The Laser Vegetation Detecting Sensor: A Full Waveform, Large-Footprint, Airborne Laser Altimeter for Monitoring Forest Resources

The use of satellite-borne large-footprint LiDAR (light detection and ranging) systems allows for the acquisition of forest monitoring data. This paper mainly describes the design, use, operating principles, installation and data properties of the new Laser Vegetation Detecting Sensor (LVDS), a LiDAR system designed and developed at the Academy of Forest Inventory and Planning (AFIP) and the Beijing Institute of Telemetry (BIT). Data from LVDS were used to calculate the mean height of forest trees on sample plots using data collected in the Hunan province of China. The results show that the full waveform data obtained by LVDS has the ability to accurately characterize forest height. The mean absolute percentage error of mean forest height per plot in flat areas was 6.8%, with a mean absolute deviation of 0.78 m. The airborne LVDS system provides prototype data sets and a platform for instrument proof-of-concept studies for China’s Terrestrial Ecosystem Carbon Monitoring (TECM) mission, which is an Earth remote sensing satellite due for launch in 2020. The information produced by LVDS allows for forest structure studies with high accuracy and coverage of large areas.

[1]  Michael A. Wulder,et al.  Estimating forest canopy height and terrain relief from GLAS waveform metrics , 2010 .

[2]  Kai Xu,et al.  Geometric Potential Assessment for ZY3-02 Triple Linear Array Imagery , 2017, Remote. Sens..

[3]  Lars M. H. Ulander,et al.  L- and P-band backscatter intensity for biomass retrieval in hemiboreal forest , 2011 .

[4]  J. Holmgren,et al.  Influence of footprint size and geolocation error on the precision of forest biomass estimates from space-borne waveform LiDAR , 2017 .

[5]  Naser El-Sheimy,et al.  Performance Analysis of Integrated Sensor Orientation , 2007 .

[6]  R. Dubayah,et al.  Estimation of tropical forest structural characteristics using large-footprint lidar , 2002 .

[7]  Sungho Choi,et al.  Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data , 2015, Remote. Sens..

[8]  Daniel Ramp,et al.  Creating vegetation density profiles for a diverse range of ecological habitats using terrestrial laser scanning , 2013 .

[9]  S. Los,et al.  Uncertainty within satellite LiDAR estimations of vegetation and topography , 2010 .

[10]  Tao Zhou,et al.  Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region , 2017, ISPRS Int. J. Geo Inf..

[11]  R. Dubayah,et al.  Large-scale retrieval of leaf area index and vertical foliage profile from the spaceborne waveform lidar (GLAS/ICESat) , 2014 .

[12]  Xiaoli Sun,et al.  The GEDI Simulator: A Large‐Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions , 2019, Earth and space science.

[13]  Peter R. J. North,et al.  Estimating forest canopy parameters from satellite waveform LiDAR by inversion of the FLIGHT three-dimensional radiative transfer model , 2017 .

[14]  Huili Gong,et al.  Differentiating Tree and Shrub LAI in a Mixed Forest With ICESat/GLAS Spaceborne LiDAR , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Zhiqiang Xiao,et al.  Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland , 2018, Remote. Sens..

[16]  Remo Bertani,et al.  Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Johannes Heinzel,et al.  Accuracy of vegetation height and terrain elevation derived from ICESat/GLAS in forested areas , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[18]  Peter M. Atkinson,et al.  Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[19]  E. Næsset Determination of mean tree height of forest stands using airborne laser scanner data , 1997 .

[20]  Michael A. Lefsky,et al.  Revised method for forest canopy height estimation from Geoscience Laser Altimeter System waveforms , 2007 .

[21]  Islam,et al.  Monitoring of changes in woodlots outside forests by multi-temporal Landsat imagery , 2018 .

[22]  Ranga B. Myneni,et al.  Modeling lidar waveforms with time‐dependent stochastic radiative transfer theory for remote estimations of forest structure , 2003 .

[23]  Sungho Choi,et al.  Application of Physically-Based Slope Correction for Maximum Forest Canopy Height Estimation Using Waveform Lidar across Different Footprint Sizes and Locations: Tests on LVIS and GLAS , 2014, Remote. Sens..

[24]  M. Herold,et al.  Nondestructive estimates of above‐ground biomass using terrestrial laser scanning , 2015 .

[25]  Randolph H. Wynne,et al.  Estimating plot-level tree heights with lidar : local filtering with a canopy-height based variable window size , 2002 .

