Canopy Height and Above-Ground Biomass Retrieval in Tropical Forests Using Multi-Pass X- and C-Band Pol-InSAR Data

Globally available high-resolution information about canopy height and AGB is important for carbon accounting. The present study showed that Pol-InSAR data from TS-X and RS-2 could be used together with field inventories and high-resolution data such as drone or LiDAR data to support the carbon accounting in the context of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) projects.

[1]  Christiane Schmullius,et al.  TanDEM-X data for aboveground biomass retrieval in a tropical peat swamp forest , 2015 .

[2]  Romeo Tatsambon Fomena,et al.  On The Role of Coherence Optimization in Polarimetric SAR Interferometry , 2005 .

[3]  Jaan Praks,et al.  Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data , 2016, Remote. Sens..

[4]  Maurizio Santoro,et al.  Experiences in Boreal Forest Stem Volume Estimation from Multitemporal C-Band InSAR , 2012 .

[5]  A. Simmons,et al.  The Concept of Essential Climate Variables in Support of Climate Research, Applications, and Policy , 2014 .

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

[7]  Terje Gobakken,et al.  Estimating spruce and pine biomass with interferometric X-band SAR , 2010 .

[8]  Marco Lavalle,et al.  Extraction of Structural and Dynamic Properties of Forests From Polarimetric-Interferometric SAR Data Affected by Temporal Decorrelation , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[9]  S. Popescu,et al.  Lidar remote sensing of forest biomass : A scale-invariant estimation approach using airborne lasers , 2009 .

[10]  R. Dennis Cook,et al.  Detection of Influential Observation in Linear Regression , 2000, Technometrics.

[11]  Lars M. H. Ulander,et al.  Regression-Based Retrieval of Boreal Forest Biomass in Sloping Terrain Using P-Band SAR Backscatter Intensity Data , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Klaus Scipal,et al.  Comparison of Aboveground Biomass Estimation From InSAR and LiDAR Canopy Height Models in Tropical Forests , 2020, IEEE Geoscience and Remote Sensing Letters.

[13]  Sandra Englhart,et al.  Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi-Temporal LiDAR Datasets , 2013, Remote. Sens..

[14]  Marco Lavalle,et al.  A Temporal Decorrelation Model for Polarimetric Radar Interferometers , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Juilson Jubanski,et al.  ICESat/GLAS Data as a Measurement Tool for Peatland Topography and Peat Swamp Forest Biomass in Kalimantan, Indonesia , 2011, Remote. Sens..

[16]  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.

[17]  Christopher J. Banks,et al.  Global and regional importance of the tropical peatland carbon pool , 2011 .

[18]  Irena Hajnsek,et al.  TanDEM-X Pol-InSAR Performance for Forest Height Estimation , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Konstantinos Papathanassiou,et al.  Forest Height Estimation by means of Pol-InSAR Limitations posed by Temporal Decorrelation , 2009 .

[20]  K. O. Niemann,et al.  Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass , 2011 .

[21]  Florian Siegert,et al.  SAR-Based Estimation of Above-Ground Biomass and Its Changes in Tropical Forests of Kalimantan Using L- and C-Band , 2018, Remote. Sens..

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

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

[24]  Seung-Kuk Lee,et al.  Estimating Mangrove Canopy Height and Above-Ground Biomass in the Everglades National Park with Airborne LiDAR and TanDEM-X Data , 2017, Remote. Sens..

[25]  Rasmus Fensholt,et al.  L-Band SAR Backscatter Related to Forest Cover, Height and Aboveground Biomass at Multiple Spatial Scales across Denmark , 2015, Remote. Sens..

[26]  Juilson Jubanski,et al.  Detection of large above-ground biomass variability in lowland forest ecosystems by airborne LiDAR , 2012 .

[27]  Terje Gobakken,et al.  Influence of Plot Size on Efficiency of Biomass Estimates in Inventories of Dry Tropical Forests Assisted by Photogrammetric Data from an Unmanned Aircraft System , 2017, Remote. Sens..

