Prediction of tree crown width in natural mixed forests using deep learning algorithm

[1]  X. Lei,et al.  Tree size inequality and competition effects on nonlinear mixed effects crown width model for natural spruce-fir-broadleaf mixed forest in northeast China , 2022, Forest Ecology and Management.

[2]  H. Temesgen,et al.  Deep learning models for improved reliability of tree aboveground biomass prediction in the tropical evergreen broadleaf forests , 2022, Forest Ecology and Management.

[3]  M. Skudnik,et al.  Artificial neural networks as an alternative method to nonlinear mixed-effects models for tree height predictions , 2022, Forest Ecology and Management.

[4]  X. Lei,et al.  Comparisons of competitor selection approaches for spatially explicit competition indices of natural spruce-fir-broadleaf mixed forests , 2022, European Journal of Forest Research.

[5]  Guangshuang Duan,et al.  Estimating crown width in degraded forest: A two-level nonlinear mixed-effects crown width model for Dacrydium pierrei and Podocarpus imbricatus in tropical China , 2021 .

[6]  Jing-hui Meng,et al.  Predicting crown width and length using nonlinear mixed-effects models: a test of competition measures using Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) , 2021, Annals of Forest Science.

[7]  Friday Nwabueze Ogana,et al.  Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm , 2021, Journal of Forestry Research.

[8]  M. Bayat,et al.  Ten-year estimation of Oriental beech (Fagus orientalis Lipsky) volume increment in natural forests: a comparison of an artificial neural networks model, multiple linear regression and actual increment , 2021 .

[9]  M. Bayat,et al.  Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest , 2021, Annals of Forest Science.

[10]  M. Bayat,et al.  Development of individual tree growth and yield model across multiple contrasting species using nonparametric and parametric methods in the Hyrcanian forests of northern Iran , 2021 .

[11]  Mwamba Kasongo Dahouda,et al.  A Deep-Learned Embedding Technique for Categorical Features Encoding , 2021, IEEE Access.

[12]  Xin Shen,et al.  Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDAR Data , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  A. Jahani,et al.  Modeling of ash (Fraxinus excelsior) bark thickness in urban forests using artificial neural network (ANN) and regression models , 2020, Modeling Earth Systems and Environment.

[14]  İlker Ercanli,et al.  Artificial intelligence with deep learning algorithms to model relationships between total tree height and diameter at breast height , 2020, Forest Systems.

[15]  İlker Ercanli,et al.  Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height , 2020, Forest Ecosystems.

[16]  Gustavo Eduardo Marcatti,et al.  Modeling of eucalyptus productivity with artificial neural networks , 2020 .

[17]  M. Bayat,et al.  Estimation of Tree Heights in an Uneven-Aged, Mixed Forest in Northern Iran Using Artificial Intelligence and Empirical Models , 2020 .

[18]  T. Ledermann,et al.  Interregional Crown Width Models for Individual Trees Growing in Pure and Mixed Stands in Austria , 2020 .

[19]  Helio Garcia Leite,et al.  Artificial neural networks on integrated multispectral and SAR data for high-performance prediction of eucalyptus biomass , 2020, Comput. Electron. Agric..

[20]  M. B. Schimalski,et al.  A comparison of machine and deep-learning algorithms applied to multisource data for a subtropical forest area classification , 2019, International Journal of Remote Sensing.

[21]  Binh Thai Pham,et al.  Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran , 2019, Comput. Electron. Agric..

[22]  Long Ye,et al.  Projecting Australia's forest cover dynamics and exploring influential factors using deep learning , 2019, Environ. Model. Softw..

[23]  D. Forrester Linking forest growth with stand structure: Tree size inequality, tree growth or resource partitioning and the asymmetry of competition , 2019, Forest Ecology and Management.

[24]  W. Keeton,et al.  Stand structure drives disparities in carbon storage in northern hardwood-conifer forests , 2019, Forest Ecology and Management.

[25]  Arshad Ali Forest stand structure and functioning: Current knowledge and future challenges , 2019, Ecological Indicators.

[26]  Prabhat,et al.  Deep learning and process understanding for data-driven Earth system science , 2019, Nature.

[27]  Dervis Karaboga,et al.  Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey , 2018, Artificial Intelligence Review.

[28]  David L. R. Affleck,et al.  Additivity of nonlinear tree crown width models: Aggregated and disaggregated model structures using nonlinear simultaneous equations , 2018, Forest Ecology and Management.

[29]  D. Raptis,et al.  A Crown Width-Diameter Model for Natural Even-Aged Black Pine Forest Management , 2018, Forests.

[30]  Changhui Peng,et al.  Application of machine-learning methods in forest ecology: recent progress and future challenges , 2018, Environmental Reviews.

[31]  A-Xing Zhu,et al.  A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping , 2018, CATENA.

[32]  Giovanni Correia Vieira,et al.  Prognoses of diameter and height of trees of eucalyptus using artificial intelligence. , 2018, The Science of the total environment.

[33]  Carlos Pedro Boechat Soares,et al.  Estimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest , 2018 .

[34]  S. Huang,et al.  Effects of competition and climate variables on modelling height to live crown for three boreal tree species in Alberta, Canada , 2018, European Journal of Forest Research.

[35]  F. Bravo,et al.  Changes in structural heterogeneity and stand productivity by mixing Scots pine and Maritime pine , 2017 .

[36]  Ram P. Sharma,et al.  Modelling crown width–diameter relationship for Scots pine in the central Europe , 2017, Trees.

[37]  YangYuqing,et al.  Allometric modelling of crown width for white spruce by fixed- and mixed-effects models , 2017 .

