Journal of the Indian Society of Remote Sensing

, Volume 47, Issue 9, pp 1599–1608 | Cite as

Regression-Based Integrated Bi-sensor SAR Data Model to Estimate Forest Carbon Stock

  • Suman SinhaEmail author
  • A. Santra
  • A. K. Das
  • L. K. Sharma
  • Shiv Mohan
  • M. S. Nathawat
  • S. Santra Mitra
  • C. Jeganathan
Research Article


The objective of this study is to estimate the forest aboveground carbon (AGC) stock using integrated space-borne synthetic aperture radar (SAR) data from COSMO-Skymed (X band) and ALOS PALSAR (L band) with field inventory over a tropical deciduous mixed forest. Carbon acts as a vital constituent in the global decision making policy targeting the impact of reducing emissions from deforestation and forest degradation (REDD) and climate change. The study proposed an approach to develop regression models for assessing the forest AGC with synergistic use of SAR bi-sensor X and L band sigma nought data. The best-fit integrated aboveground biomass (AGB) model was validated with additional sample points that produced a model accuracy of 78.6%, adjusted R2 = 0.88, RMSE = 16.6 Mg/ha, standard error of estimates of 16.03 and Willmott’s index of agreement of 0.93. Resulting modeled AGB was converted to AGC using conversion factors. L band resulted in higher accuracy of estimates when compared to X band, while the estimation accuracy enhanced on integrating X- and L-band information. Hence, the study presents an approach using integrated SAR bi-sensor X and L bands that enhance the AGB and AGC estimation accuracy, which can contribute to the operational forestry and policy making related to forest conservation, REDD/REDD+ climate change, etc.


Aboveground carbon ALOS PALSAR Backscatter COSMO-Skymed Regression 



This work was supported by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India under Grant [File Number PDF/2015/000043/EAS].


