Urban green is indispensable from an urban ecological and social point of view and fulfils important functions such as dust binding, temperature reduction, wind damping or groundwater recharge. Especially for bioclimatic modeling, knowledge of size, structure and green volume of the urban vegetation is essential. Manual mapping of vegetation structures is timeconsuming and cost-intensive and can only ever be carried out in locally limited study areas. Active and passive remote sensing technologies in combination with automated methods for information extraction offer the opportunity to record the green structure in urban areas differentiated according to vegetation types. The new globally and freely available data provided by the European Copernicus Program raises the question whether these data are suitable for mapping and quantifying the urban green structure, including an accuracy estimation. Previous studies on the usability of Sentinel-2 data for vegetation analysis were essentially limited to crop and tree species classification in open space. The approach presented here thus considers for the first time the application of this data in a purely urban environment. Here we present a modeling approach based on multiple regression models. A Sentinel-2A scene from July 4, 2015 covering the greater Dresden area served as the input data set. After atmospheric correction of the satellite image scene 10 spectral channels were available. A high-resolution vegetation cover model with a grid width of 50 cm was available as a reference data set for the entire study area (City of Dresden, Germany). This takes into account the vegetation classes deciduous trees, conifers, shrubs, low (grassy) vegetation and arable land. Thus the area share of these vegetation types could be determined aggregated for each pixel of the satellite image scene. In addition, vegetation indices (NDVI and others) were calculated using suitable channels. For the prediction of each vegetation class, estimation equations were drawn up and evaluated with regard to their quality. Especially for deciduous and coniferous trees, satisfactory model quality values could be obtained, so that the green component prediction in these cases represents a useful basis for the determination of the green structure at the building block level in urban areas.
The international project "Geo-Archaeology in the Steppe - Reconstruction of Cultural Landscapes in the Orkhon valley,
Central Mongolia" was set up in July 2008. It is headed by the Department of Pre- and Protohistoric Archaeology of
Bonn University. The project aims at the study of prehistoric and historic settlement patterns, human impact on the
environment and the relation between towns and their hinterland in the Orkhon valley, Central Mongolia. The
multidisciplinary project is mainly sponsored for three years by the German Federal Ministry of Education and Research
(BMBF) and bridges archaeology, natural sciences and engineering (sponsorship code 01UA0801C). Archaeologists of
the Mongolian Academy of Sciences and of the Bonn University, geographers of Free University Berlin, geophysics of
the Institute for Photonic Technology Jena and the RWTH Aachen University, and geographers and engineers of the
German Aerospace Centre Berlin collaborate in the development of new technologies and their application in
archaeology1. On the basis of Russian aerial photographs from the 1970s, an initial evaluation regarding potential
archaeological sites was made. Due to the poor geometric and radiometric resolution of these photographs, identification
of archaeological sites in many cases remained preliminary, and detailed information on layout and size could not be
gained. The aim of the flight campaign in September 2008 was therefore the confirmation of these sites as well as their
high resolution survey. A 10 megapixel range finder camera was used for the recording of high resolution aerial
photography. This image data is suited for accurate determination and mapping of selected monuments. The airborne
camera was adapted and mounted on an electrically operated eight propeller small drone. Apart from high resolution
geo-referenced overview pictures, impressive panoramic images and very high resolution overlapping image data was
recorded for photogrammetric stereoscopic processing. Due to the overlap of 85% along and across the track each point
in the image data is recorded in at least four pictures. Although a smaller overlap might be sufficient for generating
digital surface models (DSM), this redundancy increases the reliability of the DSM generation. Within this
photogrammetric processing digital surface models and true ortho photo mosaics with a resolution up to 2,5 cm/pixel in
X, Y, Z are derived.
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