Sensitivity of Bistatic TanDEM-X Data to Stand Structural Parameters in Temperate Forests

Synthetic aperture radar (SAR) satellite data provide a valuable means for the large-scale and long-term monitoring of structural components of forest stands. The potential of TanDEM-X interferometric SAR (InSAR) for the assessment of forest structural properties has been widely verified. However, present studies are mostly restricted to homogeneous forests and do not account for stratification in assessing model performance. A systematic sensitivity analysis of the TanDEM-X SAR signal to forest structural parameters was carried out with emphasis on different strata of forest stands (location of the study site, forest type, and development stage). Forest structure was parameterized by forest height metrics and stem volume. Results show that X-band volume coherence is highly sensitive to the forest canopy. Volume scattering within the canopy is dependent on the vertical heterogeneity of the forest stand. In general, TanDEM-X coherence is more sensitive to forest vertical structure compared to backscatter. The relations between TanDEM-X volume coherence and forest structural properties were significant at the level of a single test site as well as across sites in temperate forests in Germany. Forest type does not affect the overall relationship between the SAR signal and the forests’ vertical structure. The prediction of forest structural parameters based on the outcome of the sensitivity analysis yielded model accuracies between 15% (relative root mean square error) for Lorey’s height and 32% for stem volume. The global database of single-polarized bistatic TanDEM-X data provides an important source for mapping structural parameters in temperate forests at large scale, irrespective of forest type.

[1]  Marc Simard,et al.  Canopy Height Model (CHM) Derived From a TanDEM-X InSAR DSM and an Airborne Lidar DTM in Boreal Forest , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Maurizio Santoro,et al.  Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions , 2013, Remote. Sens..

[3]  Maurizio Santoro,et al.  Stem volume retrieval in boreal forests from ERS-1/2 interferometry , 2002 .

[4]  Jong-Sen Lee,et al.  Polarimetric SAR speckle filtering and its implication for classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[5]  Caixia Liu,et al.  Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China , 2019, Remote Sensing of Environment.

[6]  Johan E. S. Fransson,et al.  Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests , 2014, Remote. Sens..

[7]  Irena Hajnsek,et al.  Large-Scale Biomass Classification in Boreal Forests With TanDEM-X Data , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Urs Wegmüller,et al.  SAR interferometric signatures of forest , 1995, IEEE Trans. Geosci. Remote. Sens..

[9]  Sandro Martinis,et al.  The effect of vegetation type and density on X-band SAR backscatter after forest fires , 2014 .

[10]  Laurent Ferro-Famil,et al.  Estimation of Forest Structure, Ground, and Canopy Layer Characteristics From Multibaseline Polarimetric Interferometric SAR Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Stephen L. Durden,et al.  A three-component scattering model for polarimetric SAR data , 1998, IEEE Trans. Geosci. Remote. Sens..

[12]  Paris W. Vachon,et al.  Coherence estimation for SAR imagery , 1999, IEEE Trans. Geosci. Remote. Sens..

[13]  Sandra A. Brown,et al.  Monitoring and estimating tropical forest carbon stocks: making REDD a reality , 2007 .

[14]  Juha Hyyppä,et al.  The seasonal behavior of interferometric coherence in boreal forest , 2001, IEEE Trans. Geosci. Remote. Sens..

[15]  Christiane Schmullius,et al.  TanDEM-X elevation model data for canopy height and aboveground biomass retrieval in a tropical peat swamp forest , 2016 .

[16]  David Miller,et al.  The TerraSAR-X Satellite , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[17]  R. Dubayah,et al.  Combining Tandem-X InSAR and simulated GEDI lidar observations for forest structure mapping , 2016 .

[18]  Dan Johan Weydahl,et al.  Temporal Stability of X-Band Single-Pass InSAR Heights in a Spruce Forest: Effects of Acquisition Properties and Season , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Irena Hajnsek,et al.  Validation of Heights Derived from Interferometric SAR and LIDAR over the Temperate Forest Site Nationalpark Bayerischer Wald , 2005 .

[20]  Kamal Sarabandi,et al.  Estimation of forest biophysical characteristics in Northern Michigan with SIR-C/X-SAR , 1995, IEEE Trans. Geosci. Remote. Sens..

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

[22]  John D. Vona,et al.  Vegetation height estimation from Shuttle Radar Topography Mission and National Elevation Datasets , 2004 .

