A simulation method to infer tree allometry and forest structure from airborne laser scanning and forest inventories

[1]  Scott J. Goetz,et al.  The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography , 2020, Science of Remote Sensing.

[2]  Ignácio Amigo,et al.  When will the Amazon hit a tipping point? , 2020, Nature.

[3]  Juha Hyyppä,et al.  Variability of wood properties using airborne and terrestrial laser scanning , 2019 .

[4]  S. Saatchi,et al.  Landscape-level validation of allometric relationships for carbon stock estimation reveals bias driven by soil type. , 2019, Ecological applications : a publication of the Ecological Society of America.

[5]  R. Irizarry.  ggplot2 , 2019, Introduction to Data Science.

[6]  Atticus E. L. Stovall,et al.  Tree height explains mortality risk during an intense drought , 2019, Nature Communications.

[7]  N. Picard,et al.  Determinants of spatial patterns of canopy tree species in a tropical evergreen forest in Gabon , 2019, Journal of Vegetation Science.

[8]  Nicholas C. Coops,et al.  Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions , 2019, Current Forestry Reports.

[9]  M. Herold,et al.  The Importance of Consistent Global Forest Aboveground Biomass Product Validation , 2019, Surveys in Geophysics.

[10]  Stefano Tebaldini,et al.  The Status of Technologies to Measure Forest Biomass and Structural Properties: State of the Art in SAR Tomography of Tropical Forests , 2019, Surveys in Geophysics.

[11]  Sassan Saatchi,et al.  A Comparative Assessment of the Performance of Individual Tree Crowns Delineation Algorithms from ALS Data in Tropical Forests , 2019, Remote. Sens..

[12]  M. Herold,et al.  Estimating architecture-based metabolic scaling exponents of tropical trees using terrestrial LiDAR and 3D modelling , 2019, Forest Ecology and Management.

[13]  Rebecca A. Spriggs,et al.  A critique of general allometry-inspired models for estimating forest carbon density from airborne LiDAR , 2019, PloS one.

[14]  Jérôme Chave,et al.  Improving plant allometry by fusing forest models and remote sensing. , 2019, The New phytologist.

[15]  A. Huth,et al.  The Relevance of Forest Structure for Biomass and Productivity in Temperate Forests: New Perspectives for Remote Sensing , 2019, Surveys in Geophysics.

[16]  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.

[17]  M. Disney.  Terrestrial LiDAR: a three-dimensional revolution in how we look at trees. , 2018, The New phytologist.

[18]  H. Shugart,et al.  Assessing terrestrial laser scanning for developing non-destructive biomass allometry , 2018, Forest Ecology and Management.

[19]  J. Abatzoglou,et al.  Microclimatic buffering in forests of the future: the role of local water balance , 2018, Ecography.

[20]  G. Bohrer,et al.  Quantifying vegetation and canopy structural complexity from terrestrial LiDAR data using the forestr r package , 2018, Methods in Ecology and Evolution.

[21]  F. Hartig,et al.  Using synthetic data to evaluate the benefits of large field plots for forest biomass estimation with LiDAR , 2018, Remote Sensing of Environment.

[22]  Klaus Scipal,et al.  In Situ Reference Datasets From the TropiSAR and AfriSAR Campaigns in Support of Upcoming Spaceborne Biomass Missions , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[23]  David Kenfack,et al.  Global importance of large‐diameter trees , 2018 .

[24]  Philip Lewis,et al.  Realistic Forest Stand Reconstruction from Terrestrial LiDAR for Radiative Transfer Modelling , 2018, Remote. Sens..

[25]  M. Keller,et al.  Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes , 2018, Biogeosciences.

[26]  Jean‐François Bastin,et al.  Field methods for sampling tree height for tropical forest biomass estimation , 2018, Methods in ecology and evolution.

[27]  A. Huth,et al.  Linking lidar and forest modeling to assess biomass estimation across scales and disturbance states , 2018 .

[28]  N. Barbier,et al.  Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach , 2017 .

[29]  J. Chave,et al.  An individual-based forest model to jointly simulate carbon and tree diversity in Amazonia: description and applications , 2017 .

[30]  Grégoire Vincent,et al.  Mapping plant area index of tropical evergreen forest by airborne laser scanning. A cross-validation study using LAI2200 optical sensor , 2017 .

[31]  J. Chave,et al.  biomass: an r package for estimating above‐ground biomass and its uncertainty in tropical forests , 2017 .

[32]  Michele Dalponte,et al.  Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data , 2017 .

[33]  Markku Åkerblom,et al.  Automatic tree species recognition with quantitative structure models , 2017 .

[34]  Michel G.J. den Elzen,et al.  The key role of forests in meeting climate targets requires science for credible mitigation , 2017 .

[35]  Mark C. Vanderwel,et al.  Allometric equations for integrating remote sensing imagery into forest monitoring programmes , 2016, Global change biology.

[36]  Sassan Saatchi,et al.  The 2016 NASA AfriSAR campaign: Airborne SAR and Lidar measurements of tropical forest structure and biomass in support of future satellite missions , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[37]  A. Huth,et al.  The importance of forest structure to biodiversity–productivity relationships , 2017, Royal Society Open Science.

