Debba, PraveshCarranza, EJMStein, AVan der Meer, FD2010-04-132010-04-132009-07Debba, P, Carranza, EJM et al 2009. Optimum sampling scheme for characterization of mine tailings. IEEE. International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, 12-17 July 2009, pp 1-4978-1-4244-3395-7http://hdl.handle.net/10204/4016Copyright: 2009 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, 12-17 July 2009The paper describes a novice method for sampling geochemicals to characterize mine tailings. The author’s model the spatial relationships between a multi-element signature and, as covariates, abundance estimates of secondary iron-bearing minerals in mine tailings dumps. The covariates of interest, are readily, but less accurately obtainable by using airborne hyperspectral data and estimated through spectral unmixing. Via simulated annealing an optimal prospective sampling scheme for a new unvisited area is derived based on the variogram model of a previously sampled area.enGeoscienceMine tailingsRemote sensingHyperspectralExternal drift krigingVariogramSpectral unmixingAirborne hyperspectral dataVariogram modelOptimum samplingConference PresentationDebba, P., Carranza, E., Stein, A., & Van der Meer, F. (2009). Optimum sampling scheme for characterization of mine tailings. IEEE. http://hdl.handle.net/10204/4016Debba, Pravesh, EJM Carranza, A Stein, and FD Van der Meer. "Optimum sampling scheme for characterization of mine tailings." (2009): http://hdl.handle.net/10204/4016Debba P, Carranza E, Stein A, Van der Meer F, Optimum sampling scheme for characterization of mine tailings; IEEE; 2009. http://hdl.handle.net/10204/4016 .TY - Conference Presentation AU - Debba, Pravesh AU - Carranza, EJM AU - Stein, A AU - Van der Meer, FD AB - The paper describes a novice method for sampling geochemicals to characterize mine tailings. The author’s model the spatial relationships between a multi-element signature and, as covariates, abundance estimates of secondary iron-bearing minerals in mine tailings dumps. The covariates of interest, are readily, but less accurately obtainable by using airborne hyperspectral data and estimated through spectral unmixing. Via simulated annealing an optimal prospective sampling scheme for a new unvisited area is derived based on the variogram model of a previously sampled area. DA - 2009-07 DB - ResearchSpace DP - CSIR KW - Geoscience KW - Mine tailings KW - Remote sensing KW - Hyperspectral KW - External drift kriging KW - Variogram KW - Spectral unmixing KW - Airborne hyperspectral data KW - Variogram model KW - Optimum sampling LK - https://researchspace.csir.co.za PY - 2009 SM - 978-1-4244-3395-7 T1 - Optimum sampling scheme for characterization of mine tailings TI - Optimum sampling scheme for characterization of mine tailings UR - http://hdl.handle.net/10204/4016 ER -