Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

[1]  Korhonen Lauri,et al.  The use of airborne laser scanning to estimate sawlog volumes , 2008 .

[2]  M. Nilsson,et al.  A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory , 2017 .

[3]  Markus Hollaus,et al.  Airborne Laser Scanning of Forest Stem Volume in a Mountainous Environment , 2007, Sensors (Basel, Switzerland).

[4]  E. Næsset,et al.  Optimizing nearest neighbour configurations for airborne laser scanning-assisted estimation of forest volume and biomass , 2017 .

[5]  D. Whitman,et al.  Comparison of Three Algorithms for Filtering Airborne Lidar Data , 2005 .

[6]  Kaiguang Zhao,et al.  Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues , 2010, Remote. Sens..

[7]  Qing Zhu,et al.  An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy , 2014 .

[8]  Bingbo Gao,et al.  State-of-the-Art: DTM Generation Using Airborne LIDAR Data , 2017, Sensors.

[9]  Xiaoye Liu,et al.  Airborne LiDAR for DEM generation: some critical issues , 2008 .

[10]  Florian Hartig,et al.  Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass , 2014 .

[11]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[12]  George Vosselman,et al.  Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds , 2004 .

[13]  Alicia Troncoso Lora,et al.  A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables , 2015, Neurocomputing.

[14]  Nicholas C. Coops,et al.  Evaluation of Ground Surface Models Derived from Unmanned Aerial Systems with Digital Aerial Photogrammetry in a Disturbed Conifer Forest , 2019, Remote. Sens..

[15]  W. Wagner,et al.  Accuracy of large-scale canopy heights derived from LiDAR data under operational constraints in a complex alpine environment , 2006 .

[16]  C. Pérez-Cruzado,et al.  Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data , 2018, Annals of Forest Science.

[17]  Antonio Luis Montealegre,et al.  A Comparison of Open-Source LiDAR Filtering Algorithms in a Mediterranean Forest Environment , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Joanne C. White,et al.  Evaluating the impact of leaf-on and leaf-off airborne laser scanning data on the estimation of forest inventory attributes with the area-based approach , 2015 .

[19]  Nicolas H. Younan,et al.  DTM extraction of lidar returns via adaptive processing , 2003, IEEE Trans. Geosci. Remote. Sens..

[20]  Terje Gobakken,et al.  Inference for lidar-assisted estimation of forest growing stock volume , 2013 .

[21]  Alberto García-Martín,et al.  Use of low point density ALS data to estimate stand-level structural variables in Mediterranean Aleppo pine forest , 2016 .

[22]  P. Gessler,et al.  Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA , 2009 .

[23]  Alexander N. V. Graham,et al.  Effect of ground surface interpolation methods on the accuracy of forest attribute modelling using unmanned aerial systems-based digital aerial photogrammetry , 2019, International Journal of Remote Sensing.

[24]  Martin J. Wooster,et al.  Assessment of Errors Caused by Forest Vegetation Structure in Airborne LiDAR-Derived DTMs , 2017, Remote. Sens..

[25]  Juan Guerra-Hernández,et al.  Comparing Johnson's SB and Weibull Functions to Model the Diameter Distribution of Forest Plantations through ALS Data , 2019, Remote. Sens..

[26]  Marek K. Jakubowski,et al.  Tradeoffs between lidar pulse density and forest measurement accuracy , 2013 .

[27]  Eduardo González-Ferreiro,et al.  Assessing the attributes of high-density Eucalyptus globulus stands using airborne laser scanner data , 2011 .

[28]  Norbert Pfeifer,et al.  Experimental evaluation of ALS point cloud ground extraction tools over different terrain slope and land-cover types , 2014 .

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

[30]  Keith C. Clarke,et al.  An improved simple morphological filter for the terrain classification of airborne LIDAR data , 2013 .

[31]  Alessandro Montaghi,et al.  A performance comparison of machine learning methods to estimate the fast-growing forest plantation yield based on laser scanning metrics , 2015, Comput. Electron. Agric..

