Algorithm Theoretical Basis Document for GEDI Footprint Aboveground Biomass Density
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[1] S. Goetz,et al. GEDI launches a new era of biomass inference from space , 2022, Environmental Research Letters.
[2] Joanne C. White,et al. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission , 2022, Remote Sensing of Environment.
[3] Sassan Saatchi,et al. Ecosystem Sciences with NISAR , 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.
[4] Andreas Huth,et al. Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing , 2021, Remote. Sens..
[5] Jan Dirk Wegner,et al. Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles , 2021, Remote Sensing of Environment.
[6] B. Poulter,et al. Global Ecosystem Demography Model (ED-global v1.0): Development, Calibration and Evaluation for NASA's Global Ecosystem Dynamics Investigation (GEDI) , 2020 .
[7] Sruthi M. Krishna Moorthy,et al. Terrestrial laser scanning in forest ecology: Expanding the horizon , 2020 .
[8] M. Disney,et al. New 3D measurements of large redwood trees for biomass and structure , 2020, Scientific Reports.
[9] N. Picard,et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models , 2020, Nature Communications.
[10] Sean P. Healey,et al. Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation , 2020, Remote. Sens..
[11] M. Hansen,et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series , 2020 .
[12] Sorin C. Popescu,et al. Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example , 2020, Remote. Sens..
[13] Marc Simard,et al. Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California , 2020, Remote Sensing of Environment.
[14] R. Dubayah,et al. The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography , 2020, Science of Remote Sensing.
[15] Lammert Kooistra,et al. Non-destructive tree volume estimation through quantitative structure modelling: Comparing UAV laser scanning with terrestrial LIDAR , 2019, Remote Sensing of Environment.
[16] Göran Ståhl,et al. Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data , 2019, Remote Sensing of Environment.
[17] Göran Ståhl,et al. Statistical properties of hybrid estimators proposed for GEDI—NASA’s global ecosystem dynamics investigation , 2019, Environmental Research Letters.
[18] M. Herold,et al. The Importance of Consistent Global Forest Aboveground Biomass Product Validation , 2019, Surveys in Geophysics.
[19] Christoph Eck,et al. New Opportunities for Forest Remote Sensing Through Ultra-High-Density Drone Lidar , 2019, Surveys in Geophysics.
[20] Mathias Disney,et al. Innovations in Ground and Airborne Technologies as Reference and for Training and Validation: Terrestrial Laser Scanning (TLS) , 2019, Surveys in Geophysics.
[21] Stephanie A. Bohlman,et al. Tropical tree height and crown allometries for the Barro Colorado Nature Monument, Panama: a comparison of alternative hierarchical models incorporating interspecific variation in relation to life history traits , 2019, Biogeosciences.
[22] Xiaoli Sun,et al. The GEDI Simulator: A Large‐Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions , 2019, Earth and space science.
[23] S. Roxburgh,et al. A revised above-ground maximum biomass layer for the Australian continent , 2019, Forest Ecology and Management.
[24] M. Herold,et al. Estimation of above‐ground biomass of large tropical trees with terrestrial LiDAR , 2017 .
[25] Wenli Huang,et al. Implications of allometric model selection for county-level biomass mapping , 2017, Carbon Balance and Management.
[26] Carsten F. Dormann,et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure , 2017 .
[27] Hans Pretzsch,et al. Generalized biomass and leaf area allometric equations for European tree species incorporating stand structure, tree age and climate , 2017 .
[28] Jukka Heikkonen,et al. Estimating the prediction performance of spatial models via spatial k-fold cross validation , 2017, Int. J. Geogr. Inf. Sci..
[29] J. Trochta,et al. 3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR , 2017, PloS one.
[30] Daniel S Falster,et al. Testing the generality of above‐ground biomass allometry across plant functional types at the continent scale , 2016, Global change biology.
[31] Ryutaro Tateishi,et al. Estimation of Tropical Forest Structural Characteristics Using ALOS-2 SAR Data , 2016 .
[32] E. Næsset,et al. Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation , 2016, Forest Ecosystems.
[33] R. Dubayah,et al. Assessing the general patterns of forest structure: quantifying tree and forest allometric scaling relationships in the United States , 2015 .
[34] Terje Gobakken,et al. Modeling Aboveground Biomass in Dense Tropical Submontane Rainforest Using Airborne Laser Scanner Data , 2015, Remote. Sens..
[35] Scott J. Goetz,et al. The Global Ecosystem Dynamics Investigation , 2014 .
[36] David Kenfack,et al. Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks , 2014 .
[37] B. Nelson,et al. Improved allometric models to estimate the aboveground biomass of tropical trees , 2014, Global change biology.
[38] Vincent Bretagnolle,et al. Spatial leave‐one‐out cross‐validation for variable selection in the presence of spatial autocorrelation , 2014 .
