among successional Plant Functional Types (PFT) through time after a hurricane disturbance, we use long-term census data (1989–2014) collected in the Luquillo Experimental Forest, Puerto Rico. We developed an algorithm to fill missing diameter data from the long-term data set that was collected three months after Hurricane Hugo; and subsequently at five-year intervals. Both the immediate hurricane-induced mortality and subsequent mortality were lower in stems with larger diameters, but varied among successional PFTs Early, Mid, Late, and Palm. Tree growth rates were observed to decrease with time since the hurricane disturbance. Five years after the hurricane, mortality was minimal but then increased gradually with time. In contrast, recruitment was highest five years after the hurricane and then decreased with time. The palm Prestoea montana became the most abundant species in the forest after the hurricane, as it had the lowest immediate hurricane-induced and subsequent mortality, and the highest recruitment. Twenty-five years after the hurricane, the palm and the Late PFT dominate the forest after shifting species composition from pre-hurricane conditions.
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21
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Combining Area-Based and Individual Tree Metrics for Improving Merchantable and Non-Merchantable Wood Volume Estimates in Coastal Douglas-Fir Forests. REMOTE SENSING 2022. [DOI: 10.3390/rs14092204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Forest management practices can increase climate change mitigation potential through applications focused on carbon budgets. One such application involves utilizing non-merchantable material (i.e., logging residues typically piled and burned) for bio-energy. However, limited remote sensing data is available for estimating wood residues until after timber has been harvested, at which point recovery of residual wood is of little financial interest. This research utilizes a hybrid method to develop models that provide pre-harvest estimates of the amount of merchantable and non-merchantable material that would result from harvesting and investigates the scalability and transferability of such measures to the harvest block level. Models were trained using 38 plots across two sites dominated by Douglas-fir, then expanded to ten harvest blocks, and transferred to eight blocks from two sites without training data before being compared against multiple independent block-level estimates. Model results showed root mean square errors of 35% and 38% for merchantable and non-merchantable volumes, respectively. Merchantable volume estimates in blocks with training had average absolute differences from the harvest scale (9–34%) similar to transferred blocks without training (15–20%). Non-merchantable model results were also similar in both trained and transferred harvest blocks, with the pre-harvest model results having lower differences from the post-harvest geospatial versus field surveys. The results from this study show promise for hybrid methods to improve estimates of merchantable wood volume compared to conventional forest cover data approaches, and provide the ability to predict non-merchantable volumes within the range of accuracy of post-harvest residue survey methods.
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22
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Abstract
The increasing importance of forest ecosystems for human society and planetary health is widely recognized, and the advancement of data collection technologies enables new and integrated ways for forest ecosystems monitoring. Therefore, the target of this paper is to propose a framework to design a forest digital twin (FDT) that, by integrating different state variables at both tree and forest levels, creates a virtual copy of the forest. The integration of these data sets could be used for scientific purposes, for reporting the health status of forests, and ultimately for implementing sustainable forest management practices on the basis of the use cases that a specific implementation of the framework would underpin. Achieving such outcomes requires the twinning of single trees as a core element of the FDT by recording the physical and biotic state variables of the tree and of the near environment via real–virtual digital sockets. Following a nested approach, the twinned trees and the related physical and physiological processes are then part of a broader twinning of the entire forest realized by capturing data at forest scale from sources such as remote sensing technologies and flux towers. Ultimately, to unlock the economic value of forest ecosystem services, the FDT should implement a distributed ledger-based on blockchain and smart contracts to ensure the highest transparency, reliability, and thoroughness of the data and the related transactions and to sharpen forest risk management with the final goal to improve the capital flow towards sustainable practices of forest management.
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23
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Can a Hierarchical Classification of Sentinel-2 Data Improve Land Cover Mapping? REMOTE SENSING 2022. [DOI: 10.3390/rs14040989] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Monitoring of land cover plays an important role in effective environmental management, assessment of natural resources, environmental protection, urban planning and sustainable development. Increasing demand for accurate and repeatable information on land cover and land cover changes causes rapid development of the advanced, machine learning algorithms dedicated to land cover mapping using satellite images. Free and open access to Sentinel-2 data, characterized with high spatial and temporal resolution, increased the potential to map and to monitor land surface with high accuracy and frequency. Despite a considerable number of approaches towards land cover classification based on satellite data, there is still a challenge to clearly separate complex land cover classes, for example grasslands, arable land and wetlands. The aim of this study is to examine, whether a hierarchal classification of Sentinel-2 data can improve the accuracy of land cover mapping and delineation of complex land cover classes. The study is conducted in the Lodz Province, in central Poland. The pixel-based land cover classification is carried out using the machine learning Random Forest (RF) algorithm, based on a time series of Sentinel-2 imagery acquired in 2020. The following nine land cover classes are mapped: sealed surfaces, woodland broadleaved, woodland coniferous, shrubs, permanent herbaceous (grassy cover), periodically herbaceous (i.e., arable land), mosses, non-vegetated (bare soil) and water bodies. The land cover classification is conducted following two approaches: (1) flat, where all land cover classes are classified together, and (2) hierarchical, where the stratification is applied to first separate the most stable land cover classes and then classifying the most problematic once. The national databases served as the source of the reference sampling plots for the classification process. The process of selection and verification of the reference sampling plots is performed automatically. To assess the stability of the classification models the classification processes are performed iteratively. The results of this study confirmed that the hierarchical approach gave more accurate results compared to the commonly used flat approach. The median of the overall accuracy (OA) of the hierarchical classification was higher by 3–9 percentage points compared to the flat one. Of interest, the OA of the hierarchical classification reached 0.93–0.99, whereas the flat approach reached 0.90. Individual classes are also better classified in the hierarchical approach.
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24
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Rau EP, Fischer F, Joetzjer É, Maréchaux I, Sun IF, Chave J. Transferability of an individual- and trait-based forest dynamics model: A test case across the tropics. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2021.109801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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25
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Jucker T. Deciphering the fingerprint of disturbance on the three-dimensional structure of the world's forests. THE NEW PHYTOLOGIST 2022; 233:612-617. [PMID: 34506641 DOI: 10.1111/nph.17729] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 09/03/2021] [Indexed: 06/13/2023]
Abstract
Canopy gaps and the processes that generate them play an integral role in shaping the structure and dynamics of forests. However, it is only with recent advances in remote sensing technologies such as airborne laser scanning that studying canopy gaps at scale has become a reality. Consequently, we still lack an understanding of how the size distribution and spatial organization of canopy gaps varies among forests ecosystems, nor have we determined whether these emergent properties can be reconciled with existing theories of forest dynamics. Here, I outline a roadmap for integrating remote sensing with field data and individual-based models to build a comprehensive picture of how environmental constraints and disturbance regimes shape the three-dimensional structure of the world's forests.
