Influence of 3D Spruce Tree Representation on Accuracy of Airborne and Satellite Forest Reflectance Simulated in DART

Advances in high-performance computer resources and exploitation of high-density terrestrial laser scanning (TLS) data allow for reconstruction of close-to-reality 3D forest scenes for use in canopy radiative transfer models. Consequently, our main objectives were (i) to reconstruct 3D representation of Norway spruce (Picea abies) trees by deriving distribution of woody and foliage elements from TLS and field structure data and (ii) to use the reconstructed 3D spruce representations for evaluation of the effects of canopy structure on forest reflectance simulated in the Discrete Anisotropic Radiative Transfer (DART) model. Data for this study were combined from two spruce research sites located in the mountainous areas of the Czech Republic. The canopy structure effects on simulated top-of-canopy reflectance were evaluated for four scenarios (10 × 10 m scenes with 10 trees), ranging from geometrically simple to highly detailed architectures. First scenario A used predefined simple tree crown shapes filled with a turbid medium with simplified trunks and branches. Other three scenarios used the reconstructed 3D spruce representations with B detailed needle shoots transformed into turbid medium, C with simplified shoots retained as facets, and D with detailed needle shoots retained as facets D. For the first time, we demonstrated the capability of the DART model to simulate reflectance of complex coniferous forest scenes up to the level of a single needle (scenario D). Simulated bidirectional reflectance factors extracted for each scenario were compared with actual airborne hyperspectral and space-borne Sentinel-2 MSI reflectance data. Scenario A yielded the largest differences from the remote sensing observations, mainly in the visible and NIR regions, whereas scenarios B, C, and D produced similar results revealing a good agreement with the remote sensing data. When judging the computational requirements for reflectance simulations in DART, scenario B can be considered as most operational spruce forest representation, because the transformation of 3D shoots in turbid medium reduces considerably the simulation time and hardware requirements.

[1]  Alexander Berk,et al.  MODTRAN6: a major upgrade of the MODTRAN radiative transfer code , 2014, Defense + Security Symposium.

[2]  Peter Willemsen,et al.  A scalable plant-resolving radiative transfer model based on optimized GPU ray tracing , 2014 .

[3]  Carlo Calfapietra,et al.  Effect of season, needle age and elevated CO2 concentration on photosynthesis and Rubisco acclimation in Picea abies. , 2012, Plant physiology and biochemistry : PPB.

[4]  M. Fournier,et al.  The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges , 2011, Annals of Forest Science.

[5]  Xi Zhu,et al.  Foliar and woody materials discriminated using terrestrial LiDAR in a mixed natural forest , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[6]  Peter R. J. North,et al.  Three-dimensional forest light interaction model using a Monte Carlo method , 1996, IEEE Trans. Geosci. Remote. Sens..

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

[8]  E. Cienciala,et al.  Comparison of different approaches of radiation use efficiency of biomass formation estimation in Mountain Norway spruce , 2017, Trees.

[9]  M I Disney,et al.  Weighing trees with lasers: advances, challenges and opportunities , 2018, Interface Focus.

[10]  Ranga B. Myneni,et al.  The role of canopy structure in the spectral variation of transmission and absorption of solar radiation in vegetation canopies , 2001, IEEE Trans. Geosci. Remote. Sens..

[11]  R. B. Jackson,et al.  A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.

[12]  Michael E. Schaepman,et al.  A note on upscaling coniferous needle spectra to shoot spectral albedo , 2012 .

[13]  W. Verhoef,et al.  Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data , 2007 .

[14]  Heinrich Spiecker,et al.  SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds , 2015 .

[15]  J. Chambers,et al.  Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. , 2007, Trends in ecology & evolution.

[16]  Olga V. Brovkina,et al.  In situ data supporting remote sensing estimation of spruce forest parameters at the ecosystem station Bílý Kříž , 2017 .

[17]  Michael A. Lefsky,et al.  Review of studies on tree species classification from remotely sensed data , 2016 .

[18]  Michael E. Schaepman,et al.  Minimizing Measurement Uncertainties of Coniferous Needle-Leaf Optical Properties. Part II: Experimental Setup and Error Analysis , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  Joanne C. White,et al.  Remote Sensing Technologies for Enhancing Forest Inventories: A Review , 2016 .

[20]  Gregory P. Asner,et al.  A Revised Measurement Methodology for Conifer Needles Spectral Optical Properties: Evaluating the Influence of Gaps between Elements , 1999 .

[21]  Barbara Koch,et al.  Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment , 2010 .

[22]  W. Qin,et al.  3-D Scene Modeling of Semidesert Vegetation Cover and its Radiation Regime , 2000 .

[23]  E. Schulze,et al.  Leaf nitrogen, photosynthesis, conductance and transpiration : scaling from leaves to canopies , 1995 .

[24]  P. Brando,et al.  Forest health and global change , 2015, Science.

[25]  Alan H. Strahler,et al.  Finding Leaves in the Forest: The Dual-Wavelength Echidna Lidar , 2015, IEEE Geoscience and Remote Sensing Letters.

