Linking ecosystem services, urban form and green space configuration using multivariate landscape metric analysis

Context: Landscape metrics represent powerful tools for quantifying landscape structure, but uncertainties persist around their interpretation. Urban settings add unique considerations, containing habitat structures driven by the surrounding built-up environment. Understanding urban ecosystems, however, should focus on the habitats rather than the matrix. Objectives: We coupled a multivariate approach with landscape metric analysis to overcome existing shortcomings in interpretation. We then explored relationships between landscape characteristics and modelled ecosystem service provision. Methods: We used principal component analysis and cluster analysis to isolate the most effective measures of landscape variability and then grouped habitat patches according to their attributes, independent of the surrounding urban form. We compared results to the modelled provision of three ecosystem services. Seven classes resulting from cluster analysis were separated primarily on patch area, and secondarily by measures of shape complexity and inter-patch distance. Results: When compared to modelled ecosystem services, larger patches up to 10 ha in size consistently stored more carbon per area and supported more pollinators, while exhibiting a greater risk of soil erosion. Smaller, isolated patches showed the opposite, and patches larger than 10 ha exhibited no additional areal benefit. Conclusions: Multivariate landscape metric analysis offers greater confidence and consistency than analysing landscape metrics individually. Independent classification avoids the influence of the urban matrix surrounding habitats of interest, and allows patches to be grouped according to their own attributes. Such a grouping is useful as it may correlate more strongly with the characteristics of landscape structure that directly affect ecosystem function

TERRASAR-X AND RAPIDEYE DATA FOR THE PARAMETERISATION OF RELATIONAL CHARACTERISTICS OF URBAN ATKIS DLM OBJECTS

This work presents a multi-sensor data analysis concept for the parameterisation of urban landuse in comparison to ATKIS DLM reference objects (digital landscape model). An object based top-down approach is implemented and the potential of multisensor data for a primary urban landcover object classification is assessed. Urban landuse structure is developed based on relational features applied to land cover objects and compared to an aggregated DLM class legend. For a better imperviousness description an advanced imperviousness measure – the build-up impervious intensity ratio is developed that takes building height and derived floor area (based on LiDAR data) and the amount of vegetation within a search radius to every building into account. The developed concept is parameterized with test areas in Rostock and transferability is investigated with data coverage in Cologne (Germany). The work is linked to the urban work package of the DLR funded ENVILAND-2 project that aims to develop operational concepts for the use of TerraSAR-X and Rapideye data in urban mapping scenarios

Multi-Modal and Multi-Temporal Data Fusion: Outcome of the 2012 GRSS Data Fusion Contest

The 2012 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society (GRSS) aimed at investigating the potential use of very high spatial resolution (VHR) multi-modal/multi-temporal image fusion. Three different types of data sets, including spaceborne multi-spectral, spaceborne synthetic aperture radar (SAR), and airborne light detection and ranging (LiDAR) data collected over the downtown San Francisco area were distributed during the Contest. This paper highlights the three awarded research contributions which investigate (i) a new metric to assess urban density (UD) from multi-spectral and LiDAR data, (ii) simulation-based techniques to jointly use SAR and LiDAR data for image interpretation and change detection, and (iii) radiosity methods to improve surface reflectance retrievals of optical data in complex illumination environments. In particular, they demonstrate the usefulness of LiDAR data when fused with optical or SAR data. We believe these interesting investigations will stimulate further research in the related areas