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Sandbank morphology is a longshore corridor with fast morphological changes. To analyze its changes, unmanned aerial vehicle (UAV) is the preferred effective and flexible data collection platform, which can collect very high resolution images. UAV campaigns along corridor areas require well-distributed ground control points (GCP) and accurate geotagged image positions to avoid misalignment errors of generated digital surface models (DSMs). However, a sandy terrain can make the measurement of GCPs difficult or even impossible. Furthermore, UAVs are often equipped with consumer-grade devices (onboard GNSS and nonmetric cameras), which, therefore, cannot provide accurate image positions and lead to geometric distortions of the image network due to inappropriate lens distortion corrections. This article proposes a strategy to calibrate the used camera and co-register multitemporal images without the need for accurate geotagged information on the whole datasets and well-distributed GCPs. The proposed strategy includes two improvements. First, we performed semi-on-the-job self-calibration (semi-OTJSC) using UAV images with favor orientations and flight altitudes to precalibrate interior orientation parameters (IOPs) and additional lens distortion parameters. This was then followed by another OTJSC of IOPs involving images with accurate geotagged positions. Second, a “transferred aerial-triangulation (Trans-AT)” strategy is proposed to co-register two consecutive UAV datasets through AT procedure similar to GNSS-supported AT within a strip image block. The experimental results revealed that the proposed method provides the best-fit camera parameters and generates high-accuracy co-registered DSMs. This strategy contributes significantly to multitemporal camera calibration and co-registration procedures for determining morphological changes in corridor mapping.