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Affiliation(s)
- Tommaso Jucker
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, UK
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26
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Harrison PA, Camarretta N, Krisanski S, Bailey TG, Davidson NJ, Bain G, Hamer R, Gardiner R, Proft K, Taskhiri MS, Turner P, Turner D, Lucieer A. From communities to individuals: Using remote sensing to inform and monitor woodland restoration. ECOLOGICAL MANAGEMENT & RESTORATION 2021. [DOI: 10.1111/emr.12505] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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27
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Poorter L, Craven D, Jakovac CC, van der Sande MT, Amissah L, Bongers F, Chazdon RL, Farrior CE, Kambach S, Meave JA, Muñoz R, Norden N, Rüger N, van Breugel M, Almeyda Zambrano AM, Amani B, Andrade JL, Brancalion PHS, Broadbent EN, de Foresta H, Dent DH, Derroire G, DeWalt SJ, Dupuy JM, Durán SM, Fantini AC, Finegan B, Hernández-Jaramillo A, Hernández-Stefanoni JL, Hietz P, Junqueira AB, N'dja JK, Letcher SG, Lohbeck M, López-Camacho R, Martínez-Ramos M, Melo FPL, Mora F, Müller SC, N'Guessan AE, Oberleitner F, Ortiz-Malavassi E, Pérez-García EA, Pinho BX, Piotto D, Powers JS, Rodríguez-Buriticá S, Rozendaal DMA, Ruíz J, Tabarelli M, Teixeira HM, Valadares de Sá Barretto Sampaio E, van der Wal H, Villa PM, Fernandes GW, Santos BA, Aguilar-Cano J, de Almeida-Cortez JS, Alvarez-Davila E, Arreola-Villa F, Balvanera P, Becknell JM, Cabral GAL, Castellanos-Castro C, de Jong BHJ, Nieto JE, Espírito-Santo MM, Fandino MC, García H, García-Villalobos D, Hall JS, Idárraga A, Jiménez-Montoya J, Kennard D, Marín-Spiotta E, Mesquita R, Nunes YRF, Ochoa-Gaona S, Peña-Claros M, Pérez-Cárdenas N, Rodríguez-Velázquez J, Villanueva LS, Schwartz NB, Steininger MK, Veloso MDM, Vester HFM, Vieira ICG, Williamson GB, Zanini K, Hérault B. Multidimensional tropical forest recovery. Science 2021; 374:1370-1376. [PMID: 34882461 DOI: 10.1126/science.abh3629] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Lourens Poorter
- Forest Ecology and Forest Management Group, Wageningen University, Wageningen, Netherlands
| | - Dylan Craven
- Centro de Modelación y Monitoreo de Ecosistemas, Universidad Mayor, Santiago, Chile
| | - Catarina C Jakovac
- Forest Ecology and Forest Management Group, Wageningen University, Wageningen, Netherlands.,Departamento de Fitotecnia, Universidade Federal de Santa Catarina. Rod. Admar Gonzaga, Florianópolis, SC, Brazil
| | - Masha T van der Sande
- Forest Ecology and Forest Management Group, Wageningen University, Wageningen, Netherlands
| | - Lucy Amissah
- CSIR-Forestry Research Institute of Ghana, KNUST, Kumasi, Ghana
| | - Frans Bongers
- Forest Ecology and Forest Management Group, Wageningen University, Wageningen, Netherlands
| | - Robin L Chazdon
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA.,Tropical Forests and People Research Centre, University of the Sunshine Coast, Maroochydore DC, QLD, Australia
| | | | - Stephan Kambach
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Jorge A Meave
- Departamento de Ecología y Recursos Naturales, Facultad de Ciencias, Universidad Nacional Autónoma de México, Coyoacán, Mexico City, Mexico
| | - Rodrigo Muñoz
- Forest Ecology and Forest Management Group, Wageningen University, Wageningen, Netherlands.,Departamento de Ecología y Recursos Naturales, Facultad de Ciencias, Universidad Nacional Autónoma de México, Coyoacán, Mexico City, Mexico
| | - Natalia Norden
- Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Bogotá, Colombia
| | - Nadja Rüger
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.,Department of Economics, University of Leipzig, Leipzig, Germany.,Smithsonian Tropical Research Institute, Ancón, Balboa, Panama
| | - Michiel van Breugel
- SI ForestGEO, Smithsonian Tropical Research Institute, Ancón, Balboa, Panama.,Yale-NUS College, Singapore, Singapore.,Department of Biological Sciences, National University of Singapore, Singapore, Singapore
| | | | - Bienvenu Amani
- UFR Agroforesterie, Université Jean Lorougnon Guédé Daloa, Daloa, Côte d'Ivoire
| | - José Luis Andrade
- Centro de Investigación Científica de Yucatán A.C. Unidad de Recursos Naturales, Colonia Chuburná de Hidalgo, Mérida, Yucatán, Mexico
| | - Pedro H S Brancalion
- Department of Forest Sciences, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil
| | - Eben N Broadbent
- Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA
| | - Hubert de Foresta
- UMR AMAP, Institut de Recherche pour le Développement (IRD), Montpellier, France
| | - Daisy H Dent
- Smithsonian Tropical Research Institute, Ancón, Balboa, Panama.,Biological and Environmental Sciences, University of Stirling, Stirling, UK
| | - Géraldine Derroire
- CIRAD, UMR EcoFoG (AgroParistech, CNRS, INRAE, Université des Antilles, Université de la Guyane), Campus Agronomique, Kourou, French Guiana
| | - Saara J DeWalt
- Department of Biological Sciences, Clemson University, Clemson, SC, USA
| | - Juan M Dupuy
- Centro de Investigación Científica de Yucatán A.C. Unidad de Recursos Naturales, Colonia Chuburná de Hidalgo, Mérida, Yucatán, Mexico
| | - Sandra M Durán
- Earth and Atmospheric Sciences Department, University of Alberta, Edmonton, AB, Canada.,Department of Ecology and Evolutionary Biology, University of Minnesota, St. Paul, MN, USA
| | | | - Bryan Finegan
- CATIE-Centro Agronómico Tropical de Investigación y Enseñanza, Turrialba, Costa Rica
| | | | - José Luis Hernández-Stefanoni
- Centro de Investigación Científica de Yucatán A.C. Unidad de Recursos Naturales, Colonia Chuburná de Hidalgo, Mérida, Yucatán, Mexico
| | - Peter Hietz
- Institute of Botany, University of Natural Resources and Life Sciences, Vienna, Austria
| | - André B Junqueira
- Institut de Ciència i Tecnologia Ambientals, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, Spain
| | - Justin Kassi N'dja
- Departement of Bioscience, University Felix Houphouet-Boigny, Abidjan, Côte d'Ivoire
| | | | - Madelon Lohbeck
- Forest Ecology and Forest Management Group, Wageningen University, Wageningen, Netherlands.,World Agroforestry Centre, ICRAF, United Nations Avenue, Gigiri, Nairobi, Kenya
| | - René López-Camacho
- Universidad Distrital Francisco José de Caldas, Facultad de Medio Ambiente y Recursos Naturales, Bogotá, Colombia
| | - Miguel Martínez-Ramos
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, Michoacán, Mexico
| | - Felipe P L Melo
- Departamento de Botânica, Universidade Federal de Pernambuco, Recife, Brazil
| | - Francisco Mora
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, Michoacán, Mexico
| | - Sandra C Müller
- Departamento de Ecologia, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Anny E N'Guessan
- Departement of Bioscience, University Felix Houphouet-Boigny, Abidjan, Côte d'Ivoire
