Skip to main content
SpringerLink
Log in
Book cover

Data Science and Big Data Analytics pp 11–27Cite as

Genetic Algorithm Approach for Optimization of Biomass Estimation at LiDAR

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 16))

Abstract

Estimation of Biomass at ICESat/GLAS footprint level was finished by incorporating information from various sensors viz., spaceborne LiDAR (ICESat/GLAS). The biomass estimation accuracies of Genetic Algorithm were studied by optimizing the waveform parameters. Multiple linear regression equation was generated using the most important variables found by Genetic Algorithm. The results of the study were very encouraging. Optimum results were obtained using the top 18 parameters derived from GLAS waveform. The biomass was predicted at small area by Genetic Algorithm with an R2 63% of and RMSE of 18.94 t/ha using the best six variables, viz. wdistance, wpdistance, R50, Ecanopy, Home., wcentroid over to 18 independent variables. The same methodology can be used for biomass estimation over a large area. The estimation is done at Tripura area. The study finally established that Genetic approach is produced the better result to predicting AGB. The best outcome of the study was the formulation of an approach that could result in higher biomass estimation accuracies.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Anaya J, Chuvieco E, Palaciosorueta A (2009) Aboveground biomass assessment in Colombia: a remote sensing approach. For Ecol Manag 257:1237–1246

    Article  Google Scholar 

  2. IPCC (2000) Good practice guidance and uncertainty management in national greenhouse gas inventories

    Google Scholar 

  3. Viergever KM, Woodhouse IH, Marino A, Brolley M, Stuart N (2008) SAR interferometry for estimating above-ground biomass of Savanna Woodlands in Belize. In: Geoscience and remote sensing symposium, 2008, IGARSS 2008, IEEE International, pp V-290–V-293

    Google Scholar 

  4. Parmar AK (2012) A neuro-genetic approach for rapid assessment of above ground biomass: an improved tool for monitoring the impact of forest degradation, geo-information science and earth observation of the University of Twente

    Google Scholar 

  5. Duong HV (2010) Processing and application of ICES at large footprint full waveform laser range data. Doctoral thesis, Delft University of Technology, Netherlands

    Google Scholar 

  6. Lu D (2006) The potential and challenge of remote sensing-based biomass estimation. Int J Remote Sens 27(7):1297–1328

    Article  MathSciNet  Google Scholar 

  7. Eisfelder C, Kuenzer C, Dech S (2011) Derivation of biomass information for semi-arid areas using remote-sensing data. Int J Remote Sens 33:2937–2984

    Article  Google Scholar 

  8. Hall FG, Bergen K, Blair JB et al (2011) “Characterizing 3D vegetation structure from space”: mission requirements. Remote Sens Environ 115(11):2753–2775

    Article  Google Scholar 

  9. Dhanda (2013) Optimising parameters obtained from multiple sensors for biomass estimation at icesat footprint level using different regression algorithms

    Google Scholar 

  10. Roeva O (2005) Genetic algorithms for a parameter estimation of a fermentation process model: a comparison

    Google Scholar 

  11. Jensen JR (2007) Prentice Hall series in geographic information science. Pearson Prentice Hall, 592 pp

    Google Scholar 

  12. Zwally HJ, Schutz B, Abdalati W, Abshirre J, Bentley C, Brenner A, Bufton J, Dezio J, Hancock D, Harding D, Herring T, Minster B, Quinn K, Palm S, Spinhirne J, Thomas R (2002) ICESat’s laser measurements of polar ice, atmosphere, ocean and land. J Geodyn 34(3–4):405–445

    Article  Google Scholar 

  13. GALGO (2006) An R package for multivariate variable selection using genetic algorithms. Victor Trevino and Francesco Falciani School of Biosciences, University of Birmingham, Edgbaston, UK Bioinformatics

    Google Scholar 

  14. Upadhyay D (2014) An ethno-botanical study of plants found in Timli Forest Range, District Dehradun, Uttarakhand, India. Cloud Publ Int J Adv Herb Sci Technol 1(1):13–19, Article ID Med-157

    Google Scholar 

Download references

Acknowledgements

I would like to express my profounder gratitude towards Dr. Subrata Nandy (Scientist/Engr. SD, FED, IIRS) who guided me throughout this paper. He supervised me for this thought and inspired me to complete it.

Author information

Authors and Affiliations

  1. Jayoti Vidyapeeth Women’s University, Jaipur, Rajasthan, India

    Sonika & Aditi Jain

Authors
  1. Sonika
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aditi Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sonika .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

  2. School of Science and Technology, Middlesex University, London, United Kingdom

    Xin-She Yang

  3. Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Aynur Unal

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sonika, Jain, A. (2019). Genetic Algorithm Approach for Optimization of Biomass Estimation at LiDAR. In: Mishra, D., Yang, XS., Unal, A. (eds) Data Science and Big Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 16. Springer, Singapore. https://doi.org/10.1007/978-981-10-7641-1_2

Download citation

Publish with us

Policies and ethics

Search

Navigation