We propose a robust method for registering high oblique images of landscapes. Typically, an input image can be registered by matching it against a set of registered images of the same location. While this has been shown to work very well for images of popular urban landmarks, registering landscape images remains a very challenging task: For a given place, only a very small amount of registered images is generally already available on photo-sharing platforms. Moreover, the appearance of landscapes can vary drastically depending on the season and the weather conditions. For these two reasons, matching the input images with registered images in a reliable way remains a challenging task. Our contribution is two-fold: first, we show how to estimate the camera orientation for images with GPS data using a novel algorithm for horizon matching based on Dynamic Time Warping. The proposed algorithm exploits an elevation model. Each image is processed independently from the others, there is therefore no need neither for image matching or for a large set of images. This step provides a set of reliable, fully registered images. Second, and in odrer to register new images with no GPS data available, we first ask the user to provide an approximate image localization on a 2D map. Then, we exploit this prior on the camera location to efficiently and robustly constrain and guide the matching process used to register the query image. We apply our method to a case study from the Zermatt area in Southern Switzerland, and show that the method provides registrations, which are accurate enough to map each pixel to an aerial map.