Spaceborne PolInSAR and ground-based TLS data modeling for characterization of forest structural and biophysical parameters

Abstract Mapping of forest biophysical parameters is an effective tool for determination of forest inventories, vegetation modeling, and the global carbon cycle. The present study aims at quantification of the forest biophysical parameters of a sub-tropical Forest of India with Terrestrial Laser Scanner (TLS) and Polarimetric Interferometry SAR (PolInSAR) modeling approaches. TLS data was acquired in single and multiple scans and it was found that the multiple scanning approaches have the capability to provide forest parameters with very high accuracy. The RANSAC algorithm was implemented on TLS point cloud data to derive forest structural parameters. The TLS modeled DBH and stem volume exhibited a coefficient of determination of 0.88 and 0.80 and RMSE of 2.85 cm and 0.4 cu.m respectively. PolInSAR Coherence Amplitude Inversion (CAI) and RVoG techniques were implemented to retrieve forest stand height. Both the PolInSAR inversion based models were employed with the integration of complex coherences occurring in different polarimetric channels. CAI estimated the forest height precisely nearly equivalent to the field measured forest height with a coefficient of determination of 0.52, percent accuracy 85.85% and RMSE of 2.94 m. Application of the RVoG model further improved the statistics of the forest height with a coefficient of determination of 0.57, percent accuracy 89.94% and RMSE of 2.13 m. Measurement of tree height estimation using RVoG showed false vegetation height for dry riverbed and the same feature was characterized by the CAI model with zero vegetation height. The research showed that the forest vegetation height characterization of CAI model is better than the RVoG. This study has successfully investigated the potential of spaceborne PolInSAR and ground-based TLS data for forest structural and biophysical parameters retrieval.

[1]  Sushil Kumar Joshi,et al.  Spaceborne PolInSAR tomography for vertical profile retrieval of forest vegetation , 2017 .

[2]  Thomas Udelhoven,et al.  The influence of scan mode and circle fitting on tree stem detection, stem diameter and volume extraction from terrestrial laser scans , 2013 .

[3]  Priyakant Sinha,et al.  Review of the use of remote sensing for biomass estimation to support renewable energy generation , 2015 .

[4]  Uwe Stilla,et al.  A voting-based statistical cylinder detection framework applied to fallen tree mapping in terrestrial laser scanning point clouds , 2017 .

[5]  Sushil Kumar Joshi,et al.  Spaceborne PolSAR Tomography for Forest Height Retrieval , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Johan Holmgren,et al.  Tree Stem and Height Measurements using Terrestrial Laser Scanning and the RANSAC Algorithm , 2014, Remote. Sens..

[7]  Lei Wang,et al.  Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[9]  M. Herold,et al.  Data acquisition considerations for Terrestrial Laser Scanning of forest plots , 2017 .

[10]  R. Garg,et al.  Spaceborne SAR Tomography for Vertical Profile Retrieval of Forest Vegetation , 2017 .

[11]  Iain H. Woodhouse,et al.  Forest height retrieval from commercial X-band SAR products , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Shefali Agarwal,et al.  Bistatic PolInSAR Inversion Modelling for Plant Height Retrieval in a Tropical Forest , 2017 .

[13]  N. Barbier,et al.  Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach , 2017 .

[14]  Michael A. Wulder,et al.  Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters , 1998 .

[15]  J. Chambers,et al.  Tree allometry and improved estimation of carbon stocks and balance in tropical forests , 2005, Oecologia.

[16]  Steen Magnussen,et al.  Lidar supported estimators of wood volume and aboveground biomass from the Danish national forest inventory (2012–2016) , 2018, Remote Sensing of Environment.

[17]  Juha Hyyppä,et al.  Quantitative Assessment of Scots Pine (Pinus Sylvestris L.) Whorl Structure in a Forest Environment Using Terrestrial Laser Scanning , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Guo Huadong,et al.  Extended Three-Stage Polarimetric SAR Interferometry Algorithm by Dual-Polarization Data , 2016, IEEE Transactions on Geoscience and Remote Sensing.

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

[20]  Cédric Taillandier,et al.  The AfriSAR Campaign: Tomographic Analysis With Phase-Screen Correction for P-Band Acquisitions , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  R. Hall,et al.  Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume , 2006 .

[22]  Carlos López-Martínez,et al.  Assessment and Estimation of the RVoG Model in Polarimetric SAR Interferometry , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Sandra Englhart,et al.  Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use , 2011 .

[24]  C. Gough,et al.  Forest Canopy Structural Complexity and Light Absorption Relationships at the Subcontinental Scale , 2018 .

[25]  Christiane Schmullius,et al.  Assessment of the mapping of fractional woody cover in southern African savannas using multi-temporal and polarimetric ALOS PALSAR L-band images , 2015 .

[26]  Esra Erten,et al.  Retrieval of vegetation height in rice fields using polarimetric SAR interferometry with TanDEM-X data , 2017 .

[27]  R. Wynne,et al.  Analysis of a lidar voxel-derived vertical profile at the plot and individual tree scales for the estimation of forest canopy layer characteristics , 2016 .

