Unmanned aerial vehicle (UAV) perception and environmental monitoring are two low-altitude commercial applications where light field imaging technology shows significant promise by simultaneously capturing the spatial and angular information of light rays. However, existing light field cameras and super-resolution methods struggle to jointly optimize spatial and angular resolutions, limiting their effectiveness in tasks requiring precise perception and high-fidelity reconstruction. To address this challenge, this paper proposes a light field Gaussian splatting representation and a light field angular spatial super-resolution (LFASSR) method tailored for low-altitude scenarios. Specifically, we extend the conventional 3D Gaussian splatting framework into the 4D light field domain and propose a 4D light field Gaussian splatting representation. Leveraging the geometric structure of light field epipolar plane images (EPIs), we decompose the 4D representation into cascaded 2D Gaussian splatting models along horizontal and vertical directions, enabling more efficient modeling of light field geometry. Based on this representation, we develop a unified light field super-resolution framework. The framework first extracts and fuses features from the spatial, angular, and EPI domains, then constructs directional geometric feature spaces for horizontal and vertical views, where 2D Gaussian splatting is applied separately. Finally, high-quality dense-view light field images with enhanced spatial resolution are rendered via EPI-based synthesis. The suggested methodology greatly outperforms state-of-the-art methods in joint spatial-angular super-resolution, as shown by extensive experimentation on public light field benchmarks and a recently gathered low-altitude light field dataset. It achieves the best performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), while also producing visually superior results with sharp detail recovery and smooth, natural disparity transitions. Moreover, the method exhibits strong generalization capability and competitive performance in standalone light field spatial super-resolution (LFSSR) and light field angular super-resolution (LFASR) tasks.