Urban green valuation integrating biophysical and qualitative aspects

Abstract

Urban green mapping has become an operational task in city planning, urban land management, and quality of life assessments. As a multi-dimensional, integrative concept, urban green comprising several ecological, socio-economic, and policy-related aspects. In this paper, the author advances the representation of urban green by deriving scale-adapted, policy-relevant units. These so-called geons represent areas of uniform green valuation under certain size and homogeneity constraints in a spatially explicit representation. The study accompanies a regular monitoring scheme carried out by the urban municipality of the city of Salzburg, Austria, using optical satellite data. It was conducted in two stages, namely SBG_QB (10.2 km², QuickBird data from 2005) and SBG_WV (140 km², WorldView-2 data from 2010), within the functional urban area of Salzburg. The geon delineation was validated by several quantitative measures and spatial analysis techniques, as well as ground documentation, including panorama photographs and visual interpretation. The spatial association pattern was assessed by calculating Global Moran’s I with incremental search distances. The final geonscape, consisting of 1083 units with an average size of 13.5 ha, was analyzed by spatial metrics. Finally, categories were derived for different types of functional geons. Future research paths and improvements to the described strategy are outlined.

Spatiotemporal Analysis of Urban Green Areas Using Change Detection: A Case Study of Kharkiv, Ukraine

“…By the way, one of the most surprising findings is the insignificant USL in this suburban part of the latter ward with few USL parcels related to the period of 2006-2009 (Figure 13A). Spatial comparison of the green LU types with the classified USL centroids proves some conclusions made above already: the largest losses of UGAs were observed in -2006, 2006-2018.…”

“…The fifth and the fourth USL spatial classes appeared only fragmentarily to the South and South-West of the "Lesopark" area (again Shevchenkivskyi, but also Kholodnohirskyi wards), which can be explained by the large urban forest presence not far from city area prove sustainable long-term dynamics of UGAs in Kharkiv, keeping some balance between their expansion and shrinking, but with minor trending to the latter one. The latest illustration confirms the USL extremums in -2006, 2006-2018.…”

“…Using only GIS without updated satellite or airborne data could produce limited results and conclusions (Gemann-Chiari and Seeland, 2004;Ragab, 2014). Mentioned parameters can be obtained only as a united result, as we already emphasized above, if applying remotely sensed imageries and various options of GIS functionality, with which the descriptors that are efficiently useful in objective evaluation of spatial green and built-up patterns and their relationships can be obtained (Senanayake et al, 2013;Chen et al, 2014;Du et al, 2017;Lang, 2018;Batool et al, 2019).…”

“…Using QuickBird imageries, these authors provided the object-based classification approach and then calculated some weighted indexes as a parameter describing the ratio between the builtup and green areas. This approach was then developed further in relevant studies that presented the definition of green indexes and outlined a neighborhood level for the index computing because a neighborhood is an areal outline for greening practice (Gupta et al, 2012), while monitoring of UGAs in Salzburg was continued after employing QuickBird by WorldView-2 (2010) and Pleiades-A (2015) resulting imageries (Lang, 2018). These examples that illustrate extracting green regularities from the satellite data processed by GIS present representative case studies for the content of our study.…”

OBIA4RTM – towards an operational open-source solution for coupling object-based image analysis with radiative transfer modelling

“…In optical remote sensing, object-based image analysis (OBIA) methods are increasingly used, which employ segmentation techniques to split satellite images into objects and analyse them instead of individual pixels (Blaschke, 2010;Blaschke et al, 2014). Image objects are defined as aggregates of neighbouring pixels characterized by a given level of homogeneity , which can be determined based on internal variance or spatial auto-correlation (Lang, 2018). Besides that, existing data such as field parcel boundaries or mapped biotopes can serve as spatial constraints for the object boundaries.…”