| | | | - Edgar Ortiz-Malavassi
- Instituto Tecnológico de Costa Rica, Escuela de Ingeniería Forestal, Cartago, Costa Rica
| | - Eduardo A Pérez-García
- Departamento de Ecología y Recursos Naturales, Facultad de Ciencias, Universidad Nacional Autónoma de México, Coyoacán, Mexico City, Mexico
| | - Bruno X Pinho
- Departamento de Botânica, Universidade Federal de Pernambuco, Recife, Brazil
| | - Daniel Piotto
- Centro de Formação em Ciências Agroflorestais, Universidade Federal do Sul da Bahia, Itabuna, BA, Brazil
| | - Jennifer S Powers
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN, USA.,Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN, USA
| | | | - Danaë M A Rozendaal
- Plant Production Systems Group, Wageningen University and Research, Wageningen, Netherlands.,Centre for Crop Systems Analysis, Wageningen University and Research, Wageningen, Netherlands
| | - Jorge Ruíz
- Programa de Estudios de Posgrado en Geografia, Convenio Universidad Pedagogica y Tecnológica de Colombia-Instituto Geografico Agustin Codazzi, Bogotá, Colombia
| | - Marcelo Tabarelli
- Departamento de Botânica, Universidade Federal de Pernambuco, Recife, Brazil
| | - Heitor Mancini Teixeira
- Plant Production Systems Group, Wageningen University and Research, Wageningen, Netherlands.,Farming Systems Ecology, Wageningen University, Wageningen, Netherlands.,Copernicus Institute, Utrecht University, Utrecht, Netherlands
| | | | - Hans van der Wal
- Departamento de Agricultura, Sociedad y Ambiente, El Colegio de la Frontera Sur - Unidad Villahermosa, Centro, Tabasco, México
| | - Pedro M Villa
- Program of Botany, Departamento de Biologia Vegetal, Laboratório de Ecologia e Evolução de Plantas, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.,Fundación para la Conservación de la Biodiversidad (PROBIODIVERSA), Mérida, Mérida, Venezuela
| | - Geraldo W Fernandes
- Ecologia Evolutiva e Biodiversidade/DBG, ICB, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - José Aguilar-Cano
- Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Bogotá, Colombia
| | | | | | - Felipe Arreola-Villa
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, Michoacán, Mexico
| | - Patricia Balvanera
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, Michoacán, Mexico
| | | | - George A L Cabral
- Departamento de Botânica, Universidade Federal de Pernambuco, Recife, Brazil
| | | | - Ben H J de Jong
- Department of Sustainability Science, El Colegio de la Frontera Sur, Lerma, Campeche, Mexico
| | - Jhon Edison Nieto
- Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Bogotá, Colombia
| | - Mário M Espírito-Santo
- Departamento de Biologia Geral, Universidade Estadual de Montes Claros, Montes Claros, Minas Gerais, Brazil
| | - Maria C Fandino
- Fondo Patrimonio Natural para la Biodiversidad y Areas Protegidas, Bogota, Colombia
| | - Hernando García
- Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Bogotá, Colombia
| | | | - Jefferson S Hall
- SI ForestGEO, Smithsonian Tropical Research Institute, Ancón, Balboa, Panama
| | - Alvaro Idárraga
- Fundación Jardín Botánico de Medellín, Herbario JAUM, Medellín, Colombia
| | | | - Deborah Kennard
- Department of Physical and Environmental Sciences, Colorado Mesa University, Grand Junction, CO, USA
| | | | - Rita Mesquita
- Biological Dynamics of Forest Fragments Project, Environmental Dynamics Research Coordination, Instituto Nacional de Pesquisas da Amazonia, Manaus, Amazonas, Brazil
| | - Yule R F Nunes
- Departamento de Biologia Geral, Universidade Estadual de Montes Claros, Montes Claros, Minas Gerais, Brazil
| | - Susana Ochoa-Gaona
- Department of Sustainability Science, El Colegio de la Frontera Sur, Lerma, Campeche, Mexico
| | - Marielos Peña-Claros
- Forest Ecology and Forest Management Group, Wageningen University, Wageningen, Netherlands
| | - Nathalia Pérez-Cárdenas
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, Michoacán, Mexico
| | - Jorge Rodríguez-Velázquez
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, Michoacán, Mexico
| | - Lucía Sanaphre Villanueva
- Centro de Investigación Científica de Yucatán A.C. Unidad de Recursos Naturales, Colonia Chuburná de Hidalgo, Mérida, Yucatán, Mexico.,Consejo Nacional de Ciencia y Tecnologia, Centro del Cambio Global y la Sustentabilidad, Tabasco, Mexico
| | - Naomi B Schwartz
- Department of Geography, University of British Columbia, Vancouver, BC, Canada
| | - Marc K Steininger
- Department of Geographical Sciences, University of Maryland, College Park, MD, USA
| | - Maria D M Veloso
- Departamento de Biologia Geral, Universidade Estadual de Montes Claros, Montes Claros, Minas Gerais, Brazil
| | - Henricus F M Vester
- Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam, Netherlands
| | | | - G Bruce Williamson
- Biological Dynamics of Forest Fragments Project, Environmental Dynamics Research Coordination, Instituto Nacional de Pesquisas da Amazonia, Manaus, Amazonas, Brazil.,Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
| | - Kátia Zanini
- Departamento de Ecologia, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Bruno Hérault
- CIRAD, UPR Forêts et Sociétés, Yamoussoukro, Côte d'Ivoire.,Forêts et Sociétés, Université Montpellier, CIRAD, Montpellier, France.,Institut National Polytechnique Félix Houphouët-Boigny, INP-HB, Yamoussoukro, Côte d'Ivoire
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28
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The Contribution of Trees Outside of Forests to Landscape Carbon and Climate Change Mitigation in West Africa. FORESTS 2021. [DOI: 10.3390/f12121652] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
While closed canopy forests have been an important focal point for land cover change monitoring and climate change mitigation, less consideration has been given to methods for large scale measurements of trees outside of forests. Trees outside of forests are an important but often overlooked natural resource throughout sub-Saharan Africa, providing benefits for livelihoods as well as climate change mitigation and adaptation. In this study, the development of an individual tree cover map using very high-resolution remote sensing and a comparison with a new automated machine learning mapping product revealed an important contribution of trees outside of forests to landscape tree cover and carbon stocks in a region where trees outside of forests are important components of livelihood systems. Here, we test and demonstrate the use of allometric scaling from remote sensing crown area to provide estimates of landscape-scale carbon stocks. Prominent biomass and carbon maps from global-scale remote sensing greatly underestimate the “invisible” carbon in these sparse tree-based systems. The measurement of tree cover and carbon in these landscapes has important application in climate change mitigation and adaptation policies.
