TanDEM-X mission—new perspectives for the inventory and monitoring of global settlement patterns

Esch, Thomas; Taubenböck, Hannes; Roth, Achim; Heldens, Wieke; Felbier, Andreas; Thiel, Michael; Schmidt, Martin; Müller, Andreas; Dech, Stefan

doi:10.1117/1.jrs.6.061702

Cited by 123 publications

(72 citation statements)

References 43 publications

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“…This makes their separation considerably more difficult. For the identification of urban areas, the Global Urban Footprint (GUF) was therefore used as additional information, which was derived on the basis of TerraSAR-X and TanDEM-X SAR data [52]. The spatial resolution of the GUF is 0.4 arcseconds (~12 m).…”

Section: Characteristics Of the Usedmentioning

confidence: 99%

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

et al. 2018

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Abstract:The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth's surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.

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Section: Characteristics Of the Usedmentioning

confidence: 99%

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

et al. 2018

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“…For worldwide inventories of large urban constellations, globally consistent datasets are ideally suited and with increasing data availability, a number of global urban area maps have been created [33][34][35][36][37]. Also, there are several global land cover classification products from multi-source earth observation imagery which are relevant for urban area delimitation.…”

Section: Large Urban Areas Ii: Earth Observationmentioning

confidence: 99%

“…Earlier, lower-resolution examples include the Global Land Cover (GLC2000) [38], GlobCover [39], IGBP-DISCover [40] or MODIS land-cover map [41] products. More recently, the high-resolution Global Human Settlement Layer (GHSL) [42] and Global Urban Footprint (GUF) [37] datasets have become available for urban area observation.…”

Section: Large Urban Areas Ii: Earth Observationmentioning

confidence: 99%

Are We in Boswash Yet? A Multi-Source Geodata Approach to Spatially Delimit Urban Corridors

Georg

Blaschke

Taubenböck

2018

IJGI

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Abstract:The delimitation of urban space is conceptually elusive and fuzzy. Commonly, urban areas are delimited through administrative boundaries. These artificial, fixed boundaries, however, do not necessarily represent the actual built-up extent, the urban catchment, or the economic linkage within and across neighboring metropolitan regions. For an approach to spatially delimit an urban corridor-a generically defined concept of a massive urban area-we use the Boston to Washington (Boswash) region as an example. This area has been consistently conceptualized in literature as bounded urban space. We develop a method to spatially delimit the urban corridor using multi-source geodata (built-up extent, infrastructure and socioeconomic data) which are based on a grid rather than on administrative units. Threshold approaches for the input data serve to construct Boswash as varying connected territorial spaces, allowing us to investigate the variability of possible spatial forms of the area, i.e., to overcome the simple dichotomous classification in favor of a probability-based differentiation. Our transparent multi-layer approach, validated through income data, can easily be modified by using different input datasets while maintaining the underlying idea that the likelihood of an area being part of an urban corridor is flexible, i.e., in our case a factor of how many input layers return positive results.

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“…giving indication on the distribution of manmade surfaces including buildings, roads and related elements [7]. Due to its relevance for a broad set of applications, global impervious surface or general built-up area mapping has been in the focus of attention for a while with data from different satellite sensors used and various approaches implemented (overview provided by [24]), recent efforts including high resolution products such as the Global Urban Footprint (GUF) [25] and the Global Human Settlement Layer (GHSL) [26]. The DMSP-derived ISA data set is unique in a sense that it does not directly extract built-up from satellite imagery but uses artificial night lighting as proxy measure.…”

Section: Impervious Surface Area (Isa) Data Derived From Dmsp-olsmentioning

confidence: 99%

Evaluating Multi-Sensor Nighttime Earth Observation Data for Identification of Mixed vs. Residential Use in Urban Areas

Aubrecht

Torres

2016

Remote Sensing

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This paper introduces a novel top-down approach to geospatially identify and distinguish areas of mixed use from predominantly residential areas within urban agglomerations. Under the framework of the World Bank's Central American Country Disaster Risk Profiles (CDRP) initiative, a disaggregated property stock exposure model has been developed as one of the key elements for disaster risk and loss estimation. Global spatial datasets are therefore used consistently to ensure wide-scale applicability and transferability. Residential and mixed use areas need to be identified in order to spatially link accordingly compiled property stock information. In the presented study, multi-sensor nighttime Earth Observation data and derivative products are evaluated as proxies to identify areas of peak human activity. Intense artificial night lighting in that context is associated with a high likelihood of commercial and/or industrial presence. Areas of low light intensity, in turn, can be considered more likely residential. Iterative intensity thresholding is tested for Cuenca City, Ecuador, in order to best match a given reference situation based on cadastral land use data. The results and findings are considered highly relevant for the CDRP initiative, but more generally underline the relevance of remote sensing data for top-down modeling approaches at a wide spatial scale.

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TanDEM-X mission—new perspectives for the inventory and monitoring of global settlement patterns

Cited by 123 publications

References 43 publications

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

Are We in Boswash Yet? A Multi-Source Geodata Approach to Spatially Delimit Urban Corridors

Evaluating Multi-Sensor Nighttime Earth Observation Data for Identification of Mixed vs. Residential Use in Urban Areas

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