Vulnerability assessment using remote sensing: The earthquake prone megacity Istanbul, Turkey

Hazards like earthquakes are natural, disasters are not. Disasters result from the impact of a hazard on a vulnerable system or society at a specific location. The framework of vulnerability aims at a holistic concept taking physical, environmental, socio-economic and political components into account. This paper focuses on the capabilities of remote sensing to contribute up-to-date spatial information to the physical dimension of vulnerability for the complex urban system of the megacity Istanbul, Turkey. An urban land cover classification based on high resolution satellite data establishes the basis to analyse the spatial distribution of different types of buildings, the carrying capacity of the street network or the identification of open spaces. In addition, a DEM (Digital Elevation Model) enables a localization of potential landslide areas. A methodology to combine these attributes related to the physical dimension of vulnerability is presented. In this process an n-dimensional coordinate system plots the variables describing vulnerability against each other. This enables identification of the degree of vulnerability and the vulnerability-determining factors for a specific location. This assessment of vulnerability provides a broad spatial information basis for decision-makers to develop mitigation strategies

Analysis of urban sprawl at mega city Cairo, Egypt using multisensoral remote sensing data, landscape metrics and gradient analysis

This paper is intended to highlight the capabilities of synergistic usage of remote sensing, landscape metrics and gradient analysis. We aim to improve the understanding of spatial characteristics and effects of urbanization on city level. Multisensoral and multitemporal remotely sensed data sets from the Landsat and TerraSAR-X sensor enable monitoring a long time period with area-wide information on the spatial urban expansion over time. Landscape metrics aim to quantify patterns on urban footprint level complemented by gradient analysis giving insight into the spatial developing of spatial parameters from the urban center to the periphery. The results paint a characteristic picture of the emerging spatial urban patterns at mega city Cairo, Egypt since the 1970s

Mapping paddy rice in Asia: a multi-sensor, time-series approach.

Rice is the most important food crop in Asia and the mapping and monitoring of paddy rice fields is an important task in the context of food security, food trade policy and greenhouse gas emissions modelling. Two countries where rice is of special significance are China, the largest producer and importer of rice, and Vietnam, where rice exports contribute a fifth to the GDP. Both countries are facing increasing pressure in terms of food security due to population and economic growth while agricultural areas are confronted with urban encroachment and the limits of yield increase. Despite the importance of knowledge about rice production the countries official land cover products and rice production statistics are of varying quality and sometimes even contradict each other. Available remote sensing studies focused either on time-series analysis from optical sensors or from Synthetic Aperture Radar (SAR) sensors – the studies using optical sensors faced problems due to either the spatial or temporal resolution and the persistent cloud cover while SAR studies found the limited data availability and large image size to be the biggest drawbacks. We try to address these issues by proposing a paddy rice mapping approach that combines medium spatial resolution, temporally dense time-series from the optical MODIS sensors and high spatial resolution time-series from the recently launched Sentinel-1 SAR sensor. We used the 250m resolution MOD13Q1 and MYD13Q1 products as a basis for our medium resolution rice map. Prevalent cloud cover introduces noise into these timeseries which we reduced by applying a Savitzky-Golay filter. We then derived a number of time-series temporal and phenological metrics for multiple years and classified rice areas with One Class Support Vector Machines. In a next step we used this medium resolution rice map to mask Sentinel-1 Interferometric Wide Swath images and create SAR time-series from which we again derived temporal and phenological metrics and classified rice areas with machine learning algorithms to arrive at a 10m resolution rice map. This method allows concurrent, accurate and high resolution mapping of paddy rice areas from freely available data with limited requirements towards processing infrastructure and can be used as a basis for greenhouse gas and crop modelling as well as providing viable information for decision makers regarding food security, food trade, bioeconomy and mitigation after crop failure. Results of our paddy rice classification will be presented for selected study sites in China and Vietnam

Integrating Remote Sensing and Social Science - The correlation of urban morphology with socioeconomic parameters

The alignment, small-scale transitions and characteristics of buildings, streets and open spaces constitute a heterogeneous urban morphology. The urban morphology is the physical reflection of a society that created it, influenced by historical, social, cultural, economic, political, demographic and natural conditions as well as their developments. Within the complex urban environment homogeneous physical patterns and sectors of similar building types, structural alignments or similar built-up densities can be localized and classified. Accordingly, it is assumed that urban societies also feature a distinctive socioeconomic urban morphology that is strongly correlated with the characteristics of a city’s physical morphology: Social groups settle spatially with one’s peer more or less segregated from other social groups according to, amongst other things, their economic status. This study focuses on the analysis, whether the static physical urban morphology correlates with socioeconomic parameters of its inhabitants – here with the example indicators income and value of property. Therefore, the study explores on the capabilities of high resolution optical satellite data (Ikonos) to classify patterns of urban morphology based on physical parameters. In addition a household questionnaire was developed to investigate on the cities socioeconomic morphology

Towards an automated estimation of vegetation cover fractions on multiple scales: Examples of Eastern and Southern Africa