[38]  Juan Carlos Castilla-Rubio,et al.  Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm , 2016, Proceedings of the National Academy of Sciences.

[39]  Sassan Saatchi,et al.  Lidar detection of individual tree size in tropical forests , 2016 .

[40]  David Kenfack,et al.  Ecological Importance of Small-Diameter Trees to the Structure, Diversity and Biomass of a Tropical Evergreen Forest at Rabi, Gabon , 2016, PloS one.

[41]  Michele Dalponte,et al.  Tree‐centric mapping of forest carbon density from airborne laser scanning and hyperspectral data , 2016, Methods in ecology and evolution.

[42]  Susan G. Letcher,et al.  Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics , 2016, Science Advances.

[43]  Stephanie A. Bohlman,et al.  Dominance of the suppressed: Power-law size structure in tropical forests , 2016, Science.

[44]  A. Huth,et al.  The structure of tropical forests and sphere packings , 2015, Proceedings of the National Academy of Sciences.

[45]  M. G. Ryan,et al.  LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients , 2015 .

[46]  Thuy Le Toan,et al.  Computer and remote‐sensing infrastructure to enhance large‐scale testing of individual‐based forest models , 2015 .

[47]  O. Phillips,et al.  Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest , 2015 .

[48]  F. M. Danson,et al.  Terrestrial Laser Scanning for Plot-Scale Forest Measurement , 2015, Current Forestry Reports.

[49]  D. Edwards,et al.  Increasing human dominance of tropical forests , 2015, Science.

[50]  H. Beeckman,et al.  Seeing Central African forests through their largest trees , 2015, Scientific Reports.

[51]  Olivier Bouriaud,et al.  Crown plasticity enables trees to optimize canopy packing in mixed-species forests , 2015 .

[52]  Matthew A. Nunes,et al.  abctools: An R Package for Tuning Approximate Bayesian Computation Analyses , 2015, R J..

[53]  Philippe Ciais,et al.  Projected strengthening of Amazonian dry season by constrained climate model simulations , 2015 .

[54]  Rebecca A. Spriggs,et al.  A simple area-based model for predicting airborne LiDAR first returns from stem diameter distributions: an example study in an uneven-aged, mixed temperate forest , 2015 .

[55]  Sean M. McMahon,et al.  Size-related scaling of tree form and function in a mixed-age forest , 2015 .

[56]  Michael W. Palace,et al.  Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data , 2015 .

[57]  P. Cox,et al.  Observing terrestrial ecosystems and the carbon cycle from space , 2015, Global change biology.

[58]  M. Herold,et al.  Nondestructive estimates of above‐ground biomass using terrestrial laser scanning , 2015 .

[59]  Norman A. Bourg,et al.  CTFS‐ForestGEO: a worldwide network monitoring forests in an era of global change , 2015, Global change biology.

[60]  J. N. Long,et al.  Utah State University From the SelectedWorks of James Long 2014 Resistance and resilience : A conceptual framework for silviculture , 2017 .

[61]  David Kenfack,et al.  Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks , 2014 .

[62]  T. Spies,et al.  Disturbance legacies increase the resilience of forest ecosystem structure, composition, and functioning. , 2014, Ecological applications : a publication of the Ecological Society of America.

[63]  B. Nelson,et al.  Improved allometric models to estimate the aboveground biomass of tropical trees , 2014, Global change biology.

[64]  Martin Isenburg,et al.  Generating pit-free canopy height models from airborne lidar , 2014 .

[65]  Hans Pretzsch,et al.  Canopy space filling and tree crown morphology in mixed-species stands compared with monocultures , 2014 .

[66]  Jean-Jacques Boreux,et al.  Predicting tree heights for biomass estimates in tropical forests – a test from French Guiana , 2014 .

[67]  R. Valentini,et al.  Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data , 2014 .

[68]  Andreas Huth,et al.  Technical Note: Approximate Bayesian parameterization of a process-based tropical forest model , 2014, 1401.8205.

[69]  G. Asner,et al.  Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric , 2014 .

[70]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[71]  J. Lutz,et al.  The Importance of Large-Diameter Trees to Forest Structural Heterogeneity , 2013, PloS one.

[72]  R. B. Jackson,et al.  The Structure, Distribution, and Biomass of the World's Forests , 2013 .

[73]  Y. Malhi,et al.  African rainforests: past, present and future , 2013, Philosophical Transactions of the Royal Society B: Biological Sciences.

[74]  S. Goetz,et al.  A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing , 2013 .

[75]  Gregory P. Asner,et al.  The rate and spatial pattern of treefall in a savanna landscape. , 2013 .

[76]  D. Coomes,et al.  Predictable changes in aboveground allometry of trees along gradients of temperature, aridity and competition , 2012 .

[77]  J. Terborgh,et al.  Tree height integrated into pantropical forest biomass estimates , 2012 .

[78]  H. Pretzsch,et al.  Evidence of variant intra- and interspecific scaling of tree crown structure and relevance for allometric theory , 2012, Oecologia.

