Skip to main content

Advertisement

SpringerLink
Log in

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

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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

Similar content being viewed by others

References

  • 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. https://doi.org/10.1007/s12524-011-0118-y.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2017.05.010.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.asr.2015.11.010.

    Article  Google Scholar 

  • 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. https://doi.org/10.1038/nclimate3063.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.isprsjprs.2017.10.016.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2011.01.008.

    Article  Google Scholar 

  • 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. https://doi.org/10.1109/jstars.2011.2176720.

    Article  Google Scholar 

  • 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 

  • 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. https://doi.org/10.1016/j.apgeog.2018.05.011.

    Article  Google Scholar 

  • 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. https://doi.org/10.1080/10106049.2017.1386720.

    Google Scholar 

  • 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 

  • 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. https://doi.org/10.1109/jstars.2013.2241020.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2006.01.021.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.asr.2017.04.018.

    Article  Google Scholar 

  • 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. https://doi.org/10.1109/jstars.2012.2221123.

    Article  Google Scholar 

  • Lu, D. (2006). The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing, 27(7), 1297–1328. https://doi.org/10.1080/01431160500486732.

    Article  Google Scholar 

  • 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. https://doi.org/10.1080/17538947.2014.990526.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2014.01.027.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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. https://doi.org/10.3390/app7070675.

    Article  Google Scholar 

  • 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 

  • Rahman, M. M., & Sumantyo, J. T. S. (2012). Retrieval of tropical forest biomass information from ALOS PALSAR data. Geocarto International, 28(5), 382–403. https://doi.org/10.1080/10106049.2012.710652.

    Article  Google Scholar 

  • 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. https://doi.org/10.1080/10106049.2016.1188164.

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/s40725-017-0052-5.

    Article  Google Scholar 

  • 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. https://doi.org/10.2174/1875413901205010021.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2017.07.038.

    Article  Google Scholar 

  • 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. https://doi.org/10.3390/rs10040608.

    Article  Google Scholar 

  • Sarker, M. L. R. (2010). Estimation of forest biomass using remote sensing. Hong Kong: The Hong Kong Polytechnic University.

    Google Scholar 

  • 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. https://doi.org/10.1007/s10661-013-3199-y.

    Article  Google Scholar 

  • 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. https://doi.org/10.1109/tgrs.2009.2023909.

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/s13762-015-0750-0.

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/s12040-016-0692-z.

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/s42965-019-00011-6.

    Google Scholar 

  • 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. https://doi.org/10.1007/s11676-017-0511-7.

    Article  Google Scholar 

  • 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 

  • Torbick, N., Ledoux, L., Salas, W., & Zhao, M. (2016). Regional mapping of plantation extent using multisensory imagery. Remote Sensing, 8, 236. https://doi.org/10.3390/rs8030236.

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/s11676-017-0439-y.

    Article  Google Scholar 

Download references

Acknowledgements

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].

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Haldia Institute of Technology, Hatiberia, Haldia, 721657, India

    Suman Sinha, A. Santra & S. Santra Mitra

  2. Department of Space, Government of India, Space Applications Centre (ISRO), Jodhpur Tekra, Ahmedabad, Gujarat, 380 015, India

    A. K. Das

  3. Department of Environmental Science, Central University of Rajasthan, NH 8, Bandar Sindri, Ajmer, Rajasthan, 305817, India

    L. K. Sharma

  4. PLANEX, Physical Research Laboratory, Thaltej Campus, Ahmedabad, Gujarat, 380059, India

    Shiv Mohan

  5. School of Sciences, Indira Gandhi National Open University (IGNOU), Maidan Garhi, New Delhi, 110068, India

    M. S. Nathawat

  6. Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi, 835215, India

    C. Jeganathan

Authors
  1. Suman Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Santra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. K. Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. L. K. Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shiv Mohan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. S. Nathawat
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. S. Santra Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. C. Jeganathan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Suman Sinha.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sinha, S., Santra, A., Das, A.K. et al. Regression-Based Integrated Bi-sensor SAR Data Model to Estimate Forest Carbon Stock. J Indian Soc Remote Sens 47, 1599–1608 (2019). https://doi.org/10.1007/s12524-019-01004-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-019-01004-7

Keywords

Search

Navigation