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Forest biomass is a key influence for future climate, and the world urgently needs highly scalable financing schemes, such as carbon offsetting certifications, to protect and restore forests.
Biomass estimates for tropical forest
Brown, S.; and Iverson, L. 1992 · 1992
Earlier work this paper cites.
What drives tropical deforestation?: a meta-analysis of proximate and underlying causes of deforestation based on subnational case study evidence
Geist, H. J.; and Lambin, E. F. 2001 · 2001
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Carbon stock assessment for a forest-to-coffee conversion landscape in Sumber-Jaya (Lampung, Indonesia): from allometric equations to land use change analysis
Van Noordwijk, M.; Rahayu, S.; Hairiah, K.; Wulan, Y.; Farida, A.; and Verbist, B. 2002 · 2002
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Accurate Estimation of Forest Carbon Stocks by 3-D Remote Sensing of Individual Trees
Omasa, K.; Qiu, G. Y.; Watanuki, K.; Yoshimi, K.; and Akiyama, Y. 2003 · 2003
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Topics in optimal transportation
Villani, C. 2003 · 2003
Earlier work this paper cites.
Error propagation and scaling for tropical forest biomass estimates
Malhi, Y.; Phillips, O. L.; Chave, J.; Condit, R.; Aguilar, S.; Hernandez, A.; Lao, S.; and Perez, R. 2004 · 2004
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Allometric models for estimating aboveground biomass of shade trees and coffee bushes grown together
Segura, M.; Kanninen, M.; and Suárez, D. 2006 · 2006
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Managing Forests for Climate Change Mitigation
Canadell, J. G.; and Raupach, M. R. 2008 · 2008
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Planetary boundaries:exploring the safe operating space for humanity
Rockstöm, J.; Steffen, W.; K. Noone, Á. P.; Chapin, F. S.; Lambin, E.; Lenton, T. M.; Scheffer, M.; Folke, C.; Schellnhuber, H.; Nykvist, B.; Wit, C. A. D.; Hughes, T.; S. van der Leeuw, H. R.; Sörlin, S.; Snyder, P. K.; R. Costanza, U. S.; Falkenmark, M.; Karlberg, L.; Corell, R. W.; Fabry, V. J.; Hansen, J.; Walker, B.; Liverman, D.; Richardson, K.; Crutzen, P.; and Foley, J. 2009 · 2009
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Carbon stock in different ages and plantation system of cocoa: allometric approach
Yuliasmara, F.; Wibawa, A.; and Prawoto, A. 2009 · 2009
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The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations
Santoro, M.; Cartus, O.; Carvalhais, N.; Rozendaal, D. M. A.; Avitabile, V.; Araza, A.; de Bruin, S.; Herold, M.; Quegan, S.; Rodríguez-Veiga, P.; Balzter, H.; Carreiras, J.; Schepaschenko, D.; Korets, M.; Shimada, M.; Itoh, T.; Moreno Martínez, A.; Cavlovic, J.; Cazzolla Gatti, R.; da Conceição Bispo, P.; Dewnath, N.; Labrière, N.; Liang, J.; Lindsell, J.; Mitchard, E. T. A.; Morel, A.; Pacheco Pascagaza, A. M.; Ryan, C. M.; Slik, F.; Vaglio Laurin, G.; Verbeeck, H.; Wijaya, A.; and Willcock, S. 2021 · 2010
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Harmonized globadgleyl maps of above and belowground biomass carbon density in the year 2010
Spawn, S.; Sullivan, C.; and Lark, T. e. a. 2020 · 2010
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Multi-source remote sensing data fusion: status and trends
Zhang, J. 2010 · 2010
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Benchmark map of forest carbon stocks in tropical regions across three continents
Saatchi, S. S.; Harris, N. L.; Brown, S.; Lefsky, M.; Mitchard, E. T. A.; Salas, W.; Zutta, B. R.; Buermann, W.; Lewis, S. L.; Hagen, S.; Petrova, S.; White, L.; Silman, M.; and Morel, A. 2011 · 2011
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Comparison of methods for measuring and assessing carbon stocks and carbon stock changes in terrestrial carbon pools. How do the accuracy and precision of current methods compare? A systematic review protocol
Petrokofsky, G.; Kanamaru, H.; Achard, F.; Goetz, S. J.; Joosten, H.; Holmgren, P.; Lehtonen, A.; Menton, M. C. S.; Pullin, A. S.; and Wattenbach, M. 2012 · 2012
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High-Resolution Global Maps of 21st-Century Forest Cover Change
Hansen, M. C.; Potapov, P. V.; Moore, R.; Hancher, M.; Turubanova, S. A.; Tyukavina, A.; Thau, D.; Stehman, S. V.; Goetz, S. J.; Loveland, T. R.; Kommareddy, A.; Egorov, A.; Chini, L.; Justice, C. O.; and Townshend, J. R. G. 2013 · 2013
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2015 · 2015
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Natural climate solutions
Griscom, B. W.; Adams, J.; Ellis, P. W.; Houghton, R. A.; Lomax, G.; Miteva, D. A.; Schlesinger, W. H.; Shoch, D.; Siikamäki, J. V.; Smith, P.; Woodbury, P.; Zganjar, C.; Blackman, A.; Campari, J.; Conant, R. T.; Delgado, C.; Elias, P.; Gopalakrishna, T.; Hamsik, M. R.; Herrero, M.; Kiesecker, J.; Landis, E.; Laestadius, L.; Leavitt, S. M.; Minnemeyer, S.; Polasky, S.; Potapov, P.; Putz, F. E.; Sanderman, J.; Silvius, M.; Wollenberg, E.; and Fargione, J. 2017 · 2017
Earlier work this paper cites.
