Foliar and woody materials discriminated using terrestrial LiDAR in a mixed natural forest

Abstract Separation of foliar and woody materials using remotely sensed data is crucial for the accurate estimation of leaf area index (LAI) and woody biomass across forest stands. In this paper, we present a new method to accurately separate foliar and woody materials using terrestrial LiDAR point clouds obtained from ten test sites in a mixed forest in Bavarian Forest National Park, Germany. Firstly, we applied and compared an adaptive radius near-neighbor search algorithm with a fixed radius near-neighbor search method in order to obtain both radiometric and geometric features derived from terrestrial LiDAR point clouds. Secondly, we used a random forest machine learning algorithm to classify foliar and woody materials and examined the impact of understory and slope on the classification accuracy. An average overall accuracy of 84.4% (Kappa = 0.75) was achieved across all experimental plots. The adaptive radius near-neighbor search method outperformed the fixed radius near-neighbor search method. The classification accuracy was significantly higher when the combination of both radiometric and geometric features was utilized. The analysis showed that increasing slope and understory coverage had a significant negative effect on the overall classification accuracy. Our results suggest that the utilization of the adaptive radius near-neighbor search method coupling both radiometric and geometric features has the potential to accurately discriminate foliar and woody materials from terrestrial LiDAR data in a mixed natural forest.

[1]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[2]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[3]  A. MacKinnon.  Forest Structure : A Key to the Ecosystem , 2012 .

[4]  Gérard G. Medioni,et al.  Curvature-Augmented Tensor Voting for Shape Inference from Noisy 3D Data , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Andrew K. Skidmore,et al.  Effects of Canopy Structural Variables on Retrieval of Leaf Dry Matter Content and Specific Leaf Area From Remotely Sensed Data , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  C. A. Mücher,et al.  Environmental science: Agree on biodiversity metrics to track from space , 2015, Nature.

[7]  Maurice G. Kendall,et al.  The advanced theory of statistics , 1945 .

[8]  L. Penasa,et al.  Integration of intensity textures and local geometry descriptors from Terrestrial Laser Scanning to map chert in outcrops , 2014 .

[9]  Guang Zheng,et al.  Improved Salient Feature-Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components Within Terrestrial Lidar Point Cloud Data of Forest Canopies , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[10]  M. Schlerf,et al.  Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data , 2006 .

[11]  A. Skidmore,et al.  Comparing accuracy assessments to infer superiority of image classification methods , 2006 .

[12]  Michael Nobis,et al.  Automatic thresholding for hemispherical canopy-photographs based on edge detection , 2005 .

[13]  Guang Zheng,et al.  Assessing the Contribution of Woody Materials to Forest Angular Gap Fraction and Effective Leaf Area Index Using Terrestrial Laser Scanning Data , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[14]  D. Lague,et al.  3D point cloud classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology , 2012 .

[15]  Wenzhong Shi,et al.  A fuzzy topology-based maximum likelihood classification , 2011 .

[16]  Edward W. Bork,et al.  Influence of Vegetation, Slope, and Lidar Sampling Angle on DEM Accuracy , 2006 .

[17]  Lee A. Vierling,et al.  Use of a ground‐based scanning lidar for estimation of biophysical properties of western larch (Larix occidentalis) , 2007 .

[18]  J. Demantké,et al.  DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS , 2012 .

[19]  Ibtisam Ab Majid,et al.  Tree species classification using worldview-3 data , 2016, 2016 7th IEEE Control and System Graduate Research Colloquium (ICSGRC).

[20]  Lixia Ma,et al.  Determining woody-to-total area ratio using terrestrial laser scanning (TLS) , 2016 .

[21]  Farah Jahan,et al.  Fusion of Hyperspectral and LiDAR Data for Land Cover Classification , 2019 .

[22]  Philip J. Howarth,et al.  Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems , 1999 .

[23]  Hermann Gross,et al.  EXTRACTION OF LINES FROM LASER POINT CLOUDS , 2006 .

[24]  Alan H. Strahler,et al.  Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: a case study in Sierra National Forest, CA , 2014 .

[25]  Peter Bunting,et al.  Retrieving forest biomass through integration of CASI and LiDAR data , 2008 .

[26]  W. Wagner,et al.  3D vegetation mapping using small‐footprint full‐waveform airborne laser scanners , 2008 .