[39] S. Goetz,et al. Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps , 2013, Carbon Balance and Management.
[40] Thomas Esch,et al. Urban Footprint Processor—Fully Automated Processing Chain Generating Settlement Masks From Global Data of the TanDEM-X Mission , 2013, IEEE Geoscience and Remote Sensing Letters.
[41] R. Nelson,et al. Comparison of precision of biomass estimates in regional field sample surveys and airborne LiDAR-assisted surveys in Hedmark County, Norway , 2013 .
[42] S. Goetz,et al. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing , 2013 .
[43] D. Clark,et al. Tropical forest biomass estimation and the fallacy of misplaced concreteness , 2012 .
[44] M. Lefsky,et al. Mapping tropical forest biomass with radar and spaceborne LiDAR in Lopé National Park, Gabon: Overcoming problems of high biomass and persistent cloud , 2012 .
[45] S. Saatchi,et al. Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass , 2011 .
[46] J. Bryan Blair,et al. Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion , 2011 .
[47] R. B. Jackson,et al. A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.
[48] John R. Moore,et al. Allometric equations to predict the total above-ground biomass of radiata pine trees , 2010, Annals of Forest Science.
[49] D. A. King,et al. Height-diameter allometry of tropical forest trees , 2010 .
[50] Klaus Scipal,et al. The BIOMASS mission — An ESA Earth Explorer candidate to measure the BIOMASS of the earth's forests , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.
[51] M. Hansen,et al. Quantification of global gross forest cover loss , 2010, Proceedings of the National Academy of Sciences.
[52] S. Wofsy,et al. Responses of terrestrial ecosystems and carbon budgets to current and future environmental variability , 2010, Proceedings of the National Academy of Sciences.
[53] G. B. Williamson,et al. Measuring wood specific gravity...Correctly. , 2010, American journal of botany.
[54] Damien Sulla-Menashe,et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .
[55] G. Hurtt,et al. Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica , 2009 .
[56] R. Nelson,et al. Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec. , 2008 .
[57] R. Nelson,et al. Regression Estimation Following the Square-Root Transformation of the Response , 2008, Forest Science.
[58] X. Guo,et al. Canadian national biomass equations: new parameter estimates that include British Columbia data , 2008 .
[59] P. Muukkonen,et al. Generalized allometric volume and biomass equations for some tree species in Europe , 2007, European Journal of Forest Research.
[60] N. Coops,et al. Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR , 2007, Trees.
[61] W. Cohen,et al. Estimates of forest canopy height and aboveground biomass using ICESat , 2005 .
[62] W. Walker,et al. Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems , 2005 .
[63] G. Foody,et al. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions , 2003 .
[64] R. Birdsey,et al. National-Scale Biomass Estimators for United States Tree Species , 2003, Forest Science.
[65] Alan H. Strahler,et al. Global land cover mapping from MODIS: algorithms and early results , 2002 .
[66] W. Cohen,et al. Lidar remote sensing of above‐ground biomass in three biomes , 2002 .
[67] R. Dubayah,et al. Estimation of tropical forest structural characteristics using large-footprint lidar , 2002 .
[68] Stephanie A. Bohlman,et al. Quantifying the deciduousness of tropical forest canopies under varying climates , 2000 .
[69] J. Blair,et al. Modeling laser altimeter return waveforms over complex vegetation using high‐resolution elevation data , 1999 .
[70] M. Honzak,et al. Tropical Forest Biomass Density Estimation Using JERS-1 SAR: Seasonal Variation, Confidence Limits, and Application to Image Mosaics , 1998 .
[71] A. Huete,et al. A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .
[72] P. Snowdon,et al. A ratio estimator for bias correction in logarithmic regressions , 1991 .
[73] James W. Flewelling,et al. Multiplicative Regression with Lognormal Errors , 1981 .
[74] G. Baskerville. Use of Logarithmic Regression in the Estimation of Plant Biomass , 1972 .
[75] E. Mayr. Wallace's Line in the Light of Recent Zoogeographic Studies , 1944, The Quarterly Review of Biology.
[76] M. Herold,et al. Aboveground Woody Biomass Product Validation Good Practices Protocol , 2021 .
[77] S. Schulze. Estimating Biomass And Biomass Change Of Tropical Forests , 2016 .
[78] Guoqing Sun,et al. Evaluating Prospects for Improved Forest Parameter Retrieval From Satellite LiDAR Using a Physically-Based Radiative Transfer Model , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[79] R. Nelson,et al. Model-based inference for biomass estimation in a LiDAR sample survey in Hedmark County, Norway , 2011 .
[80] Mark H. Hansen,et al. Investigation into calculating tree biomass and carbon in the FIADB using a biomass expansion factor approach , 2009 .
[81] D. Clark,et al. Abundance, growth and mortality of very large trees in neotropical lowland rain forest , 1996 .