[32]  Hongyu Huang,et al.  A Comparison of Two Open Source LiDAR Surface Classification Algorithms , 2011, Remote. Sens..

[33]  K. Kraus,et al.  Determination of terrain models in wooded areas with airborne laser scanner data , 1998 .

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

[35]  Murat Uysal,et al.  Investigating performance of Airborne LiDAR data filtering algorithms for DTM generation , 2015 .

[36]  Terje Gobakken,et al.  Different plot selection strategies for field training data in ALS-assisted forest , 2010 .

[37]  K. Lim,et al.  Examining the effects of sampling point densities on laser canopy height and density metrics , 2008 .

[38]  L. Rodriguez,et al.  Stand volume models based on stable metrics as from multiple ALS acquisitions in Eucalyptus plantations , 2015, Annals of Forest Science.

[39]  F. Bravo,et al.  Assessing the robustness of variable selection methods when accounting for co-registration errors in the estimation of forest biophysical and ecological attributes , 2019, Ecological Modelling.

[40]  Eric Bastos Gorgens,et al.  Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations , 2015 .

[41]  Margarida Tomé,et al.  Equação de Volume Total, Volume Percentual e de Perfil do Tronco para Eucalyptus globulus Labill. em Portugal , 2007 .

[42]  Gottfried Mandlburger,et al.  Beyond 3-D: The New Spectrum of Lidar Applications for Earth and Ecological Sciences , 2016 .

[43]  Simon D. Jones,et al.  Assessing the Ability of Image Based Point Clouds Captured from a UAV to Measure the Terrain in the Presence of Canopy Cover , 2019, Forests.

[44]  T. Zawila-Niedzwiecki,et al.  Comparison of various algorithms for DTM interpolation from LIDAR data in dense mountain forests , 2016 .

[45]  Jorge García-Gutiérrez,et al.  Comparison of ALS based models for estimating aboveground biomass in three types of Mediterranean forest , 2016 .

[46]  Aloysius Wehr,et al.  Airborne laser scanning—an introduction and overview , 1999 .

[47]  Liviu Theodor Ene,et al.  Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass , 2016 .

[48]  Thomas Nord-Larsen,et al.  Developing an airborne laser scanning dominant height model from a countrywide scanning survey and national forest inventory data , 2010 .

[49]  E. Næsset.  Practical large-scale forest stand inventory using a small-footprint airborne scanning laser , 2004 .

[50]  Andrew Thomas Hudak,et al.  A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[51]  E. Næsset,et al.  Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data. , 2009 .

[52]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[53]  Chengcui Zhang,et al.  A progressive morphological filter for removing nonground measurements from airborne LIDAR data , 2003, IEEE Trans. Geosci. Remote. Sens..

[54]  J. Hyyppä,et al.  Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests , 2008 .

[55]  C CoopsNicholas,et al.  Unmanned aerial systems for precision forest inventory purposes: A review and case study , 2017 .

[56]  Nicholas C. Coops,et al.  Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data , 2019, Remote Sensing of Environment.

[57]  Juan de la Riva,et al.  Interpolation Routines Assessment in ALS-Derived Digital Elevation Models for Forestry Applications , 2015, Remote. Sens..

[58]  Veraldo Liesenberg,et al.  Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR Data , 2018 .

[59]  Frédéric Bretar,et al.  3-D mapping of a multi-layered Mediterranean forest using ALS data , 2012 .

[60]  Ross Nelson,et al.  How did we get here? An early history of forestry lidar1 , 2013 .

[61]  Nicholas C. Coops,et al.  Augmenting Site Index Estimation with Airborne Laser Scanning Data , 2015 .

[62]  Dominic Fawcett,et al.  Unmanned aerial vehicle (UAV) derived structure-from-motion photogrammetry point clouds for oil palm (Elaeis guineensis) canopy segmentation and height estimation , 2019, International Journal of Remote Sensing.

[63]  Gil Rito Gonçalves,et al.  A Thorough Accuracy Estimation of DTM Produced From Airborne Full-Waveform Laser Scanning Data of Unmanaged Eucalypt Plantations , 2012, IEEE Transactions on Geoscience and Remote Sensing.