MUAS 2015 > Session details
Paper 22 - Session title: Global
11:00 Global Urban Footprint – A Key Step in Characterizing the Global Human Settlements Pattern from Space
Esch, Thomas; Heldens, Wieke; Hirner, Andreas; Keil, Manfred; Marconcini, Mattia; Roth, Achim; Zeidler, Julian German Aerospace Center (DLR), Germany
The ongoing global phenomenon of people migrating to cities is referred to as urbanization and primarily manifests itself in the continuous and often rapid spatial expansion of urban agglomerations. Nevertheless, the dimension and structuring behind this process can be considered as a spatial continuum ranging from rural to urban settlements. Accordingly, gathering a detailed global knowledge about the size, form (e.g., compact or spread) and spatial distribution (e.g., dispersed or nucleated) of different types of settlements represents a major issue to better understand urbanization and develop effective mitigation, adaptation and management strategies. The German TanDEM-X mission has collected two global coverages of very high resolution synthetic aperture radar (SAR) X-band imagery with a spatial resolution of about three meters in the years 2011/2012 and 2012/2013. This radar data set provides Earth observation imagery in an extraordinary spatial detail that outmatches any existing global coverage with optical data. In this paper we present i) the technique implemented to identify and map human settlements in a so far unique spatial detail of ~12m, and ii) demonstrate the potential to characterize human settlements properties and patterns as well as the urban morphology (e.g., in terms of building height) using the worldwide data available from TanDEM-X mission. The basic approach towards the fully-automatic detection and delineation of built-up areas from very high resolution SAR imagery is presented by describing the corresponding operational processing environment – the Urban Footprint Processor (UFP) - and the resulting geo-information product - the Global Urban Footprint (GUF) settlement mask. Along with this, we introduce a procedure that exploits the information provided by the GUF settlement mask to facilitate an effective spatial and structural characterization of human settlement forms and patterns from the local to the continental scale. Moreover, we present an outlook on add-on products describing the built-up density and the average building height within the human settlements delineated in the GUF. These analyses are based on optical imagery (built-up density) and a digital elevation data generated in the context of the TanDEM-X mission. The results of the study indicate the high potential of the developed geo-information products to support the analysis of urbanization patterns, the urban-rural continuum, peri-urbanization, spatiotemporal dynamics of settlement development as well as population estimation, vulnerability assessment and global change modeling.
Paper 26 - Session title: Global
11:20 The Global Human Settlement Layer
Pesaresi, Martino; Ehrlich, D.; Ferri, S.; Florczyk, A.; Freire, S.; Halkia, M.; Julea, A.; Kemper, T.; Soille, P.; Syrris, V. EC JRC, Italy
The project GLOB-HS “Global Human Settlement Analysis for Disaster Risk Reduction” is supported by the EC JRC in the frame of the institutional research activities for the years 2014-2016. Scope of GLOB-HS is developing, testing and applying the technologies and analysis methods integrated in the JRC Global Human Settlement Analysis Platform (GHSAP) for applications in support to global disaster risk reduction initiatives (DRR). GHSAP uses geo-spatial data, primarily remotely sensed and population. GLOB-HS also cooperates with the Group on Earth Observation on SB-04-Global Urban Observation and Information, Int. partners (WB, RADI, SANSA) and the UN (incl. UNISDR on Global Assessment Reporting on DRR).
The GHS information production system was successfully tested with a globally representative set of satellite data in the metric spatial resolution range (0.5to10m-resolution), and successfully applied for the production of the first continental analysis of European built-up areas using 2.5-m-resolution input image data. During 2014 GLOB-HS delivered the preliminary results on processing of global multi-temporal satellite data for analysis of human settlement, using decametric spatial resolution imageries collected from the Landsat platform in the past 40 years. A GHS partnership was launched on 22 October 2014 at the end of a two-day workshop hosted by the JRC with the support of the Group of Earth Observation (GEO). To mark the event, a manifesto was signed. It calls for a collaborative and integrated approach to advance knowledge on GHS and an understanding on how they are changing. The increasing capabilities of Earth Observation satellites combined with rapid advances in geospatial sciences, analytical methods and computing power have made possible detailed, measurable and globally consistent descriptions of the human-made habitat. The manifesto promotes full and open access to the data offered through these advances and to the GHS information generated from them. The partnership will support initiatives such as the concurrent post-2015 processes on sustainable development, climate change and disaster risk as well as the UN Third Conference on Housing and Sustainable Urban Development (Habitat III, 2016).
