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An alternative approach for estimating above ground biomass using Resourcesat-2 satellite data and artificial neural network in Bundelkhand region of India

Environmental Monitoring and Assessment volume 189, Article number: 576 (2017) Cite this article

Abstract

Determination of above ground biomass (AGB) of any forest is a longstanding scientific endeavor, which helps to estimate net primary productivity, carbon stock and other biophysical parameters of that forest. With advancement of geospatial technology in last few decades, AGB estimation now can be done using space-borne and airborne remotely sensed data. It is a well-established, time saving and cost effective technique with high precision and is frequently applied by the scientific community. It involves development of allometric equations based on correlations of ground-based forest biomass measurements with vegetation indices derived from remotely sensed data. However, selection of the best-fit and explanatory models of biomass estimation often becomes a difficult proposition with respect to the image data resolution (spatial and spectral) as well as the sensor platform position in space. Using Resourcesat-2 satellite data and Normalized Difference Vegetation Index (NDVI), this pilot scale study compared traditional linear and nonlinear models with an artificial intelligence-based non-parametric technique, i.e. artificial neural network (ANN) for formulation of the best-fit model to determine AGB of forest of the Bundelkhand region of India. The results confirmed the superiority of ANN over other models in terms of several statistical significance and reliability assessment measures. Accordingly, this study proposed the use of ANN instead of traditional models for determination of AGB and other bio-physical parameters of any dry deciduous forest of tropical sub-humid or semi-arid area. In addition, large numbers of sampling sites with different quadrant sizes for trees, shrubs, and herbs as well as application of LiDAR data as predictor variable were recommended for very high precision modelling in ANN for a large scale study.

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Acknowledgements

We acknowledge the people who helped us during field data collection and the local villagers of the Buldelkhand area for their valuable guidance.

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Affiliations

  1. Indian Grassland and Fodder Research Institute, Gwalior Road, Jhansi, 284 003, India

    Dibyendu Deb, J. P. Singh & R. S. Chaurasia

  2. Department of Soil Science and Agricultural Chemistry, Uttar Banga Krishi Viswavidyalaya, Cooch Behar, 736 165, India

    Shovik Deb

  3. Department of Geography, Jadavpur University, Kolkata, 700032, India

    Debajit Datta

  4. Department of Agricultural Statistics, Uttar Banga Krishi Viswavidyalaya, Cooch Behar, 736 165, India

    Arunava Ghosh

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  1. Dibyendu Deb
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  2. J. P. Singh
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  3. Shovik Deb
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  5. Arunava Ghosh
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Correspondence to Shovik Deb.

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Deb, D., Singh, J.P., Deb, S. et al. An alternative approach for estimating above ground biomass using Resourcesat-2 satellite data and artificial neural network in Bundelkhand region of India. Environ Monit Assess 189, 576 (2017). https://doi.org/10.1007/s10661-017-6307-6

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  • DOI: https://doi.org/10.1007/s10661-017-6307-6

Keywords

  • Above ground biomass
  • Allometric equation
  • Artificial neural network
  • Normalized difference vegetation index
  • Satellite image