[27]  Kenji Omasa,et al.  3-D voxel-based solid modeling of a broad-leaved tree for accurate volume estimation using portable scanning lidar , 2013 .

[28]  Yi Lin,et al.  Tree species classification based on explicit tree structure feature parameters derived from static terrestrial laser scanning data , 2016 .

[29]  A. Hudak,et al.  Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability , 2009 .

[30]  Alan H. Strahler,et al.  Measuring Effective Leaf Area Index, Foliage Profile, and Stand Height in New England Forest Stands Using a Full-Waveform Ground-Based Lidar , 2011 .

[31]  Nicholas C. Coops,et al.  Tree species classification in subtropical forests using small-footprint full-waveform LiDAR data , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[32]  Martin Pfennigbauer,et al.  Improving quality of laser scanning data acquisition through calibrated amplitude and pulse deviation measurement , 2010, Defense + Commercial Sensing.

[33]  Bernhard Höfle,et al.  Comparative classification analysis of post-harvest growth detection from terrestrial LiDAR point clouds in precision agriculture , 2015 .

[34]  M. Hodgson,et al.  Accuracy of Airborne Lidar-Derived Elevation: Empirical Assessment and Error Budget , 2004 .

[35]  K. P. Reese,et al.  LANDSCAPE CHANGES WITHIN THE HISTORICAL DISTRIBUTION OF COLUMBIAN SHARP-TAILED GROUSE IN EASTERN WASHINGTON : IS THERE HOPE? , 1998 .

[36]  Fei Deng,et al.  Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests , 2013 .

[37]  F. Mark Danson,et al.  Developing a dual-wavelength full-waveform terrestrial laser scanner to characterize forest canopy structure , 2014 .

[38]  Felix Morsdorf,et al.  Canopy closure, LAI and radiation transfer from airborne LiDAR synthetic images , 2014 .

[39]  Andrew K. Skidmore,et al.  Canopy leaf water content estimated using terrestrial LiDAR , 2017 .

[40]  Rachel Gaulton,et al.  The potential of dual-wavelength laser scanning for estimating vegetation moisture content , 2013 .

[41]  M. Verstraete,et al.  Estimating leaf area distribution in savanna trees from terrestrial LiDAR measurements , 2011 .

[42]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[43]  Stuart P. Hardegree,et al.  Vegetation and slope effects on accuracy of a LiDAR-derived DEM in the sagebrush steppe , 2011 .

[44]  K. Pearson.  Mathematical Contributions to the Theory of Evolution. III. Regression, Heredity, and Panmixia , 1896 .

[45]  Behnaz Bigdeli,et al.  Fusion of hyperspectral and LIDAR data using decision template-based fuzzy multiple classifier system , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[46]  Bisheng Yang,et al.  A shape-based segmentation method for mobile laser scanning point clouds , 2013 .

[47]  Kenji Omasa,et al.  Estimation and Error Analysis of Woody Canopy Leaf Area Density Profiles Using 3-D Airborne and Ground-Based Scanning Lidar Remote-Sensing Techniques , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[48]  Xiaoye Liu,et al.  Support vector machines for tree species identification using LiDAR-derived structure and intensity variables , 2013 .

[49]  P. Radtke,et al.  Ground-based Laser Imaging for Assessing Three-dimensional Forest Canopy Structure , 2006 .

[50]  Anttoni Jaakkola,et al.  Analysis of Incidence Angle and Distance Effects on Terrestrial Laser Scanner Intensity: Search for Correction Methods , 2011, Remote. Sens..

[51]  Ross A. Hill,et al.  GOING UNDERCOVER: MAPPING WOODLAND UNDERSTOREY FROM LEAF-ON AND LEAF-OFF LIDAR DATA , 2007 .

[52]  R. Day,et al.  Validating LiDAR Derived Estimates of Canopy Height, Structure and Fractional Cover in Riparian Areas: A Comparison of Leaf-on and Leaf-off LiDAR Data , 2010 .

[53]  Andrew K. Skidmore,et al.  3D leaf water content mapping using terrestrial laser scanner backscatter intensity with radiometric correction , 2015 .

[54]  Chongcheng Chen,et al.  Accuracy of the LiDAR-derived DEM in dense shrub areas in mountainous NW US , 2011, Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services.