Remote sensing has become the most important data source for the digital elevation model (DEM) generation. DEM analyses can be applied in various fields and many of them require appropriate DEM visualization support. Analytical hill-shading is the most frequently used relief visualization technique. Although widely accepted, this method has two major drawbacks: identifying details in deep shades and inability to properly represent linear features lying parallel to the light beam. Several authors have tried to overcome these limitations by changing the position of the light source or by filtering. This paper proposes a new relief visualization technique based on diffuse, rather than direct, illumination. It utilizes the sky-view factor—a parameter corresponding to the portion of visible sky limited by relief. Sky-view factor can be used as a general relief visualization technique to show relief characteristics. In particular, we show that this visualization is a very useful tool in archaeology as it improves the recognition of small scale features from high resolution DEMs.

Iron is a key limiting micro-nutrient for marine primary productivity. It can be supplied to the ocean by atmospheric dust deposition. Volcanic ash deposition into the ocean represents another external and so far largely neglected source of iron. This study demonstrates strong evidence for natural fertilisation in the iron-limited oceanic area of the NE Pacific, induced by volcanic ash from the eruption of Kasatochi volcano in August 2008. Atmospheric and oceanic conditions were favourable to generate a massive phytoplankton bloom in the NE Pacific Ocean which for the first time strongly suggests a connection between oceanic iron-fertilisation and volcanic ash supply.

Aerial mapping and remote sensing takes another step forward with this method of modelling lidar data. The usual form of presentation, hill shade, uses a point source to show up surface features. Sky-view factor simulates diffuse light by computing how much of the sky is visible from each point. The result is a greatly improved visibility — as shown here by its use on a test site of known topography in Slovenia.

Monitoring of (surface) urban heat islands (UHI) is possible through satellite remote sensing of the land surface temperature (LST). Previous UHI studies are based on medium and high spatial resolution images, which are in the best-case scenario available about four times per day. This is not adequate for monitoring diurnal UHI development. High temporal resolution LST data (a few measurements per hour) over a whole city can be acquired by instruments onboard geostationary satellites. In northern Germany, geostationary LST data are available in pixels sized 3,300 by 6,700 m. For UHI monitoring, this resolution is too coarse, it should be comparable instead to the width of a building block: usually not more than 100 m. Thus, an LST downscaling is proposed that enhances the spatial resolution by a factor of about 2,000, which is much higher than in any previous study. The case study presented here (Hamburg, Germany) yields promising results. The latter, available every 15 min in 100 m spatial resolution, showed a high explained variance (R²: 0.71) and a relatively low root mean square error (RMSE: 2.2 K). For lower resolutions the downscaling scheme performs even better (R²: 0.80, RMSE: 1.8 K for 500 m; R²: 0.82, RMSE: 1.6 K for 1,000 m).