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Timber Volume Estimation by Double Sampling Using High Resolution Satellite Data


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1 North Eastern Space Applications Centre Dept. of Space, Umiam 793103 Meghalaya, India
     

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Double sampling or two-phase sampling design offers a variety of possibilities for effective use of auxiliary information such as those from high resolution remote sensing data. This study has been made to examine the possibilities of different forms of auxiliary information derived from remote sensing data in double sampling design and suggest an appropriate estimator for forest timber volume estimation in the context of preparing forest working plan inputs. A regression-cum-ratio estimator has been derived for double sampling using information on two auxiliary variables derived from high resolution satellite data. The estimator has been adopted for forest timber volume estimation utilizing tree crown diameter (x) and NDVI (z) as auxiliary variables. Relationship between timber volume (y) and 2 2 average crown diameter (x) has a higher value of R (0.733) as compared to that with NDVI (z), where R =0.528. It is shown that combination of these two auxiliary variables under doubling sampling design has significantly reduce the standard error of the timber volume estimate (CV of SE=11.49%) against estimates brought out by common sampling design (31% for simple random sampling, 22.34% for systematic sampling and 18.69% for stratified random sampling). This indicates that the estimator can be employed in a variety of conditions where there is good correlation of satellite derived auxiliary information with sample based ground measurements and when the cost of ground measurements is relatively high.

Keywords

Double Sampling, Regression-cum-ratio Estimator, High Resolution Satellite Data, Timber Volume Estimation, Mean Square Error.
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About The Authors

B. K. Handique
North Eastern Space Applications Centre Dept. of Space, Umiam 793103 Meghalaya
India

S. Sudhakar
North Eastern Space Applications Centre Dept. of Space, Umiam 793103 Meghalaya
India


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  • Bhusan, S., Pandey, S. and Katara, S. (2008). A class of estimators in double sampling using two auxiliary variables, Journal of Reliability and Statistical Studies,1(1),67-73.

  • Cochran, W.G. (2002). Sampling Techniques, John Wiley and Sons Rem. Sensing (Asia) Pvt. Ltd., Singapore.

  • FSI, (1996). Volume Equations for Forests of India, Nepal and Bhutan, Forest survey of India, Dehradun.

  • Frayer, W.E. and Furnival, G.M. (1999). Forest sampling designs: a history, Journal of Forestry, 97: 4-8.

  • Gregoire, T.G., Valentine, H.T. and Furnival, G.M. (1987). Sampling methods for estimating stem volume and volume increment, Forest Ecology and Management, 21: 311-323.

  • Gunawardena, A. R., Nissanka, S.P. and Dayawansa, N.D.K.( 2008). Development of Merchantable Timber Volume Estimation of Pinus caribaea Plantations using Multi-Spectral Satellite Images, Engineer, XXXXI (5), 68-73.

  • Hidiroglou, M.A. and Sarndal, C.E. (1998). Use of auxiliary variable for two phase sampling, Survey Methodology, 24(1):11-20.

  • Jensen, J.R. (1999). Introductory Digital Image processing, a Remotesensing Perspective, Prentice Hall, New Jersey.

  • Jhajj, H.S. and Walia, G.S. (2011). A generalized Difference-cum-Ratio type estimator for the population mean in double sampling, Proceedings of the Statistics 2011 Canada: 5th Canadian Conference in Applied Statistics, July 1-4, 2011, Concordia University, Montreal, Canada.

  • Johnson, E. W. (2000). Forest sampling desk reference, CRC Press, New York.

  • Kadilar, C and Cingi, H. (2003). A study on the chain ratio-type estimator, Hacettepe Journal of Mathematics and Statistics. 32: 105-108.

  • Katsch, C.H.R. and Van Laar, A. (2002). The estimation of the growing stock of eucalypt plantation forests, based on spectral signatures of satellite imagery, in South Africa. South African Forestry Journal, 194: 65-70.

  • Khoshnevisan, M., Singh, H.P., Singh, S. and Smarandache, F. (2002). A general class of estimators of population median using two auxiliary variables in double sampling, In : Randomness and optimal estimation in data sampling ( 2nd edition), American Research Press, 26- 43.

  • Kiregyera, B. (1984). Regression-type estimators using two auxiliary variables and the model of double sampling from finite populations, Metrika, 31: 215-226.

  • Kohl, M. and Kushwaha, S.P.S. (1994). A four-phase sampling method for assessing standing volume using Landsat-TM data, aerial photography and field assessments, Commonwealth Forestry Review, 73(1): 35-42.

  • Korhonen, L., Korhonen, K.T., Rautiainen, M. and Stenberg, P. (2006). Estimation of forest canopy cover:a comparison of field measurement techniques, Silva Fennica, 40(4): 577–588.

  • Lillesand, T.M. and Keifer, R.W. (2003). RemoteSensing and Image Interpretation, 5th edition, John Wiley and Sons, New York.

