Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

[1]  Rafael Calama,et al.  Multilevel linear mixed model for tree diameter increment in stone pine (Pinus pinea): a calibrating approach , 2005 .

[2]  M. Nilsson,et al.  Combining national forest inventory field plots and remote sensing data for forest databases , 2008 .

[3]  Johannes Breidenbach,et al.  Estimation of Forest Growing Stock Volume with UAV Laser Scanning Data: Can It Be Done without Field Data? , 2020, Remote. Sens..

[4]  Guillermo Trincado,et al.  A multilevel individual tree basal area increment model for aspen in boreal mixedwood stands , 2009 .

[5]  Lihu Dong,et al.  Comparison of Tree Biomass Modeling Approaches for Larch (Larix olgensis Henry) Trees in Northeast China , 2020, Forests.

[6]  Lihu Dong,et al.  Modeling Height–Diameter Relationships for Mixed-Species Plantations of Fraxinus mandshurica Rupr. and Larix olgensis Henry in Northeastern China , 2020, Forests.

[7]  Peter Surový,et al.  Very High Density Point Clouds from UAV Laser Scanning for Automatic Tree Stem Detection and Direct Diameter Measurement , 2020, Remote. Sens..

[8]  Michael J. Falkowski,et al.  A review of methods for mapping and prediction of inventory attributes for operational forest management , 2014 .

[9]  Lianjun Zhang,et al.  Developing Two Additive Biomass Equations for Three Coniferous Plantation Species in Northeast China , 2016 .

[10]  David A. Ratkowsky,et al.  Problems of hypothesis testing of regressions with multiple measurements from individual sampling units , 1984 .

[11]  Wuming Zhang,et al.  An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation , 2016, Remote. Sens..

[12]  Gherardo Chirici,et al.  A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data , 2016 .

[13]  Anshuman Bhardwaj,et al.  UAVs as remote sensing platform in glaciology: Present applications and future prospects , 2016 .

[14]  Matti Maltamo,et al.  Species-specific combination and calibration between area-based and tree-based diameter distributions using airborne laser scanning , 2016 .

[15]  M. Maltamo,et al.  Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines , 2019, Silva Fennica.

[16]  Ying Quan,et al.  A Region-Based Hierarchical Cross-Section Analysis for Individual Tree Crown Delineation Using ALS Data , 2017, Remote. Sens..

[17]  Robert J. McGaughey,et al.  Mixed-effects models for estimating stand volume by means of small footprint airborne laser scanner data. , 2008 .

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

[19]  Fengri Li,et al.  Modelling conifer crown profiles as nonlinear conditional quantiles: An example with planted Korean pine in northeast China , 2017 .

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

[21]  E. Næsset,et al.  Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data , 2015 .

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

[23]  P. Krzystek,et al.  Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data , 2012 .

[24]  Liviu Theodor Ene,et al.  Comparative testing of single-tree detection algorithms under different types of forest , 2011 .

[25]  Cheng Wang,et al.  Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux , 2018 .

[26]  Arko Lucieer,et al.  Evaluating Tree Detection and Segmentation Routines on Very High Resolution UAV LiDAR Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Zhen Zhen,et al.  Trends in Automatic Individual Tree Crown Detection and Delineation - Evolution of LiDAR Data , 2016, Remote. Sens..

[28]  M. Maltamo,et al.  Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics , 2010 .

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

[30]  Göran Ståhl,et al.  Remote sensing-assisted data assimilation and simultaneous inference for forest inventory , 2019 .

[31]  Le Wang,et al.  Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges , 2019, Remote Sensing of Environment.

[32]  R. Sharma,et al.  Modelling individual tree height–diameter relationships for multi-layered and multi-species forests in central Europe , 2018, Trees.

[33]  Eben N. Broadbent,et al.  Monitoring the structure of forest restoration plantations with a drone-lidar system , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[34]  J. Hyyppä,et al.  Tree species classification using airborne LiDAR - effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type , 2010 .

[35]  P. Meli,et al.  The effectiveness of lidar remote sensing for monitoring forest cover attributes and landscape restoration , 2019, Forest Ecology and Management.

[36]  Matti Maltamo,et al.  Predicting tree diameter using allometry described by non-parametric locally-estimated copulas from tree dimensions derived from airborne laser scanning , 2019, Forest Ecology and Management.

[37]  Q. Guo,et al.  Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods , 2010 .

[38]  M. Maltamo,et al.  Variable selection strategies for nearest neighbor imputation methods used in remote sensing based forest inventory , 2012 .

[39]  Yong Q. Tian,et al.  Estimating Basal Area and Stem Volume for Individual Trees from Lidar Data , 2007 .

[40]  Gregory S. Biging,et al.  Evaluation of Competition Indices in Individual Tree Growth Models , 1995, Forest Science.

[41]  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.