[26]  Chen Chen,et al.  Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for Maize , 2018, Remote. Sens..

[27]  Luigi Boschetti,et al.  MODIS-derived EVI, NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[28]  Michael G. Wing,et al.  Performance Comparison of a Low-Cost Mapping Grade Global Positioning Systems (GPS) Receiver and Consumer Grade GPS Receiver under Dense Forest Canopy , 2007, Journal of Forestry.

[29]  W. Cohen,et al.  Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA , 1999 .

[30]  Ross Nelson,et al.  Model effects on GLAS-based regional estimates of forest biomass and carbon , 2010 .

[31]  W. Cohen,et al.  Estimates of forest canopy height and aboveground biomass using ICESat , 2005 .

[32]  J. Blair,et al.  The Laser Vegetation Imaging Sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography , 1999 .

[33]  N. Hanan,et al.  Estimation of Woody and Herbaceous Leaf Area Index in Sub‐Saharan Africa Using MODIS Data , 2018 .

[34]  Edward T. A. Mitchard,et al.  Extending the baseline of tropical dry forest loss in Ghana (1984–2015) reveals drivers of major deforestation inside a protected area , 2018 .

[35]  Christiane Schmullius,et al.  Influence of Surface Topography on ICESat/GLAS Forest Height Estimation and Waveform Shape , 2012, Remote. Sens..

[36]  M. Lefsky,et al.  Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forests , 2001 .

[37]  Changhui Peng,et al.  Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST: A case study in Quebec, Canada , 2018 .

[38]  Congcong Li,et al.  Forest Canopy Height Extraction in Rugged Areas With ICESat/GLAS Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Sun Guo-qing Forest Vertical Parameters from Lidar and Multi-angle Imaging Spectrometer Data , 2006 .

[40]  Xiaohuan Xi,et al.  Retrieving leaf area index using ICESat/GLAS full-waveform data , 2013 .

[41]  J. Terborgh,et al.  Tree height integrated into pantropical forest biomass estimates , 2012 .

[42]  W. Cohen,et al.  Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests , 1999 .

[43]  Hao Lu,et al.  LiCHy: The CAF's LiDAR, CCD and Hyperspectral Integrated Airborne Observation System , 2016, Remote. Sens..

[44]  Victoria Meyer,et al.  Comparison of Small- and Large-Footprint Lidar Characterization of Tropical Forest Aboveground Structure and Biomass: A Case Study From Central Gabon , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[45]  Seung-Kuk Lee,et al.  Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data , 2019, Remote Sensing of Environment.

[46]  Ehsan Abdi,et al.  Accuracy and precision of consumer-grade GPS positioning in an urban green space environment , 2014 .

[47]  Nicolas Baghdadi,et al.  Capability of GLAS/ICESat Data to Estimate Forest Canopy Height and Volume in Mountainous Forests of Iran , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[48]  D. Harding,et al.  ICESat waveform measurements of within‐footprint topographic relief and vegetation vertical structure , 2005 .

[49]  Caf Beijing,et al.  Waveform Length Extraction from ICEsat GLAS Data and Forest Application Analysis , 2006 .

[50]  Dario Papale,et al.  Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data , 2018 .

[51]  Honggan Wu,et al.  Using GF-2 Imagery and the Conditional Random Field Model for Urban Forest Cover Mapping , 2016 .

[52]  H. Zwally,et al.  Overview of ICESat's Laser Measurements of Polar Ice, Atmosphere, Ocean, and Land , 2002 .

[53]  Irina Melnikova,et al.  Estimation of Leaf Area Index in a Mountain Forest of Central Japan with a 30-m Spatial Resolution Based on Landsat Operational Land Imager Imagery: An Application of a Simple Model for Seasonal Monitoring , 2018, Remote. Sens..

[54]  Nicolas Baghdadi,et al.  Coupling potential of ICESat/GLAS and SRTM for the discrimination of forest landscape types in French Guiana , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[55]  Natascha Kljun,et al.  Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems , 2017, Remote. Sens..

[56]  K. Ranson,et al.  Forest vertical structure from GLAS : An evaluation using LVIS and SRTM data , 2008 .

[57]  Göran Ståhl,et al.  Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data , 2018, Remote. Sens..

[58]  José Luis Hernández-Stefanoni,et al.  Effects of Sample Plot Size and GPS Location Errors on Aboveground Biomass Estimates from LiDAR in Tropical Dry Forests , 2018, Remote. Sens..