The GHS data update and improvement will be supported by EC using Sentinel1,2 global input data. A pre-operational phase is foreseen in the years 2016-2018. If successful, this phase will bring to the definition of a new Copernicus service in 2018+. Preliminary results of the application of the GHSL automatic information extraction workflow to Sentinel 1,2 data will be presented.
Paper 50 - Session title: Global
12:20 Mapping Infrastructure and Population for Disaster Planning in Urban Areas with Remote Sensing and Census Data
Yetman, Gregory George (1); MacManus, Kytt (2); Doxsey-Whitfield, Erin (3); Chen, Robert S (4) 1: Columbia University, United States of America; 2: Columbia University, United States of America; 3: Columbia University, United States of America; 4: Columbia University, United States of America
Understanding the interactions between environmental and human systems, and in particular supporting the applications of Earth science data and knowledge in place-based decision making, requires systematic assessment of the distribution and dynamics of human population and the built human infrastructure in conjunction with environmental variability and change. The NASA Socioeconomic Data and Applications Center (SEDAC) operated by the Center for International Earth Science Information Network (CIESIN) at Columbia University has had a long track record in developing reference data layers for human population and settlements and is expanding its efforts on topics such as intercity roads, reservoirs and dams, and energy infrastructure. SEDAC has set as a strategic priority the acquisition, development, and dissemination of data resources derived from remote sensing and socioeconomic data on urban land use change, including temporally and spatially disaggregated data on urban change and rates of change, the built infrastructure, and critical facilities.
We report here on a range of past and ongoing activities, including the Global Human Settlements Layer effort led by the European Commission’s Joint Research Centre (JRC), the Global Exposure Database for the Global Earthquake Model (GED4GEM) project, the Global Roads Open Access Data Working Group (gROADS) of the Committee on Data for Science and Technology (CODATA), and recent work with ImageCat, Inc. to improve estimates of the exposure and fragility of buildings, road and rail infrastructure, and other facilities with respect to selected natural hazards. New efforts such as the proposed Global Human Settlement indicators initiative of the Group on Earth Observations (GEO) could help fill critical gaps and link potential reference data layers with user needs. We highlight key sectors and themes that require further attention, and the many significant challenges that remain in developing comprehensive, high quality, up-to-date, and well maintained reference data layers on population and built infrastructure. The need for improved indicators of sustainable development in the context of the post-2015 development framework provides an opportunity to link data efforts directly with international development needs and investments.
Paper 57 - Session title: Global
12:00 EO4Urban: Multitemporal Sentinel-1A SAR and Sentinel-2A MSI Data for Global Urban Services
Ban, Yifang (1); Gamba, Paolo (2) 1: KTH Royal Institute of Technology, Sweden; 2: University of Pavia, Italy
With more than half of the world population now living in cities, and 2.5 billion more people expected to move into cities by 2050, urban areas pose significant challenges on the environment. Although only a small percentage of global land cover, urban areas significantly alter climate, biogeochemistry, and hydrology at local, regional, and global scales. Thus, accurate and timely information on urban land cover and their changing patterns is of critical importance to support sustainable urban development. At present, the information urban planners and decision makers needed to support planning activities are either non-existent, dated or collected through time-consuming field survey or visual interpretation of images. Through its synoptic view and the repeatability, satellite remote sensing can provide timely and accurate information necessary to map urban land cover and monitor urbanization. With the recent launch of Sentinel-1A and planned launch of Sentinel-2A in 2015, high resolution SAR and optical data with global coverage and operational reliability become routinely available. They provide excellent opportunity to develop novel methods and algorithms for operational urban services and products to support smart and sustainable planning.