[38]  Maria J. Diamantopoulou,et al.  Artificial Neural Network Models: An Alternative Approach for Reliable Aboveground Pine Tree Biomass Prediction , 2017 .

[39]  L. Fu,et al.  A generalized interregional nonlinear mixed-effects crown width model for Prince Rupprecht larch in northern China , 2017 .

[40]  Guangxing Wang,et al.  Modelling a system of nonlinear additive crown width models applying seemingly unrelated regression for Prince Rupprecht larch in northern China , 2017 .

[41]  Ameet Talwalkar,et al.  Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization , 2016, J. Mach. Learn. Res..

[42]  E. B. Görgens,et al.  Artificial Intelligence Procedures for Tree Taper Estimation within a Complex Vegetation Mosaic in Brazil , 2016, PloS one.

[43]  R. Sharma,et al.  Individual tree crown width models for Norway spruce and European beech in Czech Republic , 2016 .

[44]  J. Guldin,et al.  Using quadratic mean diameter and relative spacing index to enhance height-diameter and crown ratio models fitted to longitudinal data , 2016 .

[45]  Hans Pretzsch,et al.  Representation of species mixing in forest growth models. A review and perspective , 2015 .

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

[47]  C. Bourque,et al.  A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change , 2015, PloS one.

[48]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[49]  Maria J. Diamantopoulou,et al.  Estimation of Weibull function parameters for modelling tree diameter distribution using least squares and artificial neural networks methods , 2015 .

[50]  Wang Xinjie,et al.  Linear Mixed-Effects Models to Describe Individual Tree Crown Width for China-Fir in Fujian Province, Southeast China , 2015, PloS one.

[51]  S. Pauleit,et al.  Crown size and growing space requirement of common tree species in urban centres, parks, and forests , 2015 .

[52]  Hans Pretzsch,et al.  Characterization of the structure, dynamics, and productivity of mixed-species stands: review and perspectives , 2015, European Journal of Forest Research.

[53]  C. Collet,et al.  Crown responses to neighbor density and species identity in a young mixed deciduous stand , 2014, Trees.

[54]  C. Messier,et al.  Diversity increases carbon storage and tree productivity in Spanish forests , 2014 .

[55]  W. Cropper,et al.  Estimating Pinus palustris tree diameter and stem volume from tree height, crown area and stand-level parameters , 2014, Journal of Forestry Research.

[56]  C. VanderSchaaf Mixed-effects height–diameter models for ten conifers in the inland Northwest, USA , 2014 .

[57]  AshrafM. Irfan,et al.  Integrating biophysical controls in forest growth and yield predictions with artificial intelligence technology , 2013 .

[58]  Maria J. Diamantopoulou,et al.  Estimating Crimean juniper tree height using nonlinear regression and artificial neural network models , 2013 .

[59]  Shouzheng Tang,et al.  Nonlinear mixed-effects crown width models for individual trees of Chinese fir (Cunninghamia lanceolata) in south-central China , 2013 .

[60]  Y. Hong,et al.  Susceptibility evaluation and mapping of China’s landslides based on multi-source data , 2013, Natural Hazards.

[61]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[62]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[63]  G. Bohrer,et al.  The role of canopy structural complexity in wood net primary production of a maturing northern deciduous forest. , 2011, Ecology.

[64]  B. Muys,et al.  Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests , 2010 .

[65]  Rasmus Astrup,et al.  Competition and tree crowns: a neighborhood analysis of three boreal tree species. , 2010 .

[66]  Turan Sönmez Diameter at breast height-crown diameter prediction models for Picea orientalis. , 2009 .

[67]  A. Pommerening,et al.  The contribution of structural indices to the modelling of Sitka spruce (Picea sitchensis) and birch (Betula spp.) crowns , 2008 .

[68]  Conghe Song,et al.  Estimating tree crown size with spatial information of high resolution optical remotely sensed imagery , 2007 .

[69]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[70]  Tron Eid,et al.  An evaluation of different diameter diversity indices based on criteria related to forest management planning , 2006 .

[71]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[72]  H. Sterba,et al.  Derivation of compatible crown width equations for some important tree species of Spain , 2005 .

[73]  Maria J. Diamantopoulou,et al.  Artificial neural networks as an alternative tool in pine bark volume estimation , 2005 .

[74]  William A. Bechtold,et al.  Largest-Crown- Width Prediction Models for 53 Species in the Western United States , 2004 .

[75]  William A. Bechtold,et al.  Using crown condition variables as indicators of forest health , 2004 .

[76]  R. Valentini,et al.  A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization , 2003 .

[77]  M. Swaine,et al.  Modelling growing space requirements for some tropical forest tree species , 2003 .

[78]  Lieven Nachtergale,et al.  Spatial methods for quantifying forest stand structure development: a comparison between nearest-neighbor indices and variogram analysis , 2003 .

[79]  NLCW NLCW,et al.  A Local Basal Area Adjustment for Crown Width Prediction , 2001 .

[80]  R. Hebda,et al.  Modeling Tree-Ring Growth Responses to Climatic Variables Using Artificial Neural Networks , 2000, Forest Science.

[81]  Antti Penttinen,et al.  Statistical opportunities for comparing stand structural heterogeneity in managed and primeval forests: An example from boreal spruce forest in southern Finland , 1996 .

[82]  R. Monserud,et al.  A basal area increment model for individual trees growing in even- and uneven-aged forest stands in Austria , 1996 .

[83]  J. Krajícek,et al.  Crown competition-a measure of density. , 1961 .