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

Journal of Earth system science

Shiv Mohan5¹  L K L K Sharma2²  M S Nathawat3⁴  Anup K Das4³  C Jeganathan1⁵  Suman Sinha1⁵ 

[1] PLANEX, Physical Research Laboratory, Thaltej Campus, Ahmedabad 380 059, Gujarat, India.$$;Department of Environmental Science, School of Earth Sciences, Central University of Rajasthan, Bandarsindri 305 817, Ajmer, Rajasthan, India.$$;Space Application Centre (ISRO), Department of Space, Government of India, Jodhpur Tekra, Satellite Road, Ahmedabad 380 015, Gujarat, India.$$;School of Sciences, Indira Gandhi National Open University (IGNOU), Maidan Garhi, New Delhi 110 068, India.$$;Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi 835 215, Jharkhand, India.$$

关键词: ALOS PALSAR;    Landsat TM;    polarization;    backscatter;    tropical deciduous forest;    biomass;    Munger;    Bihar;   

DOI  :  

学科分类：天文学(综合)

来源: Indian Academy of Sciences

【 摘 要 】

Forest stand biomass serves as an effective indicator for monitoring REDD (reducing emissions fromdeforestation and forest degradation). Optical remote sensing data have been widely used to derive forestbiophysical parameters inspite of their poor sensitivity towards the forest properties. Microwave remotesensing 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 PALSARdata over Indian subcontinental tropical deciduous mixed forests located in Munger (Bihar, India). Spatialbiomass models were developed. The results using Landsat TM showed poor correlation (R^2 =0.295and 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 inimproving PALSAR estimates of forest biomass. Hence, the study recommends the combined use of bothoptical and SAR sensors for better assessment of stand biomass with significant contribution towardsoperational forestry.

【 授权许可】

Unknown