Christoph Aubrecht

The ASCAT Soil Moisture Product: A Review of its Specifications, Validation Results, and Emerging Applications

Many physical, chemical and biological processes taking place at the land surface are strongly influenced by the amount of water stored within the upper soil layers. Therefore, many scientific disciplines require soil moisture observations for developing, evaluating and improving their models. One of these disciplines is meteorology where soil moisture is important due to its control on the exchange of heat and water between the soil and the lower atmosphere. Soil moisture observations may thus help to improve the forecasts of air temperature, air humidity and precipitation. However, until recently, soil moisture observations had only been available over a limited number of regional soil moisture networks. This has hampered scientific progress as regards the characterisation of land surface processes not just in meteorology but many other scientific disciplines as well. Fortunately, in recent years, satellite soil moisture data have increasingly become available. One of the freely available global soil moisture data sets is derived from the backscatter measurements acquired by the Advanced Scatterometer (ASCAT) that is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP) satellite series. ASCAT was designed to observe wind speed and direction over the oceans and was initially not foreseen for monitoring soil moisture over land. Yet, as argued in this review paper, the characteristics of the ASCAT instrument, most importantly its wavelength (5.7 cm), its high radiometric accuracy, and its multiple-viewing capabilities make it an attractive sensor for measuring soil moisture. Moreover, given the operational status of ASCAT, and its promising long-term prospects, many geoscientific applications might benefit from using ASCAT soil moisture data. Nonetheless, the ASCAT soil moisture product is relatively complex, requiring a good understanding of its properties before it can be successfully used in applications. To provide a comprehensive overview of themajor characteristics and caveats of the ASCATsoil moisture product, this paper describes the ASCAT instrument and the soil moisture processor and near-real-time distribution service implemented by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT).A review of themost recent validation studies shows that the quality of ASCAT soil moisture product is – with the exception of arid environments –comparable to, and over some regions (e.g. Europe) even better than currently available soil moisture data derived from passive microwave sensors. Further, a review of applications studies shows that the use of the ASCAT soil moisture product is particularly advanced in the fields of numerical weather prediction and hydrologic modelling. But also in other application areas such as yield monitoring, epidemiologic modelling, or societal risks assessment some first progress can be noted. Considering the generally positive evaluation results, it is expected that the ASCAT soil moisture product will increasingly be used by a growing number of rather diverse land applications.

Integrating earth observation and GIScience for high resolution spatial and functional modeling of urban land use

Integrative analysis of remote sensing data and socioeconomic information enables the transition of land cover and urban structures into a detailed functional model of urban land use. In this paper object based image analysis is used to derive a classification of urban structures. The implementation of ALS (Airborne Laser Scanning) significantly enhances the classification of optical imagery both in terms of accuracy as well as automation. Land cover types are additionally differentiated based on their relative height above ground resulting in a 3D building model. This model forms the basis for the integration of socioeconomic data for identifying urban functions. Buildings are split into sub-buildings by creating Thiessen polygons based on geocoded address point data. Company data is linked to this address information resulting in significant refinement of the functional classification and concrete identification of building use. By means of spatial disaggregation, raster population data is distributed to potential residential buildings. The relevant potential residential capacity is calculated under consideration of building use and ALS-based height information. These additional information sources guarantee a high accuracy of disaggregation and a further refinement of the functional 3D city model, independently confirmed by a quantitative accuracy assessment.

Multi-level geospatial modeling of human exposure patterns and vulnerability indicators

In the context of disaster risk management and in particular for improving preparedness and mitigation of potential impacts, information on socioeconomic characteristics including aspects of situation-specific human exposure and vulnerability is considered vital. This paper provides an overview on available multi-level geospatial information and modeling approaches from global to local scales that could serve as inventory for people involved in disaster-related areas. Concepts and applications related to the human exposure and social vulnerability domains are addressed by illustrating the varying dimensions and contextual implications. Datasets and methods are highlighted that can be applied to assess earthquake-related population exposure, ranging from global and continental-scale population grids (with a focus on recent developments for Europe) to high-resolution functional urban system models and space–time variation aspects. In a further step, the paper elaborates on the integration of social structure on regional scale and the development of aggregative social and economic vulnerability indicators which would eventually enable the differentiation of situation-specific risk patterns. The presented studies cover social vulnerability mapping for selected US federal states in the New Madrid seismic zone as well as the advancement of social vulnerability analysis through integration of additional economic features in the index construction by means of a case study for Turkey’s provinces.

