Estimation of Above Ground Biomass Using Support Vector Machines and ALOS/PALSAR data

Thota Sivasankar; Junaid Mushtaq Lone; Sarma K. K.; Abdul Qadir; Raju P.L. N.

doi:10.15625/0866-7187/41/2/13690

Author affiliations

Authors

Thota Sivasankar North Eastern Space Applications Centre, Department of Space, Umiam-793103, Meghalaya, India
Junaid Mushtaq Lone North Eastern Space Applications Centre, Department of Space, Umiam-793103, Meghalaya, India
Sarma K. K. North Eastern Space Applications Centre, Department of Space, Umiam-793103, Meghalaya, India
Abdul Qadir Department of Geography, University of Delaware, USA
Raju P.L. N. North Eastern Space Applications Centre, Department of Space, Umiam-793103, Meghalaya, India

DOI:

https://doi.org/10.15625/0866-7187/41/2/13690

Keywords:

Synthetic aperture radar, ALOS-2, PALSAR-2, above ground biomass, support vector machines

Abstract

L-band Synthetic aperture radar (SAR) data has been extensively used for forest aboveground biomass (AGB) estimation due to its higher saturation level. However, SAR backscatter is highly influenced by the topography characteristics along with the bio-geophysical properties of vegetation and underneath soil characteristics. This has limited the accuracy of directly relating the SAR backscatter with above ground biomass in highly undulated terrain. In this study, it has been observed that terrain degree of slope and aspect plays a vital role in influencing the SAR backscatter in addition with AGB. Because of this, the degree of slope and aspect along with SAR backscatter in HH (transmit and receive polarizations are horizontal) and HV (transmit horizontal and receive vertical) polarizations have been considered as inputs for Support Vector Machine (SVM) to improve the biomass retrieval accuracy. Our results demonstrate that the accuracy of AGB estimation over hilly terrain can be significantly improved by considering topographical characteristics in addition to L-band backscatter.

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References

Attarchi S., Gloaguen R., 2014. Classifying complex mountainous forests with L-Band SAR and landsat data integration: A comparison among different machine learning methods in the Hyrcanian forest. Remote Sensing, 6(5), 3624–3647. https://doi.org/10.3390/rs6053624.

Baghdadi N., et al., 2015. Evaluation of ALOS/PALSAR L-band data for the estimation of Eucalyptus plantations above-ground biomass in Brazil. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 8(8), 3802–3811.

Baig S., Qazi W.A., Akhtar A.M., Waqar M.M., Ammar A., Gilani H., Mehmood S.A., 2017. Above Ground Biomass Estimation of Dalbergia sissoo Forest Plantation from Dual-Polarized ALOS-2 PALSAR Data. Canadian Journal of Remote Sensing, 43(3), 297–308. https://doi.org/10.1080/07038992.2017.1330143

Basak D., Pal S., Patranabis D.C., 2007. Support Vector Regression. Neuronal Information Processing-Letters and Reviews, 11(10), 203–224. https://doi.org/10.4258/hir.2010.16.4.224.

Cairns M.A., et al., 1997. Root Biomass Allocation in the World’s Upland Forests. Oecologia, 111(1), 1–11. https://doi.org/10.1007/s004420050201.

Cristianini N., Taylor J.S., 2000. An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, 2000.

Friedrichs F., Igel C., 2005. Evolutionary tuning of multiple SVM parameters. Neurocomputing, 64, 107–117.

Gao X., et al., 2012. Classification of hyperspectral image based on SVM optimized by a new particle swarm optimization. In Proc. 2nd International Conference on Remote Sensing, Environment

and Transportation Engineering, https://doi.org/10.1109/RSETE.2012.6260436.

Hamdan O., Khali Aziz H., Mohd Hasmadi I., 2014. L-band ALOS PALSAR for biomass estimation of Matang Mangroves, Malaysia. Remote Sensing of Environment, 155, 69–78. https://doi.org/10.1016/j.rse.2014.04.029.

Harrell P.A., Bourgeau-Chavez L.L., Kasischke E.S., French N.H.F., Christensen N.L., 1995. Sensitivity of ERS-1 and JERS-1 radar data to biomass and stand structure in Alaskan boreal forest. Remote Sensing of Environment, 54(3), 247–260. https://doi.org/10.1016/0034-4257(95)00127-1.

Henderson, F.M., Lewis, A.J., 1998. Manual Of Remote Sensing, Principles and Applications Of Imaging Radar, ASPRS, John Wiley, New York.

Huang W., Sun G., Ni W., Zhang Z., Dubayah R., 2015. Sensitivity of multi-source SAR backscatter to changes in forest aboveground biomass. Remote Sensing, 7(8), 9587–9609. https://doi.org/10.3390/rs70809587.