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29
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Using TLS-Measured Tree Attributes to Estimate Aboveground Biomass in Small Black Spruce Trees. FORESTS 2021. [DOI: 10.3390/f12111521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Research Highlights: This study advances the effort to accurately estimate the biomass of trees in peatlands, which cover 13% of Canada’s land surface. Background and Objectives: Trees remove carbon from the atmosphere and store it as biomass. Terrestrial laser scanning (TLS) has become a useful tool for modelling forest structure and estimating the above ground biomass (AGB) of trees. Allometric equations are often used to estimate individual tree AGB as a function of height and diameter at breast height (DBH), but these variables can often be laborious to measure using traditional methods. The main objective of this study was to develop allometric equations using TLS-measured variables and compare their accuracy with that of other widely used equations that rely on DBH. Materials and Methods: The study focusses on small black spruce trees (<5 m) located in peatland ecosystems of the Taiga Plains Ecozone in the Northwest Territories, Canada. Black spruce growing in peatlands are often stunted when compared to upland black spruce and having models specific to them would allow for more precise biomass estimates. One hundred small trees were destructively sampled from 10 plots and the dry weight of each tree was measured in the lab. With this reference data, we fitted biomass models specific to peatland black spruce using DBH, crown diameter, crown area, height, tree volume, and bounding box volume as predictors. Results: Our best models had crown size and height as predictors and outperformed established AGB equations that rely on DBH. Conclusions: Our equations are based on predictors that can be measured from above, and therefore they may enable the plotless creation of accurate biomass reference data for a prominent tree species in a common ecosystem (treed peatlands) in North America’s boreal.
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30
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Scholl VM, McGlinchy J, Price-Broncucia T, Balch JK, Joseph MB. Fusion neural networks for plant classification: learning to combine RGB, hyperspectral, and lidar data. PeerJ 2021; 9:e11790. [PMID: 34395073 PMCID: PMC8325917 DOI: 10.7717/peerj.11790] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 06/25/2021] [Indexed: 11/29/2022] Open
Abstract
Airborne remote sensing offers unprecedented opportunities to efficiently monitor vegetation, but methods to delineate and classify individual plant species using the collected data are still actively being developed and improved. The Integrating Data science with Trees and Remote Sensing (IDTReeS) plant identification competition openly invited scientists to create and compare individual tree mapping methods. Participants were tasked with training taxon identification algorithms based on two sites, to then transfer their methods to a third unseen site, using field-based plant observations in combination with airborne remote sensing image data products from the National Ecological Observatory Network (NEON). These data were captured by a high resolution digital camera sensitive to red, green, blue (RGB) light, hyperspectral imaging spectrometer spanning the visible to shortwave infrared wavelengths, and lidar systems to capture the spectral and structural properties of vegetation. As participants in the IDTReeS competition, we developed a two-stage deep learning approach to integrate NEON remote sensing data from all three sensors and classify individual plant species and genera. The first stage was a convolutional neural network that generates taxon probabilities from RGB images, and the second stage was a fusion neural network that “learns” how to combine these probabilities with hyperspectral and lidar data. Our two-stage approach leverages the ability of neural networks to flexibly and automatically extract descriptive features from complex image data with high dimensionality. Our method achieved an overall classification accuracy of 0.51 based on the training set, and 0.32 based on the test set which contained data from an unseen site with unknown taxa classes. Although transferability of classification algorithms to unseen sites with unknown species and genus classes proved to be a challenging task, developing methods with openly available NEON data that will be collected in a standardized format for 30 years allows for continual improvements and major gains for members of the computational ecology community. We outline promising directions related to data preparation and processing techniques for further investigation, and provide our code to contribute to open reproducible science efforts.
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Affiliation(s)
- Victoria M Scholl
- Earth Lab, Cooperative Institute for Research in Environmental Science, University of Colorado at Boulder, Boulder, Colorado, United States.,Department of Geography, University of Colorado at Boulder, Boulder, Colorado, United States
| | - Joseph McGlinchy
- Earth Lab, Cooperative Institute for Research in Environmental Science, University of Colorado at Boulder, Boulder, Colorado, United States
| | - Teo Price-Broncucia
- Department of Computer Science, University of Colorado at Boulder, Boulder, Colorado, United States
| | - Jennifer K Balch
- Earth Lab, Cooperative Institute for Research in Environmental Science, University of Colorado at Boulder, Boulder, Colorado, United States.,Department of Geography, University of Colorado at Boulder, Boulder, Colorado, United States
| | - Maxwell B Joseph
- Earth Lab, Cooperative Institute for Research in Environmental Science, University of Colorado at Boulder, Boulder, Colorado, United States
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31
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Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level. REMOTE SENSING 2021. [DOI: 10.3390/rs13142796] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
UAV laser scanning (ULS) has the potential to support forest operations since it provides high-density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven-aged northern hardwood stand. We investigated: (1) the transferability of raster-based and bottom-up point cloud-based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree-level (i.e., height, crown diameter (CD), DBH) and stand-level (i.e., tree count, basal area (BA), DBH-distribution) forest inventory attributes. These objectives were studied under leaf-on and leaf-off canopy conditions. Results achieved from ULS data were cross-compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments. The best results that characterized individual trees from ULS data were achieved under leaf-off conditions using a point cloud-based bottom-up ITD. The latter outperformed the raster-based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R2 = 0.29 to R2 = 0.61), and prediction of tree DBH (from R2 = 0.36 to R2 = 0.67), when compared with values that were estimated from reference TLS data. Major improvements were observed for the detection of trees in the lower canopy layer (from 9% with raster-based ITD to 51% with point cloud-based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud-based ITD). Under leaf-on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy. Under these conditions, the raster-based ITD was unable to detect low-level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder-fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7.4 cm, Bias = 3.1 cm, and R2 = 0.75. Yet, it yielded more accurate estimates of the BA (+3.5%) and DBH-distribution of the stand than did allometric models −12.9%), when compared with in situ field measurements. Results suggest that the use of bottom-up ITD on high-density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations.
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32
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Simulating the Effects of Intensifying Silviculture on Desired Species Yields across a Broad Environmental Gradient. FORESTS 2021. [DOI: 10.3390/f12060755] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In the past two decades, forest management has undergone major paradigm shifts that are challenging the current forest modelling architecture. New silvicultural systems, guidelines for natural disturbance emulation, a desire to enhance structural complexity, major advances in successional theory, and climate change have all highlighted the limitations of current empirical models in covering this range of conditions. Mechanistic models, which focus on modelling underlying ecological processes rather than specific forest conditions, have the potential to meet these new paradigm shifts in a consistent framework, thereby streamlining the planning process. Here we use the NEBIE (a silvicultural intervention scale that classifies management intensities as natural, extensive, basic, intensive, and elite) plot network, from across Ontario, Canada, to examine the applicability of a mechanistic model, ZELIG-CFS (a version of the ZELIG tree growth model developed by the Canadian Forest Service), to simulate yields and species compositions. As silvicultural intensity increased, overall yield generally increased. Species compositions met the desired outcomes when specific silvicultural treatments were implemented and otherwise generally moved from more shade-intolerant to more shade-tolerant species through time. Our results indicated that a mechanistic model can simulate complex stands across a range of forest types and silvicultural systems while accounting for climate change. Finally, we highlight the need to improve the modelling of regeneration processes in ZELIG-CFS to better represent regeneration dynamics in plantations. While fine-tuning is needed, mechanistic models present an option to incorporate adaptive complexity into modelling forest management outcomes.
