Simulating the Canopy Reflectance of Different Eucalypt Genotypes With the DART 3-D Model

Finding suitable models of canopy reflectance in forward simulation mode is a prerequisite for their use in inverse mode to characterize canopy variables of interest, such as leaf area index (LAI) or chlorophyll content. In this study, the accuracy of the three-dimensional reflectance model DART (Discrete Anisotropic Radiative Transfer) was assessed for canopies of different genotypes of Eucalyptus, having distinct biophysical and biochemical characteristics, to improve the knowledge on how these characteristics are influencing the reflectance signal as measured by passive orbital sensors. The first step was to test the model suitability to simulate reflectance images in the visible and near infrared. We parameterized DART model using extensive measurements from Eucalyptus plantations including 16 contrasted genotypes. Forest inventories were conducted and leaf, bark, and forest floor optical properties were measured. Simulation accuracy was evaluated by comparing the mean top of canopy (TOC) bidirectional reflectance of DART with TOC reflectance extracted from a Pleiades very high resolution satellite image. Results showed a good performance of DART with mean reflectance absolute error lower than 2%. Intergenotype reflectance variability was correctly simulated, but the model did not succeed at catching the slight spatial variation for a given genotype, excepted when large gaps appeared due to tree mortality. The second step consisted of sensitivity analysis to explore which biochemical or biophysical characteristics influenced more the canopy reflectance between genotypes. Perspectives for using DART model in inversion mode in these ecosystems were discussed.

[1]  Jacques Ranger,et al.  Mixed-species plantations of Acacia mangium and Eucalyptus grandis in Brazil , 2008 .

[2]  Andres Kuusk,et al.  The effect of crown shape on the reflectance of coniferous stands , 2004 .

[3]  Jean-Philippe Gastellu-Etchegorry,et al.  Recovery of forest canopy characteristics through inversion of a complex 3D model , 2002 .

[4]  Jean-Philippe Gastellu-Etchegorry,et al.  A modeling approach to assess the robustness of spectrometric predictive equations for canopy chemistry , 2001 .

[5]  Gregory P. Asner,et al.  Variability in Leaf and Litter Optical Properties: Implications for BRDF Model Inversions Using AVHRR, MODIS, and MISR , 1998 .

[6]  Jean-Philippe Gastellu-Etchegorry,et al.  Modeling of the radiation regime and photosynthesis of a finite canopy using the DART model. Influence of canopy architecture assumptions and border effects , 2000 .

[7]  M. Schaepman,et al.  Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer , 2013 .

[8]  Xiaotong Zhang,et al.  Consistent estimation of multiple parameters from MODIS top of atmosphere reflectance data using a coupled soil-canopy-atmosphere radiative transfer model , 2016 .

[9]  Nicholas C. Coops,et al.  Spectral reflectance characteristics of eucalypt foliage damaged by insects , 2001 .

[10]  Clement Atzberger,et al.  Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[11]  J. Zerubia,et al.  Mapping local density of young Eucalyptus plantations by individual tree detection in high spatial resolution satellite images , 2013 .

[12]  R. Houborg,et al.  Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data , 2007 .

[13]  Lu Su,et al.  Radiation Transfer Model Intercomparison (RAMI) exercise: Results from the second phase , 2004 .

[14]  Flávio Jorge Ponzoni,et al.  Calibration of a Species-Specific Spectral Vegetation Index for Leaf Area Index (LAI) Monitoring: Example with MODIS Reflectance Time-Series on Eucalyptus Plantations , 2012, Remote. Sens..

[15]  K. Soudani,et al.  Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass , 2008 .

[16]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[17]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[18]  G. Maire,et al.  Tree and stand light use efficiencies over a full rotation of single- and mixed-species Eucalyptus grandis and Acacia mangium plantations , 2013 .

[19]  Nadine Gobron,et al.  The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing , 2015 .

[20]  Huili Gong,et al.  Sensitivity Analysis of Vegetation Reflectance to Biochemical and Biophysical Variables at Leaf, Canopy, and Regional Scales , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Hardy Pfanz,et al.  The optical, absorptive and chlorophyll fluorescence properties of young stems of five woody species , 2016 .

[22]  W. Verhoef,et al.  Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations , 2011 .

[23]  Gérard Dedieu,et al.  Discrete Anisotropic Radiative Transfer (DART 5) for Modeling Airborne and Satellite Spectroradiometer and LIDAR Acquisitions of Natural and Urban Landscapes , 2015, Remote. Sens..

[24]  Jean-Philippe Gastellu-Etchegorry,et al.  Investigating the Utility of Wavelet Transforms for Inverting a 3-D Radiative Transfer Model Using Hyperspectral Data to Retrieve Forest LAI , 2013, Remote. Sens..

[25]  Michael E. Schaepman,et al.  Estimation of Spruce Needle-Leaf Chlorophyll Content Based on DART and PARAS Canopy Reflectance Models , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  J. Gastellu-Etchegorry,et al.  A simple anisotropic reflectance model for homogeneous multilayer canopies , 1996 .

[27]  Jean-Philippe Gastellu-Etchegorry,et al.  A model-based performance test for forest classifiers on remote-sensing imagery , 2009 .

[28]  P. Brancalion,et al.  Integrating genetic and silvicultural strategies to minimize abiotic and biotic constraints in Brazilian eucalypt plantations , 2013 .

[29]  M. Schlerf,et al.  Suitability and adaptation of PROSAIL radiative transfer model for hyperspectral grassland studies , 2013 .

[30]  Andrew K. Skidmore,et al.  Photosynthetic bark: Use of chlorophyll absorption continuum index to estimate Boswellia papyrifera bark chlorophyll content , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[31]  Nicolas Barbier,et al.  Linking canopy images to forest structural parameters: potential of a modeling framework , 2012, Annals of Forest Science.

[32]  Jean-Philippe Gastellu-Etchegorry,et al.  An LUT-Based Inversion of DART Model to Estimate Forest LAI from Hyperspectral Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[33]  M. Schaepman,et al.  Simulating imaging spectrometer data: 3D forest modeling based on LiDAR and in situ data , 2014 .

[34]  Jean-Philippe Gastellu-Etchegorry,et al.  An interpolation procedure for generalizing a look-up table inversion method , 2003 .

[35]  G. Maire,et al.  Importance of deep water uptake in tropical eucalypt forest , 2017 .

[36]  Jean-Philippe Gastellu-Etchegorry,et al.  DART: a 3D model for simulating satellite images and studying surface radiation budget , 2004 .

[37]  V. Demarez,et al.  A Modeling Approach for Studying Forest Chlorophyll Content , 2000 .