Skip to main content

Bistatic PolInSAR Inversion Modelling for Plant Height Retrieval in a Tropical Forest

Abstract

Polarimetric SAR interferometry (PolInSAR) based modeling approaches have been used by several researchers for forest height estimation using airborne and space-borne sensors. Temporal gaps between interferometric pairs due to repeat pass acquisition of SAR data induces volume decorrelation in the coherence values and this may cause error during inversion modelling for forest height estimation. Tandem-X CoSSC products were acquired by operating TerraSAR-X and Tandem-X both the sensors with maintained spatial baseline and zero temporal gap. The prime focus of the present study was to evaluate the performance of bistatic X-band PolInSAR pair for forest height retrieval. Two algorithms; three stage inversion (TSI) and coherence amplitude inversion (CAI) were implemented on the complex coherences obtained from PolInSAR processing. Both the inversion algorithms showed their potential for forest height retrieval through bistatic PolInSAR data. Relationship between field-measured and CAI based modelling showed correlation of 0.27 with RMSE 4.81 m. TSI based modelling showed better results in comparison to CAI based inversion with correlation 0.74 and RMSE of 3.79 m. This study investigated the potential of bistatic PolInSAR data over tropical forest area and it was found that space-borne bistatic SAR has great potential to preserve coherence values for forest height estimation.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. 1.

    Mitchell AL, Rosenqvist A, Mora B (2017) Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD+. Carbon Balance Manag 12:9. https://doi.org/10.1186/s13021-017-0078-9

    Article  Google Scholar 

  2. 2.

    Ingram JC, Dawson TP, Whittaker RJ (2005) Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sens Environ 94:491–507. https://doi.org/10.1016/j.rse.2004.12.001

    ADS  Article  Google Scholar 

  3. 3.

    Roy PS, Behera MD, Murthy MSR et al (2015) New vegetation type map of India prepared using satellite remote sensing: comparison with global vegetation maps and utilities. Int J Appl Earth Obs Geoinf 39:142–159. https://doi.org/10.1016/j.jag.2015.03.003

    Article  Google Scholar 

  4. 4.

    Wang J, Sammis TW, Gutschick VP et al (2010) Review of satellite remote sensing use in forest health studies. Open Geogr J 3:28–42. https://doi.org/10.2174/1874923201003010028

    Article  Google Scholar 

  5. 5.

    Pause M, Schweitzer C, Rosenthal M et al (2016) In situ/remote sensing integration to assess forest health—a review. Remote Sens. https://doi.org/10.3390/rs8060471

    Google Scholar 

  6. 6.

    Zawadzki J, Cieszewski CJ, Zasada M, Lowe RC (2005) Applying geostatistics for investigations of forest ecosystems using remote sensing imagery. Silva Fenn 39:599–618. https://doi.org/10.14214/sf.369

    Article  Google Scholar 

  7. 7.

    Treitz P, Howarth P (2000) High spatial resolution remote sensing data for forest ecosystem classification. Remote Sens Environ 72:268–289. https://doi.org/10.1016/S0034-4257(99)00098-X

    ADS  Article  Google Scholar 

  8. 8.

    Du L, Zhou T, Zou Z et al (2014) Mapping forest biomass using remote sensing and national forest inventory in China. Forests 5:1267–1283. https://doi.org/10.3390/f5061267

    Article  Google Scholar 

  9. 9.

    Wulder M (1998) Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Prog Phys Geogr 22:449–476. https://doi.org/10.1177/030913339802200402

    Article  Google Scholar 

  10. 10.

    McRoberts RE, Tomppo EO (2007) Remote sensing support for national forest inventories. Remote Sens Environ 110:412–419. https://doi.org/10.1016/j.rse.2006.09.034

    ADS  Article  Google Scholar 

  11. 11.

    Tomppo E, Olsson H, Ståhl G et al (2008) Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sens Environ 112:1982–1999. https://doi.org/10.1016/j.rse.2007.03.032

    ADS  Article  Google Scholar 

  12. 12.

    Sai Bharadwaj P, Kumar S, Kushwaha SPS, Bijker W (2015) Polarimetric scattering model for estimation of above ground biomass of multilayer vegetation using ALOS-PALSAR quad-pol data. Phys Chem Earth Parts A/B/C 83–84:187–195. https://doi.org/10.1016/j.pce.2015.09.003

    Article  Google Scholar 

  13. 13.

    Behera M, Tripathi P, Mishra B et al (2015) Above-ground biomass and carbon estimates of Shorea robusta and Tectona grandis forests using QuadPOL ALOS PALSAR data. Adv Space Res. https://doi.org/10.1016/j.asr.2015.11.010

    Google Scholar 

  14. 14.

    Treuhaft R, Goncalves F, Dos Santos JR et al (2015) Tropical-forest biomass estimation at X-band from the spaceborne Tandem-X interferometer. IEEE Geosci Remote Sens Lett 12:239–243. https://doi.org/10.1109/LGRS.2014.2334140

    ADS  Article  Google Scholar 

  15. 15.

    Askne JIH, Soja MJ, Ulander LMH (2017) Biomass estimation in a boreal forest from TanDEM-X data, lidar DTM, and the interferometric water cloud model. Remote Sens Environ 196:265–278. https://doi.org/10.1016/j.rse.2017.05.010

    ADS  Article  Google Scholar 

  16. 16.

    Kumar S, Pandey U, Kushwaha SP et al (2012) Aboveground biomass estimation of tropical forest from Envisat advanced synthetic aperture radar data using modeling approach. J Appl Remote Sens 6:63588. https://doi.org/10.1117/1.JRS.6.063588

    Article  Google Scholar 

  17. 17.