Identification of heat risk patterns in the U.S. National Capital Region by integrating heat stress and related vulnerability.

The increase in the number and severity of weather extremes (including excessive heat) potentially associated with climate change has highlighted the needs for research into risk assessment and risk reduction measures. Extreme heat events, the focus of this paper, have been consistently reported as the leading cause of weather-related mortality in the United States in recent years. In order to fully understand impact potentials and analyze risk in its individual components both the spatially and temporally varying patterns of heat and the multidimensional characteristics of vulnerability have to be considered. In this paper we present a composite index aggregating these factors to assess heat related risk for the U.S. National Capital Region in 2010. The study reveals how risk patterns are in part driven by the geographic variations of vulnerability, generally showing a clear difference between high-risk urban areas and wide areas of low risk in the suburban and rural environments. This pattern is particularly evident for the core center of the study area around the District of Columbia, which is largely characterized by high index values despite not having experienced the peak of the heat stress as compared to other regions in the metropolitan area. The article aims to set a framework for local-level heat stress risk assessment that can provide valuable input and decision support for climate adaptation planning as well as emergency managers aiming at risk reduction and optimization of resource distribution.

Roughness Mapping on Various Vertical Scales Based on Full-Waveform Airborne Laser Scanning Data

Abstract: Roughness is an important input parameter for modeling of natural hazards such as floods, rock falls and avalanches, where it is basically assumed that flow velocities decrease with increasing roughness. Seeing roughness as a multi-scale level concept ( i.e. , ranging from fine-scale soil characteristics to description of understory and lower tree layer) various roughness raster products were derived from the original full-waveform airborne laser scanning (FWF-ALS) point cloud using two different types of roughness parameters, the surface roughness ( SR ) and the terrain roughness ( TR ). For the calculation of the SR , ALS terrain points within a defined height range to the terrain surface are considered. For the parameterization of the SR, two approaches are investigated. In the first approach, a geometric description by calculating the standard deviation of plane fitting residuals of terrain points is used. In the second one, the potential of the derived echo widths are analyzed for the parameterization of

A global inventory of coral reef stressors based on satellite observed nighttime lights

In this article, we present a satellite-based approach to gather information about the threat to coral reefs worldwide. Three chosen reef stressors – development, gas flaring and heavily lit fishing boat activity – are analysed using nighttime lights data derived from the Defense Meteorological Satellite Program (DMSP) produced at the National Oceanic & Atmospheric Administration, National Geophysical Data Center (NOAA/NGDC). Nighttime lights represent a direct threat to coral reef ecosystems and are an excellent proxy measure for associated human-caused stressors. A lights proximity index (LPI) is calculated, measuring the distance of coral reef sites to each of the stressors and incorporating the stressors intensity. Colourized maps visualize the results on a global scale. Area rankings clarify the effects of artificial night lighting on coral reefs on a regional scale. The results should be very useful for reef managers and for state administrations to implement coral reef conservation projects and for the scientific world to conduct further research.

Remote sensing to map influence of light pollution on Cory’s shearwater in São Miguel Island, Azores Archipelago

Global economic and population growth increase the extent and intensity of artificial night lighting. From an ecological perspective, this is light pollution, which causes changes in reproductive physiology, migration and foraging of many species and ultimately leads to loss of biodiversity. Some seabirds are intimately linked with the light features of their environments because they are nocturnally active. We report light-induced groundings of Cory’s shearwater (Calonectris diomedea) during a 2-year study (2008 and 2009) in São Miguel Island, in the Azores archipelago, and investigate the spatial correlation of locations of grounded birds with an annual composite of remotely sensed stable lights. Results indicate that 16.7% of fledglings are attracted to lights. The exposure of shearwater colonies in the study area to artificial night lighting is low overall. Four colonies account for 87% of the grounded birds. The distance each bird was found from the closest colony was best explained by the ratio of the satellite-measured light levels at the grounding spot to the light levels at the assigned colony of origin. These results demonstrate that satellite-observed nighttime lights are sufficient to assess risk to marine birds at the scale of oceanic islands and indicate their utility for monitoring the effectiveness of programs to manage lighting to reduce risk for these species and conducting global assessments of species vulnerability. To minimize the impact on Cory’s shearwater and other marine birds, we recommend measures such as reduction and control of lighting intensity near colony locations, while continuing and re-enforcing rescue campaigns.