[79]  G. Asner,et al.  Evaluating uncertainty in mapping forest carbon with airborne LiDAR , 2011 .

[80]  F. Rocca,et al.  The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle , 2011 .

[81]  S. Higgins,et al.  TRY – a global database of plant traits , 2011, Global Change Biology.

[82]  R. B. Jackson,et al.  A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.

[83]  Andreas Huth,et al.  Statistical inference for stochastic simulation models--theory and application. , 2011, Ecology letters.

[84]  M. Fournier,et al.  The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges , 2011, Annals of Forest Science.

[85]  Katalin Csill'ery,et al.  abc: an R package for approximate Bayesian computation (ABC) , 2011, 1106.2793.

[86]  N. Coops,et al.  Assessment of standing wood and fiber quality using ground and airborne laser scanning: A review , 2011 .

[87]  G. Powell,et al.  High-resolution forest carbon stocks and emissions in the Amazon , 2010, Proceedings of the National Academy of Sciences.

[88]  O. François,et al.  Approximate Bayesian Computation (ABC) in practice. , 2010, Trends in ecology & evolution.

[89]  F. Hall,et al.  Importance of structure and its measurement in quantifying function of forest ecosystems , 2010 .

[90]  S. Goetz,et al.  Lidar remote sensing variables predict breeding habitat of a Neotropical migrant bird. , 2010, Ecology.

[91]  Geoffrey B. West,et al.  A general quantitative theory of forest structure and dynamics , 2009, Proceedings of the National Academy of Sciences.

[92]  J. Chave,et al.  Towards a Worldwide Wood Economics Spectrum 2 . L E a D I N G D I M E N S I O N S I N W O O D F U N C T I O N , 2022 .

[93]  S. Pacala,et al.  Predicting and understanding forest dynamics using a simple tractable model , 2008, Proceedings of the National Academy of Sciences.

[94]  J. Dushoff,et al.  SCALING FROM TREES TO FORESTS: TRACTABLE MACROSCOPIC EQUATIONS FOR FOREST DYNAMICS , 2008 .

[95]  Frans Bongers,et al.  Above-ground biomass and productivity in a rain forest of eastern South America , 2008, Journal of Tropical Ecology.

[96]  Peter R. J. North,et al.  Vegetation height estimates for a mixed temperate forest using satellite laser altimetry , 2008 .

[97]  Karl J Niklas,et al.  Maximum plant height and the biophysical factors that limit it. , 2007, Tree physiology.

[98]  J. Chave,et al.  Rapid decay of tree-community composition in Amazonian forest fragments , 2006, Proceedings of the National Academy of Sciences.

[99]  O. Phillips,et al.  Continental-scale patterns of canopy tree composition and function across Amazonia , 2006, Nature.

[100]  Frans Bongers,et al.  Architecture of 54 moist-forest tree species: traits, trade-offs, and functional groups. , 2006, Ecology.

[101]  J. Hyyppä,et al.  DETECTING AND ESTIMATING ATTRIBUTES FOR SINGLE TREES USING LASER SCANNER , 2006 .

[102]  P. Puttonen,et al.  Impact of stand structure on surface fire ignition potential in Picea abies and Pinus sylvestris forests in southern Finland , 2005 .

[103]  K. Itten,et al.  LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management , 2004 .

[104]  Emilio Chuvieco,et al.  Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests , 2004 .

[105]  J. Bryan Blair,et al.  Beyond potential vegetation: Combining lidar data and a height-structured model for carbon studies , 2004 .

[106]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[107]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[108]  David A. Coomes,et al.  Disturbances prevent stem size‐density distributions in natural forests from following scaling relationships , 2003 .

[109]  Karl J. Niklas,et al.  A general model for mass-growth-density relations across tree-dominated communities , 2003 .

[110]  E. Næsset.  Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data , 2002 .

[111]  F. Bongers,et al.  Crown development in tropical rain forest trees: patterns with tree height and light availability , 2001 .

[112]  D. Sabatier,et al.  The Lowland High Rainforest: Structure and Tree Species Diversity , 2001 .

[113]  James H. Brown,et al.  A general model for the structure and allometry of plant vascular systems , 1999, Nature.

[114]  Lonnie W. Aarssen,et al.  The interpretation of stem diameter–height allometry in trees: biomechanical constraints, neighbour effects, or biased regressions? , 1999 .

[115]  Richard Condit,et al.  Tropical Forest Census Plots , 1998, Environmental Intelligence Unit.

[116]  S. Thomas.  Asymptotic height as a predictor of growth and allometric characteristics in malaysian rain forest trees , 1996 .

[117]  D. A. King,et al.  Allometry and life history of tropical trees , 1996, Journal of Tropical Ecology.

[118]  Karl J. Niklas,et al.  Botanical Scaling. (Book Reviews: Plant Allometry. The Scaling of Form and Process.) , 1994 .

[119]  Henry F. Inman,et al.  The overlapping coefficient as a measure of agreement between probability distributions and point estimation of the overlap of two normal densities , 1989 .

[120]  Paul W. Holland,et al.  Two Robust Alternatives to Least-Squares Regression , 1977 .