  • Magnussen, S. and Boudewyn, P. (1998). Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators, Canadian Journal of Forest Reserach, 28: 1016-1031.

  • Mahto, B. (2001). Quantification of forest growing stock using remote sensing data for planning and management: a case study of Tikauli forest in Chitawan district, Nepal, Proc. 22ndAsian Conference on remote sensing,Nov 5-9, 2001, Singapore.

  • McRoberts, R.E., Wendt, D.G. and Liknes, G.C. (2005). Stratified estimation of forest inventory variables using spatially summarized stratifications, Silva Fennica 39(4): 559–571.

  • Mutanga, O. and Skidmore, A.K.(2004). Merging double sampling with remote sensing for a rapid estimation of fuelwood. Geocarto International,19 (4): 49-55.

  • NESAC (2007). RemoteSensing and GIS inputs for forest working plan inputs for North Cachar Hills, Project Report (NESAC-SR- 50-2007).

  • Roy, P.S. Sharma, K.P. and Jain, A., (1996). Stratification of density in dry deciduous forest using satellite remote sensing digital data—An approach based on spectral indices, Journal of Biosciences,. 21(5): 723–734.

  • Sahoo, L.N., Senapati, S.C. and Dalabehera, M. (2004). On two methods of unbiased estimation in two-phase sampling using two auxiliary variables, Bulletin of the Malaysian Mathematical Society (Second Series), 27, 73-75.

  • Sarma, K.K., Handique, B.K., Devi, H. Suchitra and Chakraborty, K. (2009). Remote Sensing and GIS based Forest Working Plan inputs for Hills Circle, North Cachar Hills District, Assam, NNRMS Bulletin, 33: 71-78.

  • Scheer, L., Akca, A., and Feldkotter, C. (1997). Efficient growing stock estimation from satellite data employing two-phased sampling with regression. Geo-Informations-Systeme, 3/97: 22-25.

  • Schreuder, H. T., Gregoire, T. G. and Wood, G. B. (1993). Sampling methods for multiresource forest inventory, John Wiley and Sons, New York.

  • Senapati, S.C. and Sahoo, L.N. (2006). An alternate class of estimators in double sampling, Bull. Malay. Math. Soc. (Second series), 29: 89-94.

  • Shiver, B.D. and Borders, B.D. (1996). Sampling Techniques for Forest Resource Inventory, John Wiley and Sons, Singapore.

  • Singh, R., Chauhan, P. and Sawan, N. (2007). A family of estimators for estimating population mean using known correlation coefficient in two phase sampling, Statistics In Transition-new series, 8(1): 89-96.

  • Sukhatme, B.V. (1962). Some ratio type estimators in two phase sampling, Journal of American Statistical Association, 57.

  • Thompson, D.N.(2000).Stratified sampling based on photo interpreted forestty pesreduces the cost of strategic forest inventory, Tas forest, 12:77-82.

  • Udaya Lakshmi, V., Murthy, M.S.R. and Dutt, C.B.S. (1998). Efficient forest resources management through GIS and remote sensing, Current Science, 75: 271-282.

  • Vries, P.G.de (1986). Sampling Theory for Forest Inventory, Springer& Verlag.


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  • Timber Volume Estimation by Double Sampling Using High Resolution Satellite Data

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Authors

B. K. Handique
North Eastern Space Applications Centre Dept. of Space, Umiam 793103 Meghalaya, India
S. Sudhakar
North Eastern Space Applications Centre Dept. of Space, Umiam 793103 Meghalaya, India

Abstract


Double sampling or two-phase sampling design offers a variety of possibilities for effective use of auxiliary information such as those from high resolution remote sensing data. This study has been made to examine the possibilities of different forms of auxiliary information derived from remote sensing data in double sampling design and suggest an appropriate estimator for forest timber volume estimation in the context of preparing forest working plan inputs. A regression-cum-ratio estimator has been derived for double sampling using information on two auxiliary variables derived from high resolution satellite data. The estimator has been adopted for forest timber volume estimation utilizing tree crown diameter (x) and NDVI (z) as auxiliary variables. Relationship between timber volume (y) and 2 2 average crown diameter (x) has a higher value of R (0.733) as compared to that with NDVI (z), where R =0.528. It is shown that combination of these two auxiliary variables under doubling sampling design has significantly reduce the standard error of the timber volume estimate (CV of SE=11.49%) against estimates brought out by common sampling design (31% for simple random sampling, 22.34% for systematic sampling and 18.69% for stratified random sampling). This indicates that the estimator can be employed in a variety of conditions where there is good correlation of satellite derived auxiliary information with sample based ground measurements and when the cost of ground measurements is relatively high.

Keywords


Double Sampling, Regression-cum-ratio Estimator, High Resolution Satellite Data, Timber Volume Estimation, Mean Square Error.

References