Spatio-temporal aspects and dimensions in integrated disaster risk management

The concept of risk has been introduced in disaster management since experiences from past years suggested that elements at risk and vulnerability should be increasingly considered within the framework of hazard and disaster management in order to reduce losses (e.g. Commission of the European Communities 2007; International Standards Organisation 2009). A disaster is defined as a ‘serious disruption of the functioning of a community or a society involving widespread human, material, economic or environmental losses and impacts, which exceeds the ability of the affected community or society to cope using its own resources’ (UNISDR 2009). The term disaster is thus not used until severe impacts on social systems, including human beings (loss of life) and associated assets (destruction of property), are caused (e.g. Johnson et al. 2006). This view was already promoted in the early stages of the last century (e.g. Queen and Mann 1925; Carr 1932) and is still seen as the key aspect in currently commonly accepted definitions such as the one cited above, which additionally highlights the event-specific exceeded coping capacity of the affected social system. The latter—for example, also identified as criterion in the EM-DAT terminology (CRED 2013)—in fact adds the domain of (social) vulnerability to the disaster concept, as it describes the disaster-specific characteristics of a system in terms of its limited ‘ability to face and manage adverse conditions [...] using available skills and resources’ (UNISDR 2009).

Advancing tsunami risk assessment by improving spatio-temporal population exposure and evacuation modeling

Tsunamis are among the most destructive and lethal of coastal hazards. These are time-specific events, and despite directly affecting a narrow strip of coastline, a single occurrence can have devastating effects and cause massive loss of life, especially in urbanized coastal areas. In this work, in order to consider the time dependence of population exposure to tsunami threat, the variation of spatio-temporal population distribution in the daily cycle is mapped and analyzed in the Lisbon Metropolitan Area. High-resolution daytime and nighttime population distribution maps are developed using ‘intelligent dasymetric mapping,’ that is, applying areal interpolation to combine best-available census data and statistics with land use and land cover data. Workplace information and mobility statistics are considered for mapping daytime distribution. In combination with a tsunami hazard map, information on infrastructure, land use and terrain slope, the modeled population distribution is used to assess people’s evacuation speed, applying a geospatial evacuation modeling approach to the city of Lisbon. The detailed dynamic population exposure assessment allows producing both daytime and nighttime evacuation time maps, which provide valuable input for evacuation planning and management. Results show that a significant amount of population is at risk, and its numbers increase dramatically from nighttime to daytime, especially in the zones of high tsunami flooding susceptibility. Also, full evacuation can be problematic in the daytime period, even if initiated immediately after a major tsunami-triggering earthquake. The presented approach greatly improves tsunami risk assessment and can benefit all phases of the disaster management process.

Long-term spatio-temporal social vulnerability variation considering health-related climate change parameters particularly affecting elderly

For assessing the social dimension of vulnerability, population exposure mapping is usually considered the essential starting point. Integration of social structure then further differentiates situation-specific vulnerability patterns on a local scale. Census data available in heterogeneous spatial reference units are still considered the standard information input for assessing potentially affected people, for example, in case of an emergency. There is a strong demand for population data in homogeneous spatial units that are independent from administrative areas. Raster representations meet this demand but are not yet available for all European countries. In this paper, we present an approach of spatial disaggregation of population data for a European transect referring to current population statistics and anticipated future prospects. Recently published data providing the degree of soil sealing are applied as basic proxy for population density in the spatial disaggregation model. In order to assess future patterns of climate change-related vulnerability, results of a European regional climate model are considered for projecting the situation in the 2030s. “Heat wave frequency” is accounted for as climate variable featuring conditions regarded as especially strenuous for elderly or physically weak persons. Integrated analysis of the population and climate prospects enables identification of hot spots in the European transect examined, that is, regions of particularly demanding projected climatic patterns as well as high population density and case-specific vulnerable structure (elderly people). Integrated and consistent spatial analyses on European scale are essential for decision support in the context of climate change impact mitigation as well as for risk communication and future safety and security considerations.