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Zhu, X. X.; Tuia, D.; Mou, L.; Xia, G.-S.; Zhang, L.; Xu, F.; and Fraundorfer, F. 2017 · 2017
Cited alongside, same era.
The misunderstood sixth mass extinction
Ceballos, G.; and Ehrlich, P. 2018 · 2018
Cited alongside, same era.
Estimating above ground biomass in a Salix plantation using high resolution UAV images
Nåfält, S. 2018 · 2018
Cited alongside, same era.
GlobBiomass - global datasets of forest biomass
Santoro, M. 2018 · 2018
Cited alongside, same era.
Small-scale forestry and carbon offset markets: An empirical study of Vermont Current Use forest landowner willingness to accept carbon credit programs
White, A. E.; Lutz, D. A.; Howarth, R. B.; and Soto, J. R. 2018 · 2018
Cited alongside, same era.
Caught in between: credibility and feasibility of the voluntary carbon market post-2020
Kreibich, N.; and Hermwille, L. 2021 · 2020
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A Novel Deep Learning Method to Identify Single Tree Species in UAV-Based Hyperspectral Images
Miyoshi, G. T.; Arruda, M. d. S.; Osco, L. P.; Marcato Junior, J.; Gonçalves, D. N.; Imai, N. N.; Tommaselli, A. M. G.; Honkavaara, E.; and Gonçalves, W. N. 2020 · 2020
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Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example
Narine, L. L.; Popescu, S. C.; and Malambo, L. 2020 · 2020
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Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks
Schiefer, F.; Kattenborn, T.; Frick, A.; Frey, J.; Schall, P.; Koch, B.; and Schmidtlein, S. 2020 · 2020
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Overstated carbon emission reductions from voluntary REDD+ projects in the Brazilian Amazon
West, T. A. P.; Börner, J.; Sills, E. O.; and Kontoleon, A. 2020 · 2020
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GainForest: Scaling Climate Finance for Forest Conservation using Interpretable Machine Learning on Satellite Imagery
Dao, D.; Cang, C.; Fung, C.; Zhang, M.; Pawlowski, N.; Gonzales, R.; Beglinger, N.; and Zhang, C. 2019 · 2019
Cited alongside, same era.
Measuring Tree Height with Remote Sensing—A Comparison of Photogrammetric and LiDAR Data with Different Field Measurements
Ganz, S.; Käber, Y.; and Adler, P. 2019 · 2019
Cited alongside, same era.
2019: Summary for Policymakers
IPCC. 2019 · 2019
Cited alongside, same era.
Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts
Lütjens, B.; Liebenwein, L.; and Kramer, K. 2019 · 2019
Cited alongside, same era.
Deep learning in remote sensing applications: A meta-analysis and review
Ma, L.; Liu, Y.; Zhang, X.; Ye, Y.; Yin, G.; and Johnson, B. A. 2019 · 2019
Cited alongside, same era.
Review on carbon storage estimation of forest ecosystem and applications in China
Sun, W.; and Liu, X. 2019 · 2019
Cited alongside, same era.
Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks
Weinstein, B. G.; Marconi, S.; Bohlman, S.; Zare, A.; and White, E. 2019 · 2019
Cited alongside, same era.
Systematic over-crediting in California’s forest carbon offsets program
Badgley, G.; Freeman, J.; Hamman, J. J.; Haya, B.; Trugman, A. T.; Anderegg, W. R.; and Cullenward, D. 2021 · 2021
Later among the works it cites.
Canopy Based Aboveground Biomass and Carbon Stock Estimation of Wild Pistachio Trees in Arid Woodlands Using GeoEye-1 Images
Bagheri, R.; Shataee, S.; and Erfanifard, S. Y. a. 2021 · 2021
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Accelerating Ecological Sciences from Above: Spatial Contrastive Learning for Remote Sensing
Bjorck, J.; Rappazzo, B. H.; Shi, Q.; Brown-Lima, C.; Dean, J.; Fuller, A.; and Gomes, C. 2021 · 2021
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McKinsey&Co: A Blueprint for Scaling Voluntary Carbon Markets to Meet the Climate Challenge
Blaufelder, C.; Levy, C.; Mannion, P.; Pinner, D.; and Weterings, J. 2021 · 2021
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Aboveground Live Woody Biomass Density
Global Forest Watch. 2019 · 2021
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Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change
IPCC. 2021 · 2021
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The global distribution and environmental drivers of aboveground versus belowground plant biomass
Ma, H.; Mo, L.; Thomas W. Crowther, D. S. M.; van den Hoogen, J.; Stocker, B. D.; Terrer, C.; and Zohner, C. M. 2021 · 2021
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Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks
Mäyrä, J.; Keski-Saari, S.; Kivinen, S.; Tanhuanpää, T.; Hurskainen, P.; Kullberg, P.; Poikolainen, L.; Viinikka, A.; Tuominen, S.; Kumpula, T.; and Vihervaara, P. 2021 · 2021
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Sourcebook for BioCarbon Fund Projects
Pearson, T.; Walker, S.; and Brown, S. 2005 · 2021
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Terrestrial biodiversity threatened by increasing global aridity velocity under high-level warming
Shi, H.; Tian, H.; Lange, S.; Yang, J.; Pan, S.; Fu, B.; and Reyer, C. P. O. 2021 · 2021
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Predicting Forest Fire Using Remote Sensing Data And Machine Learning
Yang, S.; Lupascu, M.; and Meel, K. S. 2021 · 2021
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Tropical Forest Carbon Stock Estimation using RGB Drone Imagery
Barenne, V.; Bohl, J. P.; Dekas, D.; and Engelmann, T. 2022 · 2022
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