    Lei Y, Siqueira P (2014) Estimation of forest height using spaceborne repeat-pass L-band InSAR correlation magnitude over the US state of Maine. Remote Sens 6:10252–10285. https://doi.org/10.3390/rs61110252

    ADS  Article  Google Scholar 

  18. 18.

    Cloude SR, Papathanassiou KP (1998) Polarimetric SAR interferometry. IEEE Trans Geosci Remote Sens 36:1551–1565. https://doi.org/10.1109/36.718859

    ADS  Article  Google Scholar 

  19. 19.

    Papathanassiou KP, Cloude SR (2001) Single-baseline polarimetric SAR interferometry. IEEE Trans Geosci Remote Sens 39:2352–2363. https://doi.org/10.1109/36.964971

    ADS  Article  Google Scholar 

  20. 20.

    Lee SK, Kugler F, Hajnsek I, Papathanassiou KP (2009) The impact of temporal decorrelation over forest terrain in polarimetric SAR interferometry. Eur Space Agency (Special Publ. ESA SP 668 SP)

  21. 21.

    Lee SK, Kugler F, Papathanassiou KP, Hajnsek I (2013) Quantification of temporal decorrelation effects at L-band for polarimetric SAR interferometry applications. IEEE J Sel Top Appl Earth Obs Remote Sens 6:1351–1367. https://doi.org/10.1109/JSTARS.2013.2253448

    Article  Google Scholar 

  22. 22.

    Schlund M, von Poncet F, Hoekman DH et al (2014) Importance of bistatic SAR features from TanDEM-X for forest mapping and monitoring. Remote Sens Environ 151:16–26. https://doi.org/10.1016/j.rse.2013.08.024

    ADS  Article  Google Scholar 

  23. 23.

    Karila K, Vastaranta M, Karjalainen M, Kaasalainen S (2015) Tandem-X interferometry in the prediction of forest inventory attributes in managed boreal forests. Remote Sens Environ 159:259–268. https://doi.org/10.1016/j.rse.2014.12.012

    ADS  Article  Google Scholar 

  24. 24.

    Oveisgharan S, Saatchi SS, Hensley S (2015) Sensitivity of Pol-InSAR measurements to vegetation parameters. IEEE Trans Geosci Remote Sens 53:6561–6572. https://doi.org/10.1109/TGRS.2015.2444351

    ADS  Article  Google Scholar 

  25. 25.

    Su B, Li J, Jin B, Guo J (2015) Tree height inversion algorithm with PolInSAR and nonlocal coherence estimation. Nongye Jixie Xuebao/Trans Chin Soc Agric Mach 46:268–273. https://doi.org/10.6041/j.issn.1000-1298.2015.12.036

    Google Scholar 

  26. 26.

    Zhang Y, He C, Xu X, Chen D (2016) Forest vertical parameter estimation using PolInSAR imagery based on radiometric correction. ISPRS Int J Geo-Inf. https://doi.org/10.3390/ijgi5100186

    Google Scholar 

  27. 27.

    Kumar S, Khati UG, Chandola S et al (2017) Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest. Adv Space Res 60:571–586. https://doi.org/10.1016/j.asr.2017.04.018

    ADS  Article  Google Scholar 

  28. 28.

    Cloude SR (2005) PoL-InSAR training course. Radio Sci

  29. 29.

    Cloude SR (2006) Polarization coherence tomography. Radio Sci 41:1–27. https://doi.org/10.1029/2005RS003436

    Article  Google Scholar 

  30. 30.

    Treuhaft RN, Siqueira PR (2000) Vertical structure of vegetated land surfaces from interferometric and polarimetric radar. Radio Sci 35:141–177. https://doi.org/10.1029/1999RS900108

    ADS  Article  Google Scholar 

  31. 31.

    Chen J, Zhang H, Wang C (2010) Comparison between ESPRIT algorithm and three-stage algorithm for PolinSAR. In: 2010 int. conf. multimed. technol. ICMT 2010, pp 3–5

  32. 32.

    Cloude SR, Papathanassiou KP (2003) Three-stage inversion process for polarimetric SAR interferometry. IEE Proc Radar Sonar Navig 150:125–134. https://doi.org/10.1049/ip-rsn:20030449

    Article  Google Scholar 

  33. 33.

    Wenxue F, Huadong G, Xinwu L et al (2016) Extended three-stage polarimetric SAR interferometry algorithm by dual-polarization data. IEEE Trans Geosci Remote Sens 54:2792–2802. https://doi.org/10.1109/TGRS.2015.2505707

    ADS  Article  Google Scholar 

  34. 34.

    Joshi SK, Kumar S (2017) Performance of PolSAR backscatter and PolInSAR coherence for scattering characterization of forest vegetation using single pass X-band spaceborne synthetic aperture radar data. J Appl Remote Sens 11:26022

    Article  Google Scholar 

  35. 35.

    Chekanov SV (2016) Numeric computation and statistical data analysis on the java platform. Springer, Basel

    Book  Google Scholar 

  36. 36.

    Verschuuren G (2014) Excel 2013 for scientists. Holy Macro! Books, Uniontown

    Google Scholar 

Download references

Acknowledgements

Authors would like to thank German Aerospace Center (DLR), Oberpfaffenhofen for providing us the TerraSAR-X/TanDEM-X dataset.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Shashi Kumar.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kumar, S., Garg, R.D., Kushwaha, S.P.S. et al. Bistatic PolInSAR Inversion Modelling for Plant Height Retrieval in a Tropical Forest. Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci. 87, 817–826 (2017). https://doi.org/10.1007/s40010-017-0451-9

Download citation

Keywords

  • PolInSAR
  • Space-borne bistatic SAR
  • TSI
  • CAI
  • Forest height