[28]  J. Boone Kauffman,et al.  BIOMASS, CARBON, AND NUTRIENT DYNAMICS OF SECONDARY FORESTS IN A HUMID TROPICAL REGION OF MÉXICO , 1999 .

[29]  Satoshi Tsuyuki,et al.  Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer-Broadleaf Forest: Comparison with Airborne Laser Scanning , 2018, Remote. Sens..

[30]  Andreas Huth,et al.  Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests , 2010 .

[31]  Txomin Hermosilla,et al.  Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates , 2014 .

[32]  W. Salas,et al.  Benchmark map of forest carbon stocks in tropical regions across three continents , 2011, Proceedings of the National Academy of Sciences.

[33]  Florian Siegert,et al.  Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[34]  Arief Wijaya,et al.  An integrated pan‐tropical biomass map using multiple reference datasets , 2016, Global change biology.

[35]  Terje Gobakken,et al.  Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania , 2015, Carbon Balance and Management.

[36]  Hernandez-Clemente Rocio,et al.  Impact of plot size and model selection on forest biomass estimation using airborne LiDAR: A case study of pine plantations in southern Spain , 2017 .

[37]  Chris Varekamp,et al.  High-resolution InSAR image simulation for forest canopies , 2002, IEEE Trans. Geosci. Remote. Sens..

[38]  G. Powell,et al.  High-resolution forest carbon stocks and emissions in the Amazon , 2010, Proceedings of the National Academy of Sciences.

[39]  I. Hajnsek,et al.  A tutorial on synthetic aperture radar , 2013, IEEE Geoscience and Remote Sensing Magazine.

[40]  Laurent Ferro-Famil,et al.  Multibaseline Polarimetric SAR Interferometry Coherence Optimization , 2008, IEEE Geoscience and Remote Sensing Letters.

[41]  Florian Siegert,et al.  Determination of the amount of carbon stored in Indonesian peatlands. , 2008 .

[42]  C. Schmullius,et al.  Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna , 2016 .

[43]  G. Asner,et al.  Environmental and Biotic Controls over Aboveground Biomass Throughout a Tropical Rain Forest , 2009, Ecosystems.

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

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

[46]  S. Goetz,et al.  Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps , 2012 .

[47]  Irena Hajnsek,et al.  Tropical-Forest-Parameter Estimation by Means of Pol-InSAR: The INDREX-II Campaign , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[48]  S. Saatchi,et al.  Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass , 2011 .

[49]  Alfred Stein,et al.  Assessment of Forest Above-Ground Biomass Estimation from PolInSAR in the Presence of Temporal Decorrelation , 2018, Remote. Sens..

[50]  S. Goetz,et al.  Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change , 2011 .

[51]  G. Asner,et al.  High-resolution mapping of forest carbon stocks in the Colombian Amazon , 2012 .

[52]  Lars M. H. Ulander,et al.  Digital canopy model estimation from TanDEM-X interferometry using high-resolution lidar DEM , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

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

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

[55]  Stefan Erasmi,et al.  Canopy height estimation with TanDEM-X in temperate and boreal forests , 2019, Int. J. Appl. Earth Obs. Geoinformation.

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

[57]  Shuai Jiang,et al.  Forest Height Estimation Based on Constrained Gaussian Vertical Backscatter Model Using Multi-Baseline P-Band Pol-InSAR Data , 2019, Remote. Sens..

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

[59]  Susan E. Page,et al.  Lowland tropical peatlands of Southeast Asia , 2006 .

[60]  B. K. Nkansah,et al.  On the Detection of Influential Outliers in Linear Regression Analysis , 2014 .

[61]  Laurent Ferro-Famil,et al.  Analysis of seasonal effects on forest parameter estimation of Indian deciduous forest using TerraSAR-X PolInSAR acquisitions , 2017 .

[62]  William F. Laurance,et al.  Indonesia’s REDD+ pact: Saving imperilled forests or business as usual? , 2012 .

[63]  Konstantinos Papathanassiou,et al.  Single-baseline polarimetric SAR interferometry , 2001, IEEE Trans. Geosci. Remote. Sens..