<p>Climate change remains one of the crucial global concerns, impacting ecosystems, economies, and human communities in profound ways. Accurately forecasting climate trends is essential for developing strong strategies to mitigate risks and adapt to changing conditions. Remote sensing technology, which provides extensive and continuous data on the Earth's surface, is a key tool for tracking and predicting these transformations. However, traditional models, often complex, validate vast amounts of remote sensing data, limiting their effectiveness. Given the ongoing environmental crisis, it is vital to efficiently and affordably monitor subtle shifts in biosphere across appropriate spatial and temporal scales. To address these challenges, diverse methods are introduced to maximize system accuracy. Thus, a new and adaptive model is proposed for classifying the global conservative efforts using satellite images. Primarily, the required sets of raw images are collected among the standard datasets. Subsequently, these data are given as input to the spatio-temporal cross-attention-based adaptive contrastive learning with residual long short-term memory to provide a better result. For further enhancement, the hyper-parameters in the network are optimally selected using the Addax optimization algorithm with random integer amendment strategy. Finally, the proposed work is assessed using distinct performance measures and contrasted over different traditional models. Hence, the desired value is attained to ensure the system's efficacy in managing global conditions.</p>