Vegetation cover is one of the key parameters for monitoring the state and dynamics of ecosystems. African semi-arid landscapes are especially prone to degradation due to climate change and increased anthropogenic impact on different spatial and temporal scales. In this study, a multiscale method is applied to monitor vegetation cover by deriving sub-pixel percentages of woody vegetation, herbaceous vegetation and soil. The approach is comparatively applied to two semi-arid savannas, one in Namibia and one in Kenya. The results in eastern and southern Africa demonstrate the applicability of the method to different semiarid ecosystems and to different types of remote sensing data. The presented analysis could show that continuous cover mapping is a highly suitable concept for semi-arid ecosystems, as these show gradual transitions rather than distinct borders between land cover types. Different spatial patterns of vegetation cover depending on land use practices and intensities could be revealed

Derivation of population distribution for vulnerability assessment in flood-prone German cities using multisensoral remote sensing data

Against the background of massive urban development, area-wide and up-to-date spatial information is in demand. However, for many reasons this detailed information on the entire urban area is often not available or just not valid anymore. In the event of a natural hazard – e.g. a river flood – it is a crucial piece of information for relief units to have knowledge about the quantity and the distribution of the affected population. In this paper we demonstrate the abilities of remotely sensed data towards vulnerability assessment or disaster management in case of such an event. By means of very high resolution optical satellite imagery and surface information derived by airborne laser scanning, we generate a precise, three-dimensional representation of the landcover and the urban morphology. An automatic, object-oriented approach detects single buildings and derives morphological information – e.g. building size, height and shape – for a further classification of each building into various building types. Subsequently, a top-down approach is applied to distribute the total population of the city or the district on each individual building. In combination with information of potentially affected areas, the methodology is applied on two German cities to estimate potentially affected population with a high level of accurac

Menschen zählen aus dem All. Möglichkeiten und Grenzen von Satellitendaten zur Abschätzung der Bevölkerungsentwicklung und des Gebäudebestandes in deutschen Städten

Is it possible to count the earth’s population from outer space? The answer is yes, in urban areas it is possible. However, this can only be done in an indirect manner: by identifying physical objects in the urban landscape in earth observation data and using these to estimate the number of inhabitants. Since the approach is indirect, data protection and the individual right to privacy are fully guaranteed. The data obtained using this method fill a gap, given that municipal population registers do not contain accurate population counts. However, remote sensing technology is not able to provide cadastral information. Nevertheless, as this paper shows, satellite imagery is capable of providing the basis for population estimates for small-scale areas. And, of course, remote sensing data also can be used to estimate the building stock. It would make sense to produce such estimates during the intervals between each building stock census, which is usually conducted every ten years with the population census. Remote sensing data cannot replace a population census, but can enrich the analytical power of population census data.Remote Sensing, spatial disaggregation, population estimation, census

A new high-resolution elevation model of Greenland derived from TanDEM-X

In this paper we present for the first time the new digital elevation model (DEM) for Greenland produced by the TanDEM-X (TerraSAR add-on for digital elevation measurement) mission. The new, full coverage DEM of Greenland has a resolution of 0.4 arc seconds corresponding to 12 m. It is composed of more than 7.000 interferometric synthetic aperture radar (InSAR) DEM scenes. X- Band SAR penetrates the snow and ice pack by several meters depending on the structures within the snow, the acquisition parameters, and the dielectricity constant of the medium. Hence, the resulting SAR measurements do not represent the surface but the elevation of the mean phase center of the backscattered signal. Special adaptations on the nominal TanDEM-X DEM generation are conducted to maintain these characteristics and not to raise or even deform the DEM to surface reference data. For the block adjustment, only on the outer coastal regions ICESat (Ice, Cloud, and land Elevation Satellite) elevations as ground control points (GCPs) are used where mostly rock and surface scattering predominates. Comparisons with ICESat data and snow facies are performed. In the inner ice and snow pack, the final X-Band InSAR DEM of Greenland lies up to 10 m below the ICESat measurements. At the outer coastal regions it corresponds well with the GCPs. The resulting DEM is outstanding due to its resolution, accuracy and full coverage. It provides a high resolution dataset as basis for research on climate change in the arctic

Assessing Forest Cover Dynamics and Forest Perception in the Atlantic Forest of Paraguay, Combining Remote Sensing and Household Level Data

The Upper Parana Atlantic Forest (BAAPA) in Paraguay is one of the most threatened tropical forests in the world. The rapid growth of deforestation has resulted in the loss of 91% of its original cover. Numerous efforts have been made to halt deforestation activities, however farmers’ perception towards the forest and its benefits has not been considered either in studies conducted so far or by policy makers. This research provides the first multi-temporal analysis of the dynamics of the forest within the BAAPA region on the one hand, and assesses the way farmers perceive the forest and how this influences forest conservation at the farm level on the other. Remote sensing data acquired from Landsat images from 1999 to 2016 were used to measure the extent of the forest cover and deforestation rates over 17 years. Farmers’ influence on the dynamics of the forest was evaluated by combining earth observation data and household survey results conducted in the BAAPA region in 2016. Outcomes obtained in this study demonstrate a total loss in forest cover of 7500 km 2 . Deforestation rates in protected areas were determined by management regimes. The combination of household level and remote sensing data demonstrated that forest dynamics at the farm level is influenced by farm type, the level of dependency/use of forest benefits and the level of education of forest owners. An understanding of the social value awarded to the forest is a relevant contribution towards preserving natural resources