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