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33
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Proulx R. On the general relationship between plant height and aboveground biomass of vegetation stands in contrasted ecosystems. PLoS One 2021; 16:e0252080. [PMID: 34038429 PMCID: PMC8153471 DOI: 10.1371/journal.pone.0252080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 05/09/2021] [Indexed: 12/02/2022] Open
Abstract
Ecological communities are unique assemblages of species that coexist in consequence of multi-causal processes that have proven hard to generalize. One possible exception are processes that control the biomass packing of vegetation stands; the amount of aboveground standing biomass expressed per unit volume. In this paper, I investigated the empirical and geometric underpinnings of biomass packing in terrestrial plant communities. I support that biomass packing in nature peaks around 1 kg m-3 across contrasted contexts, ranging from grasslands to forest ecosystems. Using published experimental and long-term survey data, I show that expressing biomass per unit volume cancels the effects of air temperature, species richness and soil fertility on aboveground stocks, thus providing a general comparative measure of storage efficiency in plant communities.
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Affiliation(s)
- Raphaël Proulx
- Canada Research Chair in Ecological Integrity, Centre de recherche sur les interactions bassins versants-écosystèmes aquatiques, Université du Québec à Trois-Rivières, Trois-Rivières, Canada
- * E-mail:
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34
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Maréchaux I, Langerwisch F, Huth A, Bugmann H, Morin X, Reyer CP, Seidl R, Collalti A, Dantas de Paula M, Fischer R, Gutsch M, Lexer MJ, Lischke H, Rammig A, Rödig E, Sakschewski B, Taubert F, Thonicke K, Vacchiano G, Bohn FJ. Tackling unresolved questions in forest ecology: The past and future role of simulation models. Ecol Evol 2021; 11:3746-3770. [PMID: 33976773 PMCID: PMC8093733 DOI: 10.1002/ece3.7391] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 02/04/2021] [Accepted: 02/20/2021] [Indexed: 12/13/2022] Open
Abstract
Understanding the processes that shape forest functioning, structure, and diversity remains challenging, although data on forest systems are being collected at a rapid pace and across scales. Forest models have a long history in bridging data with ecological knowledge and can simulate forest dynamics over spatio-temporal scales unreachable by most empirical investigations.We describe the development that different forest modelling communities have followed to underpin the leverage that simulation models offer for advancing our understanding of forest ecosystems.Using three widely applied but contrasting approaches - species distribution models, individual-based forest models, and dynamic global vegetation models - as examples, we show how scientific and technical advances have led models to transgress their initial objectives and limitations. We provide an overview of recent model applications on current important ecological topics and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.Synthesis. This overview shows that forest models, due to their complementarity and mutual enrichment, represent an invaluable toolkit to address a wide range of fundamental and applied ecological questions, hence fostering a deeper understanding of forest dynamics in the context of global change.
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Affiliation(s)
| | - Fanny Langerwisch
- Department of Ecology and Environmental SciencesPalacký University OlomoucOlomoucCzech Republic
- Department of Water Resources and Environmental ModelingCzech University of Life SciencesPragueCzech Republic
| | - Andreas Huth
- Helmholtz Centre for Environmental Research ‐ UFZLeipzigGermany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
- Institute of Environmental Systems ResearchOsnabrück UniversityOsnabrückGermany
| | - Harald Bugmann
- Forest EcologyInstitute of Terrestrial EcosystemsETH ZürichZurichSwitzerland
| | - Xavier Morin
- EPHECEFECNRSUniv MontpellierUniv Paul Valéry MontpellierIRDMontpellierFrance
| | - Christopher P.O. Reyer
- Potsdam Institute for Climate Impact Research (PIK)Member of the Leibniz AssociationPotsdamGermany
| | - Rupert Seidl
- Institute of SilvicultureUniversity of Natural Resources and Life Sciences (BOKU)ViennaAustria
- TUM School of Life SciencesTechnical University of MunichFreisingGermany
| | - Alessio Collalti
- Forest Modelling LabInstitute for Agriculture and Forestry Systems in the MediterraneanNational Research Council of Italy (CNR‐ISAFOM)Perugia (PG)Italy
- Department of Innovation in Biological, Agro‐food and Forest SystemsUniversity of TusciaViterboItaly
| | | | - Rico Fischer
- Helmholtz Centre for Environmental Research ‐ UFZLeipzigGermany
| | - Martin Gutsch
- Potsdam Institute for Climate Impact Research (PIK)Member of the Leibniz AssociationPotsdamGermany
| | | | - Heike Lischke
- Dynamic MacroecologyLand Change ScienceSwiss Federal Institute for Forest, Snow and Landscape Research WSLBirmensdorfSwitzerland
| | - Anja Rammig
- TUM School of Life SciencesTechnical University of MunichFreisingGermany
| | - Edna Rödig
- Helmholtz Centre for Environmental Research ‐ UFZLeipzigGermany
| | - Boris Sakschewski
- Potsdam Institute for Climate Impact Research (PIK)Member of the Leibniz AssociationPotsdamGermany
| | | | - Kirsten Thonicke
- Potsdam Institute for Climate Impact Research (PIK)Member of the Leibniz AssociationPotsdamGermany
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35
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Handheld Laser Scanning Detects Spatiotemporal Differences in the Development of Structural Traits among Species in Restoration Plantings. REMOTE SENSING 2021. [DOI: 10.3390/rs13091706] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
A major challenge in ecological restoration is assessing the success of restoration plantings in producing habitats that provide the desired ecosystem functions and services. Forest structural complexity and biomass accumulation are key measures used to monitor restoration success and are important factors determining animal habitat availability and carbon sequestration. Monitoring their development through time using traditional field measurements can be costly and impractical, particularly at the landscape-scale, which is a common requirement in ecological restoration. We explored the application of proximal sensing technology as an alternative to traditional field surveys to capture the development of key forest structural traits in a restoration planting in the Midlands of Tasmania, Australia. We report the use of a hand-held laser scanner (ZEB1) to measure annual changes in structural traits at the tree-level, in a mixed species common-garden experiment from seven- to nine-years after planting. Using very dense point clouds, we derived estimates of multiple structural traits, including above ground biomass, tree height, stem diameter, crown dimensions, and crown properties. We detected annual increases in most LiDAR-derived traits, with individual crowns becoming increasingly interconnected. Time by species interaction were detected, and were associated with differences in productivity between species. We show the potential for remote sensing technology to monitor temporal changes in forest structural traits, as well as to provide base-line measures from which to assess the restoration trajectory towards a desired state.
