Global Urban Footprint (GUF) by DLR (Thomas Esch) for Italy to Croatia.
The Global Urban Footprint by DLR (Thomas Esch) has been released and provides a global coverage of urbanized areas. Previous versions of this data set has already been used by ongoing research in our department and we will now update the data for our scientific work. It is a great source for mapping human impact on a global scale. From the DLR website: Currently, more than half of the world’s population are urban dwellers and this number is still rapidly increasing. Since settlements – and urban areas in particular – represent the centers of human activity, the environmental, economic, political, societal and cultural impacts of urbanization are far-reaching. They include negative aspects like the loss of natural habitats, biodiversity and fertile soils, climate impacts, waste, pollution, crime, social conflicts or transportation and traffic problems, making urbanization to one of the most pressing global challenges. Accordingly, a profound understanding of the global spatial distribution and evolution of human settlements constitutes a key element in envisaging strategies to assure sustainable development of urban and rural settlements.
In this framework, the objective of the “Global Urban Footprint” (GUF) project is the worldwide mapping of settlements with unprecedented spatial resolution of 0.4 arcsec (~12 m). A total of 180 000 TerraSAR-X and TanDEM-X scenes have been processed to create the GUF. The resulting map shows the Earth in three colors only: black for “urban areas”, white for “land surface” and grey for “water”. This reduction emphasizes the settlement patterns and allows for the analysis of urban structures, and hence the proportion of settled areas, the regional population distribution and the arrangement of rural and urban areas. More details at: www.dlr.de/guf
Further data portals and visualizations are available here:
Via U-TEP Website: https://urban-tep.eo.esa.int
U-TEP Geobrowser: https://urban-tep.eo.esa.int/geobrowser/?id=guf
and a ESA GUF article: http://www.esa.int/Our_Activities/Observing_the_Earth/New_map_offers_precise_snapshot_of_human_life_on_Earth
The MSc thesis by Asja Bernd titled “Mind the Gap: A Global Analysis of Grassland Fragmentation using MODIS Land Cover Data” is handed in. Very interesting results on global grassland fragmentation. Read the abstract:
Around the world, grassland and savannah ecosystems are under intense anthropogenic use, yet research has not given them much attention. One significant threat is fragmentation, reducing habitat connectivity and hindering species dispersal. Using MODIS land cover data from 2012, combined with infrastructure data derived from VMAP0 and OpenStreetMap, I assessed the fragmentation of grasslands on a global scale. The metrics applied were patch size, distance to the euclidean nearest neighbour and number of neighbours per patch. To quantify the contribution of human pressure to fragmentation, the results were correlated with the Human Influence Index and human population density. For subsets, selected from the Global 200 Ecoregions, I analysed land cover data from 2001 and 2012 to determine
trends over time. Globally, grasslands are highly fragmented by infrastructure, which reduced patch size by more than 50 %, and significantly increased isolation. Human pressure seems to act as a driver of fragmentation, diminishing patch size and the number of neighbours, while increasing the distance to neighbours. For the subsets, results varied, but two of three metrics indicated an increase in fragmentation between 2001 and 2012. In the face of declining migrations of terrestrial mammals and increasing human pressure, a better understanding of the effects of fragmentation is needed to develop adequate management and protection strategies.
Supervisor: Prof. Neil Burgess and Dr. Martin Wegmann
Asja Bernd started her MSc on ” A Global Analysis of Grassland Fragmentation” supervised by Neil Burgess (UNEP) and Martin Wegmann. She will use globally available land cover data sets and compute a variety of spatial metrics to derive the fragmentation pattern of grasslands. Methods available in R and GRASS will be applied to allow a semi-automatic global analysis. Asja is a student in the Global Change Ecology MSc program and well trained in journalism as well as natural sciences.