Multi-Spectral Lidar: Radiometric Calibration, Canopy Spectral Reflectance, and Vegetation Vertical SVI Profiles

Multi-spectral (ms) airborne lidar data are enriched relative to traditional lidar due to the multiple channels of intensity digital numbers (DNs), which offer the potential for active Spectral Vegetation Indices (SVIs), enhanced classification, and change monitoring. However, in case of SVIs, indices should be calculated from spectral reflectance values derived from intensity DNs after calibration. In this paper, radiometric calibration of multi-spectral airborne lidar data is presented. A novel low-cost diffuse reflectance coating was adopted for creating radiometric targets. Comparability of spectral reflectance values derived from ms lidar data for coniferous stand (2.5% for 532 nm, 17.6% for 1064 nm, and 8.4% for 1550 nm) to available spectral libraries is shown. Active vertical profiles of SVIs were constructed and compared to modeled results available in the literature. The potential for a new landscape-level active 3D SVI voxel approach is demonstrated. Results of a field experiment with complex radiometric targets for estimating losses in detected lidar signals are described. Finally, an approach for estimating spectral reflectance values from lidar split returns is analyzed and the results show similarity of estimated values of spectral reflectance derived from split returns to spectral reflectance values obtained from single returns (p > 0.05 for paired test).

[1]  D. Roy,et al.  Integrating disparate lidar data at the national scale to assess the relationships between height above ground, land cover and ecoregions , 2014 .

[2]  Markus Hollaus,et al.  Total canopy transmittance estimated from small-footprint, full-waveform airborne LiDAR , 2017 .

[3]  Nicholas C. Coops,et al.  Estimation of standing dead tree class distributions in northwest coastal forests using lidar remote sensing , 2009 .

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

[5]  Juha Hyyppä,et al.  Feasibility of Multispectral Airborne Laser Scanning Data for Road Mapping , 2017, IEEE Geoscience and Remote Sensing Letters.

[6]  George Vosselman,et al.  Airborne and terrestrial laser scanning , 2011, Int. J. Digit. Earth.

[7]  W. M. Benzel,et al.  USGS Spectral Library Version 7 , 2017 .

[8]  Craig A. Coburn,et al.  Investigating Multi-Spectral Lidar Radiometry: An Overview of the Experimental Framework , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[9]  Andrew M. Wallace,et al.  Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDAR Data , 2012, Remote. Sens..

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

[11]  Barbara Koch,et al.  Segmentation of forest to tree objects , 2014 .

[12]  Ants Vain,et al.  Use of Naturally Available Reference Targets to Calibrate Airborne Laser Scanning Intensity Data , 2009, Sensors.

[13]  Nicholas Wilson,et al.  A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration , 2015, Sensors.

[14]  Juha Hyyppä,et al.  Correcting Airborne Laser Scanning Intensity Data for Automatic Gain Control Effect , 2010, IEEE Geoscience and Remote Sensing Letters.

[15]  J. Hyyppä,et al.  Calibration of laser scanning intensity data using brightness targets. The method developed by the Finnish Geodetic Institute , 2012 .

[16]  J. Hyyppä,et al.  Change Detection Techniques for Canopy Height Growth Measurements Using Airborne Laser Scanner Data , 2006 .

[17]  Ahmed El-Rabbany,et al.  AIRBORNE MULTISPECTRAL LIDAR DATA FOR LAND-COVER CLASSIFICATION AND LAND/WATER MAPPING USING DIFFERENT SPECTRAL INDEXES , 2016 .

[18]  Marc Simard,et al.  Continental-Scale Canopy Height Modeling by Integrating National, Spaceborne, and Airborne LiDAR Data , 2016 .

[19]  D. Donoghue,et al.  Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data , 2007 .

[20]  Ilkka Korpela,et al.  Acquisition and evaluation of radiometrically comparable multi-footprint airborne LiDAR data for forest remote sensing , 2017 .

[21]  Timothy J. Malthus,et al.  A Multispectral Canopy LiDAR Demonstrator Project , 2011, IEEE Geoscience and Remote Sensing Letters.

[22]  Ronald J. Hall,et al.  The uncertainty in conifer plantation growth prediction from multi-temporal lidar datasets , 2008 .

[23]  Brindusa Cristina Budei,et al.  Identifying the genus or species of individual trees using a three-wavelength airborne lidar system , 2018 .

[24]  Chris Hopkinson,et al.  Investigating the Consistency of Uncalibrated Multispectral Lidar Vegetation Indices at Different Altitudes , 2019, Remote. Sens..

[25]  A. Huete,et al.  A review of vegetation indices , 1995 .

[26]  Håkan Olsson,et al.  Simulating the effects of lidar scanning angle for estimation of mean tree height and canopy closure , 2003 .

[27]  Laura Chasmer,et al.  Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment , 2016 .

[28]  Juha Hyyppä,et al.  Radiometric Calibration of LIDAR Intensity With Commercially Available Reference Targets , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Ahmed Shaker,et al.  Airborne LiDAR intensity banding: Cause and solution , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[30]  Ahmed El-Rabbany,et al.  Multispectral LiDAR Data for Land Cover Classification of Urban Areas , 2017, Sensors.

[31]  R. Clark,et al.  Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data , 2003 .

[32]  Philip Lewis,et al.  Measuring forests with dual wavelength lidar: A simulation study over topography , 2012 .

[33]  Felix Morsdorf,et al.  Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling , 2009 .

[34]  F. Mark Danson,et al.  Angular Reflectance of Leaves With a Dual-Wavelength Terrestrial Lidar and Its Implications for Leaf-Bark Separation and Leaf Moisture Estimation , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Ana Paula Kersting,et al.  Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction , 2012 .

[36]  R. Brown,et al.  Characterization of a low-cost diffuse reflectance coating , 2008 .