498 research outputs found
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 â 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 cityâs physical morphology: Social groups settle spatially with oneâs 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 â 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 â e.g. a river flood â 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 â e.g. building size, height and shape â 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 zÀhlen aus dem All. Möglichkeiten und Grenzen von Satellitendaten zur AbschÀtzung der Bevölkerungsentwicklung und des GebÀudebestandes in deutschen StÀdten
Is it possible to count the earthâs 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 farmersâ perception towards the forest and its beneïŹts has not been considered either in studies conducted so far or by policy makers. This research provides the ïŹrst 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 inïŹuences 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. Farmersâ inïŹuence 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 inïŹuenced by farm type, the level of dependency/use of forest beneïŹts 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
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