Estimates of solar radiation distribution in urban areas are often limited by the complexity of urban environments. These limitations arise from spatial structures such as buildings and trees that affect spatial and temporal distributions of solar fluxes over urban surfaces. The traditional solar radiation models implemented in GIS can address this problem only partially. They can be adequately used only for 2‐D surfaces such as terrain and rooftops. However, vertical surfaces, such as facades, require a 3‐D approach. This study presents a new 3‐D solar radiation model for urban areas represented by 3‐D city models. The v.sun module implemented in GRASS GIS is based on the existing solar radiation methodology used in the topographic r.sun model with a new capability to process 3‐D vector data representing complex urban environments. The calculation procedure is based on the combined vector‐voxel approach segmenting the 3‐D vector objects to smaller polygon elements according to a voxel data structure of the volume region. The shadowing effects of surrounding objects are considered using a unique shadowing algorithm. The proposed model has been applied to the sample urban area with results showing strong spatial and temporal variations of solar radiation flows over complex urban surfaces.
Multi-temporal synthetic aperture radar interferometry techniques (MT-InSAR) are nowadays a well-developed remote sensing tool for ground stability monitoring of areas afflicted by natural hazards. Its application capability has recently been emphasized by the Sentinel-1 satellite mission, providing extensive spatial coverage, regular temporal sampling and free data availability. We perform MT-InSAR analysis over the wider Upper Nitra region in Slovakia, utilizing all Sentinel-1 images acquired since November 2014 until March 2017. This region is notable for its extensive landslide susceptibility as well as intensive brown coal mining. We focus on two case studies, being impaired by recent activation of these geohazards, which caused serious damage to local structures. We incorporate a processing chain based on open-source tools, combining the current Sentinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS) implementation. MT-InSAR results reveal substantial activity at both case studies, exceeding the annual displacement velocities of 30 mm/year. Moreover, our observations are validated and their accuracy is confirmed via comparison with ground truth data from borehole inclinometers and terrestrial levelling. Detected displacement time series provide valuable insight into the spatio-temporal evolution of corresponding deformation phenomena and are thus complementary to conventional terrestrial monitoring techniques. At the same time, they not only demonstrate the feasibility of MT-InSAR for the assessment of remediation works, but also constitute the possibility of operational monitoring and routine landslide inventory updates, regarding the free Sentinel-1 data.
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