Prediction of Individual Tree Diameter and Height to Crown Base Using Nonlinear Simultaneous Regression and Airborne LiDAR Data

[1]  Henrik Meilby,et al.  Site-specific height growth models for six common tree species in Denmark , 2009 .

[2]  J. Coulston,et al.  Compatibility, Development, and Estimation of Taper and Volume Equation Systems , 2018, Forest Science.

[3]  Christina L. Staudhammer,et al.  Individual Tree-Based Diameter Growth Model of Slash Pine in Florida Using Nonlinear Mixed Modeling , 2013 .

[4]  S. Reutebuch,et al.  Estimating forest canopy fuel parameters using LIDAR data , 2005 .

[5]  Jari Hynynen,et al.  Predicting tree crown ratio for unthinned and thinned Scots pine stands , 1995 .

[6]  S. Popescu Estimating biomass of individual pine trees using airborne lidar , 2007 .

[7]  E. Næsset,et al.  Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve , 2002 .

[8]  B. Courbaud,et al.  Development of an individual tree-based mechanical model to predict wind damage within forest stands , 2004 .

[9]  Scott D. Roberts,et al.  Measuring heights to crown base and crown median with LiDAR in a mature, even-aged loblolly pine stand , 2009 .

[10]  Lindi J. Quackenbush,et al.  Using error-in-variable regression to predict tree diameter and crown width from remotely sensed imagery. , 2010 .

[11]  Juha Hyyppä,et al.  A comparative study of the use of laser scanner data and field measurements in the prediction of crown height in boreal forests , 2006 .

[12]  Eben N. Broadbent,et al.  Spatial partitioning of biomass and diversity in a lowland Bolivian forest: Linking field and remote sensing measurements , 2008 .

[13]  R. Sharma,et al.  Modelling individual tree height to crown base of Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus sylvatica L.) , 2017, PloS one.

[14]  Shouzheng Tang,et al.  A parameter estimation program for the error-in-variable model , 2002 .

[15]  Hua Sun,et al.  Development of a System of Compatible Individual Tree Diameter and Aboveground Biomass Prediction Models Using Error-In-Variable Regression and Airborne LiDAR Data , 2018, Remote. Sens..

[16]  Terje Gobakken,et al.  Comparing different methods for prediction of mean crown height in Norway spruce stands using airborne laser scanner data , 2010 .

[17]  B. Koch,et al.  Detection of individual tree crowns in airborne lidar data , 2006 .

[18]  W. Cohen,et al.  Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests , 1999 .

[19]  Åsa Persson,et al.  Species identification of individual trees by combining high resolution LiDAR data with multi‐spectral images , 2008 .

[20]  P. J. Curran,et al.  The importance of measurement error for certain procedures in remote sensing at optical wavelengths , 1986 .

[21]  M. Heurich Automatic recognition and measurement of single trees based on data from airborne laser scanning over the richly structured natural forests of the Bavarian Forest National Park , 2008 .

[22]  LiYun,et al.  Evaluation of nonlinear equations for predicting diameter from tree height , 2012 .

[23]  R. WeiskittelAaron,et al.  Development of height to crown base models for thirteen tree species of the North American Acadian Region , 2012 .

[24]  Bernard R. Parresol,et al.  Additivity of nonlinear biomass equations , 2001 .

[25]  Shouzheng Tang,et al.  Simultaneous equations, error-in-variable models, and model integration in systems ecology , 2001 .

[26]  G. Tonoli,et al.  Effect of natural weathering and accelerated artificial in physical, mechanical and colorimetric OSB panels. , 2017 .

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

[28]  M. Maltamo,et al.  Incorporating tree- and stand-level information on crown base height into multivariate forest management inventories based on airborne laser scanning , 2018 .

[29]  E. Næsset,et al.  Single Tree Segmentation Using Airborne Laser Scanner Data in a Structurally Heterogeneous Spruce Forest , 2006 .

[30]  S. Popescu,et al.  A voxel-based lidar method for estimating crown base height for deciduous and pine trees , 2008 .

[31]  Harold E. Burkhart,et al.  Compatible crown ratio and crown height models , 1987 .

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

[33]  D. V. Lindley,et al.  Regression Lines and the Linear Functional Relationship , 1947 .

[34]  Q. Guo,et al.  Simple method for direct crown base height estimation of individual conifer trees using airborne LiDAR data. , 2018, Optics express.

[35]  Liyong Fu,et al.  Improving Estimation of Forest Canopy Cover by Introducing Loss Ratio of Laser Pulses Using Airborne LiDAR , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Jari Vauhkonen,et al.  Estimating crown base height for Scots pine by means of the 3D geometry of airborne laser scanning data , 2010 .

[37]  Shouzheng Tang,et al.  Comparison of seemingly unrelated regressions with error-in-variable models for developing a system of nonlinear additive biomass equations , 2015, Trees.

[38]  Annika Kangas,et al.  Effect of errors-in-variables on coefficients of a growth model and on prediction of growth , 1998 .

[39]  Jason Parent,et al.  Assessing the potential for leaf-off LiDAR data to model canopy closure in temperate deciduous forests , 2014 .