InSAR-Based Tree Height Estimation of Hilly Forest Using Multitemporal Radarsat-1 and Sentinel-1 SAR Data

This article presents estimation and validation of tree heights extracted from synthetic aperture radar interferometry (InSAR) digital elevation model (DEM). InSAR technique was used to generate DEM in terms of top of the canopy digital surface model (TCDSM). Multitemporal Radarsat-1 InSAR data pair of February 12, 2004 and March 7, 2004 and Sentinel-1 data pair of December 9, 2017 and December 21, 2017 were used. The TCDSM values at ground truth locations were subtracted from the DEM obtained at forest gap areas (i.e., forest floor elevation). The satellite-derived TCDSM-based tree height was compared with the ground observed data. For this purpose, an experiment in Tundi Reserved Forest, Dhanbad, Jharkhand, India was conducted using global positioning system and altimeter. The study area is a hilly forest tract dominated by Shorea robusta (Sal) with average height of 12 m. Total 275 numbers of trees were selected at 11 locations in different elevation strata. Ground truth locations were also selected on the basis of variability, tree height categories, and accessibility in the area. It was noticed that mean absolute error (MAE) and root-mean-square error (RMSE) between in situ and estimated heights for Radarsat-1 were 1.48 and 1.53 m, respectively, whereas for Sentinel-1 in 2017, MAE and RMSE were 1.3 and 1.34 m, respectively, with strong correlation of >80%.

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

[2]  Jerome K. Vanclay,et al.  Realizing opportunities in forest growth modelling , 2003 .

[3]  P. Couteron,et al.  Predicting tropical forest stand structure parameters from Fourier transform of very high‐resolution remotely sensed canopy images , 2005 .

[4]  John Latto,et al.  The measurement of height differences in ecological studies , 1996 .

[5]  Anuj Srivastava,et al.  Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region , 2009 .

[6]  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.

[7]  Jan Askne,et al.  Interferometric tree height observations in boreal forests with SAR interferometry , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[8]  Thierry Toutin,et al.  State-of-the-art of elevation extraction from satellite SAR data , 2000 .

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

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

[11]  W. Marsden I and J , 2012 .

[12]  H. Balzter,et al.  Observations of forest stand top height and mean height from interferometric SAR and LiDAR over a conifer plantation at Thetford Forest, UK , 2007 .

[13]  Nicolas Baghdadi,et al.  Merging of airborne elevation data and Radarsat data to develop a Digital Elevation Model , 2005 .

[14]  Lars M. H. Ulander,et al.  Repeat-pass SAR interferometry over forested terrain , 1995, IEEE Transactions on Geoscience and Remote Sensing.

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

[16]  Shaun Quegan,et al.  Environmental effects on the interferometric repeat-pass coherence of forests , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Johan E. S. Fransson,et al.  Comparison between TanDEM-X- and ALS-based estimation of aboveground biomass and tree height in boreal forests , 2017 .