Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System

The airborne multi-wavelength light detection and ranging (LiDAR) system measures different wavelengths simultaneously and usually includes two or more active channels in infrared and green to acquire both topographic and hydrographic information. The reflected multi-wavelength energy can also be used to identify different land covers based on physical properties of materials. This study explored the benefits of multi-wavelength LiDAR in object-based land cover classification, focusing on three major issues: (1) the evaluation of single- and multi-wavelength LiDARs for land cover classification; (2) the performance of spectral and geometrical features extracted from multi-wavelength LiDAR; and (3) the comparison of the vegetation index derived from active multi-wavelength LiDAR and passive multispectral images. The three-wavelength test data were acquired by Optech Titan in green, near-infrared, and mid-infrared channels, and the reference data were acquired from Worldview-3 image. The experimental results show that the multi-wavelength LiDAR provided higher accuracy than single-wavelength LiDAR in land cover classification, with an overall accuracy improvement rate about 4–14 percentage points. The spectral features performed better compared to geometrical features for grass, road, and bare soil classes, and the overall accuracy improvement is about 29 percentage points. The results also demonstrated the vegetation indices from Worldview-3 and Optech Titan have similar characteristics, with correlations reaching 0.68 to 0.89. Overall, the multi-wavelength LiDAR system improves the accuracy of land cover classification because this system provides more spectral information than traditional single-wavelength LiDAR.

[1]  V. Wichmann,et al.  EVALUATING THE POTENTIAL OF MULTISPECTRAL AIRBORNE LIDAR FOR TOPOGRAPHIC MAPPING AND LAND COVER CLASSIFICATION , 2015 .

[2]  William E. Carter,et al.  Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar , 2016, Remote. Sens..

[3]  C. Briese,et al.  AIRBORNE LASER BATHYMETRY FOR DOCUMENTATION OF SUBMERGED ARCHAEOLOGICAL SITES IN SHALLOW WATER , 2015 .

[4]  Juha Hyyppä,et al.  Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating , 2017 .

[5]  Kiyun Yu,et al.  Assessing the Possibility of Landcover Classification Using Lidar Intensity Data , 2002 .

[6]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[7]  Lennart Nilsen,et al.  Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation , 2016, Remote. Sens..

[8]  Jie Shan,et al.  Building roof modeling from airborne laser scanning data based on level set approach , 2011 .

[9]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[10]  Juha Hyyppä,et al.  Automatic Detection of Buildings and Changes in Buildings for Updating of Maps , 2010, Remote. Sens..

[11]  Shuhab D. Khan,et al.  Application of multispectral LiDAR to automated virtual outcrop geology , 2014 .

[12]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[13]  Markus Hollaus,et al.  Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification , 2008, Sensors.

[14]  Przemysław Kupidura,et al.  Testing of Land Cover Classification from Multispectral Airborne Laser Scanning Data , 2016 .

[15]  Steven E. Franklin,et al.  Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests , 2012 .

[16]  Guihua Zhao,et al.  3D LAND COVER CLASSIFICATION BASED ON MULTISPECTRAL LIDAR POINT CLOUDS , 2016 .

[17]  Chang Lu,et al.  Immunomagnetic separation of tumor initiating cells by screening two surface markers , 2017, Scientific Reports.

[18]  Benjamin Koetz,et al.  Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data , 2008 .

[19]  Jungho Im,et al.  Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification , 2010 .

[20]  Gong Wei,et al.  A Multi-Wavelength Canopy LiDAR for Vegetation Monitoring: System Implementation and Laboratory-Based Tests , 2011 .

[21]  Jianping Huang,et al.  Analysis of Dust Aerosol by Using Dual-Wavelength Lidar , 2012 .

[22]  Gregory Asner,et al.  Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data , 2012, Remote. Sens..

[23]  D. N. Whiteman,et al.  Profiling of the forest fire aerosol plume with multiwavelength Raman lidar , 2014, 2014 International Conference Laser Optics.

[24]  Patricia Gober,et al.  Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.

[25]  K. Bakuła,et al.  Multispectral airborne laser scanning - a new trend in the development of LiDAR technology , 2015 .

[26]  Markus Hollaus,et al.  Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data , 2012 .

[27]  J. Shan,et al.  Urban DEM generation from raw lidar data: A labeling algorithm and its performance , 2005 .

[28]  E. Zelniker,et al.  Detection and vectorization of roads from lidar data , 2007 .

[29]  Markus H. Gross,et al.  Multi‐scale Feature Extraction on Point‐Sampled Surfaces , 2003, Comput. Graph. Forum.

[30]  Detlef Müller,et al.  Aerosol optical and microphysical retrievals from a hybrid multiwavelength lidar data set – DISCOVER-AQ 2011 , 2014 .

[31]  W. Gong,et al.  Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer , 2017, Scientific Reports.

[32]  Samia Boukir,et al.  Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests , 2011 .

[33]  Christelle Vancutsem,et al.  GlobCover: ESA service for global land cover from MERIS , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[34]  Yun Zhang,et al.  DEVELOPMENT OF A SUPERVISED SOFTWARE TOOL FOR AUTOMATED DETERMINATION OF OPTIMAL SEGMENTATION PARAMETERS FOR ECOGNITION , 2010 .

[35]  Avideh Zakhor,et al.  Tree Detection in Urban Regions Using Aerial Lidar and Image Data , 2007, IEEE Geoscience and Remote Sensing Letters.