[26]  D. Diner,et al.  Canopy Structure Parameters Derived from Multi-Angular Remote Sensing Data for Terrestrial Carbon Studies , 2004 .

[27]  N. Gobron,et al.  A semidiscrete model for the scattering of light by vegetation , 1997 .

[28]  Michael E. Schaepman,et al.  Shoot scattering phase function for Scots pine and its effect on canopy reflectance , 2012 .

[29]  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.

[30]  M. Nieuwenhuis,et al.  Retrieval of forest structural parameters using LiDAR remote sensing , 2010, European Journal of Forest Research.

[31]  Wuming Zhang,et al.  Reconstruction of Single Tree with Leaves Based on Terrestrial LiDAR Point Cloud Data , 2018, Remote. Sens..

[32]  Philip Lewis,et al.  Realistic Forest Stand Reconstruction from Terrestrial LiDAR for Radiative Transfer Modelling , 2018, Remote. Sens..

[33]  P. Stenberg,et al.  A method to account for shoot scale clumping in coniferous canopy reflectance models , 2003 .

[34]  B. Bailey,et al.  Rapid measurement of the three-dimensional distribution of leaf orientation and the leaf angle probability density function using terrestrial LiDAR scanning , 2017 .

[35]  Guang Zheng,et al.  Leaf Orientation Retrieval From Terrestrial Laser Scanning (TLS) Data , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[36]  A. Kuusk,et al.  A Directional Multispectral Forest Reflectance Model , 2000 .

[37]  V. Demarez,et al.  Modeling radiative transfer in heterogeneous 3D vegetation canopies , 1995, Remote Sensing.

[38]  R. Richter,et al.  Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction , 2002 .

[39]  Michael E. Schaepman,et al.  Influence of woody elements of a Norway spruce canopy on nadir reflectance simulated by the DART model at very high spatial resolution , 2008 .

[40]  R. Chazdon Beyond Deforestation: Restoring Forests and Ecosystem Services on Degraded Lands , 2008, Science.

[41]  Marco Heurich,et al.  Understanding Forest Health with Remote Sensing-Part II - A Review of Approaches and Data Models , 2017, Remote. Sens..

[42]  Philip Lewis,et al.  3D modelling of forest canopy structure for remote sensing simulations in the optical and microwave domains , 2006 .

[43]  Richard A. Fournier,et al.  The structural and radiative consistency of three-dimensional tree reconstructions from terrestrial lidar , 2009 .

[44]  W. Verhoef Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .

[45]  Thomas R. Loveland,et al.  A review of large area monitoring of land cover change using Landsat data , 2012 .

[46]  Philip Lewis,et al.  Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data , 2013, Remote. Sens..

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

[48]  N. Myers,et al.  The biodiversity challenge: Expanded hot-spots analysis , 1990, The Environmentalist.

[49]  M. Schaepman,et al.  Applicability of the PROSPECT model for Norway spruce needles , 2006 .

[50]  Hideki Kobayashi,et al.  A coupled 1-D atmosphere and 3-D canopy radiative transfer model for canopy reflectance, light environment, and photosynthesis simulation in a heterogeneous landscape , 2008 .

[51]  Winfried Kurth,et al.  Growth grammars simulating trees - an extension of L-systems incorporating local variables and sensitivity , 1997 .

[52]  Abdelaziz Kallel,et al.  Vegetation radiative transfer modeling based on virtual flux decomposition , 2010 .

[53]  T. D. Mitchell,et al.  Ecosystem Service Supply and Vulnerability to Global Change in Europe , 2005, Science.

[54]  Michel M. Verstraete,et al.  Raytran: a Monte Carlo ray-tracing model to compute light scattering in three-dimensional heterogeneous media , 1998, IEEE Trans. Geosci. Remote. Sens..

[55]  Jorgen B. Thomsen,et al.  Biodiversity Hotspots and Major Tropical Wilderness Areas: Approaches to Setting Conservation Priorities , 1998 .

[56]  Guang Zheng,et al.  Improved Salient Feature-Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components Within Terrestrial Lidar Point Cloud Data of Forest Canopies , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[57]  Markku Åkerblom,et al.  Non-intersecting leaf insertion algorithm for tree structure models , 2018, Interface Focus.

[58]  Pauline Stenberg,et al.  Simulations of the effects of shoot structure and orientation on vertical gradients in intercepted light by conifer canopies. , 1996, Tree physiology.

[59]  Marco Heurich,et al.  Understanding Forest Health with Remote Sensing -Part I - A Review of Spectral Traits, Processes and Remote-Sensing Characteristics , 2016, Remote. Sens..

[60]  Patrick Valduriez,et al.  OpenAlea: scientific workflows combining data analysis and simulation , 2015, SSDBM.

[61]  M. Marek,et al.  Test of Accuracy of LAI Estimation by LAI-2000 under Artificially Changed Leaf to Wood Area Proportions , 2000, Biologia Plantarum.

[62]  Michael E. Schaepman,et al.  GEOMETRICAL AND STRUCTURAL PARAMETERIZATION OF FOREST CANOPY RADIATIVE TRANSFER BY LIDAR MEASUREMENTS , 2008 .

[63]  Ahmad Al Bitar,et al.  DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.