An alternative method for snow cover mapping on satellite images by modern applied mathematics

Abstract

Continuous monitoring of snow cover is very crucial since the extend and amount of snow are key parameters for many processes closely related to ecology and climatology. Measuring the extend and amount of snow by in situ measurements are not always practical and possible due to operational and logistic reasons. Since 1960s, images taken by earth-observing satellites have been extensively used to monitor snow cover, and many parametric and nonparametric image classification methods have been proposed and applied for snow cover mapping. In this study, a novel application of nonparametric regression splines is introduced within the frame of modern applied mathematics and remote sensing. Implementation of multivariate adaptive regression splines (MARS) in image classification for snow mapping on moderate resolution imaging spectroradiometer (MODIS) images is demonstrated within a well-elaborated framework. The relation between the variations in MARS model building parameters and their effect on the predictive performance are represented in various perspectives. Performance of MARS in image classification is compared with the traditional maximum-likelihood (ML) method by using error matrices. Significant improvement in the classification accuracy of MARS models is observed as the number of basis functions and the degree of interaction increase. On three image sets out of four, the MARS approach gives better classification accuracies when compared to ML method.