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Estimation of above-ground biomass and delineation of vegetation of tropical forests using EOS-04 data


Affiliations
1 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
2 National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, India
3 Indus University, Ahmedabad 381 115, India
4 ACL Digital – Reliance Jio, Navi Mumbai 400 701, India
5 North Eastern Space Applications Centre, Umiam 793 103, India

The C-band SAR on-board EOS-04 mission provides unique opportunities to characterize forest vegetation through its sensitiveness to vegetation structure and all-weather imaging capability over regions of perpetual cloud cover. The present study has brought out the applications of EOS-04 data for estimation of above-ground biomass (AGB) of tropical deciduous forests and scrublands, mapping of forest cover and delineation of mangroves vegetation. The study suggested that EOS-04 data can be used for mapping AGB of tropical scrublands and low density forests of AGB £ 80 t/ha. The overall RMSE for all vegetation with AGB £ 80 t/ha was 15.3 t/ha (R2 – 0.49). It was shown that the integration of EOS-04 and Sentinel-2 data improved AGB estimates across biomass ranges of 0–245 t/ha (RMSE – 21.60 t/ha and 0.81). EOS-04 data was also found to be useful for the delineation of mangroves and forest vegetation using machine-learning algorithms. The study supports operational use of EOS-04 data for estimation of AGB over low biomass tropical forests and scrublands

Keywords

Above-ground biomass, C-band SAR, forest cover, mangrove vegetation, MRS data, random forest regression, tropical forests.
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  • Estimation of above-ground biomass and delineation of vegetation of tropical forests using EOS-04 data

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Authors

Anup K. Das
Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
C. Patnaik
Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
Saroj Maity
Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
M. S. S. Praveen
National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, India
R. Suraj Reddy
National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, India
G. Rajashekar
National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, India
Nilima R. Chaube
Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
Seema Mahajan
Indus University, Ahmedabad 381 115, India
Yashraj Jain
ACL Digital – Reliance Jio, Navi Mumbai 400 701, India
Dhruval Bhavsar
North Eastern Space Applications Centre, Umiam 793 103, India
Kasturi Chakraborty
North Eastern Space Applications Centre, Umiam 793 103, India
Deepak Putrevu
Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India

Abstract


The C-band SAR on-board EOS-04 mission provides unique opportunities to characterize forest vegetation through its sensitiveness to vegetation structure and all-weather imaging capability over regions of perpetual cloud cover. The present study has brought out the applications of EOS-04 data for estimation of above-ground biomass (AGB) of tropical deciduous forests and scrublands, mapping of forest cover and delineation of mangroves vegetation. The study suggested that EOS-04 data can be used for mapping AGB of tropical scrublands and low density forests of AGB £ 80 t/ha. The overall RMSE for all vegetation with AGB £ 80 t/ha was 15.3 t/ha (R2 – 0.49). It was shown that the integration of EOS-04 and Sentinel-2 data improved AGB estimates across biomass ranges of 0–245 t/ha (RMSE – 21.60 t/ha and 0.81). EOS-04 data was also found to be useful for the delineation of mangroves and forest vegetation using machine-learning algorithms. The study supports operational use of EOS-04 data for estimation of AGB over low biomass tropical forests and scrublands

Keywords


Above-ground biomass, C-band SAR, forest cover, mangrove vegetation, MRS data, random forest regression, tropical forests.



DOI: https://doi.org/10.18520/cs%2Fv126%2Fi9%2F1088-1101