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

[43]  Petteri Packalen,et al.  Edge-Tree Correction for Predicting Forest Inventory Attributes Using Area-Based Approach With Airborne Laser Scanning , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[44]  Juha Hyyppä,et al.  International Benchmarking of the Individual Tree Detection Methods for Modeling 3-D Canopy Structure for Silviculture and Forest Ecology Using Airborne Laser Scanning , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Liviu Theodor Ene,et al.  Modelling tree diameter from airborne laser scanning derived variables: A comparison of spatial statistical models , 2010 .

[46]  Lorenzo Bruzzone,et al.  A Growth-Model-Driven Technique for Tree Stem Diameter Estimation by Using Airborne LiDAR Data , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Linking tree form, allocation and growth with an allometrically explicit model , 2005 .

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

[49]  Norbert Pfeifer,et al.  A Case Study of UAS Borne Laser Scanning for Measurement of Tree Stem Diameter , 2017, Remote. Sens..

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

[51]  Chien-Shun Lo,et al.  Growth-Competition-Based Stem Diameter and Volume Modeling for Tree-Level Forest Inventory Using Airborne LiDAR Data , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[52]  Juha Hyyppä,et al.  Autonomous Collection of Forest Field Reference - The Outlook and a First Step with UAV Laser Scanning , 2017, Remote. Sens..

[53]  Marco Heurich,et al.  Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[54]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[55]  Maggi Kelly,et al.  Airborne Lidar-derived volume metrics for aboveground biomass estimation: A comparative assessment for conifer stands , 2014 .

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

[57]  S. Meng,et al.  Improved calibration of nonlinear mixed-effects models demonstrated on a height growth function. , 2009 .

[58]  Martin Isenburg,et al.  Generating spike-free digital surface models using LiDAR raw point clouds: A new approach for forestry applications , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[59]  Benjamin Wilkinson,et al.  Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes , 2020, Comput. Electron. Agric..

[60]  Q. Guo,et al.  An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China , 2017 .

[61]  M. Maltamo,et al.  Combining ALS and NFI training data for forest management planning: a case study in Kuortane, Western Finland , 2009, European Journal of Forest Research.

[62]  M. Maltamo,et al.  Calibration of area based diameter distribution with individual tree based diameter estimates using airborne laser scanning , 2014 .

[63]  M. Maltamo,et al.  The transferability of airborne laser scanning based tree-level models between different inventory areas , 2019, Canadian Journal of Forest Research.

[64]  Maggi Kelly,et al.  A New Method for Segmenting Individual Trees from the Lidar Point Cloud , 2012 .

[65]  M. Maltamo,et al.  Predicting and calibrating tree attributes by means of airborne laser scanning and field measurements , 2012 .

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

[67]  F. Uzoh,et al.  Individual tree diameter increment model for managed even-aged stands of ponderosa pine throughout the western United States using a multilevel linear mixed effects model , 2008 .

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

[69]  Arko Lucieer,et al.  An Assessment of the Repeatability of Automatic Forest Inventory Metrics Derived From UAV-Borne Laser Scanning Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[70]  Joanne C. White,et al.  Lidar sampling for large-area forest characterization: A review , 2012 .

[71]  Petteri Packalen,et al.  Effect of flying altitude, scanning angle and scanning mode on the accuracy of ALS based forest inventory , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[72]  Hua Sun,et al.  Prediction of Individual Tree Diameter Using a Nonlinear Mixed-Effects Modeling Approach and Airborne LiDAR Data , 2020, Remote. Sens..

[73]  Zhen Zhen,et al.  A graph-based progressive morphological filtering (GPMF) method for generating canopy height models using ALS data , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[74]  J. Greenberg,et al.  Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDAR , 2018, Remote Sensing of Environment.

[75]  G. Robinson That BLUP is a Good Thing: The Estimation of Random Effects , 1991 .

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

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

[78]  E. Næsset Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data , 2009 .

[79]  L. Korhonen,et al.  Nationwide airborne laser scanning based models for volume, biomass and dominant height in Finland. , 2016 .

[80]  Norbert Pfeifer,et al.  International benchmarking of terrestrial laser scanning approaches for forest inventories , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[81]  MehtätaloLauri,et al.  Evaluating marginal and conditional predictions of taper models in the absence of calibration data , 2012 .

[82]  Eija Honkavaara,et al.  Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements , 2019, Forest Ecosystems.

[83]  Qinghua Guo,et al.  Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California , 2018, Int. J. Digit. Earth.

[84]  M. Vastaranta,et al.  Predicting individual tree attributes from airborne laser point clouds based on the random forests technique , 2011 .

[85]  Adrián Pascual,et al.  Using Tree Detection Based on Airborne Laser Scanning to Improve Forest Inventory Considering Edge Effects and the Co-Registration Factor , 2019, Remote. Sens..

[86]  Ying Quan,et al.  The Feasibility of Modelling the Crown Profile of Larix olgensis Using Unmanned Aerial Vehicle Laser Scanning Data , 2020, Sensors.

[87]  Marco Heurich,et al.  Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests , 2014 .

[88]  Petteri Packalen,et al.  Forest inventories for small areas using drone imagery without in-situ field measurements , 2020 .