Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Examining spatiotemporal changes in the phenology of Australian mangroves using satellite imagery

Nicolás Younes investigated the phenology of Australian mangroves using satellite imagery, field data, and generalized additive models. He found that satellite-derived phenology changes with location, frequency of observation, and spatial resolution. Nicolás challenges the common methods for detecting phenology and proposes a data-driven approach

Semantic location extraction from crowdsourced data

Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction

GLOBAL BARE GROUND GAIN BETWEEN 2000 AND 2012 AND THE RELATIONSHIP WITH SOCIOECONOMIC DEVELOPMENT

Bare ground gain -- the complete removal of vegetation due to land use changes, represents an extreme land cover transition that completely alters the structure and functioning of ecosystems. The fast expansion of bare ground cover is directly associated with increasing population and urbanization, resulting in accelerated greenhouse gas emissions, intensified urban heat island phenomenon, and extensive habitat fragments and loss. While the economic return of settlement and infrastructure construction has improved human livelihoods, the negative impacts on the environment have disproportionally affected vulnerable population, creating inequality and tension in society. The area, distribution, drivers, and change rates of global bare ground gain were not systematically quantified; neither was the relationship between such dynamics and socioeconomic development. This dissertation seeks methods for operational characterization of bare ground expansion, advances our understanding of the magnitudes, dynamics, and drivers of global bare ground gain between 2000 and 2012, and uncovers the implications of such change for macro-economic development monitoring, all through Landsat satellite observations. The approach that employs wall-to-wall maps of bare ground gain classified from Landsat imagery for probability sample selection is proved particularly effective for unbiased area estimation of global, continental, and national bare ground gain, as a small land cover and land use change theme. Anthropogenic land uses accounted for 95% of the global bare ground gain, largely consisting of commercial/residential built-up, infrastructure development, and resource extraction. China and the United States topped the total area increase in bare ground. Annual change rates of anthropogenic bare ground gain are found as a leading indicator of macro-economic change in the study period dominated by the 2007-2008 global financial crisis, through econometric analysis between annual gains in the bare ground of different land use outcomes and economic fluctuations in business cycles measured by detrended economic variables. Instead of intensive manual interpretation of land-use attributes of probability sample, an approach of integrating a pixel- and an object- based deep learning algorithms is proposed and tested feasible for automatic attribution of airports, a transportation land use with economic importance

Mapping urban surface materials with imaging spectroscopy data on different spatial scales

This work focuses on the development of methods for mapping urban surface materials by means of imaging spectroscopy data with different spatial resolution. General findings from this work represent a sensor- and site-independent framework for the automated extraction of spectrally pure pixels using an urban image spectral library while coping with its potential incompleteness. The extraction of spectrally pure pixels serves as a basic prerequisite for the subsequent use of image analysis methods to obtain detailed urban surface material maps. These material maps enabled the determination of gradual material transitions that were finally related to complex spectral mixtures resulting from 30 m spatial resolution imaging spectroscopy data to analyse typical material compositions within certain administrative units. The findings demonstrate the great potential of using upcoming spaceborne imaging spectroscopy data for a regular area-wide mapping of surface materials in urban areas. Im Fokus dieser Arbeit stand die Entwicklung von Methoden zur Kartierung urbaner Oberflächenmaterialien mittels abbildender Spektroskopiedaten unterschiedlicher räumlicher Auflösung. Das vorgestellte Konzept zur automatisierten sensor- und ortsunabhängigen Extraktion spektral reiner Pixel aus flugzeuggetragenen Fernerkundungsdaten berücksichtigt dabei die mögliche Unvollständigkeit einer urbanen Bildspektralbibliothek. Die Extraktion spektral reiner Pixel dient als Grundvoraussetzung für den späteren Einsatz von Bildanalyseverfahren zur Gewinnung detaillierter Kartierungen urbaner Oberflächenmaterialien. Aus diesen sind Materialgradienten ableitbar, die mit den komplexen Spektralmischungen aus Hyperspektraldaten mit 30 m räumlicher Auflösung in Verbindung gebracht wurden. Die Analyse typischer Materialzusammensetzungen innerhalb städtischer Verwaltungseinheiten zeigt das enorme Potential zukünftiger Hyperspektralsatelliten für die Erfassung des Materialvorkommens von Städten

Risk-Informed Sustainable Development in the Rural Tropics

Many people live in rural areas in tropical regions. Rural development is not merely a contribution to the growth of individual countries. It can be a way to reduce poverty and to increase access to water, health care, and education. Sustainable rural development can also help stop deforestation and reduce live-stock, which generate most of the greenhouse gas emissions. However, eorts to achieve a sustainable rural development are often thwarted by oods, drought, heat waves, and hurricanes, which local communities are not very prepared to tackle. Agricultural practices and local planning are still not very risk-informed. These deciencies are particularly acute in tropical regions, where many Least Developed Countries are located and where there is, however, great potential for rural development. This Special Issue contains 22 studies on best practices for risk awareness; on local risk reduction; on several cases of soil depletion, water pollution, and sustainable access to safe water; and on agronomy, earth sciences, ecology, economy, environmental engineering, geomatics, materials science, and spatial and regional planning in 12 tropical countries

Climate Models

Climate models is a very broad topic, so a single volume can only offer a small sampling of relevant research activities. This volume of 14 chapters includes descriptions of a variety of modeling studies for a variety of geographic regions by an international roster of authors. The climate research community generally uses the rubric climate models to refer to organized sets of computer instructions that produce simulations of climate evolution. The code is based on physical relationships that describe the shared variability of meteorological parameters such as temperature, humidity, precipitation rate, circulation, radiation fluxes, etc. Three-dimensional climate models are integrated over time in order to compute the temporal and spatial variations of these parameters. Model domains can be global or regional and the horizontal and vertical resolutions of the computational grid vary from model to model. Considering the entire climate system requires accounting for interactions between solar insolation, atmospheric, oceanic and continental processes, the latter including land hydrology and vegetation. Model simulations may concentrate on one or more of these components, but the most sophisticated models will estimate the mutual interactions of all of these environments. Advances in computer technology have prompted investments in more complex model configurations that consider more phenomena interactions than were possible with yesterday s computers. However, not every attempt to add to the computational layers is rewarded by better model performance. Extensive research is required to test and document any advantages gained by greater sophistication in model formulation. One purpose for publishing climate model research results is to present purported advances for evaluation by the scientific community