Automated GIS-based derivation of urban ecological indicators using hyperspectral remote sensing and height information

Abstract

Urban ecological indicators allow the objective and quantitative characterisation of ecological conditions in a spatially continuous way by evaluating the influence of urban surface types with respect to ecological functions and ecosystem services. Although the concept had already been developed in the 1980s, the variety of existing indicators had not been widely applied yet in urban planning practice, because of the high manual mapping effort that is required for spatially differentiated urban surface mapping. This paper presents a new automated remote sensing and GIS-based system for the flexible and user-defined derivation of urban ecological indicators. The system is based on automated surface material mapping using airborne hyperspectral image data and height information. were validated using results from field-based reference mapping that had been carried out for selected city blocks situated in both cities. An accuracy assessment for these reference city blocks has revealed mean errors of approximately 4 %, confirming the suitability of the developed automated GIS-based system for flexible and efficient indicator calculation.

Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices

“…Remote sensing satellite imageries provide a timely and cost-effective approach for mapping built-up of vegetation and impervious surfaces (Arnold and Gibbons, 1996;Matthias and Martin, 2003;Behling et al, 2015). Impervious surfaces are in general defined as any material, natural or man-made, that prevents the infiltration of surface water into the soil (Arnold and Gibbons, 1996;Matthias and Martin, 2003;Weng, 2012).…”

Is lack of space a limiting factor for the development of aquaculture in EU coastal areas?

“…in the water resource management in agriculture (Rahimi et al, 2015), in the restoration of wetlands (Newcomer et al, 2014), in crop-monitoring (Wu et al, 2014), in pesticide exposure modelling for large areas (Wan, 2015) or in urban planning by automated sensing of ecological indicators (Behling et al, 2015).…”

Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

“…Numerous studies are available for estimation and mapping of impervious urban areas using remote sensing methods; Weng [6] has presented a comprehensive review of remote sensing methods for the classification of impervious urban surfaces. Studies characterizing urban surfaces using spectral information of objects are popular (to mention a few, [9,10,[31][32][33]). Initially, a high-resolution multispectral imaging has been used to discriminate urban surfaces using object-based image classification [34,35]; however, further development in the hyperspectral sensor has raised the discriminating power of an image [31].…”

“…Studies characterizing urban surfaces using spectral information of objects are popular (to mention a few, [9,10,[31][32][33]). Initially, a high-resolution multispectral imaging has been used to discriminate urban surfaces using object-based image classification [34,35]; however, further development in the hyperspectral sensor has raised the discriminating power of an image [31]. HSI suffers from autocorrelation and data redundancy in the adjacent wavebands [36,37], which leads to needing for optimal selection of bands and a powerful classifier.…”