Calibration of LANDSAT data for sparsely vegetated semi-arid rangelands
- 1 December 1986
- journal article
- research article
- Published by Informa UK Limited in International Journal of Remote Sensing
- Vol. 7 (12), 1729-1750
- https://doi.org/10.1080/01431168608948964
Abstract
A LANDSAT-based rangelands monitoring system has been designed for semi-arid chenopod shrublands in southern Australia. Simultaneous ground and LANDSAT measurements were used to test multivariate calibration methods for estimating vegetation cover. Of three methods compared, the Lwin-Maritz and inverse estimators outperformed the classical approach. Data were analysed by rangeland type and as a combined set. Calibration curves, with errors of estimation, are presented for five major rangeland types.Keywords
This publication has 11 references indexed in Scilit:
- Reflectance modelling and the derivation of vegetation indices for an Australian semi-arid shrublandInternational Journal of Remote Sensing, 1986
- The application of Landsat image data to rangeland assessment and monitoring: the development and demonstration of a land image-based resource information system (LIBRIS)Journal of Arid Environments, 1986
- Observer differences in transect counts, cover estimates and plant size measurements on range monitoring sites in an arid shrubland.The Rangeland Journal, 1984
- Spatial resolution of remotely sensed imagery A review paperInternational Journal of Remote Sensing, 1983
- The application of Landsat image data to rangeland assessment and monitoring: An example from South Australia.The Rangeland Journal, 1983
- An Analysis of the Linear-Calibration Controversy from the Perspective of Compound EstimationTechnometrics, 1982
- A Note on the Problem of Statistical CalibrationJournal of the Royal Statistical Society Series C: Applied Statistics, 1980
- Note on a calibration problemBiometrika, 1969
- A Generalization of the Gauss-Markov TheoremJournal of the American Statistical Association, 1966
- A Generalization of the Gauss-Markov TheoremJournal of the American Statistical Association, 1966