[23]  Jaan Praks,et al.  Seasonal Differences in Forest Height Estimation From Interferometric TanDEM-X Coherence Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[24]  Marc Simard,et al.  Using InSAR Coherence to Map Stand Age in a Boreal Forest , 2012, Remote. Sens..

[25]  Shaun Quegan,et al.  Forest biomass and the science of inventory from space , 2012 .

[26]  Jens Nieschulze,et al.  Implementing large-scale and long-term functional biodiversity research: The Biodiversity Exploratories , 2010 .

[27]  Phillip B. Gibbons,et al.  Forest and woodland stand structural complexity: Its definition and measurement , 2005 .

[28]  David Small,et al.  Flattening Gamma: Radiometric Terrain Correction for SAR Imagery , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Gerhard Krieger,et al.  TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry , 2006, IEEE Transactions on Geoscience and Remote Sensing.

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

[31]  Stefan Erasmi,et al.  Canopy penetration depth estimation with TanDEM-X and its compensation in temperate forests , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

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

[33]  H. Balzter.  Forest mapping and monitoring with interferometric synthetic aperture radar (InSAR) , 2001 .

[34]  Gulab Singh,et al.  Potential of Space-Borne PolInSAR for Forest Canopy Height Estimation Over India—A Case Study Using Fully Polarimetric L-, C-, and X-Band SAR Data , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[35]  Juha Hyyppä,et al.  Prediction of Forest Stand Attributes Using TerraSAR-X Stereo Imagery , 2014, Remote. Sens..

[36]  João Roberto dos Santos,et al.  Tropical-Forest Biomass Estimation at X-Band From the Spaceborne TanDEM-X Interferometer , 2015, IEEE Geoscience and Remote Sensing Letters.

[37]  Hans Pretzsch,et al.  Prediction of stem volume in complex temperate forest stands using TanDEM-X SAR data , 2016 .

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

[39]  Lars M. H. Ulander,et al.  Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data , 2017, Remote. Sens..

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

[41]  G. Krieger,et al.  A HRWS SAR system design with multi-beam imaging capabilities , 2017, 2017 European Radar Conference (EURAD).

[42]  Lars M. H. Ulander,et al.  On the Sensitivity of TanDEM-X-Observations to Boreal Forest Structure , 2019, Remote. Sens..

[43]  A. Sumida,et al.  A comparison between various definitions of forest stand height and aerodynamic canopy height , 2010 .

[44]  Rasmus Fensholt,et al.  Understanding ‘saturation’ of radar signals over forests , 2017, Scientific Reports.

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

[46]  P. Rodríguez-Veiga,et al.  Quantifying Forest Biomass Carbon Stocks From Space , 2017, Current Forestry Reports.

[47]  I. Woodhouse,et al.  Radar backscatter is not a \'direct measure\' of forest biomass , 2012 .

[48]  K. V. S. Badarinath,et al.  Analysis of ENVISAT ASAR data for forest parameter retrieval and forest type classification—a case study over deciduous forests of central India , 2007 .

[49]  Jakob van Zyl,et al.  The Shuttle Radar Topography Mission (SRTM): a breakthrough in remote sensing of topography , 2001 .

[50]  Christiane Schmullius,et al.  Properties of ERS-1/2 coherence in the Siberian boreal forest and implications for stem volume retrieval , 2007 .

[51]  J. Hyyppä,et al.  Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes , 2000 .

[52]  Göran Ståhl,et al.  Improved Prediction of Forest Variables Using Data Assimilation of Interferometric Synthetic Aperture Radar Data , 2017 .

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

[54]  M. Vastaranta,et al.  Tandem-X interferometry in the prediction of forest inventory attributes in managed boreal forests , 2015 .

[55]  Christian Ammer,et al.  Relations between forest management, stand structure and productivity across different types of Central European forests , 2018, Basic and Applied Ecology.

[56]  Terje Gobakken,et al.  Biomass and InSAR height relationship in a dense tropical forest , 2017 .

[57]  M. Vastaranta,et al.  Prediction of plot-level forest variables using TerraSAR-X stereo SAR data , 2012 .

[58]  Jaan Praks,et al.  LIDAR-Aided SAR Interferometry Studies in Boreal Forest: Scattering Phase Center and Extinction Coefficient at X- and L-Band , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[59]  D. Schimel.  Forests in the Global Carbon Cycle , 2014 .