Land cover change analysis of a Mediterranean area in Spain using different sources of data: multi-seasonal landsat images, land surface temperature, digital terrain models and texture

Rodriguez Galiano, V. and Chica-Olmo, M. (2012) Land cover change analysis of a Mediterranean area in Spain using different sources of data: multi-seasonal landsat images, land surface temperature, digital terrain models and texture. Applied Geography, 35 (1-2), 208-218. (doi:10.1016/j.apgeog.2012.06.014).

Abstract

The main objective of this study was to apply a method for the mapping and analysis of land cover changes in a Mediterranean area in southern Spain (Granada Province). The province of Granada is a complex and very heterogeneous area made up of numerous land covers that are difficult to map due to spectral similarities.

The inherent difficulty of the mapping of areas with the abovementioned characteristics was addressed by choosing a supervised classification algorithm called random forest and, in addition, by obtaining and incorporating new variables that allowed an improved land cover characterization: multi-seasonal spectral variables corresponding to different stages of land cover phenological development, variables linked to environmental gradients (digital terrain models and land surface temperature) and spatial variability structure textural measures. The same level of accuracy was obtained from the combined use of satellite images with digital terrain models or textural measures. However, the inclusion of the land surface temperature had a more moderate effect and only improved the mapping of some of the land-covers.

A post-classification change analysis was conducted by comparing two supervised classifications obtained from the application of the classifier to a selection of different spectral, terrain and textural variables of images from 1998 and 2004. The global accuracy of the change map was 86% (matching percentage). The applied method resulted in a difference in the mapping accuracy of 33% in relation to the method based on the traditional maximum-likelihood classifier and considering only the spectral variables from the Landsat TM-5 satellite sensor.

This record has no associated files available for download.

Contributors

Download statistics