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Post-mining sites have a significant impact on surrounding ecosystems. Afforestation can restore these ecosystems, but its success and speed depends on the properties of the excavated spoil substrates. Thermal infrared remote sensing brings advantages to the mapping and classification of spoil substrates, resulting in the determination of its properties. A library of spoil substrates containing spectral emissivity and chemical properties can facilitate remote sensing activities. This study presents spectral library of spoil substrates' emissivities extracted from brown coal mining sites in the Czech Republic. Extracted samples were homogenized by drying and sieving. Spectral emissivity of each sample was determined by spectral smoothing algorithm applied to data measured by a Fourier transform infrared (FTIR) spectrometer. A set of chemical parameters (pH, conductivity, Na, K, Al, Fe, loss on ignition and polyphenol content) and toxicity were determined for each sample as well. The spectral library presented in this paper also offers valuable information in the form of geographical coordinates for the locations where samples were obtained. Presented data are unique in nature and can serve many remote sensing activities in longwave infrared electromagnetic spectrum.

Commission II, ThS 17 -Smart cities KEY WORDS: Visualization and spatial analysis of urban phenomena, Airborne laser scanning, Hyperspectral thermal imagery, City structure, Thermal regime, Urban heat island. ABSTRACT:Good understanding of a city's thermal regime and its dependency on the structure of the city provides key knowledge serving as an input for long-term strategic decision-making by local governments. The urban heat island, and more specifically overheating of the streets and adjacent buildings during summer heat waves, has been pointed out as an important issue in the city of Brno, Czech Republic. A complex research effort using remote sensing techniques has started which will analyse the impact of city structure on the thermal behaviour, principally the role of vegetation in the thermal regulation of streets. Two airborne data sets were acquired: hyperspectral data using CASI, SASI and TASI sensors (ITRES, Canada) and lidar mapping using a Riegl 680i instrument (RIEGL, Austria). The thermal data were acquired on two occasions: 7 February 2015 (winter season) and 4 July 2015 (summer season).A laser scanning data-set was acquired on 22 September 2015 with a point cloud density of approximately 15 points/m 2 . Surface temperature was retrieved from the thermal hyperspectral data by applying a temperature-emissivity separation algorithm. The 3-D structure of the city was classified from the laser scanning data; we distinguished three main classes: bare land, buildings and vegetation. In the paper we present figures comparing thermal behaviour with other features collected along linear transects through the central part of the city.