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36
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Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing. REMOTE SENSING 2021. [DOI: 10.3390/rs13081592] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.
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37
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The Timber Footprint of the German Bioeconomy—State of the Art and Past Development. SUSTAINABILITY 2021. [DOI: 10.3390/su13073878] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The article gives a comprehensive overview of the roundwood equivalents (RE) consumed in the German bioeconomy from Germany and abroad between 1995 and 2015, i.e., the Timber Footprint of final Consumption (TFPcon). The calculation is based on an adapted version of Exiobase 3.4. The sustainability of roundwood procurement for the TFPcon is assessed. A systematic embedding of the tree compartments considered in the TFP in the context of national forest inventories and material flow analysis is presented. The results show that, in 2015, the total volume of the TFPcon of Germany is 90 Mm3 (slightly above the 1995 level) and is composed of 61% coniferous and 39% non-coniferous wood. Germany is strongly dependent on roundwood sourced from abroad and thus was a net importer of RE in 2015. Among the 17 countries with the largest supply of RE for the TFPcon, around one third very likely include large shares of roundwood procured from deforestation or clear-cutting. The self-sufficiency rate in 2015 was only 76%. It would be possible to increase domestic roundwood production by 8–41% (mainly in the hardwood sector) without exceeding the sustainability limits as defined in the WEHAM scenarios.
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38
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Hao S, Chen Y, Hu B, Cui Y. A classifier-combined method based on D-S evidence theory for the land cover classification of the Tibetan Plateau. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:16152-16164. [PMID: 33247405 DOI: 10.1007/s11356-020-11791-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 11/22/2020] [Indexed: 06/12/2023]
Abstract
The Tibetan Plateau (TP) is a region with high altitudes and complicated terrain conditions. Due to the special conditions of this region, it is also regarded as the third pole of the Earth. The land cover and vegetation in this region have not been extensively studied, so this study investigated the possibility of using a combined classifier that was established based on D-S evidence theory to extract the land cover of the TP. Multiple feature images were obtained based on a single classification rule, and the feature images were normalized to obtain the basic probability assignment (BPA). The BPA was used as the evidence source to represent the belief level of each type of land cover. The information for the different belief levels was combined based on the D-S evidence theory. The maximum belief level of the combination results was used to identify the land cover types on the TP. The results of this study indicate that based on the D-S evidence theory, multiple classifiers can effectively be combined to improve the classification results. This study has also revealed that more classifiers fused together to make a combined classifier did not result in the combined classifier's accuracy being higher than those of the original classifiers. Higher accuracies were only obtained when more high accuracy evidence theory was used in the classifier combination, in which case, the combined classifier's classification accuracy was also high.
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Affiliation(s)
- Shuang Hao
- School of Natural Science, Anhui Agricultural University, Hefei, 200036, China.
| | - Yongfu Chen
- Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China.
| | - Bo Hu
- Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China
| | - Yuhuan Cui
- School of Natural Science, Anhui Agricultural University, Hefei, 200036, China
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39
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Modelling the Diameter Distribution of Savanna Trees with Drone-Based LiDAR. REMOTE SENSING 2021. [DOI: 10.3390/rs13071266] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The diameter distribution of savanna tree populations is a valuable indicator of savanna health because changes in the number and size of trees can signal a shift from savanna to grassland or forest. Savanna diameter distributions have traditionally been monitored with forestry techniques, where stem diameter at breast height (DBH) is measured in the field within defined sub-hectare plots. However, because the spatial scale of these plots is often misaligned with the scale of variability in tree populations, there is a need for techniques that can scale-up diameter distribution surveys. Dense point clouds collected from uncrewed aerial vehicle laser scanners (UAV-LS), also known as drone-based LiDAR (Light Detection and Ranging), can be segmented into individual tree crowns then related to stem diameter with the application of allometric scaling equations. Here, we sought to test the potential of UAV-LS tree segmentation and allometric scaling to model the diameter distributions of savanna trees. We collected both UAV-LS and field-survey data from five one-hectare savanna woodland plots in northern Australia, which were divided into two calibration and three validation plots. Within the two calibration plots, allometric scaling equations were developed by linking field-surveyed DBH to the tree metrics of manually delineated tree crowns, where the best performing model had a bias of 1.8% and the relatively high RMSE of 39.2%. A segmentation algorithm was then applied to segment individual tree crowns from UAV-LS derived point clouds, and individual tree level segmentation accuracy was assessed against the manually delineated crowns. 47% of crowns were accurately segmented within the calibration plots and 68% within the validation plots. Using the site-specific allometry, DBH was modelled from crown metrics within all five plots, and these modelled results were compared to field-surveyed diameter distributions. In all plots, there were significant differences between field-surveyed and UAV-LS modelled diameter distributions, which became similar at two of the plots when smaller trees (<10 cm DBH) were excluded. Although the modelled diameter distributions followed the overall trend of field surveys, the non-significant result demonstrates a need for the adoption of remotely detectable proxies of tree size which could replace DBH, as well as more accurate tree detection and segmentation methods for savanna ecosystems.
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Weinstein BG, Marconi S, Bohlman SA, Zare A, Singh A, Graves SJ, White EP. A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network. eLife 2021; 10:e62922. [PMID: 33605211 PMCID: PMC7895524 DOI: 10.7554/elife.62922] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 02/15/2021] [Indexed: 01/03/2023] Open
Abstract
Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain technical challenges in turning sensor data into tangible information. Using deep learning methods, we produced an open-source data set of individual-level crown estimates for 100 million trees at 37 sites across the United States surveyed by the National Ecological Observatory Network's Airborne Observation Platform. Each canopy tree crown is represented by a rectangular bounding box and includes information on the height, crown area, and spatial location of the tree. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses and cross-region comparisons encompassing forest types from most of the United States.
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Affiliation(s)
- Ben G Weinstein
- Department of Wildlife Ecology and Conservation, University of FloridaGainesvilleUnited States
| | - Sergio Marconi
- Department of Wildlife Ecology and Conservation, University of FloridaGainesvilleUnited States
| | - Stephanie A Bohlman
- School of Forest Resources and Conservation, University of FloridaGainesvilleUnited States
| | - Alina Zare
- Department of Electrical and Computer Engineering, University of FloridaGainesvilleUnited States
| | - Aditya Singh
- Department of Agricultural & Biological Engineering, University of FloridaGainesvilleUnited States
| | - Sarah J Graves
- Nelson Institute for Environmental Studies, University of Wisconsin-MadisonMadisonUnited States
| | - Ethan P White
- Department of Wildlife Ecology and Conservation, University of FloridaGainesvilleUnited States
- Informatics Institute, University of FloridaGainesvilleUnited States
- Biodiversity Institute, University of FloridaGainesvilleUnited States
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Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning. REMOTE SENSING 2021. [DOI: 10.3390/rs13030352] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose.