Imhoff M.L., 1993. Radar Backscatter Biomass Saturation - Observations and Implications for Global Biomass Assessment. Igarss’93: Better Understanding of Earth Environment, Vols. I–Iv, 43–45.

Koch B., 2010. Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 581–590.

Jensen J.R., 2007. Remote sensing of environment: An Earth resources perspective, 2nd ed., Prentice Hall: Upper Saddle River, NJ-07458, ISBN: 0-13-188950-8.

Lone J.M., Sivasankar T., Sarma K.K., Qadir A., Raju P.L.N., 2018. Influence of slope aspect on above ground biomass estimation using ALOS-2 data. International Journal of Science and Research, 6(6), 1422–1428.

Mitchard E.T.A., Saatchi S.S., White L., Abernethy K., Jeffery K.J., Lewis S.L., Collins M., Lefsky M.A., Leal M.E., Woodhouse I.H., Meir P., 2012. Mapping tropical forest biomass with radar and spaceborne LiDAR in Lope National Park, Gabon: overcoming problems of high biomass and persistent cloud, Biogeosciences, 9(1), 179–191. https://doi.org/10.5194/bg-9-179-2012.

Morel A.C., Saatchi S.S., Malhi Y., Berry N.J., Banin L., Burslem D., Nilus R., Ong R.C., 2011. Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data. Forest Ecology and Management, 262(9), 1786–1798. https://doi.org/10.1016/j.foreco.2011.07.008.

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(7), 675. https://doi.org/10.3390/app7070675.

Pan Y., Birdsey R.A., Phillips O.L., Jackson R.B., 2013. The Structure, Distribution, and Biomass of the World’s Forests. Annual Review of Ecology, Evolution, and Systematics, 44(1), 593–622. https://doi.org/10.1146/annurev-ecolsys-110512-135914.

Patel P., Srivastava H.S., 2013. Ground truth planning for synthetic aperture radar (SAR): Addressing various challenges using statistical approach. International Journal of Advancement in Remote Sensing, GIS and Geography, 1(2), 1–17.

Patel P., Srivastava H.S., 2013. RADARSAT-2 announcement of opportunity project on soil moisture, surface roughness and vegetation parameter retrieval using SAR polarimetry. SAC/EPSA/MPSG/CVD/TDPR&D/01/13, Soar International Closing and Reporting-2013, Final Report Submitted to Canadian Space Agency (CSA) through MDA, Canada, Indian Space Research Organization (ISRO), India, 01–81.

Peregon A., Yamagata Y., 2013. The use of ALOS/PALSAR backscatter to estimate above-ground forest biomass: A case study in Western Siberia. Remote Sensing of Environment, 137, 139–146.

Rajput S.S., Shukla N.K., Gupta V.K., Jain J.D., 1996. Timber mechanics: Strength classification and grading of timber. ICFRE Publication-38, ICFRE, Dehradun, 103.

Roy P.S., et al., 2015. Development of decadal (1985-1995-2005) land use and land cover database for India. Remote Sensing, 7(3), 2401–2430. https://doi.org/10.3390/rs70302401.

Schölkopf B.L. Bartlett P., Smola A., Williamson R., 1998. Support vector regression with automatic accuracy control. Perspectives in Neural Computing: Proceedings of the 8th International Conference on Artificial Neural Networks (ICANN’98), 111–116.

Shao Z., Zhang L., 2016. Estimating forest aboveground biomass by combining optical and SAR data: A case study in genhe, inner Mongolia, China. Sensors (Switzerland), 16(6). https://doi.org/10.3390/s16060834.

Sivasankar T., Lone J.M., Sarma K.K., Qadir A., Raju P.L.N., 2018. The potential of multi-frequency multi-polarized ALOS-2/PALSAR-2 and Sentinel-1 SAR data for aboveground forest biomass estimation. International Journal of Engineering and Technology, 10(3), 797–802.

Smola A.J., Schölkopf B., 2004. A tutorial on support vector regression. Statistics and Computing, 14, 199–222.

Srivastava H.S., et al., 2011. A semi-empirical modeling approach to calculate two-way attenuation in radar backscatter from soil due to crop cover. Current Science, 100(12), 1871–1874.

Srivastava H.S., Sivasankar T., Patel P., 2018. An insight into the volume component generated from RISAT-1 hybrid polarimetric SAR data for crop biophysical parameters retrieval. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-5, 209–214.

Thumaty K.C., Fararoda R., Middinti S., Gopalakrishnan R., Jha C.S., Dadhwal V.K., 2016. Estimation of above ground biomass for central Indian deciduous forests using ALOS PALSAR L-band data. Journal of the Indian Society of Remote Sensing, 44(1), 31–39. https://doi.org/10.1007/s12524-015-0462-4.