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Small Area Estimation for Total Basal Cover in the State of Maharashtra in India

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Abstract

This chapter describes small area estimation (SAE) approach to produce the small area estimates of the total basal cover (m2/ha) for trees, shrubs and herbs for the state of Maharashtra in India. All seven forest types are defined as small areas. The analysis uses the data of survey conducted by Tropical Forest Research Institute, Jabalpur, India during the Indian Council of Forestry Research and Education’s revisiting of forestry types of India in the year 2011–12. The nested quadrats of 10 m × 10 m, 3 m × 3 m and 1 m × 1 m size for tree, shrub and herb layers respectively are the sampling units. The auxiliary data, percentage of forest cover at small area level is available from India’s State of Forest Report 2009 (FSI 2009). The results show that forest type-wise estimates of total basal cover for trees, shrubs and herbs generated by SAE approach are reliable as compared to direct survey estimates. Such disaggregate level estimates are invaluable policy information for state forest department and local resource managers.

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References

  • Ambler, R., Caplan, D., Chambers, R., Kovacevic, M., & Wang, S. (2001). Combining unemployment benefits data and LFS data to estimate ILO unemployment for small areas: An application of a modified Fay-Herriot Method. In: Proceedings of the International Association of Survey Statistician. Meeting of the ISI: Seoul, August 2001.

    Google Scholar 

  • Battese, G. E., Harter, R. M., & Fuller, W. A. (1988). An error component model for prediction of county crop areas using survey and satellite data. Journal of the American Statistical Association, 83, 28–36.

    Article  Google Scholar 

  • Breidenback, J., Northdurft, A., & Kändler, G. (2010). Comparison of nearest neighbor approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data. European Journal of Forest Research, 129, 833–846.

    Article  Google Scholar 

  • Champion, H. G., & Seth, S. K. (1968). A revised survey of forest types of India. Delhi: Govt. of India Press.

    Google Scholar 

  • Chandra, H., Aditya, A., & Sud, U. C. (2018). Localised estimates and spatial mapping of poverty incidence in the state of Bihar in India—an application of small area estimation techniques. PLoS ONE, 13(6), e0198502.

    Article  Google Scholar 

  • Chandra, H. (2013). Exploring spatial dependence in area level random effect model for disaggregate level crop yield estimation. Journal of Applied Statistics, 40, 823–842.

    Article  MathSciNet  Google Scholar 

  • Chandra, H., & Chandra, G. (2015). An overview of small area estimation techniques. In G. Chandra, R. Nautiyal, H. Chandra, N. Roychoudury, & N. Mohammad (Eds.), Statistics in Forestry: Methods and Applications (pp. 45–54). Coimbatoor: Bonfring Publication.

    Google Scholar 

  • Chandra, H., Salvati, N., & Sud, U. C. (2011). Disaggregate-level estimates of indebtedness in the state of Uttar Pradesh in India-an application of small area estimation technique. Journal of Applied Statistics, 38(11), 2413–2432.

    Article  MathSciNet  Google Scholar 

  • Das, S., Chandra, H., & Saha, U. R. (2019). District level prevalence of diarrhea disease among under-five children in Bangladesh: An application of small area estimation approach. PLoS ONE, 14(2), e0211062.

    Article  Google Scholar 

  • Fay, R. E., & Herriot, R. A. (1979). Estimation of income from small places: an application of James-Stein procedures to census data. Journal of the American Statistical Association, 74, 269–277.

    Article  MathSciNet  Google Scholar 

  • FSI. (2009). India’s state of forest report 2009, Forest Survey of India, Government of India, Dehradun, India.

    Google Scholar 

  • FSI. (2017). India’s state of forest report 2017, Forest Survey of India, Government of India, Dehradun, India.

    Google Scholar 

  • Goerndt, M. E., Monleon, V., & Temesgen, H. (2010). Relating forest attributes with area-based and tree-based LiDAR metrics for western Oregon. Western Journal of Applied Forestry, 25, 105–111.

    Article  Google Scholar 

  • Katila, M., & Tomppo, E. (2005). Empirical errors of small area estimates from the multisource national forest inventory in eastern Finland. Silva Fennica, 40(729), 742.

    Google Scholar 

  • McRoberts, R. E. (2012). Estimating forest attribute parameters for small areas using nearest neighbors techniques. Forest Ecology and Management, 272, 3–12.

    Article  Google Scholar 

  • Ohmann, J. L., & Gregory, M. J. (2002). Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon. U.S.A. Canadian Journal of Forest Research, 32, 725–741.

    Article  Google Scholar 

  • Pfeffermann, D. (2002). Small area estimation: new developments and directions. International Statistical Review, 70, 125–143.

    MATH  Google Scholar 

  • Prasad, N. G. N., & Rao, J. N. K. (1990). The estimation of the mean squared error of the small area estimators. Journal of the American Statistical Association, 85, 163–171.

    Article  MathSciNet  Google Scholar 

  • Rao, J. N. K. (2003). Small area estimation. New York: Wiley.

    Book  Google Scholar 

  • Tomppo, E. (2006). The Finnish national forest inventory. In A. Kangas & M. Maltamo (Eds.), Forest Inventory: Methodology and Applications. Dordrecht, the Netherlands: Springer.

    Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the valuable comments and suggestions of the reviewers. These led to a considerable improvement in the chapter. The work of Hukum Chandra was carried out under an ICAR-National Fellow Project at ICAR-IASRI, New Delhi, India.

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Chandra, H., Chandra, G. (2020). Small Area Estimation for Total Basal Cover in the State of Maharashtra in India. In: Chandra, G., Nautiyal, R., Chandra, H. (eds) Statistical Methods and Applications in Forestry and Environmental Sciences. Forum for Interdisciplinary Mathematics. Springer, Singapore. https://doi.org/10.1007/978-981-15-1476-0_16

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