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Leveraging TLS as a Calibration and Validation Tool for MLS and ULS Mapping of Savanna Structure and Biomass at Landscape-Scales. REMOTE SENSING 2021. [DOI: 10.3390/rs13020257] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Savanna ecosystems are challenging to map and monitor as their vegetation is highly dynamic in space and time. Understanding the structural diversity and biomass distribution of savanna vegetation requires high-resolution measurements over large areas and at regular time intervals. These requirements cannot currently be met through field-based inventories nor spaceborne satellite remote sensing alone. UAV-based remote sensing offers potential as an intermediate scaling tool, providing acquisition flexibility and cost-effectiveness. Yet despite the increased availability of lightweight LiDAR payloads, the suitability of UAV-based LiDAR for mapping and monitoring savanna 3D vegetation structure is not well established. We mapped a 1 ha savanna plot with terrestrial-, mobile- and UAV-based laser scanning (TLS, MLS, and ULS), in conjunction with a traditional field-based inventory (n = 572 stems > 0.03 m). We treated the TLS dataset as the gold standard against which we evaluated the degree of complementarity and divergence of structural metrics from MLS and ULS. Sensitivity analysis showed that MLS and ULS canopy height models (CHMs) did not differ significantly from TLS-derived models at spatial resolutions greater than 2 m and 4 m respectively. Statistical comparison of the resulting point clouds showed minor over- and under-estimation of woody canopy cover by MLS and ULS, respectively. Individual stem locations and DBH measurements from the field inventory were well replicated by the TLS survey (R2 = 0.89, RMSE = 0.024 m), which estimated above-ground woody biomass to be 7% greater than field-inventory estimates (44.21 Mg ha−1 vs 41.08 Mg ha−1). Stem DBH could not be reliably estimated directly from the MLS or ULS, nor indirectly through allometric scaling with crown attributes (R2 = 0.36, RMSE = 0.075 m). MLS and ULS show strong potential for providing rapid and larger area capture of savanna vegetation structure at resolutions suitable for many ecological investigations; however, our results underscore the necessity of nesting TLS sampling within these surveys to quantify uncertainty. Complementing large area MLS and ULS surveys with TLS sampling will expand our options for the calibration and validation of multiple spaceborne LiDAR, SAR, and optical missions.
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Abstract
Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha−1/normalized RMSE 18.8%/R² 0.76; 50 ha: 22.8 trees ha−1/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha−1, bias 0.8 m² ha−1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.
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Ross CW, Hanan NP, Prihodko L, Anchang J, Ji W, Yu Q. Woody-biomass projections and drivers of change in sub-Saharan Africa. NATURE CLIMATE CHANGE 2021; 11:449-455. [PMID: 35136420 PMCID: PMC8819706 DOI: 10.1038/s41558-021-01034-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 03/30/2021] [Indexed: 05/20/2023]
Abstract
Africa's ecosystems have an important role in global carbon dynamics, yet consensus is lacking regarding the amount of carbon stored in woody vegetation and the potential impacts to carbon storage in response to changes in climate, land use, and other Anthropocene risks. Here, we explore the socio-environmental conditions that shaped the contemporary distribution of woody vegetation across sub-Saharan Africa and evaluate ecosystem response to multiple scenarios of climate change, anthropogenic pressures, and fire disturbance. Our projections suggest climate change will have a small but negative effect on above ground woody biomass at the continental scale, and the compounding effects of population growth, increasing human pressures, and socio-climatic driven changes in fire behavior further exacerbate climate-driven trends. Relatively modest continental-scale trends obscure much larger regional perturbations, with climatic and anthropogenic factors leading to increased carbon storage potential in East Africa, offset by large deficits in West, Central, and Southern Africa.
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Affiliation(s)
- C Wade Ross
- Department of Plant and Environmental Sciences New Mexico State University, Las Cruces, NM, USA
- Tall Timbers Research Station, Tallahassee, Florida 32312, USA
| | - Niall P Hanan
- Department of Plant and Environmental Sciences New Mexico State University, Las Cruces, NM, USA
| | - Lara Prihodko
- Animal and Range Sciences, New Mexico State University, Las Cruces, NM, USA
| | - Julius Anchang
- Department of Plant and Environmental Sciences New Mexico State University, Las Cruces, NM, USA
| | - Wenjie Ji
- Department of Plant and Environmental Sciences New Mexico State University, Las Cruces, NM, USA
| | - Qiuyan Yu
- Department of Plant and Environmental Sciences New Mexico State University, Las Cruces, NM, USA
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Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning. REMOTE SENSING 2020. [DOI: 10.3390/rs13010024] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.
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Exploring the Variability of Tropical Savanna Tree Structural Allometry with Terrestrial Laser Scanning. REMOTE SENSING 2020. [DOI: 10.3390/rs12233893] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Individual tree carbon stock estimates typically rely on allometric scaling relationships established between field-measured stem diameter (DBH) and destructively harvested biomass. The use of DBH-based allometric equations to estimate the carbon stored over larger areas therefore, assumes that tree architecture, including branching and crown structures, are consistent for a given DBH, and that minor variations cancel out at the plot scale. We aimed to explore the degree of structural variation present at the individual tree level across a range of size-classes. We used terrestrial laser scanning (TLS) to measure the 3D structure of each tree in a 1 ha savanna plot, with coincident field-inventory. We found that stem reconstructions from TLS captured both the spatial distribution pattern and the DBH of individual trees with high confidence when compared with manual measurements (R2 = 0.98, RMSE = 0.0102 m). Our exploration of the relationship between DBH, crown size and tree height revealed significant variability in savanna tree crown structure (measured as crown area). These findings question the reliability of DBH-based allometric equations for adequately representing diversity in tree architecture, and therefore carbon storage, in tropical savannas. However, adoption of TLS outside environmental research has been slow due to considerable capital cost and monitoring programs often continue to rely on sub-plot monitoring and traditional allometric equations. A central aspect of our study explores the utility of a lower-cost TLS system not generally used for vegetation surveys. We discuss the potential benefits of alternative TLS-based approaches, such as explicit modelling of tree structure or voxel-based analyses, to capture the diverse 3D structures of savanna trees. Our research highlights structural heterogeneity as a source of uncertainty in savanna tree carbon estimates and demonstrates the potential for greater inclusion of cost-effective TLS technology in national monitoring programs.
