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Developing synergy regression models with space-borne ALOS PALSAR and Landsat TM sensors for retrieving tropical forest biomass

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Abstract

Forest stand biomass serves as an effective indicator for monitoring REDD (reducing emissions from deforestation and forest degradation). Optical remote sensing data have been widely used to derive forest biophysical parameters inspite of their poor sensitivity towards the forest properties. Microwave remote sensing provides a better alternative owing to its inherent ability to penetrate the forest vegetation. This study aims at developing optimal regression models for retrieving forest above-ground bole biomass (AGBB) utilising optical data from Landsat TM and microwave data from L-band of ALOS PALSAR data over Indian subcontinental tropical deciduous mixed forests located in Munger (Bihar, India). Spatial biomass models were developed. The results using Landsat TM showed poor correlation (R 2 = 0.295 and RMSE = 35 t/ha) when compared to HH polarized L-band SAR (R 2 = 0.868 and RMSE = 16.06 t/ha). However, the prediction model performed even better when both the optical and SAR were used simultaneously (R 2 = 0.892 and RMSE = 14.08 t/ha). The addition of TM metrics has positively contributed in improving PALSAR estimates of forest biomass. Hence, the study recommends the combined use of both optical and SAR sensors for better assessment of stand biomass with significant contribution towards operational forestry.

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Acknowledgements

The authors sincerely acknowledge JAXA (Japan Aerospace Exploration Agency, Japan) for providing the ALOS PALSAR data under ALOS RA-4 project. We sincerely acknowledge Space Application Centre (SAC, ISRO), Ahmedabad (India), for providing facilities to process the PALSAR data. The authors express sincere gratitude to the Department of Science and Technology (DST), Government of India for providing funds under DST-INSPIRE Program (Ref. No. DST/INSPIRE FELLOWSHIP/2010/[316]) to carry out the research to Suman Sinha.

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Authors and Affiliations

  1. Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi, 835 215, Jharkhand, India

    Suman Sinha & C Jeganathan

  2. Department of Environmental Science, School of Earth Sciences, Central University of Rajasthan, Bandarsindri, 305 817, Ajmer, Rajasthan, India

    L K Sharma

  3. School of Sciences, Indira Gandhi National Open University (IGNOU), Maidan Garhi, New Delhi, 110 068, India

    M S Nathawat

  4. Space Application Centre (ISRO), Department of Space, Government of India, Jodhpur Tekra, Satellite Road, Ahmedabad, 380 015, Gujarat, India

    Anup K Das

  5. PLANEX, Physical Research Laboratory, Thaltej Campus, Ahmedabad, 380 059, Gujarat, India

    Shiv Mohan

Authors
  1. Suman Sinha
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  2. C Jeganathan
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  3. L K Sharma
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  4. M S Nathawat
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  5. Anup K Das
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  6. Shiv Mohan
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Correspondence to Suman Sinha.

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Corresponding editor: Pradeep Kumar Thapliyal

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Sinha, S., Jeganathan, C., Sharma, L.K. et al. Developing synergy regression models with space-borne ALOS PALSAR and Landsat TM sensors for retrieving tropical forest biomass. J Earth Syst Sci 125, 725–735 (2016). https://doi.org/10.1007/s12040-016-0692-z

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

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