Estimating leaf chlorophyll and nitrogen contents using active hyperspectral LiDAR and partial least square regression method

Abstract. As hyperspectral LiDAR (HSL) combines the advantages of hyperspectral remote sensing and LiDAR, it has the potential of estimating vegetation biochemical contents at any three-dimensional (3-D) location. We investigate the capability of HSL to monitor leaf chlorophyll and nitrogen contents at a distance of 7.5 m. Using full-waveform LiDAR data obtained by HSL, the performance of the partial least square regression (PLSR) model using three strategies (only wavelength bands, only vegetation indices, and both wavelength bands and vegetation indices) is explored based on the correlation analysis of 16 wavelength bands and 16 vegetation indices. The result shows that the PLSR model can make full use of various wavelength bands and vegetation indices, having a strong ability to monitor leaf chlorophyll and nitrogen contents using full-waveform LiDAR data. The analysis can be applied for predicting leaf biochemical components of other vegetation and provides the basis for monitoring 3-D distribution of biochemical contents using HSL in the future.

[1]  Sorin C. Popescu,et al.  Fusion of lidar and multispectral data to quantify salt marsh carbon stocks , 2014 .

[2]  B. Turner,et al.  Estimating foliage nitrogen concentration from HYMAP data using continuum, removal analysis , 2004 .

[3]  Clement Atzberger,et al.  Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat , 2010 .

[4]  Luis Alonso,et al.  Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content , 2011, Sensors.

[5]  Zheng Niu,et al.  32-channel hyperspectral waveform LiDAR instrument to monitor vegetation: design and initial performance trials , 2014, Asia-Pacific Environmental Remote Sensing.

[6]  Philip N. Slater,et al.  Atmospheric effects on radiation reflected from soil and vegetation as measured by orbital sensors using various scanning directions. , 1982, Applied optics.

[7]  Anatoly A. Gitelson,et al.  Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[8]  J. Clevers.  The use of imaging spectrometry for agricultural applications , 1999 .

[9]  Matti Maltamo,et al.  Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index , 2011 .

[10]  Li Wang,et al.  Design of a New Multispectral Waveform LiDAR Instrument to Monitor Vegetation , 2015, IEEE Geoscience and Remote Sensing Letters.

[11]  Chaoyang Wu,et al.  Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation , 2008 .

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

[13]  Anming Bao,et al.  Different units of measurement of carotenoids estimation in cotton using hyperspectral indices and partial least square regression , 2014 .

[14]  Chunjiang Zhao,et al.  Variations in crop variables within wheat canopies and responses of canopy spectral characteristics and derived vegetation indices to different vertical leaf layers and spikes , 2015 .

[15]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[16]  Hengbiao Zheng,et al.  WREP: A wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops , 2017 .

[17]  Xu Chu,et al.  Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice , 2014, Plant and Soil.

[18]  Teemu Hakala,et al.  Nitrogen concentration estimation with hyperspectral LiDAR , 2013 .

[19]  Yu Huang,et al.  Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration , 2015, Remote. Sens..

[20]  Teemu Hakala,et al.  Fast and nondestructive method for leaf level chlorophyll estimation using hyperspectral LiDAR , 2014 .

[21]  J. Bryan Blair,et al.  Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion , 2011 .

[22]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[23]  Z. Niu,et al.  Estimation of leaf biochemical content using a novel hyperspectral full-waveform LiDAR system , 2014 .

[24]  Wenjiang Huang,et al.  Predicting vegetation water content in wheat using normalized difference water indices derived from ground measurements , 2009, Journal of Plant Research.

[25]  Pablo J. Zarco-Tejada,et al.  Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data , 2011 .

[26]  A. Gitelson,et al.  Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves , 2006 .

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

[28]  Wenjiang Huang,et al.  Estimation of Leaf Nitrogen and Grain Protein Content by Hyperspectral Vegetation Index in Winter Wheat , 2013 .

[29]  Jan G. P. W. Clevers,et al.  Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[31]  J. Dash,et al.  The MERIS terrestrial chlorophyll index , 2004 .

[32]  Yubin Lan,et al.  Effect of Vertical Distribution of Crop Structure and Biochemical Parameters of Winter Wheat on Canopy Reflectance Characteristics and Spectral Indices , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[33]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[34]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[35]  Zhen Wang,et al.  Recent development of hyperspectral LiDAR using supercontinuum laser , 2016, Other Conferences.

[36]  Yuwei Chen,et al.  Two-channel Hyperspectral LiDAR with a Supercontinuum Laser Source , 2010, Sensors.

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

[38]  Teemu Hakala,et al.  Technical Note: Multispectral lidar time series of pine canopy chlorophyll content , 2015 .

[39]  A. Gitelson,et al.  Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation , 1994 .

[40]  Teemu Hakala,et al.  Artificial target detection with a hyperspectral LiDAR over 26-h measurement , 2015 .

[41]  Nicholas C. Coops,et al.  Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data , 2003, IEEE Trans. Geosci. Remote. Sens..