[28]  Shashi Kumar,et al.  Aboveground biomass estimation of tropical forest from Envisat advanced synthetic aperture radar data using modeling approach , 2012 .

[29]  Corina da Costa Freitas,et al.  Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon , 2013 .

[30]  Philippe Réfrégier,et al.  Invariant Contrast Parameters of PolInSAR Homogenous RVoG Model , 2014, IEEE Geoscience and Remote Sensing Letters.

[31]  Jan Hackenberg,et al.  Applying quantitative structure models to plot-based terrestrial laser data to assess dendrometric parameters in dense mixed forests , 2018 .

[32]  Rui Guo,et al.  Effects of climate warming on net primary productivity in China during 1961–2010 , 2017, Ecology and evolution.

[33]  Irena Hajnsek,et al.  Forest Height Estimation by Means of Pol-InSAR Data Inversion: The Role of the Vertical Wavenumber , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[34]  R. Houghton,et al.  Aboveground Forest Biomass and the Global Carbon Balance , 2005 .

[35]  N. Pfeifer,et al.  Tree Stem Shapes Derived from TLS Data as an Indicator for Shallow Landslides , 2016 .

[36]  Philippe Réfrégier,et al.  Vegetation Height Estimation Precision With Compact PolInSAR and Homogeneous Random Volume Over Ground Model , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Conghe Song,et al.  Optical remote sensing of forest leaf area index and biomass , 2013 .

[38]  Yang Ruliang.  Investigation of Tree Height Retrieval with Polarimetric SAR Interferometry Based on ESPRIT Algorithm , 2011 .

[39]  C. Pérez-Cruzado,et al.  Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data , 2018, Annals of Forest Science.

[40]  Fernando Vicente-Guijalba,et al.  A Simple RVoG Test for PolInSAR Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[41]  M. Moghaddam,et al.  Vegetation characteristics and underlying topography from interferometric radar , 1996 .

[42]  Terje Gobakken,et al.  Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations , 2017, Remote Sensing of Environment.

[43]  Weimin Ju,et al.  Retrieving forest canopy clumping index using terrestrial laser scanning data , 2018, Remote Sensing of Environment.

[44]  Won-Kyung Baek,et al.  Classification of Forest Vertical Structure in South Korea from Aerial Orthophoto and Lidar Data Using an Artificial Neural Network , 2017 .

[45]  S. Gerstl,et al.  Physics concepts of optical and radar reflectance signatures A summary review , 1990 .

[46]  R. K. Dixon,et al.  Carbon Pools and Flux of Global Forest Ecosystems , 1994, Science.

[47]  Guang Zheng,et al.  Assessing the Contribution of Woody Materials to Forest Angular Gap Fraction and Effective Leaf Area Index Using Terrestrial Laser Scanning Data , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[48]  R. Treuhaft,et al.  Vertical structure of vegetated land surfaces from interferometric and polarimetric radar , 2000 .

[49]  S. Cloude.  Polarization coherence tomography , 2006 .

[50]  Konstantinos Papathanassiou,et al.  Pine Forest Height Inversion Using Single-Pass X-Band PolInSAR Data , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[51]  Shashi Kumar,et al.  Random forest regression modelling for forest aboveground biomass estimation using RISAT-1 PolSAR and terrestrial LiDAR data , 2016, Asia-Pacific Remote Sensing.

[52]  D. Lu.  The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[53]  K. A. Chowdhury,et al.  Indian woods : their identification, properties and uses , 1958 .

[54]  Y. Malhi,et al.  Tropical forests and atmospheric carbon dioxide. , 2000, Trends in ecology & evolution.

[55]  Sushil Kumar Joshi,et al.  Performance of PolSAR backscatter and PolInSAR coherence for scattering characterization of forest vegetation using single pass X-band spaceborne synthetic aperture radar data , 2017 .

[56]  Shane Cloude,et al.  The structure of oriented vegetation from polarimetric interferometry , 1999, IEEE Trans. Geosci. Remote. Sens..

[57]  Christiane Schmullius,et al.  Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico , 2018, Carbon Balance and Management.

[58]  Hamid Hamraz,et al.  Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds , 2016, 1701.00169.

[59]  Shefali Agrawal,et al.  Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest , 2017 .

[60]  Junichi Susaki,et al.  A new approach to retrieve leaf normal distribution using terrestrial laser scanners , 2015, Journal of Forestry Research.

[61]  Laurent Ferro-Famil,et al.  Remote sensing of vegetation using multi-baseline polarimetric SAR interferometry (theoretical modeling and physical parameter retrieval) , 2009 .

[62]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[63]  Diego González-Aguilera,et al.  Comparing Terrestrial Laser Scanning (TLS) and Wearable Laser Scanning (WLS) for Individual Tree Modeling at Plot Level , 2018, Remote. Sens..

[64]  Kenneth Grogan,et al.  A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring , 2016, Remote. Sens..

[65]  Andreas Fries,et al.  Estimation of Above Ground Biomass in a Tropical Mountain Forest in Southern Ecuador Using Airborne LiDAR Data , 2018, Remote. Sens..