A block compressed sensing with projected Landweber (BCS-PL) framework that incorporates the universal measurement and projected-Landweber iterative reconstruction is summarized. Based on the BCS-PL framework, an improved reconstruction algorithm for aerial imagery: block compressed sensing with adaptive-thresholding projected Landweber (BCS-ATPL), which leverages a piecewise-linear thresholding model for wavelet-based image denoising, is presented. Through analyzing the functional relation between the thresholding factors and sampling subrates, the proposed adaptive-thresholding model can effectively remove wavelet-domain noise of bivariate shrinkage. For the reconstruction quality of aerial images, experimental results demonstrate that the proposed BCS-ATPL algorithm consistently outperforms several existing BCS-PL reconstruction algorithms. With the experiment-driven methodology, the BCS-ATPL algorithm can preserve better reconstruction quality at a competitive computational cost, which makes it more desirable for aerial imagery applications.