  1. Alappat, V. O., Joshi, A. K., & Krishnamurthy, Y. V. N. (2011). Tropical dry deciduous forest stand variable estimation using SAR data. Journal of the Indian Society of Remote Sensing, 39(4), 583–589. Scholar
  2. Askne, J. I. H., Soja, M. J., & Ulander, L. M. H. (2017). Biomass estimation in a boreal forest from TanDEM-X data, lidar DTM, and the interferometric water cloud model. Remote Sensing of Environment, 196, 265–278. Scholar
  3. Behera, M. D., Tripathi, P., Mishra, B., Kumar, S., Chitale, V. S., & Behera, S. K. (2016). Above-ground biomass and carbon estimates of Shorea robusta and Tectona grandis forests using QuadPOL ALOS PALSAR data. Advances in Space Research, 57(2), 552–561. Scholar
  4. Betts, R. A., Jones, C. D., Knight, J. R., Keeling, R. F., & Kennedy, J. J. (2016). El Niño and a record CO2 rise. Nature Climate Change, 6, 806. Scholar
  5. Castillo, J. A. A., Apan, A. A., Maraseni, T. N., & Salmo, S. G. (2017). Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 70–85. Scholar
  6. Englhart, S., Keuck, V., & Siegert, F. (2011). Aboveground biomass retrieval in tropical forests—The potential of combined X- and L-band SAR data use. Remote Sensing of Environment, 115(5), 1260–1271. Scholar
  7. Englhart, S., Keuck, V., & Siegert, F. (2012). Modeling aboveground biomass in tropical forests using multi-frequency SAR data—A comparison of methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(1), 298–306. Scholar
  8. Ghasemi, N., Sahebi, M. R., & Mohammadzadeh, A. (2011). A review on biomass estimation methods using synthetic aperture radar data. International Journal of Geomatics and Geosciences, 1(4), 776–788.Google Scholar
  9. Ghosh, S. M., & Behera, M. D. (2018). Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Applied Geography, 96, 29–40. Scholar
  10. Goïta, K., Mouloungou, J., & Bénié, G. B. (2017). Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data. Geocarto International. Scholar
  11. Hamdan, O., Aziz, H. K., & Rahman, K. A. (2011). Remotely sensed L-band SAR data for tropical forest biomass estimation. Journal of Tropical Forest Science, 23, 318–327.Google Scholar
  12. Hame, T., Rauste, Y., Antropov, O., Ahola, H. A., & Kilpi, J. (2013). Improved mapping of tropical forests with optical and SAR imagery, Part II: Above ground biomass estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(1), 92–101. Scholar
  13. Hyde, P., Dubayah, R., Walker, W., Blair, J. B., Hofton, M., & Hunsaker, C. (2006). Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sensing of Environment, 102(1), 63–73. Scholar
  14. Kumar, S., Khati, U. G., Chandola, S., Agrawal, S., & Kushwaha, S. P. S. (2017). Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest. Advances in Space Research, 60(3), 571–586. Scholar
  15. Kumar, P., Sharma, L. K., Pandey, P. C., Sinha, S., & Nathawat, M. S. (2013). Geospatial strategy for tropical forest-wildlife reserve biomass estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2), 917–923. Scholar
  16. Lu, D. (2006). The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing, 27(7), 1297–1328. Scholar
  17. Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., & Moran, E. (2014). A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 9(1), 63–105. Scholar
  18. Montesano, P. M., Nelson, R. F., Dubayah, R. O., Sun, G., Cook, B. D., Ranson, K. J. R., et al. (2014). The uncertainty of biomass estimates from LiDAR and SAR across a boreal forest structure gradient. Remote Sensing of Environment, 154, 398–407. Scholar
  19. Mushtaq, H., & Malik, T. (2014). Accounting carbon dioxide emission and stratification of carbon stock in Western Ghats, India. A geospatial approach. International Journal of Remote Sensing and Geoscience, 3, 1–5.Google Scholar
  20. Nizalapur, V., Jha, C. S., & Madugundu, R. (2010). Estimation of above ground biomass in Indian tropical forested area using multi-frequency DLR-ESAR data. International Journal of Geomatics and Geosciences, 1(2), 167–178.Google Scholar
  21. Omar, H., Misman, M. A., & Kassim, A. R. (2017). Synergetic of PALSAR-2 and Sentinel-1A SAR polarimetry for retrieving aboveground biomass in Dipterocarp forest of Malaysia. Applied Sciences, 7, 675. Scholar
  22. Pandey, U., Kushwaha, S. P. S., Kachhwaha, T. S., Kunwar, P., & Dadhwal, V. K. (2010). Potential of Envisat ASAR data for woody biomass assessment. Tropical Ecology, 51(1), 117–124.Google Scholar
  23. Rahman, M. M., & Sumantyo, J. T. S. (2012). Retrieval of tropical forest biomass information from ALOS PALSAR data. Geocarto International, 28(5), 382–403. Scholar
  24. Rashid, I., Bhat, M. A., & Romshoo, S. A. (2017). Assessing changes in the above ground biomass and carbon stocks of Lidder valley, Kashmir Himalaya, India. Geocarto International, 32(7), 717–734. Scholar
  25. Rodrigues-Veiga, P., Wheeler, J., Louis, V., Tansey, K., & Balzter, H. (2017). Quantifying forest biomass carbon stocks from space. Current Forestry Reports, 3(1), 1–18. Scholar
  26. Sambatti, J. B. M., Leduc, R., Lübeck, D., Moreira, J. R., & dos Santos, J. R. (2012). Assessing forest biomass and exploration in the Brazilian Amazon with airborne InSAR: an alternative for REDD. The Open Remote Sensing Journal, 5, 21–36. Scholar
  27. Santi, E., Paloscia, S., Pettinato, S., Fontanelli, G., Mura, M., Zolli, C., et al. (2017). The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas. Remote Sensing of Environment, 200, 63–73. Scholar
  28. Santoro, M., & Cartus, O. (2018). Research pathways of forest above-ground biomass estimation based on SAR backscatter and interferometric SAR observations. Remote Sensing, 10, 608. Scholar
  29. Sarker, M. L. R. (2010). Estimation of forest biomass using remote sensing. Hong Kong: The Hong Kong Polytechnic University.Google Scholar
  30. Sharma, L. K., Nathawat, M. S., & Sinha, S. (2013). Top-down and bottom-up inventory approach for above ground forest biomass and carbon monitoring in REDD framework using multi-resolution satellite data. Environmental Monitoring and Assessment, 185(10), 8621–8637. Scholar
  31. Shimada, M., Isoguchi, O., Tadono, T., & Isono, K. (2009). PALSAR radiometric and geometric calibration. IEEE Transactions on Geoscience and Remote Sensing, 47(12), 3915–3932. Scholar
  32. Sinha, S., Jeganathan, C., Sharma, L. K., & Nathawat, M. S. (2015). A review of radar remote sensing for biomass estimation. International Journal of Environmental Science and Technology, 12(5), 1779–1792. Scholar
  33. Sinha, S., Jeganathan, C., Sharma, L. K., Nathawat, M. S., Das, A. K., & Mohan, S. (2016). Developing synergy regression models with space-borne ALOS PALSAR and Landsat TM sensors for retrieving tropical forest biomass. Journal of Earth System Science, 125(4), 725–735. Scholar
  34. Sinha, S., Santra, A., Das, A. K., Sharma, L. K., Mohan, S., Nathawat, M. S., et al. (2019). Accounting tropical forest carbon stock with synergistic use of space-borne ALOS PALSAR and COSMO-Skymed SAR sensors. Tropical Ecology. Scholar
  35. Sinha, S., Santra, A., Sharma, L., Jeganathan, C., Nathawat, M. S., Das, A. K., et al. (2018). Multi-polarized Radarsat-2 satellite sensor in assessing forest vigor from above ground biomass. Journal of Forestry Research, 29(4), 1139–1145. Scholar
  36. Sinha, S., Sharma, L. K., & Nathawat, M. S. (2013). Integrated geospatial techniques for land-use/land-cover and forest mapping of deciduous Munger forests (India). Universal Journal of Environmental Research and Technology, 3(2), 190–198.Google Scholar
  37. Torbick, N., Ledoux, L., Salas, W., & Zhao, M. (2016). Regional mapping of plantation extent using multisensory imagery. Remote Sensing, 8, 236. Scholar
  38. Waikhom, A. C., Nath, A. J., & Yadava, P. S. (2018). Aboveground biomass and carbon stock in the largest sacred grove of Manipur, Northeast India. Journal of Forestry Research, 29(2), 425–428. Scholar

Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringHaldia Institute of TechnologyHaldiaIndia
  2. 2.Department of Space, Government of IndiaSpace Applications Centre (ISRO)AhmedabadIndia
  3. 3.Department of Environmental ScienceCentral University of RajasthanAjmerIndia
  4. 4.PLANEX, Physical Research LaboratoryAhmedabadIndia
  5. 5.School of SciencesIndira Gandhi National Open University (IGNOU)New DelhiIndia
  6. 6.Department of Remote SensingBirla Institute of TechnologyRanchiIndia

Personalised recommendations