Vegetation cover seasonal changes assessment from TM imagery in a semi-arid landscape

This work evaluates the suitability of spectral mixture analysis (SMA) methods to assess vegetation cover seasonal changes in a desertification context. Our main interest is to produce remotely sensed derived maps, sensitive to vegetation activity and quite independent of the soil background. A further aim is to analyse the inter-annual variations of this magnitude for different natural vegetation species, in response to seasonal and climatic changes. Fractional vegetation cover (FVC) was obtained using a Variable Endmember Spectral Mixture Analysis (VESMA) technique. The aim is to identify the main vegetation cover and lithological units and decompose them in separate stages. The use of specific spectral signatures for each pixel allows for a better adaptation of the endmembers to local conditions, which is an important prerequisite to ensure the accuracy of fractions. The method has been tested on a well documented area, the Guadalentin river basin, located in south-eastern Spain. Unlike pine forest and stipa classes, rosmarinus, sparse shrubs and seasonal grasses classes displayed larger inter-annual variability, showing higher stress in response to water availability. A comparative analysis between FVC and the Normalized Difference Vegetation Index (NDVI) was also conducted. Average values were used as indicators of the dynamics of the vegetation cover, with the variance of each vegetation class giving similar results. The correlation between both magnitudes varied from 55% for the class with least coverage to 90% for the densest vegetation class. Regarding seasonal evolution, the average values and standard deviations of the changes in each vegetation class in specific periods were related to seasonal changes and the effects of the rainfall pattern. Significant differences were found between the two methods, with FVC showing a higher coherence.