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Ferraz A, Saatchi SS, Longo M, Clark DB. Tropical tree size-frequency distributions from airborne lidar. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2020; 30:e02154. [PMID: 32347996 DOI: 10.1002/eap.2154] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 03/26/2020] [Accepted: 03/30/2020] [Indexed: 06/11/2023]
Abstract
In tropical rainforests, tree size and number density are influenced by disturbance history, soil, topography, climate, and biological factors that are difficult to predict without detailed and widespread forest inventory data. Here, we quantify tree size-frequency distributions over an old-growth wet tropical forest at the La Selva Biological Station in Costa Rica by using an individual tree crown (ITC) algorithm on airborne lidar measurements. The ITC provided tree height, crown area, the number of trees >10 m height and, predicted tree diameter, and aboveground biomass from field allometry. The number density showed strong agreement with field observations at the plot- (97.4%; 3% bias) and tree-height-classes level (97.4%; 3% bias). The lidar trees size spectra of tree diameter and height closely follow the distributions measured on the ground but showed less agreement with crown area observations. The model to convert lidar-derived tree height and crown area to tree diameter produced unbiased (0.8%) estimates of plot-level basal area and with low uncertainty (6%). Predictions on basal area for tree height classes were also unbiased (1.3%) but with larger uncertainties (22%). The biomass estimates had no significant bias at the plot- and tree-height-classes level (-5.2% and 2.1%). Our ITC method provides a powerful tool for tree- to landscape-level tropical forest inventory and biomass estimation by overcoming the limitations of lidar area-based approaches that require local calibration using a large number of inventory plots.
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Affiliation(s)
- António Ferraz
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, 91109, USA
- Institute of Environment and Sustainability, University of California, Los Angeles, California, 90024, USA
| | - Sassan S Saatchi
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, 91109, USA
- Institute of Environment and Sustainability, University of California, Los Angeles, California, 90024, USA
| | - Marcos Longo
- NASA Postdoctoral fellow, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, 91109, USA
| | - David B Clark
- Department of Biology, University of Missouri-St. Louis, St. Louis, Missouri, 63121, USA
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Nyamjav J, Batsaikhan ME, Li G, Li J, Luvsanjamba A, Jin K, Xiao W, Wu L, Indree T, Qin A. Allometric equations for estimating above-ground biomass of Nitraria sibirica Pall. in Gobi Desert of Mongolia. PLoS One 2020; 15:e0239268. [PMID: 32991580 PMCID: PMC7526795 DOI: 10.1371/journal.pone.0239268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 09/02/2020] [Indexed: 11/24/2022] Open
Abstract
Nitraria sibirica Pall. is a shrub species belonging to the
family of Nitrariaceae. It plays pivotal role in arid ecosystems since it is
tolerant to high salinity and drought. This species is widely distributed
throughout Mongolia and it is mostly found in arid ecosystems of Mongolian Gobi
Desert. In this study, we developed allometric equations for estimating
above-ground biomass of N. sibirica using
various structural descriptors and pinpointed the best models. Variables that
precisely predicted above-ground biomass were a combination of basal diameter,
crown area, and height. The allometric growth equation constructed is not merely
helpful to achieve accurate estimations of the above-ground biomass in shrub
vegetation in the Gobi Desert of Mongolia, but also can provide a reference for
the above-ground biomass of Nitraria species growing in
analogous habitats worldwide. Therefore, our research purposes an important
advance for biomass estimation in Gobi ecosystems and complements previous
studies of shrub biomass worldwide. This study provides reasonable estimates of
biomass of N. sibirica, which will be valuable
in evaluations of biological resources, especially for quantifying the main
summer diet of Gobi bears, and also can be an alternative tool for assessing
carbon cycling in Gobi Desert.
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Affiliation(s)
- Javkhlan Nyamjav
- Laboratory of Vegetation Ecology and Plant Resources, Botanic Garden and
Research Institute, Mongolian Academy of Sciences, Ulaanbaatar,
Mongolia
| | - Munkh-Erdene Batsaikhan
- Laboratory of Vegetation Ecology and Plant Resources, Botanic Garden and
Research Institute, Mongolian Academy of Sciences, Ulaanbaatar,
Mongolia
| | - Guangliang Li
- Research Institute of Forest Ecology, Environment and Protection, Chinese
Academy of Forestry, Beijing, China
- Key Laboratory of Forest Ecology and Environment of National Forestry and
Grassland Administration, Beijing, China
| | - Jia Li
- Institute of Desertification Studies, Chinese Academy of Forestry,
Beijing, China
| | | | - Kun Jin
- Research Institute of Natural Protected Area, Chinese Academy of
Forestry, Beijing, China
| | - Wenfa Xiao
- Research Institute of Forest Ecology, Environment and Protection, Chinese
Academy of Forestry, Beijing, China
- Key Laboratory of Forest Ecology and Environment of National Forestry and
Grassland Administration, Beijing, China
| | - Liji Wu
- Inner Mongolian Hulun Lake to National Nature Reserve, Hulunbuir,
Beijing, China
| | - Tuvshintogtokh Indree
- Laboratory of Vegetation Ecology and Plant Resources, Botanic Garden and
Research Institute, Mongolian Academy of Sciences, Ulaanbaatar,
Mongolia
- * E-mail:
(AQ);
(TI)
| | - Aili Qin
- Research Institute of Forest Ecology, Environment and Protection, Chinese
Academy of Forestry, Beijing, China
- Key Laboratory of Forest Ecology and Environment of National Forestry and
Grassland Administration, Beijing, China
- * E-mail:
(AQ);
(TI)
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From Drones to Phenotype: Using UAV-LiDAR to Detect Species and Provenance Variation in Tree Productivity and Structure. REMOTE SENSING 2020. [DOI: 10.3390/rs12193184] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The use of unmanned aerial vehicles (UAVs) for remote sensing of natural environments has increased over the last decade. However, applications of this technology for high-throughput individual tree phenotyping in a quantitative genetic framework are rare. We here demonstrate a two-phased analytical pipeline that rapidly phenotypes and filters for genetic signals in traditional and novel tree productivity and architectural traits derived from ultra-dense light detection and ranging (LiDAR) point clouds. The goal of this study was rapidly phenotype individual trees to understand the genetic basis of ecologically and economically significant traits important for guiding the management of natural resources. Individual tree point clouds were acquired using UAV-LiDAR captured over a multi-provenance common-garden restoration field trial located in Tasmania, Australia, established using two eucalypt species (Eucalyptus pauciflora and Eucalyptus tenuiramis). Twenty-five tree productivity and architectural traits were calculated for each individual tree point cloud. The first phase of the analytical pipeline found significant species differences in 13 of the 25 derived traits, revealing key structural differences in productivity and crown architecture between species. The second phase investigated the within species variation in the same 25 structural traits. Significant provenance variation was detected for 20 structural traits in E. pauciflora and 10 in E. tenuiramis, with signals of divergent selection found for 11 and 7 traits, respectively, putatively driven by the home-site environment shaping the observed variation. Our results highlight the genetic-based diversity within and between species for traits important for forest structure, such as crown density and structural complexity. As species and provenances are being increasingly translocated across the landscape to mitigate the effects of rapid climate change, our results that were achieved through rapid phenotyping using UAV-LiDAR, raise the need to understand the functional value of productivity and architectural traits reflecting species and provenance differences in crown structure and the interplay they have on the dependent biotic communities.
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A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra. REMOTE SENSING 2020. [DOI: 10.3390/rs12162638] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.
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