dc.contributor.author |
Debba, Pravesh
|
|
dc.contributor.author |
Carranza, EJM
|
|
dc.contributor.author |
Stein, A
|
|
dc.contributor.author |
Van der Meer, FD
|
|
dc.date.accessioned |
2010-04-13T07:17:25Z |
|
dc.date.available |
2010-04-13T07:17:25Z |
|
dc.date.issued |
2009-07 |
|
dc.identifier.citation |
Debba, 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 1-4 |
en |
dc.identifier.isbn |
978-1-4244-3395-7 |
|
dc.identifier.uri |
http://hdl.handle.net/10204/4016
|
|
dc.description |
Copyright: 2009 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, 12-17 July 2009 |
en |
dc.description.abstract |
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. |
en |
dc.language.iso |
en |
en |
dc.publisher |
IEEE |
en |
dc.subject |
Geoscience |
en |
dc.subject |
Mine tailings |
en |
dc.subject |
Remote sensing |
en |
dc.subject |
Hyperspectral |
en |
dc.subject |
External drift kriging |
en |
dc.subject |
Variogram |
en |
dc.subject |
Spectral unmixing |
en |
dc.subject |
Airborne hyperspectral data |
en |
dc.subject |
Variogram model |
en |
dc.subject |
Optimum sampling |
en |
dc.title |
Optimum sampling scheme for characterization of mine tailings |
en |
dc.type |
Conference Presentation |
en |
dc.identifier.apacitation |
Debba, P., Carranza, E., Stein, A., & Van der Meer, F. (2009). Optimum sampling scheme for characterization of mine tailings. IEEE. http://hdl.handle.net/10204/4016 |
en_ZA |
dc.identifier.chicagocitation |
Debba, Pravesh, EJM Carranza, A Stein, and FD Van der Meer. "Optimum sampling scheme for characterization of mine tailings." (2009): http://hdl.handle.net/10204/4016 |
en_ZA |
dc.identifier.vancouvercitation |
Debba 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 . |
en_ZA |
dc.identifier.ris |
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 -
|
en_ZA |