The overall objective of this research is to evaluate multitemporal Sentinel-1A SAR and Sentinel-2A MSI data for global urban services using innovative methods and algorithms, namely KTH-SEG, a novel object-based classification method for detailed urban land cover mapping, and KTH-Pavia Urban Extractor, a robust algorithm for urban extent extraction. Ten cities around the world in different geographical and environmental conditions are selected as study areas. Sentinel-1A SAR and Sentinel-2A optical data will be acquired during vegetation season in 2015 and 2016. Historical ENVISAT ASAR and ERS-1/2 SAR data will be selected from the archives for monitoring of urban development. KTH-SEG, an advanced segmentation method will be further developed for multiresolution segmentation of Sentinel-1A SAR and Sentinel-2A MSI data based on edge-aware region growing and merging algorithm using parallel computing. The post-segmentation classification is performed using support vector machines. KTH-Pavia Urban Extractor, the proposed processing chain for urban extent extraction includes urban extraction based on spatial indices and Grey Level Co-occurrence Matrix (GLCM) textures, an existing method and several improvements i.e., parallel computing, SAR and optical data preprocessing, enhancement, fusion and postprocessing. Both KTH-SEG and KTH-Pavia Urban Extractor will be adapted, improved and applied to Sentinel-1A SAR, Sentinel-2A MSI data as well as their fusion.
Two end users have committed to participate in the project, namely the Urban and Regional Development Department at the Stockholm County Administrative Board in Stockholm, Sweden and National Geomatics Center of China at the National Administration of Surveying, Mapping and Geoinformation of China in Beijing, China. This gives us a unique opportunity to develop much needed urban services.
This research and development is expected to produce a pilot global urban services demonstrator using multitemporal Sentinel-1A SAR and Sentinel-2A MSI data. The project will contribute to i). better understanding of the urban products and services that the end users require; ii). development of novel and robust methods and algorithms for improved urban services to planners to support smart and sustainable urban development; ; iii). better understanding of the capacity of Sentinel-1A SAR and Sentinel-2A optical data for detailed urban land cover mapping and urbanization monitoring; iv). the goals and activities of GEO SB-04 Global Urban Observation and Information Task and the UN post-2015 sustainable development goals.
Paper 68 - Session title: Global
11:40 A Continuous Infrastructure Index for Mapping Human Settlements
Small, Christopher (1); Nghiem, Son (2) 1: Lamont Doherty Earth Observatory, Columbia University, Palisades, NY 10964, United States of America; 2: NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 , United States of America
The Landsat program provides more than three decades of decameter resolution multispectral observations of the growth and evolution of human settlements and development worldwide. These changes are often easy to observe visually but accurate repeatable quantification at Landsat’s resolution has proven elusive. In part, this is a consequence of the multi-scale heterogeneity and diversity of settlements worldwide. Mapping settlement extent is also confounded by the lack of a single, physically-based, definition of what constitutes urban, peri-urban and other types of settlement. We attempt to resolve both of these challenges by characterizing built environments in terms of their distinctive physical properties. This can be accomplished by combining multi-temporal optical reflectance with synthetic aperture radar backscatter measurements to identify combinations of physical properties that distinguish built environments from other types of land cover. Three well-known examples include an abundance of impervious surface, persistent deep shadow between buildings and high density of corner reflectors at meter to decameter scales. At optical wavelengths, spectral properties of land cover can be represented using standardized spectral endmember fractions to represent combinations of the most spectrally and functionally distinct components of land cover; soil and impervious substrates, vegetation, water and shadow. The spectral similarity of soils and impervious substrates that makes thematic classifications error prone can be resolved by using multi-season composites of spectral endmembers to distinguish spectrally stable impervious substrates from temporally variable soil reflectance resulting from seasonal changes in moisture content (thus albedo) and fractional vegetation cover. By representing the diversity of anthropogenic land use as a continuous mosaic of land cover it is possible to quantify the wide variety of human settlements in a way that is physically consistent, repeatable and scalable. Our strategy is to develop and test algorithms to combine multi-season Landsat and Sentinel-2 optical multispectral imagery with SRTM and Sentinel-1 C-band radar backscatter imagery to produce a Continuous Infrastructure Index (CII) to identify and map changes in the extent of anthropogenic built environments (e.g. urban, suburban, exurban, peri-urban) worldwide between 2000 and 2015. Rather than attempting to map specific features associated with built environments (e.g. impervious surfaces, buildings, roads), we characterize the combined optical and microwave response of a wide range of built environments to identify the physical properties associated with these features (e.g. spectral stability, persistent shadow, anisotropic backscatter intensity). We will then use the most persistent of these properties to derive an optimized index incorporating multiple characteristics measured by both optical and microwave sensors. Variations in relative density of stable substrate (impervious surface), building shadow and corner reflectors will be used to define a continuous space of built environment characteristics for different types of human settlement worldwide. Changes in CII between 2000 and 2015 will quantify both vertical and horizontal growth as well as temporal evolution of settlement networks worldwide.