Multisource ERS-1 and optical data for vegetal cover assessment and monitoring in a semi-arid region of Algeria

Abstract

This paper highlights advantages of using Synthetic Aperture Radar (SAR) data combined with multispectral data to improve vegetal cover assessment and monitoring in a semi-arid region of southern Algeria. We present a number of preprocessing and processing techniques using multidate optical data analysis alone and SAR ERS-1 and Landsat Thematic Mapper (TM) data integration due to aspects of radar image enhancement techniques and the study of roughness of different types of vegetation in steppic regions. Image data integration has become a valuable approach to integrate multisource satellite data. It has been found that image data from different spectral domains (visible, near-infrared, microwave) provides datasets with complementarity information content and can be used to improve the spatial resolution of satellite images. In this communication, we present a part of the cooperation research project which deals with fusing ERS-1 SAR geocoded images with Landsat TM data, investigating different combinations of integration and classification techniques. The methodology consists of several steps: (1) Speckle noise reduction by comparative performance of different filtering algorithms. Several filtering algorithms were implemented and tested with different window sizes, iterations and parameters. (2) Geometric superposition and geocoding of optical images regarding SAR ERS-1 image and resampling at unique resolution of 25 m. (3) Application of different numerical combinations of integration techniques and unsupervized classifications such as the Forgy method, the MacQueen method and other methods. The results were compared with vegetal cover mapping from aerial photographs of the region of Foum Redad in the south of the Saharian Atlas. The combinations proposed above allow us to distinguish different themes in the arid and semi-arid regions in the south of the Saharian Atlas using a colour composite image and show a good correlation between different types of land cover and land use and radar backscattering level in the SAR data which corresponds essentially to the roughness of the soil surface.