Lakes are vital ecological assets due to their role as multifunctional ecosystems that support human activities and maintain biodiversity. Their rich biodiversity helps uphold ecological balance in surrounding environments. A healthy lake ecosystem acts as a natural filter, removing pollutants and maintaining water quality, which benefits both humans and wildlife. Mapping land cover types around and within lakes is essential for monitoring and assessing biodiversity. Remote sensing data provides abundant information to analyze and monitor such ecosystems. In this study, Pulicat Lake, an ecologically significant zone influenced by both riverine and marine inputs is selected as region of interest for land cover mapping. The land cover classification is carried out using the spectral bands (1-7). To enhance the classification performance a localized approach, Segmented Principal Component Analysis (SPCA) is employed to generate an accurate land cover map of the pulicat region. The accuracy assessment is carried out using stratified random sampling method. Compared to the spectral band classification method which yielded overall accuracy of 87.25% and kappa coefficient of 0.83, the SPCA method achieved superior classification results with an overall accuracy of 90.52% and kappa coefficient of 0.90 respectively. The results emphasize that the SPCA method enhances the separability of spectrally mixed classes in the land cover classification of complex ecosystems.